--- title: "Complete Book 0" book: "MPUB 303 Research Methdology and Research Ethics" category: "General" publisher: "Ratan Prakashan Mandir Pvt. Ltd." type: "Educational Material" ---  According to Latest Syllabus Read For Sure Success In University Examination RATAN TEXT BOOK RESEARCH METHODOLOGY AND RESEARCH ETHICS M.A.Pub.Ad. (Sem-III) Dr. Madan Mohan Gurjar Published by Ratan Prakashan Mandir Pvt. Ltd. 2nd Floor, Centre Plaza, Parinay Kunj, Lajpat Kunj Marg, Agra-282002 Copyright Authors & Publishers Published by Ratan Prakashan Mandir Pvt. Ltd. 2nd Floor, Centre Plaza, Parinay Kunj, Lajpat Kunj Marg, Agra-282002 ISBN :978-81-69604-41-3 Price 384.00 only Printed at : KIDS INTERNATIONAL PVT. LTD. C-60, 61, 62, 63, EPIP, Shastripuram, Agra - 282007 Ph. : +91 9719004921 CONTENTS | Sr.No. | Topic | Page No. | |---|---|---| | CHAPTER-1 | SOCIAL SCIENCE RESEARCH - NATUREAND SCOPE | 1 | | CHAPTER-2 | METHODS OF SOCIOLOGICAL RESEARCH | 6 | | CHAPTER-3 | SCIENTIFIC METHOD | 19 | | CHAPTER-4 | BASIC ELEMENTS OF RESEARCH | 37 | | CHAPTER-5 | MEANINGAND FORMULATION OF HYPOTHESIS | 46 | | CHAPTER-6 | RESEARCH DESIGN-MEANINGAND FORMULATION OF RESEARCH DESIGN | 54 | | CHAPTER-7 | CONTENTANALYSIS | 60 | | CHAPTER-8 | MEANING AND TYPES OF SAMPLING | 73 | | CHAPTER-9 | DATACOLLECTION METHODS-OBSERVATION | 82 | | CHAPTER-10 | QUESTIONNAIRE AND SCHEDULE FORMULATIONANDAPPLICATION | 90 | | CHAPTER -11 | GRAPHIC REPRESENTATION OF DATA | 99 | | CHAPTER-12 | MEASURES OF CENTRALTENDENCY- MEAN, MEDIANAND MODE | 112 | | CHAPTER-13 | MEASURES OF DISPERSION | 119 | | CHAPTER-14 | CORRELATIONANALYSIS | 129 | | | Assignments | 134 | CHAPTER-1SOCIAL SCIENCE RESEARCH: NATURE, SCOPE, AND OBJECTIVES Structure 1 .0 Learning Objective 1.1    Introduction 1.2    Social Research 1.3    Aims of Social Research 1.4    Motivating Factor of Social Research 1.5    Basic Assumptions of Social Research 1.6    Vitality of Social Research 1.7    Self Check Exercise 1.8    Summary 1.9    Glossary 1.10   Answer to Self Check Exercise 1.11   Terminal Questions 1.12   Suggested Readings 1 .0 Learning Objectives After studying this lesson, the learner will be able: -      To understand about the Meaning, Nature and Significance of Social Science Research. -      To knows about the different aims of Social Research -      To Comprehend about the basic Assumptions of Social Research To analyze about the utility of Social Research in day to day life. 1.1    Introduction One of the Characteristics of an under developed country’ attempting to develop rapidly is its enthusiasm for research. Research in simple words can be said to be theory building and theory testing. Research isundertaken in order to solve the problems being faced by a man, knowledge is grown for the sake of solving the existing problems of a modern world. Further, research is neither reading nor writing a text book. It is not even a haphazard looking for facts. It is essentially a systematic enquiry seeking facts through objectives, verifiable methods in order to discover the relationship among them and to deduce from them broad principles or laws. Broadly, speaking there arethree types of research, although it is difficult, and sometimes almost impossible, to isolate them. The difference between them is only one of emphasis as to which fact is more important. These three types are: Firstly the discovery of facts, as in social survey. Secondly Research, which largely interprets already available information i.e., it makes use of secondary data. It does not mean that this type of research does not at all .collect primary data or new facts, but it only means that the emphasis is more on the analysis and interpretation of the existing information. Finally Research of a purely theory type. The essence of this type is building up of the higher reaches of a pure theory based only distinctly upon primary and secondary data and sometimes on the basis of purereasoning. 1.2    Social Research According to P.V. Young, “Social research may be defined as: A scientific undertaking which by means of logical and systematized techniques, aims to: 1)    Discover new4cts or verify and test old facts. 2)    Analysis of their sequence, inter-relationships and casual explanations which are derived with in anappropriate theoretical frame of reference. 3)    Develop new scientific tools, concepts and theories which would facilitate reliable and valid studyof human behaviour. Encyclopedia of the Social Sciences defines social research is a systematic method of exploring analysing and concept realizing social life in order to “extend correct or verify knowledge, aids in construction of atheory or in practice of an art.” 1.3    Aims of Social/Research From the above given definitions of the social research, the aim and Purpose of social research is thecollection, analysis, saving and generalization of facts and there by attain a grasp of general principles underlying social facts. This helps in predicting and controlling of future course of action. The aims of social researchcan be classified in to two categories: 1.    Theoretical Aims: Social research studies-man in relation to other men and therefore; its subject matter is comprised of social facts. A Social fact includes all biological, ecological, cultural and anthropological data relevant to explanation of mans behavior in relation to society. Social research also aims at knowledge of social facts. Thus, the primary object of social. Research is to get true an intimate knowledge of human society and its functioning, to know and understand the laws that are operating behind various social activitiesof man. 2.    Utilitarian Aims : According to P.V Young “The primary goal of research immediate or distantis to understand social life and thereby gain a greater measure of control over social behavior.” Human society suffers from a number of social evils. And all these evils or at least most of them have their roots in theorganisation of human society and it’s working. The utilitarian view should not lead us to concede that the purpose of the social research is to find aremedy for all evils. 1.4    Motivating Factors of Social Research P.V., Young has mentioned motivating factors of social’ research. 1.    Curiosity about unknown : According to Young, curiosity is an internsic trait of human mind acompelling drive in the exploration of man’s surroundings.” It is a natural instinct in the mankind. Even a small child is curious about the unknown objects that he notices around him and tries to understand them in his own way. The same curiosity drives a scientist to explore unknown factor’s working behind the social phenomena. 2.    Desire to understand the cause and effect of wide spread social problems : The search for cause and effect relationship has been more relentless than almost any other scientific effort upon which human energies have been spent. More and more research is undertaken to dispel doubts and uncertainties which result from inadequate conceptions of underlying factor shaping social processes. People do not wantonly an account of events but want to know how they happened. 3.    Appearance of novel and unanticipated situations : Man is often faced with many acute and difficult social problems. An ordinary person reacts emotionally to these but a social scientist sits down dispassionately to find out their cause and thus evolve a lasting solution to such intricate problems. In quite a large number of cases such problems have inspired the social scientists to go into details and study the basicfactors causing these problems. 4.    Desire to discover new and test old scientific procedures as an efficient way to gain useful and fundamental knowledge: Such research is not in fact a research in social phenomena, but a research in techniques or methods used in social research. A number of such cases have been made to evolve better and most refined techniques for dealing with social problems. Of late there has been growing emphasis uponthe use of quantitative or statistical method in social research in order to make it more definite and mathematicallyprecise. 1.5    Basic Assumptions of Social Research 1.    Existence of cause and effect relationship : It has to be accepted as a basis of social research thatthere exists cause and effect relationship between various. Social activities. These cause always produce similar results and therefore, if they are known they can be used effectively in checking the evils resulting from them. 2.    Existence of sequence or law in social activities: Another assumption is that various social activities do not occur in a haphazard random way. There is some system, some trend behind them. If this trend orsystem is located it is possible to Predict the future course of social phenomena. 3.    Possibility of detached study: Although man is a part of the society which he is investigating, yet it is possible for him to study it apart from him. This own feelings and emotions would not be reflected in the study. Although it is very difficult task, it is not impossible. 4.    Existence of ideal type: In society, everyone is not entirely different from each other. People may be grouped into fairly homogeneous classes known as ideal type. The deduction drawn from the study of the group may be made possible to apply to the whole type. 5.    Possibility of Representative Sample: It is generally assumed that a sample, representative of the group may be drawn and the deduction from the study of the sample may be made applicable to the whole group. 1.6    Utility of Social Research 1.    Social Control : Planning is not confined to the field of economics alone, it has an equal importance in the field of social organisation. We can no longer remain passive on looking and let the society take its own route whether towards good or evil. The leaders of society must guide its path for an orderly marchtowards preplanned goals. A control over society is possible only when we have a complete knowledge of the organisation and working of society and its various institutions, their inter-relationship mat guide human behaviour. All this can be achieved only through a scientific study of society. 2.    Social Cohesion : The study of society creates better understanding between different social groups it reveals the underlying unity in the midst of apparent disparity, and thus helps to create the feeling of oneness, sympathy and understanding in place of racial prejudice. If such understanding create a large part of national and international problems would be solved without much difficulty. 3.    Social Welfare : Social Welfare or removal of social evils can be achieved through social research. Social research/helps us to judge the magnitude of social evils and thus take necessary step to remove them with a large number of legislation and reformative measures. We trace their origin to the reports of social surveys. Social research helps us to find out not only the magnitude but also the real cause of evils and thus helps us to devise ways to hit at the root of these evils. 3.    Social Prediction : Social research helps us to formulate social laws which show relationship between social facts and their causes. Once the existence of causative factors and their magnitude is known, we canpredict the result. Although accurate prediction in majority of cases is not possible due to complexity of socialphenomena, variety of causative factors and their instability Yet certain broad trends ca definitely be located.: Statistical methods can successfully be used for such prediction. 5.    Social growth : Social research helps in growth of society on right lines. Every Society is subject to grow in the direction in which it is influenced by its own structure, institutions, social values, etc. But it is equally affected by our knowledge of our own ‘ society as well as other societies. Man is a rational being and gains by experience. The future path of social progress is, therefore conditioned by one knowledge of ourselves an other people. Social research thus helps in guiding the trend of social growth on more proper times and towards cherished goals. 6.    Perfection of tool of Research : As social-research progresses the tools of analysis and methods of research become more perfect with use. New tools are devised to give more an accurate and precise results. As the tools of research becomes more perfect, the science of sociology itself more towards greater perfection. 1.7    Self Check Exercise a.    What do you mean by Social Research? b.    What are the aims and purpose of Social Research? c.    Describe about motivating factors of Social Research? 1.8    Summary Social Science Research is the activity of gathering, analyzing and interpreting information for a variety of Social, Economic, Educational and Political purposes. It has a critical appreciation of contemporary society and social issues based on a sound foundations of social theory and research methodology. Social Science Research facilitate the better understanding between different groups of society which create the feeling of oneness, Sympathy and understanding in place of racial prejudice. It helps in growth of societyon right path and tends towards development. 1.9    Glossary Systematic-acting according to a fixed plan or method Haphazard-lacking any obvious principles of organizationCuriosity- a strong desire to know or learn something. Cohesion- the action or fact of forming a united whole. 1.10    Answer to Self-Check Exercise (a) See 1.1 and 1.2 (b) See 1.3 (c) See 1.4 1.11    Terminal Questions a.    Describe in detail about the basic assumptions of Social Research? b.    Write a note on utility of Social Research. 1.12    Suggested Readings •    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. •    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. •    Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. •    Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. •  Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. •  Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. •    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. •    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. ***** 14 CHAPTER-2TYPE OF RESEARCH: PURE & APPLIED, EXPLORATORY, DESCRIPTIVE AND ACTION RESEARCH Structure 2.0 Learning Objectives 2.1    Introduction 2.2    Types of Research: 2.2.1    Pure Research 2.2.2    Applied Research 2.3    Exploratory Research 2.4    Descriptive Research 2.5    Action Research 2.6    Self Check Exercise 2.7    Summary 2.8    Glossary 2.9    Answer to Self Check Exercise 2.10    Terminal Questions 2.11    Suggested Readings 2.0 Learning Objectives After studying this lesson, the learner will be able: To understand types of research methods. To know about the importance of pure and applied research. To get the knowledge about descriptive, exploratory and action research. 2.1    Introduction Research is a process to discover new knowledge. In the Code of Federal Regulations (45 CFR 46.102(d)) pertaining to the protection of human subjects research is defined as: “A systematic investigation (i.e., the gathering and analysis of information) designed to develop or contribute to generalizable knowledge.” The National Academy of Sciences states that the object of research is to “extend human knowledge of the physical, biological, or social world beyond what is already known.” Research is different than other forms of discovering knowledge (like reading a book) because it uses a systematic process called the Scientific Method. The Scientific Method consists of observing the world around you and creating a hypothesis about relationships in the world. A hypothesis is an informed and educated prediction or explanation about something. Part of the research process involves testing the hypothesis, and then examining the results of these tests as they relate to both the hypothesis and the world around you. When a researcher forms a hypothesis, this acts like a map through the research study. It tells the researcher which factors are important to study and how they might be related to each other or caused by a manipulation that the researcher introduces (e.g. a program, treatment or change in the environment). With this map, the researcher can interpret the information he/she collects and can make sound conclusions about the results. Research can be done with human beings, animals, plants, other organisms and inorganic matter. When research is done with human beings and animals, it must follow specific rules about the treatment of humans and animals that have been created by the U.S. Federal Government. This ensures that humans and animals are treated with dignity and respect, and that the research causes minimal harm. No matter what topic is being studied, the value of the research depends on how well it is designed and done. Therefore, one of the most important considerations in doing good research is to follow the design or plan that is developed by an experienced researcher who is called the Principal Investigator (PI). The PI is in charge of all aspects of the research and creates what is called a protocol (the research plan) that all people doing the research must follow. By doing so, the PI and the public can be sure that the results of the research are real and useful to other scientists. 2.2    Types of Research Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it. It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated. Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilizes the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors. There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several. 2.2.1    Pure Research Pure research, also known as basic research or fundamental research refers to scientific or academic investigations that aim to expand knowledge and understanding in a particular field without any immediate practical application or commercial goal. It is driven by curiosity, the pursuit of knowledge, and a desire to explore the unknown. The primary objective of pure research is to discover new theories, principles, or concepts, or to deepen our understanding of existing ones, rather than focusing on specific practical outcomes or applications. It often involves theoretical and experimental work conducted in laboratories, research institutions, or academic settings. Here are some key characteristics and aspects of pure research: •    Knowledge expansion: Pure research seeks to uncover new knowledge, push the boundaries of human understanding, and provide a foundation for further scientific and technological advancements. It aims to explore fundamental questions, mechanisms, and phenomena in various disciplines. •    Curiosity-driven: Pure research is primarily motivated by curiosity and a desire to explore the unknown. Researchers often pursue questions that intrigue them or have the potential to contribute to the overall knowledge base of their field. •    Unrestricted by immediate applications: Unlike applied research, which focuses on solving specific problems or developing practical applications, pure research is not constrained by short-term goals or commercial considerations. Its focus is on generating knowledge for knowledge's sake. •    Long-term impact: Although the practical applications of pure research may not be immediately evident, it often serves as the foundation for future applied research or technological advancements. Many significant scientific breakthroughs and discoveries have emerged from pure research efforts. Interdisciplinary nature: Pure research often transcends disciplinary boundaries, as researchers explore connections, common principles, and patterns across different fields. It encourages collaboration and the exchange of ideas between researchers from diverse backgrounds. •    Rigorous methodology: Pure research follows rigorous scientific methodologies, including hypothesis formulation, experimentation, data collection, analysis, and peer review. It relies on empirical evidence and logical reasoning to draw conclusions and contribute to the existing body of knowledge. •    Knowledge dissemination: The results of pure research are typically shared through academic publications, conferences, and scientific forums. This enables other researchers and scholars to build upon the findings, verify or challenge them, and contribute to the ongoing scientific discourse. •    Uncertain outcomes: Pure research involves exploring uncharted territories, and the outcomes are not always predictable. Researchers may encounter unexpected results or pursue paths that lead to dead ends. However, even negative results contribute to the collective knowledge by narrowing down possibilities and guiding future research directions. Examples of pure research include investigations into the fundamental particles of the universe, the exploration of deep space and celestial bodies, deciphering the mechanisms of biological processes, studying the intricacies of human cognition, and investigating the nature of complex mathematical systems. These endeavors contribute to our understanding of the world, even if their practical applications are not immediately evident. Pure research plays a vital role in advancing scientific knowledge, fostering innovation, and building the foundation for applied research and technological breakthroughs. By expanding our understanding of the natural world and fundamental principles, it opens up new possibilities and drives progress in various fields. 2.2.2    Applied Research Applied research, also known as practical research, refers to scientific or scholarly investigations conducted with the specific purpose of solving practical problems, improving existing processes, or developing new technologies and applications. Unlike pure research, applied research focuses on immediate or near-term outcomes and has direct relevance and applicability to real-world situations. Here are some key characteristics and aspects of applied research: •    Problem-solving orientation: Applied research is driven by the desire to address specific issues or challenges in various domains, such as medicine, engineering, agriculture, business, or social sciences. It aims to provide practical solutions or improvements to existing systems, processes, or technologies. •    Goal-oriented: Applied research is conducted with well-defined objectives and practical outcomes in mind. Researchers aim to develop specific products, technologies, methods, or interventions that can be implemented in real-world settings to bring about tangible benefits. •    Collaboration with stakeholders: Applied research often involves collaboration between researchers and external stakeholders, such as industry partners, government agencies, nonprofit organizations, or community groups. These collaborations help ensure that the research is aligned with the needs and requirements of the end-users or beneficiaries. •    Emphasis on implementation: Applied research not only focuses on generating knowledge but also on implementing and translating that knowledge into practical applications. Researchers work on developing prototypes, conducting field trials, or testing interventions to demonstrate the feasibility and effectiveness of their solutions. •    Action-oriented methodology: Applied research employs a range of methodologies, including experimental studies, case studies, surveys, simulations, and modeling, to gather data and evaluate the effectiveness of proposed solutions. It emphasizes the practical relevance of research findings and their potential impact. •    Time-sensitive outcomes: Applied research aims to produce timely outcomes that can be readily applied or implemented. The results are often expected to address immediate needs or offer improvements over existing methods, technologies, or practices. •    Feedback loop and iterative process: Applied research often involves an iterative process of developing, testing, and refining solutions based on feedback and evaluation. Researchers gather data, analyze results, make adjustments, and retest their interventions to ensure optimal performance and applicability. •    Knowledge transfer and dissemination: While applied research is primarily focused on practical outcomes, it also contributes to the body of knowledge in the respective field. Researchers disseminate their findings through publications, technical reports, conferences, or workshops, allowing others to learn from their experiences and build upon their work. Examples of applied research include developing new medical treatments, improving manufacturing processes, designing sustainable energy systems, optimizing transportation logistics, creating effective educational interventions, or implementing social policy interventions. These endeavors are driven by the need to address specific challenges and provide tangible solutions that have immediate impact and practical applications. Applied research bridges the gap between theoretical knowledge and practical implementation, driving innovation, economic growth, and social progress. It helps industries, organizations, and societies overcome obstacles, improve efficiency, and enhance the quality of life. By translating scientific knowledge into actionable solutions, applied research plays a crucial role in addressing real-world problems and shaping our daily lives. 2.3    Exploratory Research Exploratory research is a type of research conducted to explore and gain initial insights into a relatively unexplored or poorly understood subject or phenomenon. It is typically conducted when the existing knowledge or information about a topic is limited or when there is a need to generate new ideas or hypotheses. Exploratory research is characterized by its open-ended and flexible nature, allowing researchers to gather preliminary data and form a foundation for further investigation. Here are some key characteristics and aspects of exploratory research: •    Investigating new or underexplored areas: Exploratory research aims to delve into areas that have not been extensively studied or are not well understood. It seeks to identify and explore new topics, emerging trends, or phenomena for which little information or theory exists. •    Generating new insights and ideas: The primary goal of exploratory research is to generate new insights, ideas, or hypotheses. Researchers often use qualitative methods such as interviews, observations, focus groups, or case studies to gather data and explore the subject in depth. These methods allow for a flexible and open-ended approach to data collection and analysis. •    Formulating research questions or hypotheses: Exploratory research helps researchers develop research questions or formulate initial hypotheses that can guide further investigation. It provides a foundation for more focused and targeted research by identifying key variables, relationships, or patterns. •    Flexible research design: Exploratory research adopts a flexible research design that allows for iterative and adaptive approaches. Researchers may modify their research questions, methods, or sampling strategies based on the emerging findings and insights obtained during the research process. •    Qualitative data collection and analysis: Exploratory research often relies on qualitative data collection methods, which enable researchers to capture rich and detailed information. Techniques such as interviews, focus groups, content analysis, or ethnographic observations help researchers explore perspectives, experiences, and context-specific factors. •    Small sample sizes: Exploratory research typically involves smaller sample sizes compared to quantitative research methods. Researchers focus on gathering in-depth information from a select group of participants or cases, aiming for a comprehensive understanding of the subject rather than statistical generalizability. •    Inductive reasoning: Exploratory research employs inductive reasoning, where researchers develop theories or generalizations based on the patterns or themes observed in the collected data. It allows for the discovery of new insights and the formulation of initial hypotheses that can be further tested in subsequent research. •    Foundation for further research: The findings and insights gained from exploratory research provide a foundation for further investigation. They can inform the design of more focused and rigorous studies, such as descriptive research, experimental research, or quantitative surveys. Examples of exploratory research include preliminary studies conducted before embarking on a larger research project, pilot studies to test the feasibility of research methods, exploratory interviews or focus groups to gain initial insights into a specific topic, or observational studies to explore behaviors or phenomena in a naturalistic setting. Exploratory research plays a crucial role in expanding knowledge, identifying research gaps, and formulating hypotheses for further investigation. It helps researchers explore new avenues of inquiry, generate innovative ideas, and lay the groundwork for more comprehensive and targeted studies in various fields of research. 2.4    Descriptive Research Descriptive research is a type of research that aims to describe and document the characteristics, behaviors, or phenomena of a particular subject or population. It focuses on providing an accurate and detailed account of the topic under investigation without attempting to explain causal relationships or make predictions. Descriptive research is concerned with answering the questions of "what," "where," "when," and "how" rather than "why." Here are some key characteristics and aspects of descriptive research: •    Objective description: Descriptive research aims to objectively describe and report the characteristics, features, or behaviors of a specific subject or population. It provides a comprehensive account of the topic under investigation, often using quantitative methods, statistical analysis, and data visualization. •    Data collection methods: Descriptive research typically involves the collection of data through various methods, such as surveys, questionnaires, interviews, observations, or existing data sources. Researchers aim to gather information from a representative sample or the entire population of interest to ensure the accuracy and reliability of the descriptive findings. •    Quantitative data analysis: Descriptive research often utilizes quantitative data analysis techniques to summarize and present the collected data. Researchers employ statistical measures such as averages, frequencies, percentages, correlations, or graphical representations to provide a clear and concise description of the data. •    Cross-sectional design: Descriptive research frequently adopts a cross-sectional design, where data is collected at a specific point in time or during a specific period. This allows researchers to capture a snapshot of the subject or population under study at a given moment. •    Population and sample: Descriptive research involves defining the population of interest, which could be a specific group, community, organization, or a broader target population. Researchers then select a representative sample from this population to collect data, ensuring that the findings can be generalized to the larger population. •    Summarizing and presenting data: Descriptive research focuses on summarizing and presenting data in a clear and accessible manner. This can involve using tables, charts, graphs, or other visual representations to communicate the characteristics, patterns, or trends observed in the data. •    Reporting findings: Descriptive research aims to provide a detailed and objective account of the research findings. Researchers typically present their results in research reports, academic papers, or presentations, providing a comprehensive description of the topic, population, data collection methods, and the statistical analyses conducted. •    Foundation for further research: Descriptive research often serves as a foundation for further research or more advanced forms of inquiry. The findings and insights gained from descriptive research can inform the development of hypotheses, the design of experimental studies, or the formulation of more complex research questions. Examples of descriptive research include surveys conducted to gather demographic data, observational studies documenting behaviors in a specific setting, census reports summarizing population characteristics, market research studies providing information about consumer preferences, or case studies describing unique phenomena or events. Descriptive research plays a critical role in providing a detailed and accurate description of various subjects, populations, or phenomena. It helps in documenting existing conditions, identifying patterns, and establishing a foundation for further research or decision-making processes in fields such as social sciences, marketing, education, public health, and many others. 2.5    Action Research Action research is a systematic approach to problem-solving and knowledge generation that is conducted in real-world contexts. It involves a cyclical process of planning, acting, observing, and reflecting, with the aim of bringing about practical change and improving the situation or condition being studied. Elaborating on action research, let's break down the key components: •    Purpose: Action research is driven by a specific purpose or goal, usually related to addressing a problem or improving a particular situation. It is commonly used in various fields such as education, healthcare, social work, and organizational development. •  Collaboration:  Action research often involves collaboration between researchers, practitioners, and stakeholders who have a vested interest in the issue being studied. This collaborative approach ensures that diverse perspectives are considered and that the research is relevant and meaningful to those involved. •    Cyclical Process: Action research follows a cyclical process that typically consists of several iterative stages: a.    Planning: The researchers identify the research question or problem they want to address, define the goals and objectives, and plan the research design and methods to be used. b.    Acting: In this stage, the planned actions or interventions are implemented in the real-world setting. These actions could involve implementing new practices, making changes to existing processes, or introducing innovative solutions. c.    Observing: During the action phase, data is collected through various methods such as observations, interviews, surveys, or document analysis. The researchers closely monitor and document the outcomes and effects of the implemented actions. d.    Reflecting: The collected data is analyzed and interpreted to gain insights into the effectiveness of the actions taken. Researchers reflect on the findings and draw conclusions about what worked, what didn't, and what needs to be modified or improved. 1.    Participatory Approach: Action research emphasizes the active participation of all stakeholders involved. It values the knowledge and expertise of practitioners and encourages their involvement in all stages of the research process. This participatory approach ensures that the research findings are more likely to be relevant, accepted, and implemented. 2.    Continuous Learning and Improvement: Action research is a continuous learning process. The findings and insights generated through each cycle of action and reflection inform subsequent cycles, leading to further improvements and refinements. It allows for ongoing adaptation and refinement of interventions based on the evolving understanding of the problem and its context. 3.    Contextualized Knowledge Generation: Action research aims to generate practical knowledge that is contextually relevant and applicable. The research findings are not intended for abstract theory development but are focused on generating actionable insights and solutions that can be implemented to bring about positive change in the real world. Overall, action research serves as a bridge between theory and practice by engaging practitioners and researchers in a collaborative effort to improve real-world situations. It combines scientific rigor with practical applicability, making it a valuable approach for addressing complex problems and fostering positive change. 2.6    Self Check Exercise a.    What do you mean by pure and applied research? b.    Describe about types of research? 2.7    Summary In summary, the significance of research lies in its ability to expand knowledge, address practical challenges, bridge gaps in understanding, foster innovation, and contribute to the academic community. By conducting meaningful and rigorous research, researchers can make valuable contributions to their respective fields and contribute to the betterment of society as a whole. 2.8    Glossary Research- a detailed and careful study of something to find out more information about it Applied research: Applied research is a non-systematic way of finding solutions to specific research problems or issues. 2.9    Answer to Self Check Exercise (a) See 2.1 and 2.2 (b) See 2.3 (c) See 2.4 2.10    Terminal Questions a.    Describe in detail about the pure and applied research? b.    Write a note on descriptive and action research. 2.11    Suggested Readings •    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. •    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. •    Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. •    Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. •  Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. •  Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. •    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. •    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. ***** CHAPTER-3 REVIEW OF LITERATURE Structure 3.0 Learning Objectives 3.1    Introduction 3.2    Historical Development and Background 3.3    Conceptual Framework and Theoretical Perspectives 3.4    Methodological Approaches 3.5    Gaps in Knowledge and Research Questions 3.6    Self Check Exercise 3.7    Summary 3.8    Glossary 3.9    Answer to self check exercise 3.10 Terminal Questions 3.11    Suggested Readings 3.0 Learning Objectives After studying this lesson, the learner will be able: -       To understand literature review. -       To know the significance of literature review in research. -       To comprehend about the different steps in literature review. -      To analyze conceptual framework and theoretical analysis. -      To understand the methodological approach in literature review. 3.1    Introduction A literature review is a critical and systematic examination of the existing body of published literature, scholarly articles, books, dissertations, conference proceedings, and other sources relevant to a particular research topic or question. It serves as an essential component of any research study, as it helps researchers understand the current state of knowledge, identify gaps, and situate their research within the broader academic context. The purpose of a literature review is multi-fold: •    Familiarize with Existing Knowledge: A literature review allows researchers to familiarize themselves with the existing body of knowledge on their research topic. By reviewing relevant literature, researchers can gain insights into the theories, concepts, models, and findings that have been explored by previous scholars. This step is crucial for researchers to build a foundation of knowledge and avoid duplicating previous work. •    Identify Gaps and Research Questions: Literature reviews help identify gaps or limitations in the existing literature. By critically analyzing and synthesizing previous studies, researchers can identify areas where further investigation is needed. These gaps form the basis for research questions or hypotheses, guiding the focus and direction of the current study. •    Evaluate Methodologies and Approaches: Literature reviews provide researchers with an opportunity to assess the methodologies, research designs, and data collection techniques employed in previous studies. By critically evaluating the strengths and weaknesses of different approaches, researchers can make informed decisions about the methods they will use in their own research. This evaluation helps ensure that the chosen methodology is appropriate for addressing the research questions and objectives. •    Demonstrate Scholarly Understanding: A literature review showcases the researcher's understanding of the current state of knowledge and theoretical perspectives within their research field. By synthesizing and summarizing the findings of previous studies, researchers demonstrate their ability to critically analyze and evaluate existing research. This step is crucial for establishing the researcher's credibility and expertise in the chosen research area. •    Contextualize and Justify the Research: A literature review provides the necessary context for the research study. By reviewing the existing literature, researchers can situate their work within the broader academic discourse and justify the significance of their research. This step helps researchers demonstrate the novelty and relevance of their study and contributes to the overall scholarly conversation. When conducting a literature review, it is important to follow a systematic approach. This involves searching for relevant sources, critically evaluating their quality and relevance, organizing the literature based on key themes or subtopics, and synthesizing the information to provide a comprehensive overview. The literature review should also include proper citations and referencing to acknowledge the original authors and sources. Overall, a literature review is a rigorous and comprehensive examination of existing knowledge on a specific research topic. It not only provides a foundation for the current study but also contributes to the advancement of knowledge by identifying gaps and stimulating further research. 3.2    Historical Development and Background The historical development and background of the literature review can be traced back to the emergence of academic research and the evolution of scholarly communication. While the specific practice of conducting literature reviews may vary across disciplines, the fundamental principles and purposes have remained consistent throughout history. Early History: The roots of literature review can be found in ancient Greece, where scholars such as Aristotle and Plato engaged in critical examination and discussion of existing philosophical works. However, the modern concept of literature review began to take shape during the Renaissance and Enlightenment periods in Europe. During this time, scholars sought to systematically gather and analyze existing knowledge in various fields, leading to the establishment of early scholarly societies and academies. Emergence of Academic Journals: The 17th and 18th centuries witnessed the rise of academic journals, which became crucial platforms for sharing and disseminating research findings. These journals played a significant role in the development of the literature review as they provided a means for researchers to access and review previous studies. Scholars would refer to these publications to identify the state of knowledge, build upon existing work, and contribute to the collective understanding of a subject. Standardization of Research Methods: In the late 19th and early 20th centuries, the formalization and standardization of research methods and methodologies began to take shape. Scholars recognized the importance of systematic approaches to gathering and analyzing information, leading to the establishment of protocols for conducting literature reviews. Researchers increasingly recognized the value of reviewing and synthesizing existing literature to inform their own studies and avoid duplicating efforts. Information Explosion and Information Retrieval Systems: The latter half of the 20th century witnessed a significant increase in the volume of scholarly literature being published. The growth of academic disciplines, the advancement of research methods, and the emergence of specialized fields led to an explosion of information. This expansion necessitated the development of information retrieval systems, such as bibliographic databases and library catalogs, to facilitate the efficient search and retrieval of relevant literature. Evolution of Literature Review Practices: With the advent of digital technologies and the internet, the landscape of literature reviews underwent a transformative shift. Online databases, search engines, and electronic resources made it easier for researchers to access and review a wide range of literature from diverse disciplines. This expanded accessibility and convenience greatly facilitated the literature review process, enabling researchers to conduct more comprehensive and interdisciplinary reviews. In recent years, the literature review process has further evolved with the emergence of citation management tools, systematic review methodologies, and the emphasis on evidence-based practices. The importance of transparency, replicability, and rigor in conducting literature reviews has also gained prominence, leading to the development of guidelines and standards for conducting high-quality reviews. Overall, the historical development of the literature review can be seen as a response to the growth of knowledge, the need for systematic inquiry, and the desire to build upon and contribute to existing research. It has evolved from a practice rooted in critical examination and discussion to a structured and methodical process that plays a vital role in shaping contemporary research endeavors. 3.3    Conceptual Framework and Theoretical Perspectives In a literature review, the conceptual framework and theoretical perspectives provide the underlying structure and theoretical basis for understanding the research topic. They help researchers analyze and interpret the existing literature and provide a framework for their own study. Here's a closer look at the conceptual framework and theoretical perspectives in a literature review: •    Conceptual Framework: A conceptual framework is a structure of concepts, theories, and ideas that guide the understanding and exploration of a particular phenomenon or research topic. It provides a lens through which researchers can organize and analyze the existing literature. The conceptual framework helps researchers identify the key variables, relationships, and factors that are relevant to the research topic. In a literature review, the conceptual framework serves as a roadmap for understanding and organizing the reviewed literature. It helps researchers determine the scope and boundaries of their study, identify gaps in knowledge, and establish the theoretical foundation for their research. The conceptual framework can be developed based on existing theories, models, or conceptual frameworks from related fields or previous studies. •    Theoretical Perspectives: The theoretical perspectives in a literature review refer to the established theories or conceptual frameworks that researchers draw upon to interpret and analyze the existing literature. These perspectives provide the lens through which researchers view and make sense of the reviewed studies. Theoretical perspectives can be discipline-specific or interdisciplinary, depending on the nature of the research topic. Researchers often adopt specific theoretical perspectives to guide their literature review and gain insights into the phenomenon under investigation. Theoretical perspectives can help researchers identify common themes, patterns, or explanatory frameworks across the reviewed literature. They 30 also assist researchers in identifying gaps, contradictions, or unresolved debates within the existing literature. When discussing the theoretical perspectives in a literature review, researchers should critically evaluate the strengths, weaknesses, and applicability of the theories to their research topic. They should also identify any gaps or limitations in the existing theoretical frameworks and explain how their research contributes to addressing those gaps. The conceptual framework and theoretical perspectives in a literature review work together to provide a structure for understanding and analyzing the existing literature. They guide researchers in organizing the reviewed studies, identifying research gaps, and formulating research questions or hypotheses. By establishing a theoretical foundation, researchers can situate their own study within the broader academic context and contribute to the advancement of knowledge in their field. 3.4    Methodological Approaches The methodological approach in a literature review refers to the systematic process researchers employ to search for, select, evaluate, and synthesize relevant literature. It involves the use of specific strategies and criteria to ensure that the literature review is comprehensive, rigorous, and unbiased. Here's a closer look at the key elements of the methodological approach in a literature review: •    Defining Inclusion and Exclusion Criteria: Researchers need to establish clear inclusion and exclusion criteria to determine which sources will be considered for the literature review. These criteria may include factors such as publication date range, language, geographic scope, study design, and relevance to the research question or topic. Defining these criteria ensures that the literature review focuses on the most relevant and reliable sources. •    Conducting Comprehensive Literature Search: Researchers should employ a systematic and comprehensive approach to search for relevant literature. This may involve using multiple databases, search engines, and other sources such as bibliographies and reference lists of relevant articles. The search strategy should be transparent and reproducible, ensuring that no important sources are missed. The use of appropriate keywords, search operators, and filters can help refine the search and retrieve relevant studies. •    Screening and Selection Process: Once the initial set of literature is gathered, researchers need to screen the titles and abstracts to determine their relevance to the research question. Full-text articles of potentially relevant studies are then assessed to determine their eligibility for inclusion. This screening and selection process should be conducted independently by multiple reviewers to minimize bias and enhance reliability. Disagreements can be resolved through discussion or by involving a third reviewer. •    Quality Assessment and Critical Appraisal: After selecting the studies, researchers need to critically appraise the quality and rigor of the included literature. This evaluation assesses the credibility, methodological soundness, and internal validity of the studies. Various tools and checklists can be used to guide this assessment process, depending on the type of studies included (e.g., randomized controlled trials, qualitative studies, systematic reviews). Quality assessment helps researchers determine the strength of evidence and potential biases in the literature. •    Data Extraction and Synthesis: Researchers systematically extract relevant data from the selected studies. This may include details such as study characteristics, sample size, research methods, key findings, and conclusions. The extracted data are then synthesized, typically using a narrative or thematic approach. Researchers identify common themes, patterns, or gaps in the literature, and summarize the main findings and arguments. The synthesis should be conducted in a transparent and systematic manner, ensuring that the results accurately reflect the reviewed literature. •    Ethical Considerations: Researchers need to consider ethical aspects when conducting a literature review. This may involve obtaining necessary permissions for using copyrighted material, protecting participants' confidentiality, and appropriately citing and acknowledging the original authors and sources. Adhering to ethical guidelines ensures the integrity and credibility of the literature review. By following a robust methodological approach, researchers can ensure that the literature review is comprehensive, transparent, and of high quality. This approach enhances the reliability and validity of the findings, enabling researchers to draw meaningful conclusions and contribute to the scholarly understanding of the research topic. 3.5    Gaps in Knowledge and Research Questions Gaps in knowledge refer to areas within the existing literature where further research is needed or where significant knowledge limitations exist. Identifying these gaps is an essential step in the literature review process as it helps researchers understand the state of current knowledge and determine the unique contribution their research can make. Here's a closer look at gaps in knowledge and how they relate to research questions: • Identification of Gaps in Knowledge: During the literature review, researchers critically analyze and synthesize the existing literature to identify areas where knowledge is lacking or incomplete. These gaps can manifest in various ways: a.    Inconsistencies or Contradictions: Researchers may encounter conflicting findings, contradictory theories, or divergent interpretations within the literature. These inconsistencies suggest the need for further investigation to clarify or reconcile the discrepancies. b.    Unexplored Aspects: The literature may highlight certain aspects of the research topic that have received limited attention or have not been adequately explored. Researchers may find gaps in terms of specific populations, contexts, or phenomena that require further investigation. c.    Methodological Limitations: Researchers may identify limitations in the methodologies or research designs employed in previous studies. These limitations could include small sample sizes, lack of diversity in participants, or reliance on self-report measures. Addressing these methodological gaps can strengthen the overall quality and reliability of future research. d.    Evolving Knowledge: Some research areas are dynamic, with ongoing developments and emerging trends. Researchers may identify gaps resulting from the need to update or expand upon existing knowledge in light of recent advancements or changes in the field. • Formulation of Research Questions: Once the gaps in knowledge have been identified, researchers can formulate research questions or hypotheses that address these gaps. Research questions are specific inquiries that guide the focus of the study and help generate new knowledge. These questions should be: a.    Clear and Specific: Research questions should be well-defined and clearly articulate what the study aims to investigate. They should address the identified gaps in knowledge and provide a specific focus for the research. b.    Relevant and Significant: Research questions should align with the significance and relevance of the research topic. They should address important issues or gaps in knowledge that have implications for theory, practice, or policy. c.    Feasible and Answerable: Research questions should be realistic and feasible to address within the scope of the study. They should be answerable through the chosen research methods and available resources. d.    Aligned with Research Objectives: Research questions should be closely aligned with the overall objectives of the study. They should reflect the purpose of the research and guide the data collection, analysis, and interpretation processes. By formulating research questions that address the identified gaps in knowledge, researchers ensure that their study contributes to the advancement of knowledge in the field. These research questions guide the subsequent stages of the research process, including study design, data collection, analysis, and interpretation, ultimately leading to new insights and understanding. 3.6 Self Check Exercise a.    Discuss the importance of objectivity in literature review. b.   Trace the interrelationship concept hypothesis and theory with the use of example. c.    Access the role of hypothesis in making a research design. d.    Discuss the characteristics of Scientific Research. 3.7    Summary Scientific- based on or characterized by the methods and principles of science or systematic or methodical. Predictability- The ability to be predicted. Generality- a statements or principal having general rather than specific validity or force. Objectively- the quality of being objective. Hypothesis- a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation. 3.8    Glossary 3.9    Answer to self check exercise (a) See 3.1 & 3.2 (b) See 3.3 3.10 Terminal Questions e.    Discuss the importance of objectivity in literature review. f.    Trace the interrelationship concept hypothesis and theory with the use of example. 3.11 Suggested Readings •    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. •    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. •    Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. •    Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. •  Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. •  Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. •    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. •    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. ***** 36 Suggested Readings CHAPTER-4 BASIC ELEMENTS OF RESEARCH: RESEARCH PROBLEM, SELECTION AND FORMULATION Structure 4.0 Learning Objectives 4.1    Introduction 4.2    Selection of a Problem for Research 4.2. 0 The Mode of Selection 4.2.1    Source of Problems 4.2.2   Process of Identifications 4.2.3   Criteria of Selection 4.3    Formulation of the Selected Problem 4.4    Construction of Frequency Table 4.5    Principles of Table Constructions 4.6    Self Check Exercise 4.7    Summary 4.8    Glossary 4.9    Answer to Self-Check Exercise 4.10    Terminal Questions 4.11    Suggested Readings 4.0 Learning Objectives After studying this lesson, the learner will be able: -      To understand about the basic elements of research. -       To know about the selection criteria of a problem for research. -       To learn construction of frequency Table. -       To learn construction of frequency Table. -      To Comprehend the Principles of Table Construction. 4.1    Introduction Research is an organized endeavour. Like any other organized work, research requires proper planning. Planning means deciding in advance. Planning of research means deciding the question or issue to be studied, setting the objectives of the study and determining the means of achieving those objectives. R is an intellectual process. It requires intellectual curiosity, intelligence, imagination and vision; and knowledge of methodology of research. Planning systematizes the research work. It eliminates aimless intellectual wandering. It gives direction to the work. Planning Process The planning stage of a research project involves the following steps: 1.    Selection of a problem for research: This involves identification of a few problems and selectingone out of them, after evaluating the alternatives against certain selection criteria. 2.    Formulation of the selected problem: The selected problem is defined and transformed intoresearchable questions. 3.    Formulation of hypotheses: The propositions to be tested are set up. 4.    Conceptualization: The concepts associated with the problem under study are operationally definedand measurement devices are designed. 5.    Research plan or design: This plan covers all the aspects of the selected, research work and servesas a blue-print for the endeavour. All these aspects of research planning are discussed in detail in the following sections. 4.2    SELECTION OF A PROBLEM FOR RESEARCH The selection of a problem is the first step in research. The term ‘problem’ means a question or issue to be examined. The selection of a problem for research is not an easy task; itself is a problem: It is leastamenable to formal methodological treatment. Vision an imaginative insight, plays an important role in thisprocess. This problem of selection arises when a student has to undertake research as a part of his course requirement. In some universities a project study is prescribed as a requirement for even undergraduate programmes. In several universities a project study is a partial requirement of postgraduate programmes like M. Com., M.B.A., M.S.W., etc. Research is an important requirement of M. Phil, and of Doctoral degree programmes. In each of these cases, a student has to select a problem for his research. Similarly, faculty members of colleges and universities select problems for research as an academic pursuit. In case of projects sponsored by planning bodies, departments of government, industries or other organizations, the sponsors themselves invariably suggest the problems to be studied. The nature of the problem to be selected depends upon the level at which the research is done. A problem appropriate for undergraduate/master degree students will necessarily be a modest one. The emphasis is upon the learning process of a beginner. A problem to be selected for M. Phil/Ph.D. programme must be a major problem requiring comprehensive treatment. In this case, the emphasis is upon both skill development and contribution to knowledge. On the other hand, a problem to be selected by an experienced academic researcher must be a complex problem meant for making a significant contribution to the development or refinement of theory or to policymaking. 4.2. 0 The Mode of Selection The students who undertake research as a course requirement do their research work under the guidance of a professor. What should be the mode of selection in their cases? Should a problem be suggested by the guide or be selected by the researcher himself? A beginner in research may, of course, prefer the first choice. He will be happy if the problem is suggested by the guide, for his problem of selection is easily solved. But is such suggestion appropriate? The suggestion of the problem by the guide means an imposition. It is an attempt to get some work done rather than to train the student to do research on his own. It destroys spontaneity. The problem suggested by the guide may not be as topic in which the student is really interested. Then he may not find pleasure instudying it, and the study will become an unwanted burden to him. Therefore, it is better to choose the problem oneself of course, the guide can help the candidate to help himself. He may dig out the candidate’s area of interest and show him the way. The actual selection should be choice of the candidate himself. One with a critical, curious and imaginative mind and is sensitive to practical problems could easily identifyproblems for study. 4.2.1    Sources of Problems The sources from which one may be able to identify research problems or develop problem awareness are: 1.    Reading: When we critically study books and articles relating to the subject of our interest, pertinent questions may arise in our mind. Similarly, areas of research may strike to our mind when we read research reports. 2.    Academic experience: Classroom lectures, class discussions, seminar discussions and out-ofclass exchanges of ideas with fellow students and professors will suggest many stimulating problems to be studied. 3.    Daily experience: Life is dynamic. We learn new things and undergo new experiences every day. If we are alert, inquisitive and sensitive to life situations, we may hit upon questions worth of investigation, “It isa mark of scientific genius to be sensitive to difficulties where less gifted people pass untroubled by doubts.1 The story about Newton justifies to this. Though apples might have fallen on the heads of people 42 before Newton’s time, only the sensitive Newton applied his mind on this event which led to the discovery of “Law of Gravitation.” 4.    Exposure to field situations: Field visits, internship training and extension work provide exposure to practical problems which call for study. 5.    Consultations: Discussions with experts, researchers, administrators and business executives will help a researcher to identify meaningful problems for research. 6.    Brain Storming: Intensified discussion within a group of interested persons may often be a means ofidentifying pertinent questions, and of developing new ideas about a problem.2 7.    Research: Research on one problem may suggest problems for further research. 8.    Intuition: Sometimes new ideas may strike to one’s mind like a flash. Reactive mind is a spring ofknowledge. 4.2.2    Process of Identification The process of identification of problems for academic research may consist of the following steps: 1.    Selection of the discipline : The discipline or subject in which one proposes to do research may be selected, e.g.. Economics, Commerce, Management, Technology, Psychology etc. The selection of the discipline is easy. One can select any subject, which one has studied thoroughly and which has interested him most. Where one has to do research in his field of specialization (e.g., Marketing Management or Finance Management or Personnel Management in M.B.A. Programme), one has to choose the subject of one’s specialization. 2.    Demarcating the broad area or a particular aspect of the selected subject: The second step is to select a particular aspect of the selected subject.. For example, if the selected subject is Financial Management, then one may select capital budgeting, financial leverage, working capital management or profit management as specific area of study. One who says that one is willing to do research on any aspect of discipline does not mean business or does not know oneself. One should identify his specific area of interest. Interest in a particular area of a subject develops out ofeducational background, reading a good book or inspiration received from a professor. 3.    Identifying two or more specific topics in the selected broad area: This is the final step in identification of problem. This requires a grasp of the branch of the subject as a whole and awareness of work already done on it. A review of concerned literature including research theses and survey of research published by the Research Councils like Indian Council of Social Science Research, New Delhi, intensivereading and reflective thinking, and discussion with the guide will help a student in identifying specific topics or issues for research. Both an uncharted path and a well-trodden path are dangerous to a beginner in research. If he chooses an unexplored problem, he has to grope in the dark and may get frustrated. On the other hand, if he selects a problem, which has already been thoroughly studied, he may not learn anything new nor contribute anything to the knowledge. It is desirable for him to adopt a via media approach. He may identify problems on which he can get the guidance of a few articles or books. A student cannot just select anyone of the identified problems for his research. He has to evaluate themfor choosing the most appropriate one. How can this evaluation be done? Against what criteria? 4.2.3    Criteria of Selection The selection of one appropriate researchable problem out of the identified problems requires evaluation of those alternatives against certain criteria. These criteria may be grouped into : (a)    Internal (or personal) criteria or factors, and (b)    External criteria or factors. Internal criteria consist of (1) researcher’s interest, (2) researcher’s competence, and (3) researcher’s own resource; finance and time. External factors include (1) researchability of the problem, (2) its importance and urgency, (3) novelty of the problem, (4) feasibility, (5) facilities, (6) usefulness and social relevance, and (7) Research personnel. Researcher’s interest: The problem should interest the researcher and be a challenge to him. Without interest and curiosity, he may not develop sustained perseverance. Even a small difficulty may become an excuse for discontinuing the study. Interest in a problem depends upon the researcher’s educational background, experience, outlook andsensitivity. Researcher’s competence: A mere interest in a problem will not do. The researcher must be competentto plan and carry out a study of the problem. He must have the ability to grasp and deal with it He must possess adequate knowledge of the subject matter, relevant methodology and statistical procedures. Researcher’s own resources: In the case of a research to be done by a researcher on his own, consideration of his own financial resource is pertinent. Does the cost involved in conducting the study of the problem iswithin the means of the researcher? If it is beyond his means, he will not be able to complete the work, unlesshe gets some external financial support. Time resource is more important than finance. Research is a timeconsuming process. What is the time that the researcher can be able to spare for the research work? Is it adequate to meet the time requirements of the problem? If not, the work cannot be completed within-theprescribed time limit. As it is difficult to foresee the eventual time constraint, it .is desirable to over-estimate the time requirement and to under-estimate the time availability. In this connection, available tidbits of time (say 5 or 10 minutes at a time) should not be counted, as nothing could be done in 5 or 10 minutes, only large chunks of time available should be counted. Researchability : The problem should be researchable, i.e., amenable for finding answers to the questions involved in it through scientific method. “Although every problem in science involves a question or a series of questions, not every question qualifies as a scientific problem”3. To be researchable a question must be one for which observation or’ other data collection in the real world can provide the answer. Many questions cannot be answered on the basis of information alone. They may involve value elements, e.g., what is merit for the purpose of employee promotion? What is ‘fairness’ “to the workers? Some questions may not be researchable because procedures or techniques are inadequate, e.g., how will a new fiscal policy affectdistributive justice? Which new management trainees have potential for top management? Importance and urgency: Problems requiring investigation are unlimited, but available research efforts are very much limited. Therefore in selecting problems for research, their relative importance and 46 significance should be considered. An important and urgent problem should be given priority over an unimportant one. For example, in industrial management today, problems of productivity, capacity utilization, motivation and industrial unrest are more important than problems of financial leverage, profit planning, vertical/horizontal integration, marketing etc. Research must be focused on useful and urgent problems. Novelty or originality: The problem must have novelty. There is no use of wasting one’s time andenergy on a problem already studied thoroughly by others. This does not mean that replication is always needless. In social sciences in some cases, it is appropriate to replicate (repeat) a study in order to verify the validity of its findings to a different situation. Feasibility : A problem may be a new one and also important, but if research on it is not feasible, it cannot be selected. Hence feasibility is a very important consideration. 3.    Merton R., et al, (ed.), Sociology Today - Problems and Prospects, New York: Harper and Row, p. 92. Some of the questions that should be considered in examining the feasibility are : •    Are suitable research techniques such as measurement devices and techniques of analysis available? •    Are accurate and reliable data available? The reliability of the findings depends upon the quality of data. In some cases, available data may be tinged by emotions. Ethnic conflicts, strikes and lockouts, poverty and affluence are examples of topics heavily weighted by emotions. •    Will the authorities of the concerned institutions extend the required cooperation in furnishing data or permit access to records? Some organizations like commercial banks, sole proprietary and partnership concerns and private limited companies do not easily extend cooperation to researchers. •    Will the respondents be willing to be interviewed? •    Can the study be completed within the time available? On the basis of the consideration of the above questions, the feasibility of the problem should be determined. Facilities: Research requires certain facilities such as well equipped library facility, suitable and competent guidance, data processing facility, etc. Hence, the availability of the facilities relevant to the problem mustbe considered. Usefulness and social relevance: Above all, the study of the problem should make ‘significant’ contribution to the concerned body of knowledge or to the solution of some significant practical problem. It should be socially relevant. This consideration is particularly important in the case of higher-level academicresearch and sponsored research. Research personnel: Research undertaken by professors and by research organizations require the services of investigators and research officers. But in India and other developing countries, research has not yet become a prospective profession. Hence, talented persons are not attracted to research projects. Employment in research projects is just considered as a stop-gap arrangement pending securing a regular placement. Therefore appropriate qualified and experienced research personnel are not easily available for the study of some problems. 4.3    FORMULATION OF THE SELECTED PROBLEM The problem selected for research may initially be vague. The question to be studied or the 48 problem to be solved may not be clear. Why the answer/solution is wanted also may not be known. Hence, the selected problem should be defined and formulated. This is a difficult process. It requires intensive reading of a few selected articles or chapters in books in order to understand the nature of the selected problem; The reading at this stage should be focused on the ‘classics’ and research papers on the topic Gunnar Myrdal’s The Challenge of World Poverty in the study of anti-poverty programmes, Peter F. Drucker’s The Effective Executive in the study of managerial effectiveness, Harold’s paper on Dynamic theory in Growth Economics are a few examples of classics. The researcher should read such selected literatures, digest, think and reflect upon what is read and digested. He should also discuss with learned persons. Then only can he gain insight into the chosen problem and be able to define and formulate it. 4.4    CONSTRUCTION OF FREQUENCY TABLE Frequency tables provide a “shorthand” summary of data. The importance of presenting statistical data in tabular form needs no emphasis. Tables facilitate comprehending masses of data at a glance; they conserve space and reduce explanations and descriptions to a minimum. They give a visual picture of relationships between, variables and categories. They facilitate summation of items and the detection oferrors and omissions and they, provide a basis for computations. It is important to make a distinction between the general purpose tables and specific tables. The general- purpose tables are primary or reference tables designed to include large amounts of source data in convenient and accessible form. The special purpose tables are analytical or derivate ones that demonstrate significant relationships in the data or the results of statistical analysis. Tables in reports of government on population, vital statistics, agriculture, industries etc., are of general-purpose type. They represent extensive repositories of statistical information. Special purpose tables are found in monographs, research reports and articlesand are used as instruments of analysis. In research, we are primarily concerned with special purpose tables. Components of a table: The major components of a table are; A.    Heading (i)    Table Number (ii)    Title of the Table (iii)    Designation of units B.    Body (i)    Sub-head: Heading of all rows or blocks of stub items (ii)    Body head; Headings of all columns or main captions and their sub-captions. (iii)    Field/body: The cells in rows and columns C.    Notations (i)    Footnotes, wherever applicable (ii)    Source, wherever applicable The format of a Frequency Table is Presented below: TABLE   NUMBERTITLE OF THETABLE Designation in units Stub-head    Caption         Or Column head        Box head Y    N      Total 1    …… 2    ……. 3    ……                    Sub captions                     Field/body 4    …….                                                 (Data only) Notation s Footnote sSource: 4.5    Principles of Table Construction There are certain generally accepted principles of rules relating to construction of tables. They are: (a)    Every table should have a title. The title should represent a succinct description of the contents’ of the table. It should be clear and concise. It should be placed above the body of the table. (b)    A number to facilitate easy reference should identify every table. The number can be centered abovethe title. The table numbers should run in a consecutive serial order. Alter-natively tables in Chapter1 be numbered as 1.1, 1.2, 1 ; in Chapter 2 as 2.1, 2.2; 2.3. and so on. (c)    The captions (or column headings) should be clear and brief. (d)    The units of measurement under each heading must always be indicated. (e)    Any explanatory footnotes concerning the table itself are placed directly beneath the table and in order to obviate any possible confusion with the textual footnotes such reference symbols as theasterisk (*) dagger (+) and the like may be used. (f)    If the data in a series of tables have been obtained from different sources, it is ordinarily advisable to indicate the specific sources in a place just below the table. (g)    Usually lines separate columns from one another. Lines are always drawn at the top and bottom of the table and below the captions. (h)    The columns may be numbered to facilitate reference. (i)    All column figures should be properly aligned. Decimal points and ‘plus’ or ‘minus’ signs should be inperfect alignment. (j)   Columns and rows that are to be compared with one another should be brought close together. (k)   Totals of rows should be placed at the extreme right column and totals of columns at the bottom. (l)    In order to emphasize the relative significance of certain categories, different kinds of type, spacingand identifications can be used. (m)    The arrangement of the categories in a table may be chronological, geographical, alphabetical oraccording to magnitude. Numerical categories are usually arranged in descending order of magnitude. (n)    Miscellaneous and exceptional items are generally placed in the last row of - the table. (o)    Usually the larger number of items is listed vertically. This means that a table’s length is more than itswidth. (p)    Abbreviations should be avoided whenever possible and ditto marks should not be used in a table (q)    The table should be made as logical, clear, accurate and simple as possible. 4.6    Self Check Exercise a.    What do you mean by planning of Research? b.    Discuss about the sources of problem for Research? c.    Elaborate about the Process of identification of Problems for research. 4.7    Summary Planning of research mean deciding the Question or issue to be studies, setting the objectives of the study and determining. The means of achieving those objections research is an intellectual process which requires intellectual curiosity, intelligence, imaginations & vision as well as knowledge methodology of research. Research Problem need to be checked out on the basis of feasible and researchable problem which is interesting to you and within your competence and manageable within the available time and resources and at the same time, has some importance and social relevance, and for which required facilitiesare available. The researcher should read selected literature. The researcher should discuss with learned persons. Tables facilitate comprehending masses of data at a glance. They conserve space and reduce explanations and descriptions to a minimum. 4.8    Glossary Research Design- a blue print of research Consultations- Discussions with experts Novelty-the quality of being new, original or unusual. 4.9    Answer to Self Check Exercise (a) See 4.1 (b) See 4.2.2. (c) see 4.2.3 4.10    Terminal Questions a.    What do you mean by Research Design? Discuss about the process of Research Design. b.    Discuss about the principle of rules relating to construction of tables? 4.11    Suggested Readings •    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. •    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. •    Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. •    Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. •  Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. •  Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. •    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. •    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. ***** CHAPTER-5HYPOTHESES: DEFINITION, FEATURES, TYPES AND TESTING PROCEDURES. Structure 5 .0 Learning Objectives 5.1    Introduction 5.2    Meaning of Hypothesis 5.3    Characteristics of Hypothesis 5.4    The Role of Hypothesis 5.5    Types of Hypothesis 5.6    How a Hypothesis originate 5.7    Criteria of useful Hypothesis 5.8    Formulation of Hypothesis 5.9    Testing of Hypothesis 5.10         Self-Check Exercise 5.11       Summary 5.12        Glossary 5.13        Answer to Self-Check Exercise 5.14         Terminal Questions 5.15        Suggested Reading 5.0 Learning Objectives After studying this lesson, the learner will be able: -      To understand the Meaning, Concept and Role of Hypothesis in research. -       To discuss about the characteristic of a good Hypothesis. -      To know about types of Hypothesis. -      To analyze that how Hypothesis originate and what are the criteria of useful Hypothesis. -      To comprehend about the method of Testing of a Hypothesis. 5.1    Introduction Discovering facts, establishing relationship between them and explaining situations and events so as finally to lead to rational generalizations and whenever possible to help to predict, constitute the process and purpose-of research. The process itself is broadly in four Successive stages, namely the formulation of a hypothesis, of a theory, and of a law culminating, if possible, in axioms. These four stages were developments from the initial stage of a hypothesis to the final one of an axiom. When a researcher observes the known facts and takes up a problem for analysis, he first has to start somewhere and this point of start is hypothesis. 5.2    Meaning of Hypothesis The word hypothesis is compound of two words ‘hypo’ and thesis and literally hypo means under or below and thesis means a reasoned Theory of rational viewpoint. Accordingly, hypothesis would mean a theory which is not fully reasoned in other words, hypothesis is a theory entertained in order to study the facts and examine the validity of the theory.. According to Coffey, “a hypothesis is an attempt at explanation, a provisional supposition made in order to explain scientifically some facts or phenomenon.” And according to Lohen and Nagel, “A hypothesis directs our search for the order.” It is not essential for a hypothesis to be necessarily true. Hence, hypothesis can be explained as a tentative solution posed on a cursory observation of known and available data and “adopted provisionally to explain certain events and to guide in the investigation of others. It is, in fact a possible solution to the problem. 5.3    Characteristics of Hypothesis The truth of a hypothesis involves observation, imaginative thinking and anticipation and deductive verification These are: 1.    Observation: Observation is a pre-condition of formulation of hypothesis. Unless, we perceive a difficultly or problem and do not feel the inner goading for solving it, we do not reflect. Therefore, observation is the first stage of hypothesis making. 2.    Reflection: Having felt a difficulty and need for a solution, we consider the problem by perceiving the relevant facts for example we see a sea in high tide and also find clear moon above. Now we appreciate a relation which is based upon an experience, namely where ever there is high tide there is full moon and never otherwise as far as we experience. Having established a relationship between two facts we know to formulate an answer for the way of this relation. This answer is hypothesis. 3.    Deduction: The third and the last step in this process is examination of hypothesis for various deductions possible from it and their mutual Compatibility and Correspondence with already known facts. There deductions are extremely useful in rejecting ill-formed hypothesis. 4.    Verification: Actually, verification is post hypothesis formulation and therefore it is not step in its formulation, but in as much as one interest in making hypothesis is not purely academic or theoretical, we wish to solve our difficulty and this difficultly can be solved if we actually test our hypothesis. 5.4    The Role of Hypothesis : It has been stated that the hypothesis is a suggested explanation on the basis of existing knowledge but with a mind open to a change of view if facts gathered at the / enquiry suggest a different explanation. Its defined purpose is to indicate the direction / of the investigation and to suggest what facts are collected. Without it research becomes unfocused and merely a pointless empirical wandering because, the function of a hypothesis is to direct our search for order among facts. The Suggestions formulated in any hypothesis may be a solution to the problem. The hypothesis, therefore gives point to the enquiry, without it the investigator may collect non-essential and even useless data and may also overload really significant and useful ones. Such unplanned gatherings of data results in waste of time and effort and rarely leads to discovery of unexpected relation between facts. It is this purpose that the hypothesis serves in an investigation. 5.5    Types of Hypothesis 1.    ‘Explanatory or Descriptive Hypothesis: A hypothesis may be about a cause of a phenomenon or about the law of which is an instance. A hypothesis about cause is explanatory whereas a hypothesis about law is descriptive. 2.    Tentative hypothesis: Where a phenomenon cannot be fully understood because of technical difficulties, we make tentative hypothesis about it and see how for this is successful in explaining. Sometimes we siminaltaneonsly test two or more hypothesis. The famous hypothesis about propagation of light namely, wave theory and corpuscular theory of light and both explain the phenomenon the Ught and none of them is final. They are tentative. 3.    Representative fictions: Some hypothesis consist of assumption as to the minute structure and operation of bodies. From the nature of the case, these assumptions-can never be proved by direct means. Their only merit is their suit ability to express the phenomenon. The hypothesis is based upon imaginative reasoning and it primarily involves thinking with the help of concrete instances. This hypothetical reasoning is abstract. A hypothesis which proves to be correct becomes a theory or law. The law of generation was a hypothesis in Newton’s mind but when proved to be true it became a law. 5.6    How a hypothesis originate There is no particular method of forming hypothesis. The choice of hypothesis depends upon Scientist’s range of knowledge and his nature abilities. There are however, certain methods to hypothesis making. These are: 1.    Induction by Simple Enumeration: We see that all roses irrespective of colour and size are sweet smelling and from this observation we form the hypothesis that rose is a sweet-smelling flower. 2.    Method of agreement: If we find that various objects in a group have a common circumstances we form a hypothesis about it on this basis for example, if all guests in a marriage party feel giddy and sick after taking food, we shall conclude that good must be poisoned. 3.    Analogy: It we observe some common features among various things then we form a hypothesis on this basis. For example, Suppose we find, that some hill people we came across are very simple. Now, we shall suppose that other hill people are also simple, because they are inhabitants of hills. This hypothesis about hill people is posed on analoging reasoning. 4.    Concomitant variation : Sometimes we form hypothesis about two phenomena by observing certain relationship between them. For example if we find medicines in attractive containers sell more than those in simple containers, we may form a hypothesis that all medicines presented in attractive containers will sell more than before. 5.    Personal experience and individual reactions may give rise to hypothesis. Some investigators have the unique capacity of perceiving interesting patterns in apparently jump facts. 5.7    Criteria of useful hypothesis All hypothesis are not equally helpful to the enquiry and some ara perhaps not at all. The researcher, therefore, has to separate the more from the less useful ones. In this process some important Considerations would help him: 1.    Conceptual Clarity : Since concepts have a particular significance in a particular science and in particular context they should be clearly defined in a communicable form, and with reference to usage in current research, otherwise, evaluation of the previous ideas and Continuity of Scientific work become difficult. This require discussing concepts with co-workers. 2.    A moral empirical reference: Science has no place for moral judgements and, therefore, no usable hypothesis can embody percepts. As far as possible, words which convey a moral judgement, such as should, ought, good, bad, etc. have to be avoided. Even where attitudes and opinions are under investigation the researcher should be interested in facts and not in his own thoughts. 3.    Specific and precise : The hypothesis must be precise and specific, if, it must help to detail all operations and predictions connected with it in the process of investigation. Specificity helps to avoid the use of selective evidence, thus increasing the validity of the findings. Precision demands that the hypothesis is not in general terms. This can be achieved by breaking-up a single hypothesis into its component sub hypothesis. Although, it is a laborious process, it enables the clarification of the relationship between data and the conclusion, in addition to making the task of the researcher more manageable. 4.    Relevant Techniques : An investigation to be practical should relate the hypothesis to the investigational techniques feasible in the particular science e.g. while the experimental technique is not feasible but necessary in physical sciences, it would not be equally so in social science and any hypothesis which very largely has to depend upon experimentation would be out of place until the technique itself has been developed adequately. This inquires in the researcher a sound knowledge of techniques to test the hypothesis and, thus to formulate practical questions. 5.    Relation to a body of theory : Science grows and is commutative, and each researcher adds his little quote is this process of growth. When therefore a hypothesis is based on a body of existing theories it is likely to make better contribution to knowledge. 5.8    Formulation of Hypothesis To have good, precise and testable hypothesis a great deal of thought and time has to be devoted the carefully the hypothesis is formulated the easier will be the further investigation and the more accurate the verification. Sometimes the researcher has to start with tentative statements, and sometimes a number of possible hypothesis have to be considered and discarded either in the very beginning of the enquiry or in the course of it. The researcher must bear in mind that a hypothesis is not formed merely when an area Pf study, either territorial, or of a problem. It is however, possible to get at the workable hypothesis if some important things are borne is mind. First the researcher should clear up his mind of accepted beliefs and solutions for the existence of a problem automatically means that it has not been satisfactorily answered. Secondly the researcher should concentrate on the nature of the problem to enable him to reach relevant facts. This demands adequate knowledge of the theoretical framework in the context of which he has to formulate and verify the hypothesis thirdly the 60 researcher must also be familiar with the techniques of phrasing the hypothesis properly so as to avoid vague terms and concepts Fourthly he should further specify the validity test to be applied to the hypothesis. He should avoid, as already stated, value judgements and keep his mind open often he has to familiatrise himself with alternative ways of collecting the facts and verifying them and carefully select which of these he would follow. Finally it is useful generally to choose problems helping to refute, qualify or support existing theories. 5.9    Testing of a Hypothesis In this age of science and logic, we do not admit anything as valid until a satisfactory’ test of its validity is carried out. We may often start with an assertion or hypothesis are use one research data to prove or disprove it. Any hypothesis is put with known statistical procedures and unless such tests are carried out; as research study may not be treated as complete. If the observed data is to be in consistent with the hypothesis, the hypothesis is rejected and if there is agreement it is accepted. Formulation of hypothesis and their verification are fundamental aspect of research. A hypothesis is a speculation about the phenomena. It is based on the common sense, theory or empirics facts. This hypothesis is a necessary link between theory and the investigation which leads to discovery of additions of knowledge. Testing process is usually based on null hypothesis (According to R.A. Fisher,” Null Hypothesis is the hypothesis which is tested for possible rejection under the assumption that it is true”). Which assumes no difference or zero sampling error, sometimes null’ hypothesis is rejected even when it is true. Such an error is known as ac - type error. On the other hand if it is accepted when it is false, it is known as P-type error. Large sample tests of Population parameters are usually carried out by using the standard errors of sample statistics the following results are given for the benefit of a researcher to enable him to solve different problems of this type: Standard error of mean (X)= Standard error of median (M)= Standard error of quartile (Q)= Standard error of standard deviation = Where a is the standard deviation for x Standard error of Coefficient of correlation (r)= Standard error of regression coefficient (x on y)= Standard error for difference of means   (X¯ ,- X¯ )= 12 a vn 1.25336 a -—— √n 1.3626 a -=— √n a √2n 1-r2 √2n aY √1 - r2 aX al2   a122 n1     n2 Where ox ay are the standard deviations for x and y respectively. Procedure of Testing : 1.    If the observed difference between sample statistic arid population parameter is larger than standard error, the difference is said to be significant and if the observed difference is less than 2 standard error, the difference is not significant at 5% level of significance. 2.    If the observed difference between sample statistic and population parameter is larger than 3 standard error, the difference is significant and if the observed difference is less than 3 standard error; the difference is not significant at 1% level. Small Sample Testing of hypothesis in small samples is carried out with the help of sampling distributions, ‘t’ distribution, ‘F’ distribution and ‘x2’ (chi-square) distribution are the principle distributions used in testing procedures. For testing the difference between sample mean and population mean, the difference between means of two randomly drawn samples and other sample statistics such as correlation and regression coefficient etc. ‘t’ distribution is frequently used. Tests for variances are conducted by using ‘F’ distribution, ‘x2’ distribution is likewise used in testing of hypothesis and also as a test of goodness of fit, some known forms of ‘t’ distribution are : | (1) t    = | X¯-m | Where al α1 ≠ α2 and | |---|---|---| | n-1 | α/√n | m + m are two means | | (2) tn +n -2 | m 1 +m2 | Read as ‘Y’ distribution with | | 1    2 | √α12   a 12 n 1  +   n 1 | n-1 degrees of freedom. | | | | Where r is coefficient of | | (3) tn-1 = | e√n-2 r√1-n2 | correlation | Testing Procedure: When the given sample data, the value of t’ or T’ or x’ statistics is calculated and then such a value is compared with the tabulated value of these distribution corresponding to the given number of degrees of freedom. It calculated value is less than the tabulated value. The difference is not treated as significant and if the difference is considered as significant interpretation of these results depends upon the type and language of the problem. Chi-Square Test The x’ test applies only to discrete data, committed rather than measured value. The test is the test of independence, the idea that one is not affected by, or related to ‘ another variable. It is used to estimate the likelihood that some factor other than chance’ (sampling ‘error) accounts for the apparent relationship. Since the null hypothesis states that there is no relationship, the test merely evaluates the probability that the observed relationship results from chance. Many a times, we observe results from the randomly drawn samples and these results may not agree with the results that would be obtained from theoretical distribution under the normal rates of probability. The laws of probability suggest that if a uniform die is tossed (say) six times, each face must turn upwards once. But in actual conduct of this operation, it may not be so. Therefore, we are always interested in testing statistically whether the observed frequencies from an experiment are significantly different from theoretical expected frequencies. In such cases x’ distribution renders great help to the researchers and the statistical analysts. Many a time we use this distribution for testing the goodness of it of a given theoretical distribution under known hypothesis. The formula for determining Chi-square is x2 n-1 n = z 2=1 E2 Where     0     =     Observed frequency E =    Expected frequency n- 1    = Degree for freedom Procedure for testing of HypothesisThese are: 1.    Determination of the problem: First and foremost important work of hypothesis is the determination of the problem or we have to understand clearly what decisions we need about it. The decision can be either acceptance or rejection of hypothesis. 2.    Setting up of a Null hypothesis: A null hypothesis is based upon the nature of problem. 3.    Selection level of significance: The researcher predetermines the level of significance which is based on the nature of the problem and his own judgement. Generally a hypothesis is tested for 1% or 5% level of significance. 4.    Computation of Standard Error: After determination of level of significance standard error is calculated. Various statistical measures have different standard errors. A suitable statistics is chosen based on the nature of hypothesis we have to test. (5 ) Calculation of Significance Ratio: To know significance ratio (T) we divide difference between sample statistics and parameters from standard error. x - ^ (Significance Ratio) T =   ------ xc 6.    Interpretation: Here we compare the predetermined critical value and significance ratio. In the value of significance ratio is less than critical ratio, value of significance ratio is greater than critical ratio difference between sample statistics and population the difference between sample statistics and population parameter will be insignificant and is merely due to parameter will be significant and on the other hand if sampling fluctuations. 5.10    Self-Check Exercise a.    What do you mean by Hypothesis? Discuss about different types of Hypothesis. b.    How a Hypothesis originate? Discuss in Details. c.    Elaborate about the important criteria of a good Hypothesis. d.    Discuss the procedure for testing of Hypothesis. 5.11    Summary When a researcher observes the known facts and takes up a problem for analysis, he first has to start somewhere and this starting point is hypothesis. It would mean a theory which is not fully reasoned. Hypothesis is a theory entertained in order to study the facts and examine the validity of the theory. The truth of a hypothesis involves observation, imaginative thinking, anticipation and deductive verification criteria of a good hypothesis includes conceptual clarity, a moral empirical reference specific and precise, relevant techniques, relation to a body of theory and so on. Thus hypothesis clarified the researcher in determining the level of significance which is based on the nature of the problem and his own judgement. 5.12    Glossary Reflection - serious thought or consideration. Observation - the action or process of closely observing or monitoring something. Analogy - a comparison between one thing and another. Empirical – based on, concerned with or verifiable by observation. 5.13    Answer to Self-Check Exercise (a) See 5.2 & 5.5 (b) See 5.6 9c) See 5.7 (d) See 5.9 5.14    Terminal Questions (a)    Discuss the role of Hypothesis in research procedure. (b)    Elaborate about the procedure for testing of Hypothesis emphasizing chi-square test. 5.15    Suggested Readings •    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. •    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. •    Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. •    Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. •  Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. •  Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. •    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. •    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. ***** CHAPTER-6RESEARCH DESIGN: DEFINITION, CONTENTS AND TYPES Structure 6.0 Learning Objectives 6.1    Introduction 6.2    Need for Research Design 6.3    Features of a Good Design 6.4    Steps in Research Design 6.5    Important Concepts Relating to Research Design 6.6    Different Research Design 6.7    Exploratory Research Studies 6.7.1    Method of Exploratory Studies 6.7.2 Descriptive Research Studies 6.7.3    Hypothesis Testing Research Studies 6.8    Self-Check Exercise 6.9    Summary 6.10    Glossary 6.11    Answer to Self-Check Exercise 6.12    Terminal Questions 6.13S uggested Readings 6.0 Learning Objectives After studying this lesson the learner will be able: To understand about the meaning and concept of Research Design. To Comprehend different steps in Research Design. To know about important concepts relating to Research Design. To describe about different types of Research Design. 6.1    Introduction A research design is the logical and systematic planning and directing of a piece of research. The design results from translating a general scientific method/model into varied research procedures. The design has to be geared to the available time and money, to the availability of data to the extent to which it is desirable or possible to impose upon person and social organisations which might supply the data. The research design is nothing but a plan of research According to this data are collected, processed and analysed. The research design differs depending upon the research purpose. 6.2    Need for Research design: Research design is needed because it facilitates the smooth sailing of the various research operations, thereby making research as efficient as possible yielding maximum information with minimum expenditure of effort, time and money. Research design stands for advance planning of the methods to be adopted for collecting the relevant data and the techniques to be used in the analysis, keeping in view the objective of the research and the availability of staff, time and money. Preparation of research design should be done with great care as any error can upset the entire project. Research design infect, has a great bearing on the reliability of the results arrived at and as such constitutes the firm foundation of the entire edifice of the research work. Hence, is imperative that an efficient and appropriate design must prepared before starting research operations. The design helps the researches to organize his ideas in a form whereby will be possible for him-to look for flaws and inadequacies. 6.3    Features of a Good Design A good research- design is often characterised by adjectives like flexible, appropriate, efficient and economical and so on. Generally, the design which minimizes the bias and maximizes the reliability of data collected and analysed is considered a good design. Similarly, a design which yields maximum information and provides an opportunity for considering many different aspects of a problem is considered most appropriate and efficient design in respect of many research problems. Hence, the question is related to the purpose or objective of the research problem and also with the nature of the problem to be studied. A research design appropriate for a particular research problem, usually involves the consideration of the following factors; i)    the means of obtaining information; ii)    the availability and skills of the researches and his staff, if any, iii)    the objective of the problem to be studied, iv)    the nature of the problem to be studied; and v)    the availability of time and money for research work 6.4    Steps in Research Design: 1.    Selection of a problem and definition: The problem selected for study should be defined clearly in operational terms so that the researcher knows positively what facts he is looking for and what is relevant to the study. Besides the operational definition of a problem; the problem selected should be practicable in cost of time and money. If the criteria of validity and reliability of results are to be fulfilled; such problems, as are unmanageable by the researcher, should not be selected for the design. 2.    Sources of data: Once the problem is selected, it is the duty of the research to state clearly the various sources of information such as library, documents, field work, etc. 3.    Nature of study: The research design should be expressed in relation to the nature of’ study to be undertaken. The choice of the statistical, experimental or comparative type of study should be made at this stage so that the following steps in planning may have relevance to the proposed problem. 4.    Objective of Study: Whether the design aims at a theoretical understanding or presupposes a “welfare” motion must be explicit at this point. Stating the object of the study aids not only in clarity of the design but also in a sincere response from the respondents. 5.    Socio-Cultural Context: A research design is always set to a context which has a social and cultural bearing on the individuals. 6.    Temporal Context : The geographical limit of the design should also be referred to at this stage so that the research related to the hypothesis is applicable to particular social groups only. 7.    Dimensions: It is physically impossible to analyse the data collected from a large universe. Hence, the selection of an adequate and representative sample is the watchword in the research. Depending upon the dimension of the proposed study, such a sample of the large population can be selected to facilitate a practical design. 8.    Basis of selection : The mechanics of drawing a random, stratified, purposive, etc. When followed carefully, will produce a scientifically valid sample in an impressive manners. 9.    Techniques of data collection: Relevant to the study design a suitable technique has to be adopted for the collection of required data. The relative merits of each method of data collection will help in the choice of suitable technique. Once the collecting data is complete; analysis, coding and presentation of report naturally follow. 6.5    Important Concepts Relating to Research Design 1.    Dependent and independent variables: A concept which can take on different quantitative values us called variable, e.g. weight, height, income, etc. Qualitative phenomena (or attributes) are also quantified on the basis of the presence or absence of the concurring attributes. Phenomena which can take on quantitatively different values even in decimal points are called continuous variables. If one variable depends upon or is a consequence of the other variable, it is termed as dependent variable and the variable that is antecedent to the dependent variable is termed as independent variable. For instance, if we say that height depends upon age, then height is a dependent variable and the age is independent variable. 2.    Extraneous variable: Independent variables that are not related to the purpose of study, but may affect the dependent variable are termed as extraneous variable. 3.    Control : One important characteristic of a good research design is to minimize the influence or effect of extraneous variable(s). The technical term ‘control’ is used when we design the study minimizing the effects of extraneous in, dependent variables. In exponential researches, the term ‘control’ used to refer to restrain experimental conditions. 4.    Confounded relationship : When a prediction or a hypothesised relationship is to be tested by scientific methods, it is termed as research hypothesis. The research hypothesis is a predictive statement that relates an independent variable to a dependent variable. Usually a research hypothesis must contain, at least one independent and one dependent variable. Predictive statements, which are not to be objectively verified or the relationships that are as seemed but not to be tested, are not termed research hypothesis. 5.    Experimental and non-experimental hypothesis testing Research: When the purpose of research is to test research hypothesis, it is termed as hypothesis-testing research. It can be experimental or the nonexperimental design. Research in which the dependent variable is manipulated is termed experimental hypothesis-testing research and are search in which in dependent variable is not manipulated is called nonexperimental hypothesis-testing research. 6.    Experimental and control groups: In an experimental hypothesis-testing research when a group is exposed to usual conditions, it is termed as control group but when the group is exposed to some novel or special condition, it is termed as experimental group, it is possible to design studies which include only experimental groups or studies which include both experimental and control groups. 7.    Treatments : The different conditions under which experimental and control groups are put are usually referred to as treatments. 8.    Experiment : The process of examining the truth of a statistical hypothesis, relating to some research problem is known as an experiment. 9.    Experimental units : The pre determined plots or the blocks where different treatments are used, are known as experimental units. Such experimental units must be selected (defined) very carefully. 6.7 Exploratory Research Studies : The studies having the first of the purposes are generally called exploratory studies. Here the major emphasis is to understand or discovery of ideas and insights. Therefore, the research design must be flexible enough to permit the consideration of many different aspects of aphenomenon. Purpose/functions of Exploratory Studies: (a)    Formulating a problem for more precise investigation or of developing hypothesis. (b)    Increasing the investigator’s familarity with the phenomenon he wishes to investigate in a sequence, more highly structured study or with the setting in which he plans to carry out such a study. (c)    Clarifying concepts (d)    Establishing priorities for further research. (e)    Gathering the information about practical possibilities for carrying out - research in real life settings. (f)    Providing a census of problems regarded as urgent by people working in the field of social relations. Social research is relatively a new subject and hence the work has been done. An investigator might find that no more work has been done on his particular subject so he has no guidelines to follow. In this case, exploratory research is necessary to obtain the experience that will be helpful in formulating relevant hypothesis for more definite investigations. And in case of problems about which little knowledge is available an exploratory study is usually most appropriate. Exploratory research is also an initial step in a continuous research process. In practice, the most difficult portion of an inquiry is its initiation. The most careful methods during the later stages of an investigation are of little value if in correct or irrelevant start has been made. 6.7.1    Methods of Exploratory Studies: There are 3 methods: 1.    The Survey of literature: It is most simple and fruitful method of formulating precisely the research problem or developing hypothesis. Hypothesis stated by earlier workers may be reviewed and their usefulness be evaluated as a basis for further research. It may also be considered whether the already stated hypothesis suggest new hypothesis, in this way the researcher should review and build upon the work already done by others, but in cases where hypothesis have not yet been formulated, his task is to review the available material for deriving the relevant hypothesis from it. Besides, the bibliographical survey of studies, already made in one’s area of interest may as well be made by the researcher for precisely formulating the problem. He should also make an attempt to apply concepts and theories developed in different research contexts to the area in which he is himself working. Sometimes the works of creative writes also provide a fertile ground for hypothesis formulation and as such may be looked into by the researcher. 2.    The Experience Survey : That is a survey of people who have had practical experience with the problem to be studied. Probably, only a small proportion of existing knowledge and experience is ever put into written form. Many people, in the course of their everyday experiences are in a position to observe the effects of alternative decisions and actions with respect to problem of human relations. Such specialist require in the routine of that work, a reservoir of experience that could be of tremendous value in helping the social scientist to become aware of important influences operating in any situation be may be called upon to study. It is the purpose of an experience survey together and synthesize such experiences. 3.    The Analysis of insight Stimulating examples : Scientists working or relatively unformulated areas, where there is little experience to serve as guide, have found intensive .study of selected examples to be a particularly fruitful method for stimulating insights and suggesting hypothesis for research. The focus may be on individuals in situations, on groups or on communities. 6.7.2    Descriptive Research Studies Studies which around taken to portray accurately the characteristics of a particular individuals, situations or a group and to determine the frequency with which something occurs or with which it is associated with something also, a major consideration is accuracy therefore, a design is needed that will minimize bias and maximize the reliability of the evidence collected. Such studies are termed as descriptive studies. The design in such studies, must focus attention on the following : (a)    Formulating the objectives of study (what the study is about and why is it being made?) (b)    Designing the methods of data collection (what techniques of gathering data will be adopted?) (c)    Selecting the sample (how much material will be needed?) (d)    Collecting the data (where can the required data be found and with what time period should the data be related?) (e)    Processing and analysing data (f)    Reporting the findings. 6.7.3    Hypothesis- Testing Research Studies It is also known as experimental studies. These studies are those where the researcher tests the hypothesis of casual relationships between variables. Such studies require procedures that will not only reduce bias and increase reliability, but will permit drawing inferences about casualty. Usually experiments meet the requirement. Hence, when we talk of research design in such studies, we often mean the design of experiments. 6.8    Self-Check Exercise a.    What do you understand by Research Design? Discuss about features of a good design. b.    Describe important steps in Research Design. c.    Discuss about different types of Research Design. Emphasizing on exploratory Research studies. 6.9    Summary There are several research designs and the researcher must decide its advance of collection and analysis of data as to which design would prove to be more appropriate for his research. He must give due weight to various points such as the type of universe and its nature, the objective of his study, the source list or the sampling frame, desired standard of accuracy and the like when taking a decision in respect of the design for his research. 6.10    Glossary Design – a plan or drawing produced to show the look and function or other object before it is made. Variable- not consistent or having a fixed pattern Experimental- based on untested ideas or technique and not yet established or finalized. 6.11    Answer to Self-Check Exercise (a) See 6.1 & 6.3 (b) See 6.4 (c) See 6.6 6.12    Terminal Questions a. Critically examine the differences about Dependent and Independent Variables? 6.13    Suggested Readings •    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. •    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. •  Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. •    Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. •  Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. •  Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. •    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. •    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. ***** CHAPTER-7 SURVEY RESEARCH AND CASE STUDY METHOD Structure 7.0 Learning Objectives 7.1    Introduction 7.2    Survey Research Definition and Purpose 7.2.1    Survey Design 7.2.2    Questionnaire Design 7.2.3    Data Collection 7.2.4    Pre-Testing 7.3    Case Study Method 7.3.1    Definition and Purpose 7.3.2   Case Selection and Design 7.3.3   Data Collection 7.3.4    Ethical Considerations 7.4    Data Analysis 7.4.1    Strengths 7.4.2    Limitations 7.5    Self Check Exercise 7.6    Summary 7.7    Glossary 7.8    Answer to self-check exercise 7.9    Terminal Questions 7.10    Suggested Readings 7.0 Learning Objectives 7.1    Introduction In social sciences and various research fields, survey research and case study methods are two commonly used approaches for data collection and analysis. These methods enable researchers to gather valuable insights into complex phenomena, investigate relationships between variables, and generate rich qualitative and quantitative data. This chapter provides an overview of survey research and case study methods, highlighting their strengths, limitations, and key considerations when applying them in research. Survey methodology is "the study of survey methods". As a field of applied statistics concentrating on human-research surveys, survey methodology studies the sampling of individual units from a population and associated techniques of survey data collection, such as questionnaire construction and methods for improving the number and accuracy of responses to surveys. Survey methodology targets instruments or procedures that ask one or more questions that may or may not be answered. Researchers carry out statistical surveys with a view towards making statistical inferences about the population being studied; such inferences depend strongly on the survey questions used. Polls about public opinion, public-health surveys, market-research surveys, government surveys and censuses all exemplify quantitative research that uses survey methodology to answer questions about a population. Although censuses do not include a "sample", they do include other aspects of survey methodology, like questionnaires, interviewers, and non-response follow-up techniques. Surveys provide important information for all kinds of public-information and research fields, such as marketing research, psychology, health-care provision and sociology. 7.2    Survey Research Definition and Purpose Survey research involves the collection of data from a sample of individuals through the administration of standardized questionnaires or interviews. The primary purpose of survey research is to obtain information about opinions, attitudes, behaviors, and characteristics of a population. Surveys can be conducted through various modes, including online surveys, telephone interviews, or in-person paper questionnaires. A single survey is made of at least a sample (or full population in the case of a census), a method of data collection (e.g., a questionnaire) and individual questions or items that become data that can be analyzed statistically. A single survey may focus on different types of topics such as preferences (e.g., for a presidential candidate), opinions (e.g., should abortion be legal?), behavior (smoking and alcohol use), or factual information (e.g., income), depending on its purpose. Since survey research is almost always based on a sample of the population, the success of the research is dependent on the representativeness of the sample with respect to a target population of interest to the researcher. That target population can range from the general population of a given country to specific groups of people within that country, to a membership list of a professional organization, or list of students enrolled in a school system (see also sampling (statistics) and survey sampling). The persons replying to a survey are called respondents, and depending on the questions asked their answers may represent themselves as individuals, their households, employers, or other organization they represent. Survey methodology as a scientific field seeks to identify principles about the sample design, data collection instruments, statistical adjustment of data, and data processing, and final data analysis that can create systematic and random survey errors. Survey errors are sometimes analyzed in connection with survey cost. Cost constraints are sometimes framed as improving quality within cost constraints, or alternatively, reducing costs for a fixed level of quality. Survey methodology is both a scientific field and a profession, meaning that some professionals in the field focus on survey errors empirically and others design surveys to reduce them. For survey designers, the task involves making a large set of decisions about thousands of individual features of a survey in order to improve it 7.2.1    Survey Design a) Sampling: Researchers need to carefully select a representative sample of participants from the target population to ensure generalizability of findings. Sampling techniques, such as random sampling or stratified sampling, are commonly employed to achieve a representative sample. In research of human subjects, a survey is a list of questions aimed for extracting specific data from a particular group of people. Surveys may be conducted by phone, mail, via the internet, and also at street corners or in malls. Surveys are used to gather or gain knowledge in fields such as social research and demography. Survey research is often used to assess thoughts, opinions and feelings. Surveys can be specific and limited, or they can have more global, widespread goals. Psychologists and sociologists often use 73 surveys to analyze behavior, while it is also used to meet the more pragmatic needs of the media, such as, in evaluating political candidates, public health officials, professional organizations, and advertising and marketing directors. Survey research has also been employed in various medical and surgical fields to gather information about healthcare personnel’s practice patterns and professional attitudes toward various clinical problems and diseases. Healthcare professionals that may be enrolled in survey studies in include physicians, nurses, and physical therapists among others. A survey consists of a predetermined set of questions that is given to a sample. With a representative sample, that is, one that is representative of the larger population of interest, one can describe the attitudes of the population from which the sample was drawn. Further, one can compare the attitudes of different populations as well as look for changes in attitudes over time. A good sample selection is key as it allows one to generalize the findings from the sample to the population, which is the whole purpose of survey research. In addition to this, it is important to ensure that survey questions are not biased such as using suggestive words. This prevents inaccurate results in a survey. 7.2.2    Questionnaire Design b) Questionnaire Design: Developing well-structured and unbiased questionnaires is essential. Researchers should pay attention to question wording, response options, and question sequence to minimize response bias and ensure clarity for participants. Questionnaires are frequently used in quantitative marketing research and social research. They are a valuable method of collecting a wide range of information from a large number of individuals, often referred to as respondents. What is often referred to as "adequate questionnaire construction" is critical to the success of a survey. Inappropriate questions, incorrect ordering of questions, incorrect scaling, or a bad questionnaire format can make the survey results valueless, as they may not accurately reflect the views and opinions of the participants. Different methods can be useful for checking a questionnaire and making sure it is accurately capturing the intended information. Initial advice may include: consulting subject-matter experts using questionnaire construction guidelines to inform drafts, such as the Tailored Design Method, or those produced by National Statistical Organisations.Empirical tests also provide insight into the quality of the questionnaire. This can be done by: conducting cognitive interviewing. By asking a sample of potential-respondents about their interpretation of the questions and use of the questionnaire, a researcher can Carrying out a small pretest of the questionnaire, using a small subset of target respondents. Results can inform a researcher of errors such as missing questions, or logical and procedural errors. Estimating the measurement quality of the questions. This can be done for instance using test-retest, quasi-simplex, or mutlitrait-multimethod models. Predicting the measurement quality of the question. This can be done using the software Survey Quality Predictor (SQP) 7.2.3    Data Collection c) Data Collection: Surveys can be self-administered or interviewer-administered. Researchers should consider the mode of data collection that best suits their research objectives, resources, and participant preferences. Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a research component in all study fields, including physical and social sciences, humanities, and business. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. The goal for all data collection is to capture evidence that allows data analysis to lead to the formulation of credible answers to the questions that have been posed. Regardless of the field of or preference for defining data (quantitative or qualitative), accurate data collection is essential to maintain research integrity. The selection of appropriate data collection instruments (existing, modified, or newly developed) and delineated instructions for their correct use reduce the likelihood of errors. QA's focus is prevention, which is primarily a cost-effective activity to protect the integrity of data collection. Standardization of protocol, with comprehensive and detailed procedure descriptions for data collection are central for prevention. The risk of failing to identify problems and errors in the research process is often caused by poorly written guidelines. Listed are several examples of such failures: •    Uncertainty of timing, methods and identification of the responsible person •    Partial listing of items needed to be collected •    Vague description of data collection instruments instead of rigorous step-by-step instructions on administering tests •    Failure to recognize exact content and strategies for training and retraining staff members 75 responsible for data collection •    Unclear instructions for using, making adjustments to, and calibrating data collection equipment •    No predetermined mechanism to document changes in procedures that occur during the investigation 7.2.4 Pre-Testing d ) Pre-testing: Prior to data collection, it is advisable to pre-test the survey instrument with a small sample to identify potential issues with question wording, response options, or survey flow. Pre-test probability and post-test probability (alternatively spelled pretest and posttest probability) are the probabilities of the presence of a condition (such as a disease) before and after a diagnostic test, respectively. Post-test probability, in turn, can be positive or negative, depending on whether the test falls out as a positive test or a negative test, respectively. In some cases, it is used for the probability of developing the condition of interest in the future. Test, in this sense, can refer to any medical test (but usually in the sense of diagnostic tests), and in a broad sense also including questions and even assumptions (such as assuming that the target individual is a female or male). The ability to make a difference between pre- and post-test probabilities of various conditions is a major factor in the indication of medical tests. The pre-test probability of an individual can be chosen as one of the following: •    The prevalence of the disease, which may have to be chosen if no other characteristic is known for the individual, or it can be chosen for ease of calculation even if other characteristics are known although such omission may cause inaccurate results •    The post-test probability of the condition resulting from one or more preceding tests •    A rough estimation, which may have to be chosen if more systematic approaches are not possible or efficient In clinical practice, post-test probabilities are often just estimated or even guessed. This is usually acceptable in the finding of a pathognomonic sign or symptom, in which case it is almost certain that the target condition is present; or in the absence of finding a sine qua non sign or symptom, in which case it is almost certain that the target condition is absent. In reality, however, the subjective probability of the presence of a condition is never exactly 0 or 100%. Yet, there are several systematic methods to estimate that probability. Such methods are usually based on previously having performed the test on a reference group in which the presence or absence on the condition is known (or at least estimated by another test that is considered highly accurate, such as by "Gold standard"), in order to establish data of test performance. These data are subsequently used to interpret the test result of any individual tested by the method. An alternative or complement to reference group-based methods is comparing a test result to a previous test on the same individual, which is more common in tests for monitoring. Theoretically, the total risk in the presence of multiple risk factors can be estimated by multiplying with each relative risk, but is generally much less accurate than using likelihood ratios, and is usually done only because it is much easier to perform when only relative risks are given, compared to, for example, converting the source data to sensitivities and specificities and calculate by likelihood ratios. Likewise, relative risks are often given instead of likelihood ratios in the literature because the former is more intuitive. Sources of inaccuracy of multiplying relative risks include: •    Relative risks are affected by the prevalence of the condition in the reference group (in contrast to likelihood ratios, which are not), and this issue results in that the validity of posttest probabilities become less valid with increasing difference between the prevalence in the reference group and the pre-test probability for any individual. Any known risk factor or previous test of an individual almost always confers such a difference, decreasing the validity of using relative risks in estimating the total effect of multiple risk factors or tests. Most physicians do not appropriately take such differences in prevalence into account when interpreting test results, which may cause unnecessary testing and diagnostic errors. •    A separate source of inaccuracy of multiplying several relative risks, considering only positive tests, is that it tends to overestimate the total risk as compared to using likelihood ratios. This overestimation can be explained by the inability of the method to compensate for the fact that the total risk cannot be more than 100%. This overestimation is rather small for small risks, but becomes higher for higher values. For example, the risk of developing breast cancer at an age younger than 40 years in women in the United Kingdom can be estimated at 2%. Also, studies on Ashkenazi Jews has indicated that a mutation in BRCA1 confers a relative risk of 21.6 of developing breast cancer in women under 40 years of age, and a mutation in BRCA2 confers a relative risk of 3.3 of developing breast 77 cancer in women under 40 years of age. From these data, it may be estimated that a woman with a BRCA1 mutation would have a risk of approximately 40% of developing breast cancer at an age younger than 40 years, and woman with a BRCA2 mutation would have a risk of approximately 6%. However, in the rather improbable situation of having both a BRCA1 and a BRCA2 mutation, simply multiplying with both relative risks would result in a risk of over 140% of developing breast cancer before 40 years of age, which can not possibly be accurate in reality. 7.3 Case Study Method 7.3.1    Definition and Purpose Case study methods involve in-depth, holistic investigations of a specific phenomenon, individual, group, organization, or event. The purpose of case studies is to provide a detailed understanding of the context, processes, and dynamics surrounding a particular case. Case studies can utilize various sources of data, such as interviews, documents, observations, and archival records. The case method is a teaching approach that uses decision-forcing cases to put students in the role of people who were faced with difficult decisions at some point in the past. It developed during the course of the twentieth-century from its origins in the casebook method of teaching law pioneered by Harvard legal scholar Christopher C. Langdell. In sharp contrast to many other teaching methods, the case method requires that instructors refrain from providing their own opinions about the decisions in question. Rather, the chief task of instructors who use the case method is asking students to devise, describe, and defend solutions to the problems presented by each case. 7.3.2    Case Selection and Design Researchers must carefully choose cases that are representative and relevant to their research question. Cases can be selected based on theoretical sampling, typical or atypical examples, or purposeful sampling to maximize the richness of data. A case study is an in-depth, detailed examination of a particular case (or cases) within a real-world context. For example, case studies in medicine may focus on an individual patient or ailment; case studies in business might cover a particular firm's strategy or a broader market; similarly, case studies in politics can range from a narrow happening over time like the operations of a specific political campaign, to an enormous undertaking like, world war, or more often the policy analysis of real-world problems affecting 78 multiple stakeholders. Generally, a case study can highlight nearly any individual, group, organization, event, belief system, or action. A case study does not necessarily have to be one observation (N=1), but may include many observations (one or multiple individuals and entities across multiple time periods, all within the same case study). Research projects involving numerous cases are frequently called cross-case research, whereas a study of a single case is called within-case research. Case study research has been extensively practiced in both the social and natural sciences. There are multiple definitions of case studies, which may emphasize the number of observations (a small N), the method (qualitative), the thickness of the research (a comprehensive examination of a phenomenon and its context), and the naturalism (a "real-life context" is being examined) involved in the research. There is general agreement among scholars that a case study does not necessarily have to entail one observation (N=1), but can include many observations within a single case or across numerous cases. For example, a case study of the French Revolution would at the bare minimum be an observation of two observations: France before and after a revolution. John Gerring writes that the N=1 research design is so rare in practice that it amounts to a "myth". The term cross-case research is frequently used for studies of multiple cases, whereas within-case research is frequently used for a single case study. John Gerring defines the case study approach as an "intensive study of a single unit or a small number of units (the cases), for the purpose of understanding a larger class of similar units (a population of cases)". According to Gerring, case studies lend themselves to an idiographic style of analysis, whereas quantitative work lends itself to a nomothetic style of analysis. He adds that "the defining feature of qualitative work is its use of noncomparable observations—observations that pertain to different aspects of a causal or descriptive question", whereas quantitative observations are comparable. According to John Gerring, the key characteristic that distinguishes case studies from all other methods is the "reliance on evidence drawn from a single case and its attempts, at the same time, to illuminate features of a broader set of cases". Scholars use case studies to shed light on a "class" of phenomena. 7.3.3    Data Collection Case study data collection involves multiple methods, including interviews, observations, document analysis, or audiovisual materials. Triangulation of data sources helps establish credibility and validity. The case study method is a very popular form of qualitative analysis and involves a careful and complete observation of a social unit, be that unit a person, a family, an institution, a cultural group or even the entire community. It is a method of study in depth rather than breadth. The case study places more emphasis on the full analysis of a limited number of events or conditions and their interrelations. The case study deals with the processes that take place and their interrelationship. Thus, case study is essentially an intensive investigation of the particular unit under consideration. The object of the case study method is to locate the factors that account for the behaviour-patterns of the given unit as an integrated totality. According to H. Odum, “The case study method is a technique by which individual factor whether it be an institution or just an episode in the life of an individual or a group is analysed in its relationship to any other in the group.”5 Thus, a fairly exhaustive study of a person (as to what he does and has done, what he thinks he does and had done and what he expects to do and says he ought to do) or group is called a life or case history. Burgess has used the words “the social microscope” for the case study method.”6 Pauline V. Young describes case study as “a comprehensive study of a social unit be that unit a person, a group, a social institution, a district or a community.”7 In brief, we can say that case study method is a form of qualitative analysis where in careful and complete observation of an individual or a situation or an institution is done; efforts are made to study each and every aspect of the concerning unit in minute details and then from case data generalisations and inferences are drawn. • Characteristics: The important characteristics of the case study method are as under: 1.    Under this method the researcher can take one single social unit or more of such units for his study purpose; he may even take a situation to study the same comprehensively. 2.    Here the selected unit is studied intensively i.e., it is studied in minute details. Generally, the study extends over a long period of time to ascertain the natural history of the unit so as to obtain enough information for drawing correct inferences In the context of this method we make complete study of the social unit covering all facets. Through this method we try to understand the complex of factors that are operative within a social unit as an integrated totality. 4 Under this method the approach happens to be qualitative and not quantitative. Mere quantitative information is not collected. Every possible effort is made to collect information concerning all aspects of life. As such, case study deepens our perception and gives us a clear insight into life. For instance, under this method we not only study how many crimes a man has 80 done but shall peep into the factors that forced him to commit crimes when we are making a case study of a man as a criminal. The objective of the study may be to suggest ways to reform the criminal. 5. In respect of the case study method an effort is made to know the mutual interrelationship of causal factors. 6. Under case study method the behaviour pattern of the concerning unit is studied directly and not by an indirect and abstract approach. 7. Case study method results in fruitful hypotheses along with the data which may be helpful in testing them, and thus it enables the generalised knowledge to get richer and richer. In its absence, generalised social science may get handicapped. Evolution and scope: The case study method is a widely used systematic field research technique in sociology these days. The credit for introducing this method to the field of social investigation goes to Frederic Le Play who used it as a hand-maiden to statistics in his studies of family budgets. Herbert Spencer was the first to use case material in his comparative study of different cultures. Dr. William Healy resorted to this method in his study of juvenile delinquency, and considered it as a better method over and above the mere use of statistical data. Similarly, anthropologists, historians, novelists and dramatists have used this method concerning problems pertaining to their areas of interests. Even management experts use case study methods for getting clues to several management problems. In brief, case study method is being used in several disciplines. Not only this, its use is increasing day by day. Assumptions: The case study method is based on several assumptions. The important assumptions may be listed as follows: (i) The assumption of uniformity in the basic human nature in spite of the fact that human behaviour may vary according to situations. (ii) The assumption of studying the natural history of the unit concerned. (iii) The assumption of comprehensive study of the unit concerned. Major phases involved: Major phases involved in case study are as follows: (i) Recognition and determination of the status of the phenomenon to be investigated or the unit of attention. (ii) Collection of data, examination and history of the given phenomenon. (iii) Diagnosis and identification of causal factors as a basis for remedial or developmental treatment. (iv) Application of remedial measures i.e., treatment and therapy (this phase is often characterised as case work). (v) Follow-up programme to determine effectiveness of the treatment applied. Advantages: There are several advantages of the case study method that follow from the various characteristics outlined above. Mention may be made here of the important advantages. (i) Being an exhaustive study of a social unit, the case study method enables us to understand fully the behaviour pattern of the concerned unit. In the words of Charles Horton Cooley, “case study deepens our perception and gives us a clearer insight into life…. It gets at behaviour directly and not by an indirect and abstract approach.” (ii) Through case study a researcher can obtain a real and enlightened record of personal experiences which would reveal man’s inner strivings, tensions and motivations that drive him to action along with the forces that direct him to adopt a certain pattern of behaviour. (iii) This method enables the researcher to trace out the natural history of the social unit and its relationship with the social factors and the forces involved in its surrounding environment. (iv) It helps in formulating relevant hypotheses along with the data which may be helpful in testing them. Case studies, thus, enable the generalised knowledge to get richer and richer. (v) The method facilitates intensive study of social units which is generally not possible if we use either the observation method or the method of collecting information through schedules. This is the reason why case study method is being frequently used, particularly in social researches. (vi) Information collected under the case study method helps a lot to the researcher in the task of constructing the appropriate questionnaire or schedule for the said task requires thorough knowledge of the concerning universe. (vii) The researcher can use one or more of the several research methods under the case study method depending upon the prevalent circumstances. In other words, the use of different methods such as depth interviews, questionnaires, documents, study reports of individuals, letters, and the like is possible under case study method. (viii) Case study method has proved beneficial in determining the nature of units to be studied along with the nature of the universe. This is the reason why at times the case study method is alternatively known as “mode of organising data”. (ix) This method is a means to well understand the past of a social unit because of its emphasis of historical analysis. Besides, it is also a technique to suggest measures for improvement in the context of the present environment of the concerned social units. (x) Case studies constitute the perfect type of sociological material as they represent a real record of personal experiences which very often escape the attention of most of the skilled researchers using other techniques. (xi) Case study method enhances the experience of the researcher and this in turn increases his analysing ability and skill. (xii) This method makes possible the study of social changes. On account of the minute study of the different facets of a social unit, the researcher can well understand the social change then and now. This also facilitates the drawing of inferences and helps in maintaining the continuity of the research process. In fact, it may be considered the gateway to and at the same time the final destination of abstract knowledge. Se8h(xiii) Case study techniques are indispensable for therapeutic and administrative purposes. They are also of immense value in taking decisions regarding several management problems. Case data are quite useful for diagnosis, therapy and other practical case problems. Limitations: Important limitations of the case study method may as well be highlighted. (i) Case situations are seldom comparable and as such the information gathered in case studies is often not comparable. Since the subject under case study tells history in his own words, logical concepts and units of scientific classification have to be read into it or out of it by the investigator. (ii) Read Bain does not consider the case data as significant scientific data since they do not provide knowledge of the “impersonal, universal, non-ethical, non-practical, repetitive aspects of phenomena.”8 Real information is often not collected because the subjectivity of the researcher does enter in the collection of information in a case study. (iii) The danger of false generalisation is always there in view of the fact that no set rules are followed in collection of the information and only few units are studied. (iv) It consumes more time and requires lot of expenditure. More time is needed under case study method since one studies the natural history cycles of social units and that too minutely. (v) The case data are often vitiated because the subject, according to Read Bain, may write what he thinks the investigator ants; and the greater the rapport, the more subjective the whole process is. (vi) Case study method is based on several assumptions which may not be very realistic at times, and as such the usefulness of case data is always subject to doubt. (vii) Case study method can be used only in a limited sphere., it is not possible to use it in case of a big society. Sampling is also not possible under a case study method. (viii) Response of the investigator is an important limitation of the case study method. He often thinks that he has full knowledge of the unit and can himself answer about it. In case the same is not true, then consequences follow. In fact, this is more the fault of the researcher rather than that of the case method 7.3.4 Ethical Considerations Researchers must adhere to ethical guidelines, ensuring informed consent, confidentiality, and respect for the rights and privacy of individuals or organizations involved in thecase study. In social science research, issues of research ethics, informed consent, and research protocols often arise, and research of Wikipedia is no exception. Rules and laws established after controversial studies like the Milgram experiment and Stanford prison experiment require researchers to design their studies such that they do no harm to participants. Researchers are expected to adhere to professional codes of ethics. Where required, they may also need to obtain permission to carry out research of Wikipedia editors from appropriate bodies at their research institutions. 7.4    Data Analysis Analysis of case study data involves a thorough examination of the collected information to identify patterns, themes, and relationships within the case. Researchers often employ qualitative analysis techniques, such as thematic analysis, content analysis, or narrative analysis, to interpret and make sense of the data. The analysis process involves coding, categorizing, and synthesizing the data to generate meaningful insights and draw conclusions. Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis. Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. 7.4.1    Strengths •    Case studies offer in-depth and contextualized insights into complex phenomena, allowing researchers to explore multiple variables and factors influencing the case. •    They provide a holistic view, considering both individual and environmental factors, and can generate rich and detailed data. •    Case studies can be exploratory, explanatory, or descriptive, depending on the research objectives. 7.4.2    Limitations •    The findings of case studies are specific to the case under investigation and may not be generalizable to broader populations or contexts. •    Researchers' subjectivity and biases may influence the selection of cases, data collection, and interpretation of results. •    Case studies can be time-consuming and resource-intensive, requiring extensive data collection and analysis efforts. 7.5    Self Check Exercise (a)    What do you mean by Survey Research? Discuss about the nature of survey research. (b)    Describe the characteristic of case study methods? 7.6    Summary Survey research and case study methods are valuable research approaches that offer unique strengths and limitations. Survey research allows researchers to collect data from a large sample and provides generalizability, while case study methods offer in-depth understanding and rich contextual insights. The choice between survey research and case study methods depends on the research question, objectives, available resources, and the depth of understanding required. By understanding the nuances and considerations associated with these methods, researchers can effectively utilize them to generate meaningful and robust findings in their studies. 7.7    Glossary 7    Analysis - detailed examination of the elements or structure of something. Interview – a meeting of people face to face, especially for consultation. 8    Case Study – a process or record of research into the development of a particular person, group or situation over a period of time. 7.7    Answer to self-check exercise (a)      See      7.1.1      &      7.1.2 (b)      See      7.2.3      (c)      See      7.3. 7.8    Suggested Readings •    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. •    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. •    Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. •    Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. •  Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. •  Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. •    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. •    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. ***** CHAPTER-8 SAMPLING: CONCEPT AND TYPES Structure 8.0 Learning Objectives 8.1    Introduction 8.2    Census Method 8.2.1    Merits of Census Method 8.2.2    Criticism of Census Method 8.3    Sampling Method 8.3.1    Essentials of Sampling 8.3.2    Merits of Sampling Method 8.3.3    Limitations of Sampling Method 8.4    Types of Sampling 8.4.1    Random Sampling 8.4.2    Merits of Random Sampling 8.4.3    Limitations of Random Sampling 8.4.4    Restricted Random Sampling 8.4.5    Systematic Sampling 8.4.6    Multi-Stage Sampling 8.5    Non-Random Sampling Method 8.5.1    Judgement Sampling 8.5.2    Convenience Sampling 8.5.3    Quota Sampling 8.6    Self-Check Exercise 8.7    Summary 8.8    Glossary 8.9    Answer to Self Check Exercise 8.10    Terminal Questions 8.11    Suggested Readings 8.0 Learning Objectives After studying this lesson, the learner will be able: -      To understand meaning and types of sampling. -      To comprehend about essentials of sampling. -      To differentiate between Random and non-random sampling. 8.1    Introduction In every piece of research a crucial issue is whether the research conclusions can be generalized beyond the immediate pool of research subjects. This is an important element of external validity. The best way to assure that the results can be generalized beyond a single study is to draw a representative sample. As many times it happens that when a secondary data are not available for the problem under study a division method survey research is often focused on establishing a relationships or measuring the incidence of characteristics of some population rather than assessing casual connections among variables. Because of this the incorporation of representative sampling may be an essential aspect of the research design. In Surveys for this reason generalisation of results to other population is more often possible with survey research design than with the other kinds of research the information required for this may be obtained by following methods. 8.2    Census Method Under the Census or complete enumeration Survey method data are collected for each and every unit of population of universe which is the complete set of items which are of interest to any particular situation. 8.2.1    Merits of Census Method These are Data are obtained from each and every unit of the population. The results obtained are likely to be more representative, accurate and reliable It is an appropriate method of obtaining information on rare events such as areas under some crops and field there of etc. This is the reason why throughout world the population data are obtained by conducting a census generally every 10 years by the census method (d) Data of a complete enumeration census can be widely exploited as a basis for various surveys. 8.2.2    Criticism of Census Method Despite these advantages the census method is not very popularly used in practice. The effort money and time required for carrying out complete enumeration will generally be extremely large and in many cases cost may be so prohibitive that the very idea of collecting information may have to be dropped. This is truer in underdeveloped countries where resources constitute big constraint. Also if the population is infinite or the evaluation process destroys the population unit, the census method cannot be adopted. 8.3    Sampling Method A sample as the name implies is a smaller representation of a larger whole. The observation of some phenomenon in complete detail would involve such a mass of data that analysis would be slow and tedious. Moreover to analyse larger quantities of material is wasteful when a smaller amount would suffice. Thus the use of sampling allows for more adequate scientific work by making the time of the scientific worker count. Instead of spending many hours over the analysis of mass of material from one point of view he may use that time to examine a smaller amount of material from many points of view or, in other words, to do a more intensive analysis of fewer cases. As we see that sampling is simply the process of learning about the population on the basis-of sample drawn from it the process of sampling involves three elements. (a)    Selection of the a ample (b)    Collecting the information, and (c)    Making an inference about the population. The three elements cannot be generally considered in isolation from one another Sample selection, data collection and estimation are all inter-woven and each has an impact on the others sampling is not haphazard selection, it embodies definite rules for selecting sample. But having followed a set of rules for sample selection, we cannot consider the estimation process independent of it estimation is guided by the manner in which the sample has been selected. As sampling is a tool which helps to know the characteristics of the universe or population by examining only a small part of it. The values obtained from the study of sample, such as the average and dispersion are known as statistics and values for the population are called parameters. 8.3.1    Essentials of Sampling If the sample results are to have worthwhile meaning it is necessary that a sample possesses the following essentials: 1.    Representativeness: A sample should be so selected that it truly represents the universe otherwise the results obtained may be misleading. To ensure representativeness the random method of selection should be used. 2.    Adequacy: The size of sample should be adequate otherwise it may not represent the characteristics of the universe. 3.    Homogeneity: when we talk of homogeneity we mean that there is no basic difference in the nature of units of the universe and that of the sample. If two samples from the same universe are taken, they should give more or less the same result. 8.3.2    Merits of Sampling Methods 1.    Sampling saves labour: A smaller staff is required both for field work and for collecting and processing the data. 2.    Less time consuming: Since the sample is a study of a part of the population, considerable time is saved when a sample survey is carried out. Time saved not only in collecting data but also in processing it for these reasons a sample provides more timely data is practice than that of a census method. 3.    More-accuracy: Although the sampling technique involves certain in accuracies owing to sampling errors, the result obtained is generally more reliable than that obtained from a complete count. There are several reasons for it first, it is always possible to determine the extent of sampling errors secondly, it is possible to avail of the services of experts and to impart training to the’ investigators in a sample survey which further reduces the possibility of errors. Follow up work can also be undertaken much more effectively in the sampling method indeed even a complete census can only be tested for a accuracy by some type of sampling check. 4.    The sample method is often used to judge the accuracy the information obtained on a census basis for example, in the population census which is conducted very often after 10 years in one country. The field officers employ the sample method to “determine the accuracy of information obtained by the enumerators on the census basis. 5.    More detailed information : The sampling technique saves time and money, it is possible to collect more detailed information in a sample survey. 8.3.3    Limitations of Sampling Method Despite the various advantages of sampling, it is not altogether free from limitations. Some of the difficulties involved in a sampling are listed below: 1.    A sample survey must be carefully planned and executed otherwise the results may be in accurate and misleading. Of course even for a complete count, care must be taken, but serious errors may arise in sampling if the sampling procedure is not perfect. 2.    Sampling generally requires the services of the experts, if only for consultation purposes. In the absence of qualified and experienced persons, the information obtained from sample cannot be relied upon, in India, shortage of experts in the sampling field is a serious hurdle in the way of reliable statistics. 3.    At times, the sampling plan may be so complicated that it requires more time, labour and money than a complete count this is so if the size of the sample is a large proportion of the total population and if complicated weighed procedures are used with each additional complication in the survey the chance of errors multiply and greater care has to be taken which, in turn, means more time and labour. 4.    If the information is required for each and every unit in the domain of study, a complete enumeration survey unnecessary. 8.4    Type of Sampling The various methods of sampling or different sampling designs can be grouped under two broad heads: 8.4.1    Random Sampling As the name implies the selection of the units to be included in the sample employs chance. Quite the opposite of what the name implies, however the sample is not chosen on an accidental basis in this sense, it is not random but is carefully planned. A random sample is one which is so drawn that the researcher from all pertinent points of view has no reason to believe a bias will result in other words, the units of the universe must be so arranged that- the selection process gives equiprobability of selection to every unit in that universe this really means that the researcher does not know his universe sufficient well to duplicate it exactly in his sample. What he is therefore doing is attempting to randomize his ignorance in this area. Unfortunately, when dealing with social this must be more difficult than when „ dealing with such things as pattern of heads and tails, or combination of figures on dice, people do not exist in such nicely divided patterns. Some are easier to locate then others, and some may refuse to respond. Moreover, there are few lists available which will guarantee a complete definition of universe. Further, although sampling can sometimes be checked against such sources as census data, using the characteristics of age, sex, location, education and race, it is frequently the universe we are studying has not been defined in any of these terms. To ensure randomness of selection one may adopt either of the following method. a.    Lottery Method : This is’ very popular method of taking a random sample. Under this method, all items of the universe are numbered or named on separate slips of paper of identical size and shape. These slips are folded and then mixed up in a container. A blindfold selection is then made of the number of slips required to constitute the desired sample size.. The selection of item thus depends upon entirely on chance. The method would be quite clear with the help of example. If we want to take sample of 10 persons out of population of 100 persons, the procedure is to write the name of all 100 persons on separate slips of paper, fold and then mix and pickup any slip, this method is very popular in lottery drawn where a decision about the prizes is to be made. b.    Table of Random numbers Lottery method becomes quite cumbersome to use as the size of population increases. An alternative method of random selection is that of using the random number table. The random numbers are generally obtained by some mechanism which when repeated a large number of times ensure approximation. 8.4.2    Merits of Random Sampling 1.    Since the selection of Items in the sample depends entirely upon chance. There is no possibility of personal bias affecting the results. 2. As compared to judgement sampling, a random sampling represents the universe in a better way. As the size of the sample increases, it becomes increasingly representative of the population. 3.    The analyst can easily assess the accuracy of this estimate because sampling errors follow the principles of chance. The theory of random sampling is further developed than that of any other type of sampling which enables the analyst to provide the most reliable information at the least cost. 8.4.3    Limitations of Random Sampling 1.    The use of random sampling necessitates a completely catalogued universe from which to draw the sample. But it is often difficult for the investigator to have up to date lists of all the items of the population to be sampled. This restricts the use of this method in economic and business data where very often we have to employ restricted random sampling. 2.    The size of the sample required to ensure statistical reliability is usually larger under random sampling than stratified sampling. 3.    Form the point of view of field survey it has been claimed that cases selected by random sampling tend to be .too widely dispersed geographically and that the time and cost of collecting data becomes too large. 4.    Random re-sampling may produce the most non random looking results e.g.; 13 cards form a well shuffled pack of play.ing cards may be of one suit: But the probability of this type of occurrence is very low. 8.4.4    Restricted Random Samplinga. Stratified Sampling : While stratified sampling is placed here in distinction to random sampling, this does not mean that it does not employ randomness. Actually, it depends upon randomness but combines this with another method calculated to increase representativeness. Because the method does improve representativeness, it allows the use of a smaller sample than does random sampling with greater precision and consequent savings in time and money. When this method of sampling is adopted, the population is divided into different groups or classes called starts and a sample is drawn from each stratum at random. Merits I.    More representative: Since the population is divided into various strats and then a sample is draw from each stratum, there is little possibility of any essential group of population being completely excluded. A more representative sample is thus secured. II.    Greater accuracy: Stratified sampling ensures greater accuracy. The accuracy is maximum if each stratum is so framed that if consists of uniform or homogeneous items. III.    Greater geographical concentration: As compared with random sample, stratified samples can be more concentrated geographically i.e., units from the different strats may be selected in such a way that all of them are localised in one geographical area this would greatly reduce the time and expense of interviewing. Limitations I.    Utmost care must be exercised in dividing the population in various strates. Each stratum must contain, an possible homogeneous items as otherwise the results may not be reliable if proper stratification of the population is not done the sample may have the effect of bias. II.    The items from each stratum should be selected at random. But this may be difficult to achieve in the absence of skilled sampling supervision and a random selection within each statum may not be ensured. 8.4.5    Systematic sampling A systematic sample is formed by selecting one unit at random and then selecting additional units at evenly spaced intervals until the sample has been formed. The method is popularly used in those cases where a complete test of the population from which the sample is to be drawn is available. Merits:- 1.    The systematic sampling design is simple and convenient to adopt. 2.    The time and work involved in sampling by this method are relatively smaller. 3.    The results obtained are also found to be generally satisfactory provided care is taken to see that there are no periodic failures associated with the sampling interval. Limitations : The main limitation of the method is that it becomes less representative if we are dealing with populations having hidden periodicities. 8.4.6    Multi-Stage Sampling As the same implies this method refers to a sampling procedure which is carried out- in several stages. The material is regarded as made up for a number of second stage sampling units, each of which is made of a number of second stage units, etc. At first, the first stage units are sampled by some suitable method, such as simple random sampling then, a sample of second stage units is selected from each of the selected first stage units, again by some suitable method which may be the same as or different from the method employed for the first stage units further, the stages may be added as required. Merits:- 1.    Multi: Stage sampling introduces flexibility in the sampling method which is lacking in other methods, it enables existing divisions and subdivisions of the population to be used as units at various stages and permits the field work to be concentrated and yet larger area to be covered. 2.    Sub: division into second stage’ (i.e. the construction of the second stage framed) need to be carried out for only those first stage units which are included in the sample. It is therefore, particularly valuable’ in surveys of under developed areas where no frame is generally sufficiently detailed and accurate for subdivision of the material into reasonably small sampling units. Limitations However, a multi-stage is in general less accurate than a sample containing the same number of final stage into which have been selected by some suitable single stage process. 8.5    Non-Random Sampling 8.5.1    MethodsJudgement Sampling: In judgement sampling the choice of sample items depend exclusively on discretion of a judge. In other words, the investigator exercises his judgement in the choice and includes those items in the sample which he thinks are most typical of the universe with regard to the characteristics under investigation. Merits: (i)    When only a small number of sampling units is in the universe, simple random sampling selection may miss the more important elements where as judgement selection would certainly include them in the sample. (ii)    When we want to study some unknown traits of a population, some of whose characteristics are known, we may then stratify the population according to these known properties and select sampling units from each stratum on the basis of judgement. This method is used to obtain a more representative sample. (iii)    In solving everyday business problems and making public policy making, discussions cannot wait for probably sample designs. Judgement sampling is then the only practical method to arrive at solutions to their urgent problems. Limitations : This method, though simple is not scientific because the population units to be sample many be affected by the personal prejudice or bias of the investigator. Thus, judgement sampling involves the risk that the investigator may establish with foregone conclusions by including those items in the sample which conform of his preconceived notions. 8.5.2    Convenience Sampling A convenience sample is obtained by selecting convenient population units. The method of convenience sampling is also called the chunk. A chunk refers to that fraction of the population being investigated which is selected neither by probability nor by judgement but by convenience. A sample is obtained from reading list which is available at the point of research. Hence, results obtained by following convenience sampling method can hardly be representative of the population. As convenience leads away from reality. 8.5.3    Quota-Sampling Above discussed types embodied the feature of randomness. Thus insuring that every member of a population has a calculable chance of being included in sample. A wide variety of procedures go under the name of quota sampling but what distinguishes them all fundamentally from probability sampling it-that, once the general break down of the sample is decided (eq. how many men and women, how many people in each age group and in each social groups to be included) and the quota assignment are allocated to the interviewers, the choice of the actual sample units to fit into this frame work is left to the interviewers. Quota sampling is therefore a method of stratified sampling in which the selection within the strata is non-random. It is this nonrandom element that constitutes its greatest weakness. 8.6    Self Check Exercise a.    What do you mean by sampling? Differentiate between census method and Sample Method? b.    Elaborate essentials of Sampling? c.    Discuss about Random and Non-Random Sampling. d.    Define stratified sampling? Discuss its merit and demerits? 8.7    Summary A sample refers to the smaller representations of a longer whole. Sampling allows you to more adequate scientific work by making the time of the scientific worker count. On the basis of sample drawn from the population the process of sampling involves selection of a sample, collecting the information and Making and inference about the population. Different types of sampling design can be grouped under random and nonrandom sampling. Random sampling refers. The units of the universe must be so arranged that the selection process give equiprobability of selection to very units in that universe. Non-random sampling refers to the judgemental sampling, Convenience sampling and quota sampling. 8.8    Glossary •    Census - an official count or survey, especially of a population. •    Stratify - from or arrange into strata. •    Probability – the quality or state of being probable. •    Adequacy - the state or quality of being adequate. 8.9    Answer to Self Check Exercise (a) See 8.1 (b) See 8.3.1 (c) See 8.4.1 & 8.5 (d) See 8.4.4 8.10    Terminal Questions a.    What are the limitations of sampling methods? b.    Critically examine stratified sampling and Multi stage sampling. c.    Write a note on Quota-Sampling. 8.11    Suggested Readings •    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. •    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. •    Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. •    Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. •  Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. •  Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. •    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. •    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. ***** CHAPTER-9METHODS OF DATA COLLECTION: DOCUMENTS, OBSERVATION, INTERVIEW AND QUESTIONNAIRE Structure 9.0 Learning Objectives 9.1    Introductions 9.2    Meaning of an observation 9.3    Purpose of observation 9.4    Advantages of observation 9.5    Limitations of observation 9.6    Types of observation 9.7    Structured observation 9.8    Unstructured observation 9.9    Recording unstructured observation 9.10    Increasing the accuracy of observation 9.11    The Limitations of Participants observation 9.12    Problem Faced while using the Technique of observations 9.13    Self-Check Exercise 9.14    Summary 9.15    Glossary 9.16    Answer to Self-Check Exercise 9.17    Terminal Questions 9.18    Suggested Readings 9.0 Learning Objectives After studying this lesson, the learner will be able: -    To understand meaning & concept of observation. -    To discuss about advantages and disadvantages of observation. -    To comprehend about different types of observation. -    To know about the problems faced while using the techniques of observation. 9.1    Introduction: It is one of the oldest techniques of obtaining information. For the past few years there has been a great increase in the use of observation methods in the study of social phenomenon. These experiences have indicated that direct observation of social behaviour can provide reliable and conceptually meaningful data in field studies as well as in laboratory experimentation. The increase in use of observers has been accompanied by an increase in methodological sophistication in observation methods. The accumulated knowledge of biologists, physicists and other natural scientists is built upon centuries of system and observation, much of it of phenomena in their natural surroundings rather than in laboratory. The relatively infrequent use of observational methods by social scientists is surprising, especially when one reflects that they are literally surrounded by their subject matter, that they have to open their eyes and observe their fellow men and women and the institutions and societies they have created. As a means of general orientations, observation certainly plays as much a part in the social as in any of the sciences. A social scientist can hardly avoid being influenced in his choice of research problem, his ideas and his theories, by what he observes, around him. Observation as a systematic method of’ collecting data must be suitable for investigating the problem in which the social scientists are interested and should be reasonably reliable and objective. 9.2    Meaning of observation- by the concise oxford dictionary Defines it as accurate watching and noting of phenomena as they occur in nature, with regard to cause and effect or mutual relations. Observation can fairly be called the classic method of scientific inquiry as it is a primary tool. Observation becomes a scientific technique to the extent that it : i)    servers a formulated research purpose, ii)    is planned systematically, iii)    is recorded systematically and related to more general propositions rather than being presented as a set of interesting curiosity and iv)    is subject to checks and controls on validity and reliability. Many types of data required by the social scientists as evidence in research can be obtained through direct observation. Suppose that he is interested in how members of different groups behave towards each other, when some activity brings them into contact or in the manner in which a mother rears her infants or in comparing the quality of housing occupied by different social strata of population, etc. To obtain these and many other types of data, he precedes best by observing the appropriate situations. Observation now is a perfect method of social investigation and probably the most popular one in gaining knowledge of social phenomena. 9.3    Purpose of observation Observation may serve the investigator in his research purposes in several ways: 1.    It enables the observer to gain insights that can be later tested by other techniques. 2.    Observation may be used to gather supplementary data that may help to interpret findings obtained by other techniques. 3.    It may be used as the primary method of data collection in studies designed to provide accurate descriptions of situations or to test casual hypothesis, 4.    Observation is used to perceive the nature and .extent of significant interrelated elements within complex social phenomena, culture patterns or human conduct. 5.    The greatest asset of observational techniques is that they make it possible to record behaviour as it occurs. Observation is an invaluable aid for studying small associations, communities, etc. in action and for nothing how people live and how they react in a given situation by observing them which provides a clear and authentic picture of a given situation and requires less dependence on securing people’s cooperation as a contrast to the Interview method. 9.4    Advantages of Observation Observation as a method of social research has been used quite extensively everywhere. Following are the chief advantages of the method. 1.    It reveals the reality: If other methods are used, like questionnaire, or interview the person being questioned can give a biased opinion and reality may not emerge. Observation is a more dependable way of collecting data in the form of measurement, for e.g. of distance, time. 2.    Only source of collecting certain data: Sometimes it is difficult to depend upon other sources for statistical information. There are some issues that can be better understood by observation only. There are certain situations that cannot be interpreted or translated. They have to be observed for e.g., Indian festivals, marriages, etc. Conclusions can be drawn only after observing the situation. 3.    Simplest Method : Observation is the simplest and most non-technical method. Although scientific observation is not so easy, yet it is easier than other methods to follow by nature. A man is habituated to observe things that attract his attention. Observation is also perfected with the theoretical knowledge of phenomena which can be gained easily. 4.    Useful in framing hypothesis : Observation is one of the main sources of formulating hypothesis, The social researcher observes various activities of the people around him. Continuous observation, even if casual and unplanned, may reveal certain sequences generally form the basis of new hypothesis. 5.    Greater accuracy : Observation at times affords greater accuracy than other methods. In case of interview or case history method, the researcher has to depend on the information supplied by other people. It is thus an indirect method and the researcher has no device to check the accuracy of the statement of other people. But in case of observation the researcher need not depend upon information supplied by other people blind folded. The data collected through observation is thus generally more valid and reliable than data collected either through interview or case history. 6.    More convincing results: The data collected observation is more convincing than otherwise. When the information has been supplied by others, there is always some doubt in mind of the people about its validity. Such data may therefore, lack the force of conviction. In case of observation, the researcher has observed things for himself and no doubt about their correctness. He can therefore, pursue the generalization with greater force of conviction. 7.    A Common Method for all sciences: Observation is a common method for all sciences. Interview is the method for all sciences. Interview is the method that is used specially in case of social sciences but Observation is common to all the sciences and naturally has greater universality of practice. The rules of observation are the same in all sciences, whether physical or social. A common method is most commonly followed and accepted as a tool. Some subjects cannot express themselves: To deal with those subjects which cannot express themselves, for example, children, mental cases, animals, etc. Their activities can only be observed closely and conclusions can be drawn on basis. 9.5    Limitations of observations 1.    Impossible to predict the spontaneous occurrence : It is often impossible to predict the spontaneous occurrence of an event precisely though to enable us to be present to observe it. If an anthropologist wishes to learn about marriage ceremonies by observation on rather than through interviews, he has to wait until a wedding to which he has access takes place. 2.    Our sense organs operate in a highly variable, erratic and selective manner. Much of the observation depends upon the state of mind and body in a given situation e.g. an investigator is tired worried, he will not observe the things in a proper way. 3.    Unless the observed phenomena are studied in relation to a social process or a particular way of life, they are in capable of explaining reality or of furthering social exploration of other associated elements. 4.    Practical difficulties : Researcher faces practical difficulties when applying observational techniques in some occurrences e.g. sexual behaviour or a family crisis may not be accessible to direct observation. 5.    Observation may lead to some inference with regard to his frame of references which from the standpoint of view of past experiences result into false perception. 9.6    Types of Observation Observational methods may be broadly grouped in the following: 1.    Structured observation or Non-Participant observation. 2.    Unstructured observation or Participant Observation. Pauline V. Young in his book Scientific Social Surveys and Research has categorized them as : 1 . Structured observation as controlled observation. 2 . Unstructured observation as Non-Controlled Observation. 9 .7 Structured Observation Observational techniques, often used in studies design to provide systematic description to test casual hypothesis are known as structured observation. The major difference in unstructured and structured is that this is more systematic studies. The investigator knows what aspects of the group activity are relevant for his research purposes and is in a position to develop a specific plan for making and recording of observations before he starts collecting data. 9 .8 Unstructured Observation The technique of unstructured observation has been contributed mainly by social anthropology where it has frequently taken the form of participant observation. In this form of observation, the observer takes on to some extent at least the role of the member of a group and participates in its functioning. In an exploratory study, the observational procedures are’ likely to be relatively Unstructured and the observer is more likely to participate in group activities. With this method, the observer joins in the daily life of the group or organisation he is studying, he watches what happens to the members of the community and how they behave and he also engages in conversations with them to find out their reactions to and interpretations of the events that have occurred, He studies the life of the community as a whole, the relationships between its members and its activities and institutions. In Participant observation, as observer has to answer the following four questions. 1.    What should he observed? 2.    How should observation be recorded? 3.    What procedures should be used to try to assure the accuracy of observation. 4.    What relationship should exist between the observer and the observed, how can such a relationship be established? 1.    The Content of observation; or What should be observed? In an exploratory, study, where Unstructured observation is most likely to be used one does not know in advance which aspects of the situation are going to be the most relevant. No hard and fast rule can be framed for the purpose nevertheless it may be helpful to provide a check list such as the one which follows. The list indicates significant elements of every social situation it suggests directions of observation that may otherwise be overlooked in other words they can be said to be the points to be kept in mind by the researcher while following unstructured observation. 1. The Participant: The researcher should lie aware about the members of the group he is going to observe as well as the group. He should know; a.    about their age, sex, occupation and background. b.    What sort of relationships prevails between various members of the group, how they are related to one another. Are they strangers or do they know one another? Are they members of some collectivity and if so what kind for e.g. an informal friendship group a fraternity or club, a factory a church? c.    What structures or groupings exist among the participants. 2.    The setting : A social situation may occur m different settings e.g. a drugstore cinema hall etc. About the setting one wants to know, in addition to its appearance, what kind of behaviour it encourages, prevents, discourage or prevents or social characteristics of setting may be described in terms of what kind of behaviour are likely to be perceived as expected or unexpected approved or disapproved, conforming or distant. 3.    The Purpose : Is there some official purpose that has brought the participants together or have they been brought together by chance ? How do participants react to official purpose’ e.g. with acceptance or with rejection. What goals other than the official purpose do the participants seen to be pursuing? Are the goals of the various participants compatible or antagonistic. 4.    The Social Behaviour: Here one wants to know what actually occur. What do the participants do, how do they do it and with whom and with what do they do it? With respect to behaviour one usually wants to know the following : a)    What was the stimulus or event that initiated it. b)    What appears to be its objective. c)    Toward whom or what is the behaviour directed. d)    What is the form of activity entailed in the behaviour (e.g. talking, running, driving a car, gesturing, sitting) e)    What are the qualities of the behaviour (e.g. its intensity, persistence unassamess, appropriates duration) f)    What are its effects (e.g. what behaviour does it evoke from others)? 5.    Frequency and Duration : Here one wants To know the answer to such questions as the following when did the situation occur? How long did it last? Is it a recurring type of situation or unique? If it recurs, how frequently does it occur? What are the occasions that give rise to it. If a researcher takes into account the above given points, he can identify the events to be observed. 9.9    Recording Unstructured Observation: In recording unstructured observation a researcher has to take care of two things when should he take notes and how should the notes be kept? The researcher should be active and alert and should prepare notes when the event is occurring i.e. simultaneously. This results in a minimum of selective bias and distortion through memory. There are many situations, however, in which note taking on the spot is not feasible because it would disturb the naturalness of the situation or arouse the suspicions of the persons observed. Moreover, constant note taking may interfere with the quality of observation. The observer may easily lose relevant aspects of the situation if he divides his attention between observing and writing. In situations in which, detailed note taking is not feasible, the memory of the observer may be too heavily taxed if recording is postponed until the observational period is over. For such situations it is well to acquire the habit of setting down significant key words in an almost imperceptible manner, using a small sheet of paper, the back of an envelope or other inconspicuous, material, If the amount to be recorded is so great that this method does not satisfy the observes, he may well decide, if it is at all feasible; to retire from an on-going situation for a few minutes every hour or two to make more detailed notes. The description of the events should be written as soon as possible in a narrative form. In order simplify’ matters, an observer can maintain an index. It would help the observer, to avoid wasting hours searching through his notes for items he remembers vaguely but cannot locate. The index should contain the following information number or date of observation notes (or of interview) group chiefly involved, names of persons observed or interviewed & perhaps also of persons discussed by them and a brief summary of what is covered is the notes. Along with recording the information in the form of notes, an observer can also take photographs photographs tend to present accurately a mass of detail, which accept to escape the human reporter. Whenever, possible photographs should be introduced in a series which might illustrate various aspects of situations. 9.10    Increasing The Accuracy of Observation This is necessary because of the element of subjectivity an observation. Two methods can be adopted to ensure accuracy. Use of sound recording instruments: An observer a use sound recording instruments in order to ensure the accuracy of the observation and if sound does not come into picture he can use an instrument to record estures. But this method has its limitations because these instruments are too costly and not every researcher can afford them. So the second method can be used. To send two or more people to observe the same event id ensure the accuracy of observations because when vo or more observers are watching and recording in the time problem area, they have opportunities to compare their findings and check bias. But the persons who are sent to observe the event ve to be chosen with great care. They should not be the a common cultural background and having similar training. If such is the case, their results are bound to tally and it would be concluded that the observation is accurate which is a wrong conclusion. Although Participant Observation has the merit of providing a means of studying a whole system with its many inter-relationships in great detail. It has certain serious draw backs also. 9.11    The Limitations of Participant Observation 1.    A risk with participant observation is that the role adopted by the observer will restrict his understanding of the situation- Riley called it as biased-view point effect. In playing a clearly defined role in the community the observer’s understanding of the situation is thereby restricted. He will have access to only to sources of information associated with that role, by being friendly to some members, of the community he will be cutting himself off from others. A complete picture of a situation can rarely be obtained by a single observer. 2.    If the observer’s role involves him closely in the community, his vision may be distorted, what is novel and noteworthy initially may, after a period of intensive contact with the community, be passed over as common. In order to remove this defect of participant’s observation, observers should be recruited from outside the community, for at least at the start of the research, they will see things in a fresh light. 3.    Participant observation is a highly individual technique. The success of the participant observation’s approach depends on his skill and personality. So, if the technique is to be successful, participant observers must have special abilities. 4.    An observer might have preconceived ideas and, notions about people and situations he is observing and he might record what he think he sees instead ‘of recording what he actually observes. 9.12    Problems Faced While Using the Technique of Observation 1.    What should be observed: If the observer is not clear as to what he must observe i.e., if the observation is at random or not systematical then the results would not be correct and reliable. 2.    How should observation be recorded: Another problem faced by a how he should record the data obtained after observation. If the data –is not recorded at a proper time, the investigator is liable to forget the information as he has to depend entirely upon his memory while using this technique. 3.    What that procedure should be used to try to ensure the accuracy of observation. The observer also faces along with the problem how the data should be recorded, the procedure to ensure the accuracy of observation. This problem becomes more complicated due to the element of subjectivity in observation. 4.    What relationship should exist between the observer and the observed and how can such a relationship be established. 9.13    Self-Check Exercise a.    What do you mean by observation? b.    What is the purpose of observation? Discuss its advantages. c.    Critically examine structured and unstructured observation. d.    Discuss the points which should be kept in mind by the researcher while following unstructured observation. 9.14    Summary Observation plays a significant role in social science research. A Social Scientist can hardly avoid being influenced in his choice of research problem, his ideas and his theories, by what he observed around him. Observation as a systematic method of collecting data must be suitable for investigating the problem in whichthe social scientists are interested and should be reasonably reliable and objective. It is now a perfect method of social investigation and probably the most popular one in gaining knowledge of social phenomena. It is an invaluable aid for studying small associations, communities etc. observation as a method of social researchhas been used quite extensively everywhere. 9.15    Glossary Observation – the action or process for closely observing or monitoring something or someone. Structured – construct or arrange according to plan. Unstructured – without formal organization or structure. 9.16    Answer to self-check exercise (a) See 9.2 (b) See 9.3 & 9.4 (c) See 9.6.1 & 9.6.2 (d) See 9.6.2 9.17    Terminal Questions a.    Comment on recording unstructured observation. b.    Discuss about the problem faced by the researcher while using the technique of observation. 9.18    Suggested Readings •    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. •    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. •  Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. •    Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. •  Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. •  Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. •    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. •    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. ***** CHAPTER-10DATA PROCESSING: EDITING, CODING AND TABULATION Structure 10.0 Learning Objectives 10.1    Introduction 10.2    Editing 10.3    Manual Editing 10.4    Computer Assisted Editing 10.5    Coding 10.6    Open Coding 10.7    Closed Coding 10.8    Tabulation 10.9    Frequency Tables 10.10    Cross Tabulation 10.11    Charts and Graphs 10.12    Self-Check Exercise 10.13    Summary 10.14    Glossary 10.15    Answer to Self-Check Exercise 10.16    Terminal Questions 10.17    Suggested Reading 10.0 Learning Objectives After studying this lesson, the learner will be able: -       To tell about data processing. -      To comprehend editing, tabulating and coding 10.1    Introduction In any research study or survey, data processing plays a crucial role in transforming raw data into meaningful and analyzable information. This chapter focuses on three essential steps in the data processing phase: editing, coding, and tabulation. These steps ensure the accuracy, consistency, and usability of the collected data. By understanding these processes, researchers can efficiently manage and analyze their data, leading to reliable and valuable outcomes Data processing refers to the conversion of raw data into meaningful and useful information. It involves various operations and techniques to manipulate, organize, analyze, and transform data to extract valuable insights and support decision-making processes. Elaborating on data processing involves discussing the stages, methods, and technologies used in the process. Here's a breakdown of the key aspects of data processing: •    Data Collection: The first step in data processing is gathering data from various sources such as databases, files, sensors, surveys, social media, or web scraping. The data can be structured (organized in a specific format like spreadsheets or databases) or unstructured (not organized, like text documents or social media posts). •    Data Cleaning: Once the data is collected, it often requires cleaning to remove errors, inconsistencies, duplicates, and missing values. Data cleaning involves techniques like data validation, outlier detection, and data imputation to ensure data quality and accuracy. •    Data Transformation: In this stage, data is transformed into a consistent and usable format. It involves tasks like data integration (combining data from multiple sources), data normalization (rescaling data to a common range), aggregation (summarizing data), and feature engineering (creating new features based on existing ones). •    Data Analysis: Data analysis encompasses a range of techniques to uncover patterns, relationships, and insights from the processed data. It involves statistical analysis, data mining, machine learning, and other analytical methods. Exploratory data analysis techniques like visualization and summary statistics help in understanding the data and identifying trends or correlations. •    Data Storage: After processing and analysis, data needs to be stored in a structured manner for efficient retrieval and future use. Traditional databases (e.g., SQL databases) or newer technologies like NoSQL databases or data lakes can be used based on the specific requirements of the data. •    Data Interpretation: Once the data is processed and analyzed, it needs to be interpreted to derive meaningful insights. This involves understanding the results in the context of the business problem or research question and drawing conclusions based on the findings. •    Decision Making: The ultimate goal of data processing is to facilitate informed decision making. The processed data and insights gained from analysis are used to support decisionmaking processes in various domains, such as business, healthcare, finance, marketing, and more. Technologies commonly used in data processing include programming languages like Python or R, statistical packages (e.g., pandas, NumPy), data manipulation tools (e.g., SQL), data visualization libraries (e.g., Matplotlib, Tableau), and machine learning frameworks (e.g., scikit-learn, TensorFlow). It's worth noting that data processing is an iterative and ongoing process. As new data becomes available, it can be incorporated into the existing data processing pipeline to update analyses and insights. Additionally, advancements in technologies like big data processing, cloud computing, and artificial intelligence have expanded the possibilities and efficiency of data processing, allowing organizations to handle large volumes of data and extract more meaningful insights. 10.2    Editing Editing is the initial stage of data processing, where data is thoroughly reviewed and checked for errors, inconsistencies, and completeness. The purpose of editing is to identify and rectify any discrepancies or issues in the collected data, ensuring its quality and integrity. The editing process can be carried out manually or using computer-based editing tools, depending on the volume and complexity of the data. Editing plays a crucial role in the research process as it ensures that the final research document is clear, concise, accurate, and adheres to the required standards. Elaborating on editing in research involves discussing the different stages, techniques, and considerations involved in the editing process. Here are some key aspects of editing in research: •    Structural Editing: Structural editing focuses on the overall organization, flow, and logical structure of the research document. It involves checking the introduction, literature review, methodology, results, discussion, and conclusion sections for coherence, relevance, and clarity. Structural editing ensures that the research paper follows a logical progression, and the main ideas are effectively communicated. •    Language Editing: Language editing involves reviewing the text for grammar, punctuation, spelling, and syntax errors. It aims to ensure that the language used is clear, concise, and follows the appropriate writing style (e.g., APA, MLA). Language editing also includes improving sentence structure, removing ambiguity, and enhancing readability. •    Style and Formatting: In research, adherence to specific style guidelines is crucial. The editor ensures that the document conforms to the required citation style, referencing format, font size, line spacing, and other formatting guidelines. This includes checking in-text citations, references, footnotes, headings, tables, and figures to ensure consistency and accuracy. •    Content Review: Content review involves evaluating the accuracy and completeness of the information presented in the research document. The editor checks for factual errors, inconsistencies, and logical flaws in the arguments or data analysis. Content review also involves verifying that the research follows ethical considerations, data integrity, and research protocols. •    Clarity and Coherence: Editors strive to make the research document clear and coherent to the intended audience. They ensure that complex concepts are explained effectively, jargon is minimized or defined, and the overall writing style is appropriate for the target readership. Editors also work to improve the logical flow of ideas and transitions between paragraphs and sections. •    Review of References: Editors verify the accuracy and completeness of the reference list and in-text citations. They ensure that all sources are properly cited, consistent with the chosen citation style, and correspond to the references listed. Editors may also cross-check references to ensure they are up to date and relevant to the research topic. •    Peer Review Feedback: If the research document has undergone peer review, the editor may incorporate feedback and suggestions provided by the reviewers. They consider the comments and recommendations to improve the clarity, accuracy, and overall quality of the research paper. It's important to note that editing is not limited to correcting errors but also involves enhancing the overall quality and impact of the research document. Professional editors, with subject matter expertise in the research field, can provide valuable insights and suggestions to improve the clarity, rigor, and presentation of the research work. Additionally, researchers should allow sufficient time for editing and revision, and consider seeking feedback from colleagues or mentors to gain different perspectives on their work. Collaboration with editors or proofreaders can greatly contribute to the quality and effectiveness of the final research document. 10.3    Manual Editing In manual editing, the data is carefully examined to detect errors and inconsistencies manually. The researcher or a team of experts meticulously review each data entry for missing values, invalid responses, outliers, and logical inconsistencies. For example, if a survey question asks for age, and an entry indicates "999" years, it can be identified as an error and corrected or removed. Manual editing allows for a detailed analysis of the data but can be time-consuming and prone to human errors. Manual editing in research refers to the process of reviewing, revising, and refining a research document by human editors. Unlike automated spell checkers or grammar tools, manual editing involves a comprehensive and in-depth examination of the research content. Elaborating on manual editing in research involves discussing the specific tasks, considerations, and benefits associated with this process. Here are some key aspects of manual editing in research: •    Language and Grammar: Manual editing focuses on correcting grammar, punctuation, syntax, and spelling errors in the research document. Editors pay close attention to sentence structure, word choice, and overall clarity of expression. They ensure that the language used is appropriate for the target audience and adheres to the conventions of academic writing. •    Style and Formatting: Editors review the document to ensure compliance with the specified style guide or formatting requirements. They check for consistency in headings, subheadings, font styles, citation format, referencing style, and other formatting elements. Editors also ensure that figures, tables, and other graphical elements are properly labeled and referenced. •    Clarity and Coherence: Manual editing aims to enhance the clarity, coherence, and overall flow of ideas in the research document. Editors carefully examine the logical structure of the paper, identifying any gaps in the argument, ambiguities, or inconsistencies. They suggest revisions to improve the organization of sections, transitions between paragraphs, and overall readability. •    Technical Accuracy: Editors verify the accuracy of the technical content in the research document. They ensure that data, calculations, formulas, statistical analyses, and other technical aspects are correct and properly presented. Editors may also check for the appropriate use of terminology, definitions, and explanations of concepts to ensure accuracy and precision. •    Consistency and Conformance: Manual editing ensures consistency throughout the research document. Editors check for consistent use of terminology, abbreviations, acronyms, and capitalization. They also verify that references and citations are consistently formatted and correctly cited throughout the document. •    Fact-Checking and Citations: Editors may perform fact-checking to ensure the accuracy of statements, claims, and references mentioned in the research document. They verify that all cited sources are accurately referenced, and the information provided is supported by credible and up-to-date references. Editors may also suggest additional sources or citations to strengthen the research work. •    Contextual Improvement: Manual editing involves considering the context of the research topic, research question, and target audience. Editors may provide suggestions to improve the introduction, literature review, discussion, and conclusion sections to ensure they are relevant, engaging, and effectively communicate the research findings. Benefits of Manual Editing in Research: •    Enhanced Clarity: Manual editing helps clarify complex concepts and ensure that the research is understandable to the intended audience. •    Improved Quality: Manual editing improves the overall quality, rigor, and presentation of the research work, enhancing its credibility and impact. •    Language Proficiency: Editors with expertise in academic writing and the specific research field can provide valuable insights to improve the language and expression in the document. •    Objective Perspective: Editors bring an objective perspective to the research document, identifying areas that may be unclear or need further development. •    Compliance with Standards: Manual editing ensures that the research document adheres to the required academic and formatting standards. It's important to involve professional editors or proofreaders who have subject matter expertise in the research field to ensure the accuracy and effectiveness of the manual editing process. Collaborating with editors can significantly contribute to refining and strengthening the final research document. 10.4    Computer Assisted Editing With the advancements in technology, computer-assisted editing tools have become popular in data processing. These tools employ automated algorithms and checks to identify potential errors or inconsistencies in the data. They can flag missing values, out-of-range responses, inconsistent patterns, and logical errors. Researchers can then review and correct the flagged entries efficiently. Computer-assisted editing saves time and reduces human errors, especially when dealing with large datasets. Computer-assisted editing in research refers to the use of software tools and technologies to support the editing process of research documents. It involves the utilization of computer programs and applications to automate or assist with various editing tasks. Elaborating on computer-assisted editing in research involves discussing the specific tools, techniques, and benefits associated with this approach. Here are some key aspects of computer-assisted editing in research: •    Grammar and Spell Checkers: Computer-assisted editing tools often include grammar and spell checkers that automatically detect and highlight grammatical errors, punctuation 113 mistakes, misspelled words, and typos in the research document. These tools provide suggestions for corrections and help ensure that the document is free from basic language errors. •    Style and Formatting Tools: Software applications designed for editing research papers often include features that facilitate adherence to specific style guidelines and formatting requirements. These tools help with tasks such as formatting citations, managing reference lists, ensuring consistent heading styles, and handling other formatting elements according to the specified style (e.g., APA, MLA). •    Plagiarism Detection: Plagiarism detection software is commonly used in research editing to identify instances of copied or improperly cited content. These tools compare the research document against a vast database of published works, online sources, and other documents to identify potential matches or similarities. Plagiarism detection tools help ensure that proper credit is given to the original sources and prevent academic misconduct. •    Language Enhancement Tools: Some computer-assisted editing tools offer advanced language enhancement features. These tools provide suggestions for improving sentence structure, word choice, readability, and overall clarity of expression. They can highlight awkward phrasing, excessive wordiness, or potential areas for improvement, helping researchers enhance the language and coherence of their work. •    Reference Management Software: Reference management software tools assist researchers in organizing and managing references throughout the research process. These tools facilitate the creation of citation libraries, automatically format references in various citation styles, and generate bibliographies or reference lists. They help ensure consistency and accuracy in referencing, saving time and effort in the editing process. •    Collaboration and Version Control: Editing research documents often involves collaboration among multiple authors or reviewers. Computer-assisted editing tools enable seamless collaboration, allowing multiple users to work on the same document simultaneously. They provide features for tracking changes, adding comments, and managing different versions of the document. This streamlines the editing and revision process, enhancing collaboration and efficiency. Benefits of Computer-Assisted Editing in Research: •    Time Efficiency: Computer-assisted editing tools can automate routine editing tasks, saving time and effort for researchers. •    Consistency: These tools help ensure consistency in grammar, formatting, and referencing throughout the research document. •    Enhanced Accuracy: Software tools can identify errors that may be overlooked in manual editing, improving the overall accuracy of the research work. •    Language Enhancement: Language enhancement features assist researchers in improving the clarity and effectiveness of their writing. •    Streamlined Collaboration: Computer-assisted editing tools facilitate collaboration and version control, enabling seamless communication and feedback among authors and reviewers. While computer-assisted editing tools offer numerous benefits, it is important to note that they should be used as aids and not replacements for manual editing. Human editors with subject matter expertise are still valuable in addressing complex editing issues, verifying technical accuracy, and ensuring the research document meets the highest standards 10.5    Coding Coding is the process of transforming qualitative or open-ended responses into quantitative or categorical data. It involves assigning numerical codes or labels to responses to facilitate data analysis. Coding enables researchers to organize and classify data, making it suitable for statistical analysis and interpretation. The coding process may vary depending on the nature of the data and the research objectives. Coding in research refers to the process of categorizing and organizing qualitative data to identify patterns, themes, or concepts. It involves systematically assigning labels or codes to segments of data, such as interview transcripts, survey responses, or text documents, to facilitate analysis and interpretation. Elaborating on coding in research involves discussing the purpose, techniques, and considerations associated with this process. Here are some key aspects of coding in research: •    Purpose of Coding: The primary purpose of coding is to make sense of qualitative data and extract meaningful insights. Coding helps researchers identify recurring themes, concepts, or patterns within the data, which can then be used to answer research questions or explore specific phenomena. Coding also helps organize and structure the data, making it more manageable for analysis. •    Techniques of Coding: There are different techniques of coding in research, and the choice depends on the research methodology, data type, and research objectives. Some commonly used coding techniques include: 1.    Open Coding: In open coding, the researcher closely examines the data and generates initial codes without preconceived categories or assumptions. It involves a line-by-line analysis, identifying concepts, ideas, or themes from the data. 2.    Axial Coding: Axial coding involves organizing the open codes into categories and exploring relationships between these categories. It helps establish connections, hierarchies, or causal relationships between codes. 3.    Selective Coding: Selective coding is the final stage of coding, where the researcher identifies the central or core codes that capture the main themes or concepts in the data. It involves refining and consolidating codes into a coherent framework. 4.    Thematic Coding: Thematic coding involves identifying and categorizing patterns or themes within the data. It focuses on identifying recurring ideas, concepts, or phenomena that emerge from the data. 5.    Code Development: During the coding process, researchers develop a coding scheme or framework to guide the analysis. The coding scheme includes a set of codes or labels that represent different concepts or themes identified in the data. The scheme is often iterative, with codes being refined or added as new insights emerge from the analysis. 6.    Inter-coder Reliability: Inter-coder reliability is the degree of agreement between multiple coders when applying codes to the data. It is important to ensure consistency and accuracy in the coding process. Researchers may employ techniques like independent coding, coding consensus meetings, or coding comparison to establish and assess inter-coder reliability. 7.    Software for Coding: There are several software tools available to assist with the coding process, known as computer-assisted qualitative data analysis software (CAQDAS). These tools, such as NVivo, ATLAS.ti, or MAXQDA, provide features to facilitate coding, organize data, and support the analysis of qualitative data. They allow researchers to efficiently manage large volumes of data, explore relationships between codes, and visualize patterns or themes. 8.    Reflexivity and Iteration: Coding is an iterative process that requires constant reflection and refinement. Researchers should be mindful of their own biases, preconceptions, and assumptions that may influence the coding process. Regular reflection and engagement with the data help ensure a rigorous and thorough analysis. Coding in research allows researchers to identify and analyze patterns, themes, and concepts within qualitative data. It provides a structured and systematic approach to making sense of complex information, leading to the generation of meaningful insights and the development of robust research finding 10.6 Open Coding In open coding, qualitative data such as interviews or open-ended survey responses are analyzed to identify common themes, concepts, or categories. Researchers read through the data and assign codes to different responses based on their content. This process allows for the emergence of new categories and insights, providing a deeper understanding of the data. Open coding is often iterative, as researchers refine and revise codes during the analysis process. Open coding is a qualitative data analysis technique used in research to identify, categorize, and label concepts, ideas, or themes from raw data. It is a fundamental step in grounded theory methodology, which aims to develop theories or explanations based on empirical observations rather than preconceived hypotheses. Open coding involves a line-by-line examination of the data, breaking it down into discrete units for analysis. Here are some key aspects of open coding in research: •    Purpose of Open Coding: The primary purpose of open coding is to systematically explore and identify concepts, ideas, or phenomena in the data. It allows researchers to generate initial codes without preconceived categories or assumptions, enabling the emergence of themes or patterns directly from the data. Open coding helps in understanding the data at a granular level and lays the foundation for further analysis. •    Process of Open Coding: The process of open coding typically involves the following steps: a. Familiarization: Researchers immerse themselves in the data by reading or listening to it 117 repeatedly to gain a deep understanding of its content. b.    Line-by-Line Analysis: Researchers code the data by identifying and labeling discrete units or segments. These units can be phrases, sentences, or paragraphs that capture a distinct idea or concept. c.    In-Vivo Codes: In open coding, researchers often use in-vivo codes, which are labels taken directly from the participants' own words. This preserves the participants' perspectives and ensures that the codes are grounded in the data. d.    Conceptualization: As codes are generated, researchers begin to conceptualize and categorize them. Similar codes are grouped together, and relationships between codes may start to emerge. e.    Iteration: The process of open coding is iterative. Researchers go back and forth between the data and the emerging codes, continually refining and modifying the codes as new insights and patterns emerge. •    Codebook Development: As open coding progresses, researchers develop a codebook or coding scheme that documents the codes and their definitions. The codebook provides a reference for consistent coding and helps in organizing and analyzing the data. •    Reflexivity: Open coding requires researchers to be reflexive and aware of their own biases and assumptions. Researchers should critically examine their interpretations and challenge any preconceptions that may influence the coding process. Reflexivity ensures a more objective and rigorous analysis. •    Software Tools: While open coding can be done manually with pen and paper, qualitative data analysis software, such as NVivo, ATLAS.ti, or MAXQDA, can assist in managing and organizing the codes and data. These tools provide features to facilitate the coding process, enable efficient data retrieval, and support further analysis. Open coding allows researchers to explore and uncover themes, concepts, or patterns directly from the data. It provides a flexible and exploratory approach to qualitative analysis, enabling the emergence of new insights and theories. The codes generated during open coding serve as building blocks for subsequent stages of analysis, such as axial coding and selective coding, in order to develop a comprehensive understanding of the research topic. 10.7    Closed Coding Closed coding is used when predetermined categories or concepts are already established. In closed coding, researchers assign codes to the responses based on the predefined categories. For example, in a survey about food preferences, predetermined categories could be vegetarian, vegan, pescatarian, etc. Closed coding provides a more structured and systematic approach to data analysis, ensuring consistency across responses. Closed coding is a qualitative data analysis technique used in research to categorize and organize data based on predefined concepts or themes. Unlike open coding, which allows for the emergence of codes directly from the data, closed coding involves applying pre-established codes to the data. It is often used after initial rounds of open coding or in studies that have a predetermined theoretical framework or coding structure. Here are some key aspects of closed coding in research: •    Predefined Codes: In closed coding, researchers develop a set of predefined codes or categories based on existing theories, prior research, or research questions. These codes represent concepts or themes that the researchers anticipate or expect to find in the data. •    Application of Codes: Researchers systematically apply the predefined codes to the data by categorizing relevant segments or excerpts. Each segment of data is assigned one or more codes that best capture the content or meaning of that segment. •    Structured Analysis: Closed coding provides a structured approach to data analysis, as it involves organizing and classifying data based on predetermined codes. This allows for easier comparison and identification of patterns across different segments or cases. •    Codebook Development: Researchers create a codebook that contains the predefined codes and their definitions. The codebook serves as a reference guide for consistent coding across the data and ensures that all coders have a shared understanding of the codes. •    Iterative Process: Closed coding is an iterative process. As researchers analyze the data using predefined codes, they may encounter new themes or concepts that were not initially considered. In such cases, they may refine or add new codes to capture these emergent patterns. •    Data Reduction: Closed coding helps in reducing the complexity of the data by categorizing it into pre-established codes or themes. This simplification allows researchers to focus on specific aspects of the data and facilitates the identification of key findings or patterns. •    Quantitative Analysis: Closed coding can be useful when researchers intend to conduct quantitative analyses or statistical tests on the coded data. By assigning predetermined codes, researchers can easily convert qualitative data into quantitative data for further statistical analysis. •    Theory Confirmation: Closed coding can be particularly relevant when researchers aim to test or confirm existing theories or hypotheses. By using predefined codes, researchers can assess the presence or frequency of specific concepts or themes in the data, providing empirical evidence to support or challenge theoretical propositions. Closed coding provides a structured and systematic approach to qualitative data analysis. It allows researchers to apply predefined codes to the data, enabling efficient organization, comparison, and analysis. While closed coding may be less exploratory than open coding, it is valuable in studies where the research questions are well-defined, theories or frameworks guide the analysis, or when researchers seek to quantify qualitative data. 10.8    Tabulation Tabulation is the final step in data processing, where the coded data is organized into tables, charts, or graphs. Tabulation allows for a concise and visual representation of the data, making it easier to identify patterns, trends, and relationships. Depending on the research objectives, different types of tables and graphical representations can be used, such as frequency tables, cross-tabulations, bar charts, or scatter plots. Tabulation in research refers to the process of summarizing and presenting data in a structured tabular format. It involves organizing and condensing collected data into tables, allowing for easy comparison, analysis, and interpretation. Elaborating on tabulation in research involves discussing the purpose, steps, and considerations associated with this process. Here are some key aspects of tabulation in research: • Purpose of Tabulation: The primary purpose of tabulation is to present data in a concise and organized manner to facilitate analysis, interpretation, and reporting. Tabulated data allows researchers to identify patterns, trends, relationships, and distributions within the dataset. It provides a visual representation of the data that can be easily understood and communicated to others. Steps in Tabulation: a.    Identify Variables: The first step in tabulation is to identify the variables or characteristics of interest in the research study. Variables can be categorical (e.g., gender, occupation) or numerical (e.g., age, income). b.    Design Tables: Researchers determine the structure and layout of the tables based on the identified variables. Each table typically focuses on one or more specific variables, with rows representing different categories or groups and columns representing different attributes or measurements. c.    Data Entry: Researchers enter the collected data into the appropriate cells of the tables. This involves transferring the information from the data collection instruments (e.g., surveys, questionnaires) or electronic datasets into the tabulation software or spreadsheets. d.    Frequency Distribution: Researchers calculate the frequency distribution for categorical variables. This involves counting the number of occurrences or responses for each category and representing them in the table. e.    Statistical Measures: For numerical variables, researchers may calculate statistical measures such as mean, median, standard deviation, or other relevant statistics. These measures can be included in the tables to provide a summary of the numerical data. f.    Cross-tabulation: Cross-tabulation involves creating tables that show the relationship between two or more variables. This helps identify associations, dependencies, or interactions between different variables. •    Presentation and Visualization: After tabulating the data, researchers may use additional techniques for visual presentation, such as creating charts, graphs, or diagrams to aid in data interpretation. Visual representations can enhance the understanding and communication of the research findings. •    Data Integrity and Accuracy: Ensuring data integrity and accuracy is crucial during the tabulation process. Researchers need to carefully review and validate the entered data, check for errors, and resolve any inconsistencies or missing values before proceeding with analysis. •    Software Tools: Researchers often utilize specialized software tools for data tabulation, such as spreadsheet software (e.g., Microsoft Excel, Google Sheets), statistical packages (e.g., SPSS, Stata), or dedicated tabulation software. These tools provide features and functions to efficiently create, manipulate, and analyze tables. Tabulation plays a significant role in summarizing and presenting research data in a meaningful way. It simplifies complex datasets, enables comparisons and analysis, and assists in drawing conclusions or making informed decisions based on the research findings. Effective tabulation ensures that researchers can effectively communicate their results to the intended audience, including colleagues, stakeholders, or readers of research reports or publications. 10.9    Frequency Tables Frequency tables display the number or percentage of occurrences of different categories or responses. They provide a clear summary of the distribution of data, allowing researchers to identify the most common or rare occurrences. Frequency tables are particularly useful when dealing with categorical or nominal data. 10.10    Cross Tabulation Cross-tabulations, also known as contingency tables, display the relationship between two or more variables. They enable researchers to examine how variables are related or influenced by one another. Cross-tabulations are often presented in a matrix format, where rows represent one variable, columns represent another variable, and the cells contain the frequency or percentage of occurrences. 10.11    Charts and Graphs Charts and graphs are visual representations of data that help in understanding complex patterns and trends quickly. Bar charts, line graphs, pie charts, and scatter plots are commonly used to present data visually. These graphical representations provide a visual overview of the data, making it easier to communicate findings to a wider audience. 10.12    Self-Check Exercise 11    What do you mean by data processing? 12    Discuss the advantages of tabulation and editing in research. 13    Describe different types charts and graphs in research? 10.13    Summary Data processing involves several crucial steps, including editing, coding, and tabulation. Editing ensures data accuracy, consistency, and completeness. Coding facilitates the transformation of qualitative data into quantitative or categorical data, allowing for statistical analysis. Tabulation organizes the coded data into tables and graphs, making it visually accessible and facilitating data interpretation. By following these steps diligently, researchers can obtain reliable and meaningful insights from their collected data, contributing to the success of their research endeavors. 10.14    Glossary 14    Factual – concerned with what is actually the case. Opinion – a view or judgement formed about something. 15    Ambiguity – the quality of being open to more than one interpretation. Schedule- a plan for carrying out a process, an appendix to formal documents. 10.15    Answer to Self-Check Exercise 16  (a) See 10.1 (b) See 10.2 & 10.3 9c) See 10.4 & 10.4.1 (d) See 10.1 & 10.7 (e) See 10.10 10.16    Terminal Questions 17    Explain general principles of data processing. 18    Elaborate types of tabulation. 19    Write a note on frequency tables. 10.17 Suggested Reading 20    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. 21    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. 22    Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. 23    Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. 24  Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. 25  Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. 26    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. 27    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. ***** Suggested Readings CHAPTER -11GRAPHIC REPRESENTATION: GRAPHS OF TIME SERIES AND GRAPHS OF FREQUENCY DISTRIBUTIONS Structure 11.0 Learning Objectives 11.1    Introduction 11.2    Different types of Diagrams 11.3    Bar Diagrams 11.4    Pie Diagrams 11.5    Frequency Curves 11.6    Histogram 11.7    Frequency Polygon 11.8    Histogram 11.9    Self Check Exercise 11.10    Summary 11.11    Glossary 11.12    Answer to Self Check Exercise 11.13    Terminal Questions 11.14    Suggested Readings 11.0 Learning Objectives After studying this lesson, the learner will be able: To comprehend the process of graphic representation of data. To understand about different types of Diagrams. 11.1    Introduction For a researcher is social sciences, knowledge of the principles of constructing ‘graphs and charts is indispensable. Sometimes graphical representation of data is more effective in attracting the attention of the reader than any other method. If the results of ‘statistical investigations do not reach their destination simply because they are not presented in a sufficiently effective fashion, the labour of the researcher go waste. “Graphs and charts are especially valuable in renders large masses of statistical data clear and comprehensible. The meaning of series of figures in textual or tabular form may be difficult for the mind to grasp or retain. Properly constructed graphs and charts relieve the mind of burdensome details of portraying facts concisely, logically and simply. Graphs and charts, by emphasizing new and significant relationships, also may be of immense service in discovering new facts and in developing hypotheses”1 The purpose of research would be achieved if the statistical data are made more meaningful, and intelligible so that even an ordinary reader could understand the implications hidden in the data. It would not be an exaggeration to say that the method of presenting facts is often more instrumental in their acceptance than the nature of the facts themselves.2 But while presenting the data in the graphic form, proper care should be taken so that poorly constructed charts and graphs may not misrepresent or distort the -otherwise good report. Graphical representation, of data, however, suffers from certain limitations Mostly, graphs cannot show so many sets of facts as may be shown in a table. Secondly, whereas in the table, exact values are shown, only approximate values can be shown on the graph. Thirdly, drawing of charts and graphs requires a certain amount of time to construct since each one as an original drawing. But these limitations are more than compensated by the added effectiveness which the graphical re presentation possesses in comparison to tabular presentation. 11.2    Different Types of Diagrams A large number of diagrams and graphs are used in research studies, depending upon the nature and scope of data. We shall, however, discuss the main diagrams and / graphs which are commonly used in presenting research data. These are listed below: 1.    Bar diagrams. 2.    Pie Diagrams. 3.    Frequency curves 4.    Histogram. 5.    Frequency Polygon. 6.    Historigram. 11.3    Bar Diagrams These are un-dimensional diagrams used to present research data with the help of bars constructed vertically or horizontally. Bar diagrams can take any of the following forms: (a)    Simple Bar Diagram, (b)    Sub-divided Bar Diagram. (c)    Multiple Bar Diagram. (a)    Simple Bar Diagram : This is a simple diagram constructed with any suitable scale. Let us consider the illustration given below: Illustration 1 The admissions in the faculty of social sciences during 1981-82 are given below. Present this information with the help of bar diagram. Department No. of students admitted 1.    Economics             60 2.    Commerce           40 Figure I shows the bar diagram for the data given above. Bars have been constructed vertically proportional to the enrolment in each department. The departments have been shown along the horizontal axis end the number of students admitted along the Y-axis. The researcher may note that bars are separated from each other by a small but distinct margin. The scale along X-axis as also along Y-axis is kept uniform. Such a bardiagram can also be constructed by drawing bars horizontally. (b)    Sub-divided Bar Diagram: Many a times however, information for the same unit is given in different sub-sections and it is desirable to present such an information in sub-sections only. Under such situations, simple bar diagrams do not serve the purpose. Such data are presented with the help of a subdivided bar-diagram. Let us again take an illustration. Illustration 2 : The data given below shows the employment situation in India and Pakistan. Represent these data by a suitable bar-diagram. | | | India | Pakistan | Percentage | |---|---|---|---|---| | | | (000)50 | (000)40 | Employment | | Employed in Agriculture | 100 | 50 | 40 | 50 | | ” ” Industry | 40 | 10 | 16 | 10 | | ” ” Transport Services | 20 | 5 | 8 | 5 | | ” ” Construction | 20 | 10 | 8 | 10 | | ” ” Trade | 30 | 5 | 12 | 5 | | ” ” Services | 40 | 20 | 16 | 20 | | Total Employment | 250 | 100 | 100 | 100 | Fig. 2 Sub-divided Bar Diagrams Fig. 3 Figures 2 and 3 show the construction of sub-divided bar diagrams. Figure 2 shows the construction of an absolute sub-divided bar diagram. Two vertical bars constructed in proportion to total employment in the two countries. Method of construction is same as in the case of a simple bar diagram. The two bars thus constructed are sub-divided according to the size of employment in each sector of the economy. These sub-divisions are shown differently by using different colours or different designs for purposes of clarity and comparison. However, since comparison in absolute numbers is misleading, the sub-division figures are usually converted into percentages and then shown on a sub-divided bar diagram as in figure 3. From figure 2, it would appear that India is more predominantly agricultural than Pakistan which, however, is not the position. Figure 3 clearly shows that a smaller percentage of labour force is engaged in agriculture in India compared to Pakistan and ‘so on. Therefore, for rational comparisons, it is always essential to convert absolute figures into percentages before constructing a sub-divided bar diagram. A sub-divided bar diagram is more informative and of greater utility. It gives detailed information about each sub-section of a major unit of information and also shows a comparative picture of different situations. (e) Multiple Bar Diagram : Sometimes the nature of information is such that neither simple nor subdivided bar diagrams represent the data precisely. In such cases, use is made of another type of bar diagram known as Multiple bar diagram. Illustration 3 The population data of three different towns in three censuses is given below. Use a suitable diagram to present these data pictorially. | Census . | 1951 (000) | 1961 (000) | 1971 (000) | |---|---|---|---| | Towns | | | | | A | 50 | 60 | 10 | | B | 40 | 30 | 50 | | C | 60 | 60 | 50 | Fig.4 Multiple Bar Diagram Figure 4 shows a multiple bar diagram. It may be noted that these are three major units of information comprising of three census periods, 1951, 1961 and 1971. But each period presents information about three different components (Towns). It may, however, be noted that the three towns are not the sub-divisions of decennial census figure and hence cannot be shown on a sub-divided bar diagram. In such cases we use only multiple bar diagrams as shown above. Different designs or colours may be used to show different towns distinctly. Within each composite census unit there is clear distance left but among three towns, a smaller uniform distance is used to separate the bars. 11.4 Pie-Diagram Sometimes circular diagrams are used to represent varied information. A circle is drawn with any suitable radius. The angle of 360° at the centre of the circle is divided in proportion to the size of each individual item of information. Yet on other occasions different circles are drawn whose radii are chosen in proportion to the individual figures on an appropriate scale. Construction of Pie-diagrams has been illustrated below with the help of an illustration. Illustration 4 Draw a Pie-diagram to represent the following arbitrary numerical data : Country India China USA USSR UIC Total Population (in millions) 250 350 100 150 50 900 Before drawing a pie diagram, the sum of numbers to be shown on the diagram is calculated and equated to 160° Accordingly, in our illustration 900=360° 360 or I =         = 0.4 = C 900 C = 0 - 4 is known as adjustment or correction factor. Multiplying each of the population figures by this correction factor yields the angular position of each individual item at the centre of the circle. Choose any appropriate radius and draw a circle and mark each angular section distinctly to complete the construction of a Pie-diagram as shown in figure 5. Fig. 5 Pie-Diagram We can also draw different circles to represent this information by selecting radii proportional to different figures on a suitable scale. Say, for example, we use 1 cm radius for India, then other radii shall be 1-4, 0-4, 0.6 and 0-2 respectively. In many cases, researchers make use of squares and many other types of diagrams according to the nature of data and purpose of its communication. Several attractive pictograms are also used in the presentation of data. 11.5 Frequency Curves These are graphs used to represent frequency distributions. Usually frequencies are shown along verticalaxis and variable values along X-axis. Frequency curves have theoretical importance as these show the nature of frequency distributions. The following illustration makes the idea clear : Illustration 5 Construct a frequency graph for the distribution given below : | X  : | 0 | 1 | 2 | 3 | 4 | 5 | 6 | |---|---|---|---|---|---|---|---| | Y  : | 4 | 6 | 9 | 15 | 10 | 16 | 8 | Fig. 6. Frequency Curve Figure 6 shows the frequency curve for the data given above. X-values are shown against the horizontal axis and frequency values along the vertical axis. Frequency values are plotted against X-values in the two dimensional plane. By joining the plotted values with a free hand, we obtain the frequency curve. It is evident from figure 6 that frequency graph has an irregular shape. It may have several peaks and troughs in it and may not exhibit any regular behaviour. However, in some specific cases, it may have a well defined shape. Some such cases are discussed below: Case I : Symmetrical frequency curves Illustration 6 Construct frequency curves for the following data | (a) | X     : | 1 | 2 | 3 | 4 | 5 | 6 | 7 | |---|---|---|---|---|---|---|---|---| | | f             : | 2 | 3 | 5 | 8 | 5 | 3 | 2 | | (b) | X     : | 1 | 2 | 3 | 4 | 5 | 6 | 7 | | | f             : | 1 | 2 | 5 | 10 | 5 | 2 | 1 | | (c) | X     : | 1 | 2 | 3 | 4 | 5 | 6 | 7 | | | f             : | 2 | 3 | 4 | 6 | 4 | 3 | 2 | Fig. 7 Symmetrical Frequency curves The data of illustrations 5 (a), (b) and (c) are shown graphically in figure 7 (a), (b) and (c) respectively. It may be noted that all these curves, are symmetrical graphs and are yet different from each other. Curve (a) is symmetrical bell shaped graph which is having normal altitude neither too peaked nor too Hat. Such a curve is known as normal frequency curve. Curve (b) is symmetrical bat highly peaked at the centre. Such a curve has more than normal altitude and is known as Lepto-Kurtic frequency curve. Against this, curve (c) is also a symmetrical curve but has a flat top. Such a curve is known as Plati-Kurtic curve. Sometimes frequency curves also have known asymmetrical forms. Such curves are either concentrated towards left or towards right depending upon the nature of distribution. Besides these known shapes under specific conditions, frequency graphs are irregular in shape. Case II. Cumulative Frequency Graph (O give) This curve is the graphic presentation of cumulative frequency distribution. This is an ever-increasing graph and does not slope downward unless frequencies are cumulated from below. If sometimes frequency values for some values of X are zero so that cumulative frequency values remain same over a range of x-values, the cumulative frequency curve may level off (run parallel to x axis) but in no case shall it slope downward. Such a graph is also known by the name of o give. Illustration 7 Draw an o give for the data given below | Class interval : | 0-4 | 4-8 | 8-12 | 12-16 | 16-20 | 20-24 | 24-28 | |---|---|---|---|---|---|---|---| | Frequency (f) | 4 | 5 | 8 | 6 | 7 | 3 | 2 | | Cumulative | | | | | | | | | frequency (f) | 4 | 9 | 17 | 23 | 30 | 33 | 35 | Fig. 8 Cumulative Frequency Graph Figure 8 shows the cumulative frequency graph for the data given in illustration 7. The researcher may note that cumulative frequency values are plotted against the upper limits of class intervals. Compared to frequency curves, these graphs have regular shapes and do not have peaks and troughs. Since at any point of the curve, we can get the number of times the values occur till that X or below that X (X stands for classintervals), the graph is occasionally also referred to as a “Less than Curve”. 11.6 Histogram Histogram is the diagrammatic presentation of a continuous grouped frequency distribution. Like a frequency curve, histogram also has lot of theoretical importance. It shows the nature of a distribution and helps in characterizing its features. Illustration 8 Construct a histogram for the data given below : Class interval 0-4   4-8    8-12   12-16  16-20 20-24  24-28 Frequency 4      5      7      9      6      3      2 Fig. 9 Histogram Figure 9 shows the Histogram for the distribution given in illustration 8. The length of the class interval is shown along X-axis and the corresponding frequencies along Y-axis. Continuous rectangles with classintervals as the width and corresponding frequency as height are constructed to complete the preparation of a histogram. As is evident histogram can be constructed only when data are given in the form of a continuous grouped frequency distribution. However, many a times, data are presented ‘in a discrete distribution or with unequal intervals and the researcher is interested in constructing a histogram. Under such situations, the distributions are properly adjusted and then a histogram constructed. A brief mention of some such cases is given below: Case I.    Unequal Class Intervals If a distribution is continuous but has unequal class intervals such as 0-3, 3-10, 10-20, 20-25, etc, an ordinary histogram cannot be constructed as such. Two options are given to researcher. He may even out his class intervals by redistributing the data with equal intervals. As such a new distribution will come into existence and an ordinary histogram can be constructed as before. The second option open to researcher is that he may adjust the respective frequencies by dividing each individual frequency with the corresponding class interval. Then again a histogram can be constructed as usual with unequal class-intervals and adjusted frequencies. Case II.    Discrete Distribution If a frequency distribution is discrete such as 1-4, 5-9, 10 -14 etc. a histogram cannot be constructed as such. In such a situation we use a correction factor (adjustment factor) as 0.5. Sometimes this is also known as continuity factor. This factor is subtracted from each of the lower limits and added to each of the upper limits. The group shall then look like 0.5-4.5, 4.5-9-5, 9.5-14.5 etc. which transforms, the original distribution to a continuous form. Again a histogram can be constructed as usual. Case III.    Ungrouped Data Suppose a distribution is given in an ungrouped manner such as : X  :       2      6      10     14     18 f    :        3        5        7       2        1 In this case, the data are converted into a continuous grouped distribution before a histogram can be constructed. The common difference of X-values (4) is treated as the class-interval (c). Then lower and upper limits are worked out as X -C/2 and X+C/2 respectively. The continuous grouped frequency distribution shall, therefore, be as 0-4, 4-8, 8-12, 12-16, 16-20 and now a histogram can be constructed as usual. 11.7    Frequency Polygon Frequency polygon is obtained by joining top middle points of the rectangles of a histogram and extending the graph to meet both the axis. Consider again the distribution given in illustration 8. Figure 9 shows its histogram. Mark the mid-points on the top of each rectangle and join these points by a free hand. Extend this graph on both sides to meet the axis to complete the drawing of a frequency polygon. This is shown in figure 10. Fig. 10. Frequency Polygon 11.8    Histogram This is the graph representing a historical series (time-series). Data recorded over a period of time is plotted on graph with horizontal axis representing time and vertical axis representing time-series observations. Let us consider the following illustration: The data given below shows the production of wheat over a period of time. Represent the data graphically. Year 1961, 62, 63, 64, 65, 66, 67, 68, 69, 70 Production (Quintals) 40, 42, 50, 45, 55, 40, 60, 65, 55, 70 Fig. 11 The productin data on a historigram It .may be observed that a historigram graph has no regular shape. It depends upon the annual production figures. Such a graph has often many peaks and troughs “in it pointing to different production situations from year to year. However, if the first and the last (initial and the terminal) points on this graph are joined by a straight line, it shows the trend in business which may be growing, declining or stagnating depending upon whether such a straight line has increasing or declining slope or is running parallel to the horizontal axis. 11.9    Self Check Exercise a.    What do you mean by graphic representation? b.    Discuss about different types of diagrams important for graphic representation of Data. c.    Elaborate the use of Frequency Polygon in research? 11.10    Summary Graphic representation of data is very important tools of research and development. It made more effective in attracting the attention of the reader than any other method. Properly constructed graphs and charts relives the mind of burdensome details of portraying facts concisely, logically and simple. Graphs and chart by emphasizing new and facts and is developing hypothesis. Bar diagrams. Pie Diagram, frequency curves, Histogram, Frequency Polygon and Historigram are the main diagrams and graphs which are commonly used in presenting research data. 11.11    Glossary Intangible – unable to be touched, not having physical presence. Histogram – a diagram consisting of rectangles whose area is proportional to the frequency of a variable and whose width is equal to the class interval. Polygon – a plane figure with at least three straight sides and angles, and typically five or more. 11.12    Answer to Self-Check Exercise (a) See 11.1 (b) See 11.2 (c) 11.7 11.13    Terminal Questions a.    Discuss about the limitations of graphic representation. b.    Illustrate bar diagram and its importance in research. c.    Write a note on Histogram. 11.14    Suggested Readings •    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. •    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. •    Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. •    Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. •    Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. •    Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. •    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. •    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. ***** CHAPTER-12ETHICS WITH RESPECT TO RESEARCH, INTELLECTUAL HONESTY AND RESEARCH INTEGRITY Structure 12.0 Learning Objectives 12.1    Introduction 12.2    Ethics in Research 12.3    Informed Consent 12.4    Confidentiality and Privacy 12.5    Minimizing Harm 12.6    Conflicts of Interest 12.7    Intellectual honesty 12.8    Research Integrity 12.9    Responsible Publication and Peer Review 12.10    Self-Check Exercise 12.11    Summary 12.12    Glossary 12.13    Answer to Self-Check Exercise 12.14    Terminal Questions 12.15    Suggested Readings 12.0 Learning Objectives After studying this lesson, the learner will be able: -    To understand about the meaning and concept ethics in research. -    To comprehend about conflicts of interest, informed consent and confidentiality. -    To know the research integrity and intellectual honesty. 12.1    Introduction Ethics, intellectual honesty, and research integrity are fundamental principles that guide ethical conduct in research. This chapter explores the ethical considerations researchers must uphold, the importance of intellectual honesty, and the principles of research integrity. 12.2    Ethics in Research Ethics in research involves ensuring the protection, rights, and welfare of individuals and communities involved in the research process. Researchers must adhere to ethical guidelines, institutional policies, and relevant regulations. Key ethical considerations include informed consent, confidentiality, privacy, minimizing harm, avoiding conflicts of interest, and respecting cultural and social norms. Ethical review boards or institutional review boards (IRBs) play a crucial role in evaluating and approving research protocols to ensure ethical compliance. 12.3    Informed Consent Obtaining informed consent is a cornerstone of ethical research. Researchers must ensure that participants fully understand the purpose, risks, benefits, and procedures involved in the study before agreeing to participate. Informed consent should be voluntary, based on comprehensive information, and obtained without coercion. Researchers should also consider the capacity and autonomy of participants, especially when involving vulnerable populations, such as children, prisoners, or individuals with cognitive impairments. 12.4    Confidentiality and Privacy Respecting confidentiality and privacy is paramount in research. Researchers must take appropriate measures to protect the identity and personal information of participants. Data should be anonymized or de-identified whenever possible and secure data storage and transmission protocols should be implemented. Researchers should inform participants about the extent to which their data will be confidential and the circumstances in which it may be disclosed, ensuring transparency and trust. 12.5    Minimizing Harm Researchers have a responsibility to minimize harm to participants and avoid exposing them to unnecessary risks. They should conduct a thorough risk assessment and take steps to mitigate potential physical, psychological, social, or legal harm. Participants' well-being should be prioritized throughout the research process, and interventions should be in place to address any adverse effects that may arise during or after the study. 12.6    Conflicts of Interest Researchers must maintain objectivity and avoid conflicts of interest that could compromise the integrity of the research. Conflicts of interest can arise from financial interests, personal relationships, or institutional affiliations that may unduly influence the research process or its outcomes. Researchers should disclose any potential conflicts of interest and take appropriate steps to manage or mitigate their impact on the research. 12.7    Intellectual honesty Intellectual honesty is the foundation of research integrity. It involves presenting one's work accurately, honestly, and transparently. Researchers should appropriately acknowledge and credit the work of others, including references and citations to relevant sources. Plagiarism, fabrication, falsification, or selective reporting of data are serious breaches of intellectual honesty and can undermine the credibility and trustworthiness of research. 12.8    Research Integrity Research integrity encompasses the ethical conduct of research and the responsible handling of data. It involves upholding the highest standards of honesty, transparency, and accountability throughout the research process. Researchers should ensure the accuracy, reliability, and reproducibility of their work. They should adhere to recognized research methodologies, use appropriate research design, collect and analyze data rigorously, and report findings truthfully. Research integrity also entails being open to scrutiny, responding to critiques, and correcting errors promptly. 12.9    Responsible Publication and Peer Review Researchers have a responsibility to publish their work responsibly, ensuring that it meets the standards of quality, accuracy, and integrity. Peer review plays a crucial role in upholding research integrity, providing a system of checks and balances by which experts in the field evaluate the scientific rigor and ethical soundness of research manuscripts. Researchers should engage in fair and constructive peer review, and they should not engage in practices such as ghostwriting, plagiarism, or undue authorship attribution. 12.10    Self-Check Exercise d.    What do you understand by ethics with respect to research? Discuss about ethics in detail. e.    Describe importance of ethics and research integrity in social science research. 12.11    Summary Ethics, intellectual honesty, and research integrity are essential pillars of responsible research conduct. Adhering to ethical guidelines, obtaining informed consent, protecting confidentiality, and minimizing harm are crucial considerations in research involving human participants. Intellectual honesty requires transparent and accurate reporting of research, giving proper credit to others, and 142 avoiding plagiarism or data manipulation. Research integrity emphasizes the highest standards of research conduct, including rigorous methodologies, responsible publication practices, and openness to scrutiny. By upholding these principles, researchers contribute to the credibility, trustworthiness, and societal impact of their work. 12.12    Glossary Ethics – the study of what is right and wrong in human behavior Research Integrity- Research integrity means conducting research in a way which allows others to have trust and confidence in the methods used and the findings that result from this. 12.13    Answer to Self-Check Exercise (a) See 12.1 & 12.3 (b) See 12.4 (c) See 12.6 12.14    Terminal Questions 1.    Describe importance of ethics and research integrity in social science research. 12.15    Suggested Readings •    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. •    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. •    Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. •   Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. •   Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. •   Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. •    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. •    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. CHAPTER-13 MEASURES OF CENTRAL TENDENCY: MEAN, MEDIAN AND MODE Structure 13.0 Learning Objectives 13.1    Introduction 13.2    Nature of Average 13.3    Objectives of an Average 13.4    Characteristics of a good Average 13.5    Different types of Average 13.6    Arithmetic Mean 13.6.1    Advantages of Arithmetic Mean 13.6.2    Limitations of Arithmetical Mean 13.6.3    Weight Arithmetic Mean 13.7    Median 13.7.1    Advantages of Median 13.7.2    Limitations of Median 13.8    Mode 13.8.1    Using Mode 13.8.2    Advantages of Mode 13.8.3    Limitations of Mode 13.9    Relationship between Mean, Median and Mode 13.10    Self-Check Exercise 13.11    Summary 13.12    Glossary 13.13 Terminal Questions 13.14    Suggested Readings 13.0 Learning Objectives After studying this lesson, the learner will be able: -      To understand about the measures of Central Tendency. -      To comprehend the nature, objectives and characteristics of an average. -      To learn how to we use Mean, Median and Mode in research process. -      To analyze the relationship between Mean, Median and Mode. 13.1    Introduction Data can be condensed into one single value which may have the characteristics of the whole data. Such a single value is known as the central value or average. Concept of average is used most commonly in everyday talk when we talk of a man with average income, we generally mean with mediocre income. But in Statistics the term average means the most typical or the most representative value of a group of members. It means a value that lies somewhere between the two extremes the highest and the lowest the greatest and the smallest, the tallest and the shortest and so on. This is why an average is also called a measure of central tendency. 13.2    Nature of an average Average is, therefore, a value which may be repeated the greatest number of times in a distribution, or it may be exactly in the middle of distribution, or it may be some other value based or more sophisticated methods of calculation. Whatever be an average, it stands for a representative value possessing the characteristics of distribution. 13.3    Objectives of an Average: i)    To get one single value which represents the characteristics of the entire data. ii)    To facilitate comparison for analysis. 13.4    Characteristics of a GoodAverageThese are - i)    It must be simple to understand ii)    It must be easy to calculate iii)    It must be well-defined iv)    It must not be influenced by extreme values v)    It must be based on all the items of distribution vi)    It must have the sampling stability vii)    It must be capable of further algebraic treatment. 13.5    Different types of averages :These are i)    Arithmetic mean: simple, and weighted ii)    Median iii)    Mode vi)    Geometric mean v)    Harmonic mean Here, however, we limit our study to the first 3 types only. 13.6    Arithmetic Mean This is the most popular and commonly used average, e.g. average output, average profits, etc. Thus when a layman talks about an average, he means this average Statisticians call it the arithmetic mean or simply the mean. Arithmetic mean can be defined as the sum-total of the values of items divided by the total number of items. Let us take a very simple example of the marks secured by 4 students in a class test. Let us say they have secured 10, 16, 13 and 9 marks respectively. The mean of the observation would be : 10 + 16 + 13 + 9 = 12 marks 4 13.6.1    Advantages of Arithmetic Mean Arithmetic mean is the most popular measure of central tendency since it offers the following advantages: 1.    It is easiest to calculate and simplest to understand. 2.    It is based on the value of every item in distribution. 3.    Since, it has a rigid mathematical formula, everyone gets the same average. There is no scope for personal bias. 4.    It can be used for further algebraic treatment in much better way than the median or mode. 5.    It is reliable and thus comparisons can be made with definiteness. 6.    It balances the values on either side can be calculated even without arranging the data in an order. 7.    Mean is a calculated value and not a positional average. 13.6.2    Limitations of Arithmetical Mean In spite of its wide use and simplicity, the mean suffers from the following limitations: 1.    It is greatly affected by extreme values and is hence not a representative value, e.g., the mean of 200, 400, 600 and 4000 is 1300, which is not representative. 2.    Mean cannot be calculated accurately even if one item is missing. 3.    In many cases the mean may appear to be unreal, e.g. per capita number of cars 0.132 in a country. 4.    In the case of distribution with open-end classes mean can be calculated only if certain assumptions are made with regard to the class limits. 13.6.3    Weighted Arithmetic Mean Weighted arithmetic mean is an improvement over the simple arithmetic mean. Simple mean give equal importance to all the values, while weighted arithmetic mean gives relative importance to more significant items in the distribution. The term weight is used in the sense of relative importance of each item. The formula of weighted arithmetic mean is: Xw = Ewx Ew Where Xw = the weighted arithmetic mean w = Weights x = Variable Steps in calculation : (i)    Depote the variable as x (ii)    Denote the weights assigned as W. (iii)    Multiply W with x to get ∑Wx (iv)    Sum up weights to get ∑W (v)    Divide ∑Wx with ∑W 13.7    Median Mean is greatly affected by the values of extreme items. It may not therefore be a representative value of distribution. Suppose the income of 5 persons are Rs. 100, 200, 500, 700 and Rs. 8000 respectively. The mean income is Rs 1950 which is 19 1/2 times of Rs. 100. Thus, the average is not representative. In order to avoid such an unrealistic situation positional average like the median is used. Median can he defined as the value or size of the middle item when items are arranged in a series. In other words, median divides a series into two parts. On one hand lie items having value lower than the median and on the other there are items having values higher than the median. The number of items located or positioned on the either side of the median is the same, e.g. Incomes of 5 persons are Rs 200, 250, 300, 400 & Rs. 600 respectively. The median is its. 300. Where the number of items is even. The median is supposed to lie mid-way between the two middle items. Suppose the incomes of 6 persons are Rs 200, 250, 300, 400, 600 and Rs. 680 respectively, the median would be mid way between Rs. 300 and Rs 400. Thus, we take simple average of Rs. 300 and 400 which is Rs. 350. 13.7.1    Advantages of Median 1.    It is easy to calculate and is also understood very easily. 2.    It is not affected by the values of extreme items, e.g. median of 5,10,15,25; 100 is 15 while the mean is 31.15 is certainly more representative. 3.    It is a good average to measure qualitative data, e.g. education health, etc. 4.    It can be determined graphically, but mean cannot be so determined 5.    Mean is greatly affected by the extreme values in greatly skewed distribution, such as price or income distributions. In such cases median is a very useful measure. 6.    It is also useful in case of open-end classes distributions. 13.7.2    Limitation of Median 1.    It is necessary to arrange the data to calculate median, but other averages do not need this. 2.    Its value is not determined by each and every item. It is only a positional average. 3.    It cannot be subjected to further algebraic treatment as mean can be. 4.    It can be error if the number of items is small: 5.    its computation cannot be as exact as the mean in all cases. 13.8    Mode Mode is a measure of central tendency of great importance. Mode can be defined as the value which occurs most frequently in a series. In other words, it is a value which has the greatest frequency in a distribution, e.g. the daily wages of 10 workers in factory are Rs. 20, 21,21, 22, 23, 23, 23, 23, 25, 26, 26 Here mode or modal value is 23. Graphically, it is that value on the x-axis which is just below the highest point or peak of the frequency curve. It is also known as the most typical or fashionable value of the distribution because it is around this value that items concentrate most heavily above diagram would give an idea about the mode. Mode Mode is at the point at which the curve reaches the maximum height. Mode is a value which is repeated most often but it may not be the value of the majority of items. 13.8.1    Using Mode Arithmetic mean is a very useful measure when we talk about situations such as average, productivity, etc. Similarly, median is a useful measure of central tendency where certain qualitative aspect is involved, e.g., health, welfare, etc. Both means and median may not be representative values of distributions, e.g. the most common wage, most common height, etc. cannot be represented with the help of the mean. Also median may not represent the data truly in many cases. Study 5.observations: 10, 20, 30, 1000, 1000, the median is 30, which is ridiculous. It is mode which may be a better measure in the above cases. 13.8.2    Advantages of Mode 1.    It is the most typical value. Hence, it is most useful in the study of wages, incomes, etc. 2.    It can also study various types of qualitative data very effectively, e.g. when we talk about the people’s choice and preference, we talk about the mode. 3.    Its value can be ascertained with case even in the case of open end classes. 4.    Its value is not affected by extremely large or small items. 5.    Its value can also be determined graphically. 13.8.3 Limitations of Mode 1.    It is difficult to determine mode in all cases. There are bi-mddal and multi modal series. 2.    Its value is not-based on each and every item of data. 3.    It cannot be subjected to algebraic treatment. 4.    11 is a very effective measure to study quantitative data. 5.    Since, it is not a rigidly defined measure, it is an unstable average. There are many formulas for calculating mode and thus there may be many values of mode. 13.9    Relationship between Mean, Median and Mode. It is a distribution the value of these averages coincide, i.e; they are the same, we say it is symmetrical distribution. Thus, their values do not coincide in those cases where the distribution is skewed or asymmetrical when distribution is moderately skewed “Kail Pearson” has expressed the relationship between the three measures with the help of the following equation: Mode = Mean – 3 ( Mean- Median) Or Mode – 3 Median – 2 Mean and Median = Mode + 2/3 (Mean- Mode) Thus if we are given the values of any of these two averages, the value of the third can be determined. 13.10    Self Check Exercise a.    What do you mean by Average? What are the characteristic of a good average? b.    What are the advantages and disadvantages of Arithmetic Mean? c.    Define Median? What are the advantages and disadvantages of Median? d.    Define Mode? What are the advantages and disadvantages of Mode? 13.11    Summary In statistics, the term average mean the most typical or the most representative value of a group of member. It mean a value that lies somewhere between the two extremes. The highest and the lowest. The greatest and the smallest, the tallest and the shortest and so on. This is why an average is also called a measure of control tendency. Arithmetic Mean can be defined as the sum-total of the value of items divided by the total number of item. It is easiest to calculate and simplest to understand. Median is the value or size of the middle item when items are arranged in a series. Mode is a value which occurs most frequently in a series. Mode is a value which has the greatest frequency in a distribution. 13.12    Glossary Average- a number expressing the control or typical value in a set of data Mode- a way or manner in which something occurs or is expressed Median-denoting or relating to a value or quantity lying at the midpoint of frequency distributions of observed value. Answer to Self-Check Exercise (a) See 13.2 & 13.4 (b) See 13.6 and 13.6.2 (c) See 13.7 13.13    Terminal Questions a.    Write a note on weighted Arithmetic Mean? b.    Elaborate the relationship between Mean, Median and Mode? 13.14    Suggested Readings •    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. •    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. •    Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. •    Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. •  Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. •  Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. •    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. •    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. ***** ***** CHAPTER-14 RESEARCH METHODLOGY AND PRACTICE EVALUATION Structure 14.0 Learning Objectives 14.1    Introduction 14.2    Defining Research Methodology 14.3    Importance of Research Methodology in Practice Evaluation 14.4    Research Design 14.5    Data Collection Methods 14.6    Sampling Techniques 14.7    Data Analysis Procedure 14.8    Ethical Considerations 14.9    Self-Check Exercise 14.10    Summary 14.11    Glossary 14.12    Answer to Self-Check Exercise 14.13    Terminal Questions 14.14    Suggested Readings 14.0 Learning Objectives After studying this lesson, the learner will be able: -    To understand the meaning of correlation. -    To know the importance of Measuring correlation -    To evaluate different types of correlation. -    To comprehend about the methods of measuring correlation. 14.1    Introduction Research methodology is a crucial aspect of any scientific investigation, including the field of practice evaluation. It provides a systematic framework for conducting studies, gathering data, and analyzing results. In this chapter, we will explore the key components of research methodology and how they apply to practice evaluation. 14.2    Defining Research Methodology Research methodology refers to the overall approach and techniques employed to answer research questions or test hypotheses. It encompasses various elements, such as research design, data collection methods, sampling techniques, and data analysis procedures. A robust research methodology ensures that the study is rigorous, reliable, and replicable. 14.3    Importance of Research Methodology in Practice Evaluation Practice evaluation aims to assess the effectiveness, efficiency, and impact of interventions, programs, or policies in real-world settings. A sound research methodology is vital to ensure that practice evaluation studies yield valid and meaningful results. It helps researchers identify the most appropriate methods and tools for evaluating practice, collect accurate data, and interpret findings accurately. 14.4    Research Design Research design is a crucial component of research methodology. It outlines the overall plan for conducting the study and addresses key decisions, such as the study's purpose, type, scope, and timeline. In practice evaluation, common research designs include experimental, quasiexperimental, and non-experimental designs, depending on the availability of control groups and randomization. 14.5    Data Collection Methods Data collection methods are essential for gathering information relevant to practice evaluation. They can include surveys, interviews, focus groups, observations, and document reviews. The selection of data collection methods depends on the research question, the nature of the practice being evaluated, and the availability of resources. It is essential to use reliable and valid instruments or develop new ones when necessary. 14.6    Sampling Techniques Sampling involves selecting a subset of individuals, organizations, or settings from the larger population of interest. The choice of sampling technique depends on the research design and the accessibility of the population. Common sampling techniques include random sampling, stratified sampling, purposive sampling, and convenience sampling. Proper sampling ensures that the study's findings can be generalized to the target population accurately. 14.7    Data Analysis Procedure Data analysis involves transforming collected data into meaningful information and drawing conclusions from the findings. In practice evaluation, both qualitative and quantitative data analysis methods can be employed. Quantitative analysis may involve statistical tests, such as regression analysis or t-tests, to examine relationships and significance. Qualitative analysis often involves coding, thematic analysis, or discourse analysis to identify patterns and themes in textual or observational data. 14.8    Ethical Considerations Ethical considerations are fundamental to any research endeavor. In practice evaluation, researchers must ensure the protection of human subjects, obtain informed consent, maintain confidentiality, and address any potential conflicts of interest. Ethical guidelines and institutional review boards (IRBs) play a vital role in safeguarding the rights and welfare of participants involved in practice evaluation studies. 14.9    Self-Check Exercise 15  (a) See 14.1 and 14.2 (b) See 14.3.1 (c) See 14.3.2 (d) See 14.4.2 14.10    Summary This chapter has provided an overview of research methodology and its application to practice evaluation. A robust research methodology is essential for conducting rigorous and meaningful evaluations. It guides researchers in making informed decisions about research design, data collection methods, sampling techniques, and data analysis procedures. By employing sound research methodology, practice evaluators can contribute to the evidence base for effective interventions and policies, ultimately improving the quality of practice and benefiting the individuals and communities they serve. 14.11    Glossary 16    Correlation – a mutual relationship or connection between two or more things. 17    Scatter Diagram – a graph in which the values of two variables are plotted along two axes, the pattern ofthe resulting points revealing any correlation present. 14.12    Terminal Questions a.    Elucidate linear and non-linear (curvilinear) correlation. b.    Write a note on Scatter Diagram Method of measuring correlation. 14.13    Suggested Readings •    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. •    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. •    Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. •    Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. •    Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. •    Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. •    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. •    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. ***** CHAPTER-15 COMPUTER: IT’S ROLE IN RESEARCH Structure 15.0 Learning Objectives 15.1    Introduction 15.2    Data Analysis 15.3    Literature Review 15.4    Experimental Design 15.5    Data Management 15.6    Collaboration and Communication 15.7    Data Visualisation 15.8    Ethical Consideration 15.9    Self-Check Exercise 15.10    Summary 15.11    Glossary 15.12    Answer to Self-Check Exercise 15.13    Terminal Questions 15.14    Suggested Readings 15.0 Learning Objectives 15.1    Introduction The use of computers has revolutionized the field of research, providing researchers with powerful tools and capabilities to enhance various aspects of the research process. This chapter explores the role of computers in research and highlights their impact on data analysis, literature review, experimental design, data management, and collaboration. Problem solving is an age old activity. The development of electronic devices, specially the computers, has given added impetus to this activity. Problems which could not be solved earlier due to sheer amount of computations involved can now be tackled with the aid of computers accurately and rapidly. Computer is certainly one of the most versatile and ingenious developments of the modern technological age. Today people use computers in almost every walk of life. No longer are they just big boxes with flashing lights whose sole purpose is to do arithmetic at high speed but they make use of studies in philosophy, psychology, mathematics and linguistics to produce output that mimics the human mind. The sophistication in computer technology has reached the stage that it will not be longer before it is impossible to tell whether you are talking to man or machine. Indeed, the advancement in computers is astonishing. To the researcher, the use of computer to analyse complex data has made complicated research designs practical. Electronic computers have by now become an indispensable part of research students in the physical and behavioural sciences as well as in the humanities. The research student, in this age of computer technology, must be exposed to the methods and use of computers. A basic understanding of the manner in which a computer works helps a person to appreciate the utility of this powerful tool. Keeping all this in view, the present chapter introduces the basics of computers, especially it. answers questions like: What is a computer? How does it function? How does one communicate with it? How does it help in analysing data? •    THE COMPUTER AND COMPUTER TECHNOLOGY A computer, as the name indicates, is nothing but a device that computes. In this sense, any device, however crude or sophisticated, that enables one to carry out mathematical manipulations becomes a computer. But what has made this term conspicuous today and, what we normally imply when we speak of computers, are electronically operating machines which are used to carry out computations. In brief, computer is a machine capable of receiving, storing, manipulating and yielding information such as numbers, words, pictures. The computer can be a digital computer or it can be a analogue computer. A digital computer is one which operates essentially by counting (using information, including letters and symbols, in coded form) where as the analogue computer operates by measuring rather than counting. Digital computer handles information as strings of binary numbers i.e., zeros and ones, with the help of counting process but analogue computer converts varying quantities such as temperature and pressure into corresponding electrical voltages and then performs specified functions on the given signals. Thus, analogue computers are used for certain specialised engineering and scientific applications. Most computers are digital, so much so that the word computer is generally accepted as being synonymous with the term ‘digital computer’. Computer technology has undergone a significant change over a period of four decades. The present day microcomputer is far more powerful and costs very little, compared to the world’s first electronic computer viz. Electronic Numerical Integrator and Calculator (ENIAC) completed in 1946. The microcomputer works many times faster, is thousands of times more reliable and has a large memory. The advances in computer technology are usually talked in terms of ‘generations’.* Today we have the fourth generation computer in service and efforts are being made to develop the fifth generation computer, which is expected to be ready by 1990. The first generation computer started in 1945 contained 18000 small bottle-sized valves which constituted its central processing unit (CPU). This machine did not have any facility for storing programs and the instructions had to be fed into it by a readjustment of switches and wires. The second generation computer found the way for development with the invention of the transistor in 1947. The transistor replaced the valve in all electronic devices and made them much smaller and more reliable. Such computers appeared in the market in the early sixties. The third generation computer followed the invention of integrated circuit (IC) in 1959. Such machines, with their CPU and main store made of IC chips, appeared in the market in the second half of the sixties. The fourth generation computers owe their birth to the advent of microprocessor—the king of chips—in 1972. The use of microprocessor as CPU in a computer has made real the dream of ‘computer for the masses’. This device has enabled the development of microcomputers, personal computers, portable computers and the like. The fifth generation computer, which is presently in the developing stage, may use new switch (such as the High Electron Mobility Transistor) instead of the present one and it may herald the era of superconducting computer. It is said that fifth generation computer will be 50 times or so more faster than the present day superfast machines. So far as input devices in computers are concerned, 159 the card or tape-based data entry system has almost been replaced by direct entry devices, such as Visual Display Unit (VDU) which consist of a TV-like screen and a typewriter-like key board which is used for feeding data into the computer. Regarding output devices, the teleprinter has been substituted by various types of low-cost high speed printers •    COMPUTERS AND RESEARCHERS Performing calculations almost at the speed of light, the computer has become one of the most useful research tools in modern times. Computers are ideally suited for data analysis concerning large research projects. Researchers are essentially concerned with huge storage of data, their faster retrieval when required and processing of data with the aid of various techniques. In all these operations, computers are of great help. Their use, apart expediting the research work, has reduced human drudgery and added to the quality of research activity. To constitute an indispensable part of their research equipment. The computers can perform many statistical calculations easily and quickly. Computation of means, standard deviations, correlation coefficients, ‘t’ tests, analysis of variance, analysis of covariance, multiple regression, factor analysis and various nonparametric analyses are just a few of the programs and subprograms that are available at almost all computer centres. Similarly, canned programs for linear programming, multivariate analysis, monte carlo simulation etc. are also available in the market. In brief, software packages are readily available for the various simple and complicated analytical and quantitative techniques of which researchers generally make use of. The only work a researcher has to do is to feed in the data he/she gathered after loading the operating system and particular software package on the computer. The output, or to say the result, will be ready within seconds or minutes depending upon the quantum of work. Techniques involving trial and error process are quite frequently employed in research methodology. This involves lot of calculations and work of repetitive nature. Computer is best suited for such techniques, thus reducing the drudgery of researchers on the one hand and producing the final result rapidly on the other. Thus. different scenarios are made available to researchers by computers in no time which otherwise might have taken days or even months. The storage facility which the computers provide is of immense help to a researcher for he can make use of stored up data whenever he requires to do so. Thus, computers do facilitate the research work. Innumerable data can be processed and analyzed with greater ease and speed. Moreover, the results obtained are generally correct and reliable. Not only this, even the design, pictorial graphing and report are being developed with the help of computers. Hence, researchers should be given computer education and be trained in the line so that they can use computers for their research work. Techniques involving trial and error process are quite frequently employed in research methodology. This involves lot of calculations and work of repetitive nature. Computer is best suited for such techniques, thus reducing the drudgery of researchers on the one hand and producing the final result rapidly on the other. Thus. different scenarios are made available to researchers by computers in no time which otherwise might have taken days or even months. The storage facility which the computers provide is of immense help to a researcher for he can make use of stored up data whenever he requires to do so. Thus, computers do facilitate the research work. Innumerable data can be processed and analyzed with greater ease and speed. Moreover, the results obtained are generally correct and reliable. Not only this, even the design, pictorial graphing and report are being developed with the help of computers. Hence, researchers should be given computer education and be trained in the line so that they can use computers for their research work. Researchers interested in developing skills in computer data analysis, while consulting the computer centers and reading the relevant literature, must be aware of the following steps: (i) data organisation and coding; (ii) storing the data in the computer; (iii) selection of appropriate statistical measures/techniques; (iv) selection of appropriate software package; (v) execution of the computer program. A brief mention about each of the above steps is appropriate and can be stated as under: First of all, researcher must pay attention toward data organisation and coding prior to the input stage of data analysis. If data are not properly organised, the researcher may face difficulty while analysing their meaning later on. For this purpose the data must be coded. Categorical data need to be given a number to represent them. For instance, regarding sex, we may give number 1 for male and 2 for female; regarding occupation, numbers 1, 2, and 3 may represent Farmer, Service and Professional respectively. The researcher may as well code interval or ratio data. For instance, I.Q. Level with marks 120 and above may be given number 1, 90–119 number 2, 60–89 number 3, 30–59 number 4 and 29 and below number 5. Similarly, the income data classified in class intervals such as Rs. 4000 and above, Rs. 3000–3999, Rs. 2000–2999 and below Rs. 2000 may respectively be represented or coded as 1, 2, 3 and 4. The coded data are to be put in coding forms (most systems). Call for a maximum of 80 columns per line in such forms) at the appropriate space meant for each variable. Once the researcher knows how many spaces each variable will occupy, the variables can be assigned to their column numbers (from 1 to 80). If more than 80 spaces are required for each subject, then two or more lines will need to be assigned. The first few columns are generally 161 devoted for subject identity number. Remaining columns are used for variables. When large number of variables are used in a study, separating the variables with spaces make the data easier to comprehend and easier for use with other programs. Once the data is coded, it is ready to be stored in the computer. Input devices may be used for the purpose. After this, the researcher must decide the appropriate statistical measure(s) he will use to analyse the data. He will also have to select the appropriate program to be used. Most researchers prefer one of the canned programs easily available but others may manage to develop it with the help of some specialised agency. Finally, the computer may be operated to execute instructions. The above description indicates clearly the usefulness of computers to researchers in data analysis. Researchers, using computers, can carry on their task at faster speed and with greater reliability. The developments now taking place in computer technology will further enhance and facilitate the use of computers for researchers. Programming knowledge would no longer remain an obstacle in the use of a computer. In spite of all this sophistication we should not forget that basically computers are machines that only compute, they do not think. The human brain remains supreme and will continue to be so for all times. As such, researchers should be fully aware about the following limitations of computer-based analysis: 1. Computerised analysis requires setting up of an elaborate system of monitoring, collection and feeding of data. All these require time, effort and money. Hence, computer based analysis may not prove economical in case of small projects. 2. Various items of detail which are not being specifically fed to computer may get lost sight of. 3. The computer does not think; it can only execute the instructions of a thinking person. If poor data or faulty programs are introduced into the computer, the data analysis would not be worthwhile. The expression “garbage in, 15.2    Data Analysis Computers play a crucial role in data analysis, enabling researchers to efficiently process and analyze large volumes of data. Statistical software packages, such as SPSS, R, or SAS, provide a wide range of statistical techniques and algorithms to explore, interpret, and draw conclusions from research data. These tools facilitate complex analyses, including regression, factor analysis, clustering, and data visualization, allowing researchers to uncover patterns, relationships, and trends in their data. Data Science Process Raw Data Collected Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis. Analysis refers to dividing a whole into its separate components for individual examination. Data analysis is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. Data is collected and analyzed to answer questions, test hypotheses, or disprove theories. Statistician John Tukey, defined data analysis in 1961, as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." There are several phases that can be distinguished, described below. The phases are iterative, in that feedback from later phases may result in additional work in earlier phases. The CRISP framework, used in data mining, has similar steps. 15.3    Literature Review The computer's role in literature review is invaluable, as it provides access to vast repositories of academic journals, research papers, and scholarly databases. Online search engines, digital libraries, and citation databases, such as Google Scholar or PubMed, allow researchers to quickly and efficiently locate relevant literature on a specific topic. Additionally, reference management software, like EndNote or Mendeley, assists researchers in organizing, annotating, and citing sources, streamlining the literature review process. A literature review is a critical and comprehensive examination of existing published research, scholarly articles, books, and other relevant sources related to a particular research topic or question. It serves as an essential component of the research process, helping researchers gain a thorough understanding of the current state of knowledge and identify gaps or areas for further investigation. Here's a breakdown of the key elements and purpose of a literature review in research: Purpose of a Literature Review: •    To establish the research context: A literature review provides the background and context for the research topic, helping to situate the study within the existing body of knowledge. •    To identify gaps or research questions: By reviewing previous studies, researchers can identify areas that have not been adequately addressed or areas that require further investigation. •  To support the research rationale: A literature review helps justify the need for the research by demonstrating the significance, relevance, and potential contribution of the study to the existing knowledge. •  To identify theoretical frameworks and methodologies: Researchers can identify and evaluate the theories, concepts, and methodologies used in previous studies, which can inform their own research design and methodology. Process of Conducting a Literature Review: •    Define the research question or objective: Clearly define the specific focus or objective of the literature review to guide the search and analysis process. •    Identify relevant sources: Conduct a systematic search using databases, libraries, academic journals, and other sources to gather relevant scholarly articles, books, reports, and other literature related to the research topic. •    Evaluate and select sources: Evaluate the credibility, relevance, and quality of the selected sources. Focus on scholarly peer-reviewed publications and ensure that the sources are recent and representative of different perspectives. •    Analyze and synthesize the literature: Read and analyze each source to extract key information and concepts. Identify common themes, trends, patterns, and gaps in the literature. Compare and contrast the findings and arguments presented in different sources. •    Organize the review: Structure the literature review based on themes, chronology, or other logical approaches. Provide a clear and coherent narrative that guides readers through the relevant literature and its implications for the research. •    Critically evaluate the literature: Assess the strengths and weaknesses of the reviewed literature. Identify any biases, limitations, or conflicting findings. Highlight areas where further research is needed. •    Write the literature review: Present the findings of the literature review in a well-structured manner, integrating the key themes, discussions, and findings from the analyzed sources. Ensure that proper citations and references are provided to acknowledge the original authors. Contribution to Research: •    Identifying research gaps: A literature review helps researchers identify areas where further investigation is needed, leading to the formulation of research questions or hypotheses. •    Establishing theoretical foundations: By reviewing existing literature, researchers can identify and utilize relevant theoretical frameworks or models to guide their research design and analysis. •    Informing research methodology: The literature review can guide researchers in selecting appropriate research methodologies, data collection techniques, and analysis methods based on the strengths and weaknesses identified in previous studies. •    Building on existing knowledge: Researchers can build upon the findings and conclusions of previous studies to expand knowledge and contribute to the research field. In summary, a literature review provides a comprehensive overview of existing research, identifies gaps and research questions, supports the rationale of the study, and guides researchers in designing their own research. It serves as a critical foundation for conducting high-quality and relevant research. 15.4    Experimental Design Computers facilitate the design and execution of experiments by offering software applications for experimental design and simulation. These tools enable researchers to model and simulate experiments, analyze variables, and optimize experimental conditions. They also assist in sample size calculations, randomization, and control group allocation, ensuring rigorous experimental design and reducing potential biases. Experimental design in research refers to the systematic planning and organization of a study to investigate the relationship between variables and test research hypotheses. It involves making deliberate choices about the design, manipulation of variables, and control of extraneous factors to ensure reliable and valid results. Here are key elements and considerations in experimental design: •    Research Question and Hypotheses: Clearly define the research question and formulate testable hypotheses that specify the expected relationship between variables. •    Independent and Dependent Variables: Identify the independent variable(s) that are manipulated or controlled by the researcher and the dependent variable(s) that are measured or observed as the outcome or response. •    Experimental Groups and Control Group: Determine the different groups or conditions in the experiment. The experimental groups receive the manipulated variable(s), while the control group serves as a baseline or comparison group that does not receive the manipulation. •    Randomization: Randomly assign participants to different experimental conditions or groups to minimize the effects of confounding variables and increase the likelihood of generalizability. Randomization helps ensure that participant characteristics are evenly distributed across groups. •    Sample Size and Power Analysis: Determine the appropriate sample size based on the research objectives and statistical power analysis. Adequate sample size is crucial for detecting meaningful effects and obtaining reliable results. Experimental Design Types: •    Between-Subjects Design: Different groups of participants are assigned to different experimental conditions, and each group experiences only one condition. •    Within-Subjects Design: The same participants are exposed to all experimental conditions, and their responses are compared across the conditions. •    Mixed Design: Combines elements of between-subjects and within-subjects designs, with different groups experiencing different conditions, while within each group, participants experience all conditions. •    Randomization and Counterbalancing: Randomize the order of conditions or counterbalance the order across participants to control for order effects and ensure equal exposure to all conditions. •    Control of Extraneous Variables: Minimize the influence of extraneous variables that may affect the dependent variable(s) by implementing appropriate control measures. This may include random assignment, matching, or holding certain variables constant. •    Validity and Reliability: Design the experiment to ensure internal and external validity. Internal validity refers to the degree to which the study accurately measures the effect of the independent variable, while external validity refers to the generalizability of the findings to the broader population or real-world contexts. Use reliable and valid measures and consider potential threats to validity. •    Data Collection and Analysis: Determine the data collection methods, tools, and statistical analyses appropriate for the research question and variables involved. Plan for data collection procedures and data management to ensure accuracy, consistency, and reliability. •    Ethical Considerations: Adhere to ethical guidelines, obtain informed consent, protect participant confidentiality, and minimize potential risks or harm to participants throughout the study. By carefully considering these elements and making informed decisions, researchers can design experiments that yield valid, reliable, and meaningful results, advancing scientific understanding in their respective fields. 15.5    Data Management Managing research data efficiently and securely is critical in any study. Computers provide a range of data management tools and techniques to streamline this process. Electronic data capture systems allow for the collection, storage, and organization of research data in a structured and easily accessible format. Data management software, like REDCap or OpenClinica, aids in data validation, data cleaning, and ensuring data integrity throughout the research project. 15.6    Collaboration and Communication Computers enhance collaboration among researchers by enabling seamless communication and sharing of research materials. Email, instant messaging, and video conferencing platforms facilitate 168 real-time communication, irrespective of geographical boundaries. Online collaboration tools, such as shared document repositories or project management platforms, enable multiple researchers to collaborate on the same project, share resources, and track progress. Collaboration and communication are crucial elements in the research process, facilitating the exchange of ideas, expertise, and resources among researchers and stakeholders. They play a vital role in advancing knowledge, fostering innovation, and maximizing the impact of research. Here's an overview of collaboration and communication in research: Collaboration in Research: •    Interdisciplinary Collaboration: Collaborating across different disciplines enables researchers to bring diverse perspectives and expertise to address complex research questions. It promotes cross-fertilization of ideas, encourages innovation, and allows for a comprehensive understanding of multifaceted problems. •    Teamwork and Networking: Collaborative research often involves working in teams. Researchers can leverage each other's strengths, skills, and knowledge to achieve shared research goals. Networking with peers, experts, and institutions can expand research opportunities, facilitate access to resources, and foster professional growth. •    Division of Labor: Collaboration allows for the division of tasks and responsibilities among team members based on their expertise and interests. Researchers can focus on their specific areas while collectively contributing to the larger research project. Efficient delegation enhances productivity and promotes efficient resource allocation. •    Data Sharing and Pooling: Collaborative research encourages data sharing and pooling across research teams or institutions. It enables the analysis of larger datasets, facilitates more robust statistical analyses, and supports generalizability and replication of research findings. Communication in Research: •    Effective Research Proposal Writing: Clear and effective communication is essential when writing research proposals. Researchers should articulate their research objectives, methodologies, significance, and expected outcomes to convey the value and feasibility of their proposed research. •    Regular Team Meetings: Regular team meetings foster open communication, collaboration, and coordination among team members. They provide opportunities to discuss research progress, address challenges, and align efforts towards shared goals. Clear and efficient communication within the team enhances productivity and cohesion. •  Peer Review and Feedback: Engaging in peer review processes enables researchers to obtain constructive feedback from experts in the field. Peer review helps improve research quality, identify potential weaknesses, and enhance the validity and reliability of research outcomes. •    Conference Presentations and Publications: Disseminating research findings through conference presentations and publications enables researchers to communicate their work to the wider scientific community. Effective communication in these forums enhances the visibility, impact, and recognition of research outcomes. •    Public Engagement and Outreach: Communicating research to the general public promotes understanding, raises awareness of research impact, and fosters public support. Researchers can engage in science communication activities, such as public talks, media interviews, or writing for popular science publications, to make research accessible and relevant to broader audiences. •    Ethical and Responsible Communication: Researchers should uphold ethical principles in their communication, including accuracy, transparency, and integrity. Proper attribution of sources, avoidance of plagiarism, and responsible dissemination of research findings contribute to the trustworthiness and credibility of the research. By fostering collaboration and effective communication, researchers can leverage collective expertise, enhance research outcomes, and promote the broader societal impact of their work. Collaboration and communication also nurture a culture of shared learning and continuous improvement in the research community. 15.7    Data Visualisation Computers provide researchers with powerful data visualization tools to present research findings in a visually compelling and understandable manner. Graphs, charts, and interactive visualizations help researchers communicate complex data patterns and relationships effectively. Data visualization software, like Tableau or ggplot, allows researchers to create engaging visuals that 170 enhance the clarity and impact of research presentations and publications. Data visualization in research refers to the graphical representation and presentation of data to facilitate understanding, interpretation, and communication of research findings. It involves transforming complex data sets into visual formats such as charts, graphs, maps, or infographics that convey patterns, trends, and relationships more effectively. Data visualization enhances the accessibility and impact of research by providing visual representations that are easily comprehensible, engaging, and memorable. It enables researchers to explore, analyze, and communicate data in a way that supports data-driven decision-making and enhances the overall quality and effectiveness of research outcomes. 15.8    Ethical Consideration The use of computers in research brings ethical considerations related to data privacy, confidentiality, and security. Researchers must ensure the protection of participants' personal information and comply with relevant data protection regulations. It is crucial to implement secure data storage systems, password protection, and encryption methods to safeguard research data from unauthorized access or breaches. Ethical considerations in research are essential to ensure the protection, welfare, and rights of participants and to maintain the integrity and credibility of the research process. Ethical guidelines help researchers navigate potential ethical challenges and make informed decisions throughout the research journey. Here are some key ethical considerations in research: •    Informed Consent: Obtain informed consent from participants before their involvement in the research. Participants should be fully informed about the study's purpose, procedures, potential risks and benefits, confidentiality, and their right to withdraw at any time. Informed consent should be voluntary, documented, and obtained in a manner appropriate for the participants' age, language, and cultural background. •    Privacy and Confidentiality: Safeguard the privacy and confidentiality of research participants. Protect personal and sensitive information collected during the study by using secure data storage methods and anonymizing data when possible. Ensure that participants' identities and personal information are kept confidential, unless explicit permission is obtained or required by law. •    Research Ethics Review: Seek ethical review and approval from relevant institutional review boards (IRBs), research ethics committees, or similar bodies. These entities evaluate research proposals to ensure compliance with ethical guidelines, protect participants' rights, and assess potential risks and benefits associated with the research. •    Risk Assessment and Minimization: Conduct a thorough risk assessment to identify and minimize potential physical, psychological, social, or legal risks to participants. Implement appropriate measures to minimize harm and ensure participant well-being throughout the research process. Provide support services or referrals when necessary. •    Respect for Participants: Treat participants with respect, dignity, and fairness. Ensure that power dynamics are appropriately managed, avoiding exploitation or coercion. Respect cultural, religious, and personal beliefs of participants and be sensitive to potential cultural biases or stereotypes. •    Data Integrity and Transparency: Maintain the integrity and accuracy of research data. Ensure transparency in data collection, analysis, and reporting. Follow sound research practices, accurately represent findings, and avoid fabricating, falsifying, or manipulating data. •    Authorship and Publication: Give proper credit and recognition to individuals who have made significant contributions to the research. Authorship should be based on substantial intellectual contributions and adhere to recognized authorship guidelines. Avoid plagiarism and provide proper citations and references. •    Conflict of Interest: Disclose any potential conflicts of interest that may compromise the objectivity or integrity of the research. Be transparent about financial, professional, or personal relationships that could influence the research design, analysis, or interpretation of results. •    Responsible Conduct: Conduct research with honesty, integrity, and professionalism. Adhere to relevant laws, regulations, and ethical guidelines specific to the research field or discipline. Seek ongoing education and training in research ethics. •    Reporting of Ethical Concerns: Promptly address and report any ethical concerns or violations that may arise during the research process. Follow appropriate channels for 172 reporting, such as supervisors, institutional ethics committees, or regulatory bodies. Adhering to ethical considerations is crucial to protect the rights and well-being of research participants, maintain public trust, and uphold the integrity of the research community. It is essential for researchers to familiarize themselves with relevant ethical guidelines, seek guidance when needed, and conduct research in a responsible and ethical manner. 15.9    Self-Check Exercise 1.    Discuss about computer and its application. 2.    Describe role of computer in research. 15.10    Summary Computers have become indispensable tools in research, transforming various aspects of the research process. From data analysis and literature review to experimental design, data management, collaboration, and data visualization, computers offer researchers enhanced efficiency, accuracy, and productivity. By harnessing the power of computers effectively and considering the associated ethical considerations and limitations, researchers can unlock new possibilities and advance knowledge in their respective fields. 15.11    Glossary Computer – an electronic machine that can store, find and arrange information, calculate amounts and control other machines Design – a plan or drawing produced to show the look and function or other object before it is made.Variable- not consistent or having a fixed pattern 15.12    Answer to Self-Check Exercise (a) See 15.1 & 15.3 (b) See 14.4 15.13    Terminal Questions a. Critically examine role of computer in research? 15.14    Suggested Readings •    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. •    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. •    Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. •    Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. •    Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. •    Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. •    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. •    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. CHAPTER-16 REPORT WRITING: CONTENT & STYLE OF REPORT WRITING Structure 16.0 Learning Objectives 16.1    Introduction 16.2    Purpose and Structure of a Report 16.3    Clarity and Conciseness 16.4    Use of Graphics and Visual 16.5    Accuracy and Objectivity 16.6    Organization and Coherence 16.7    Tone and Style 16.8    Proofreading and Editing 16.9    Self-Check Exercise 16.10    Summary 16.11    Glossary 16.12    Answer to Self-Check Exercise 16.13    Terminal Questions 16.14    Suggested Readings 16.0 Learning Objectives 16.1    Introduction Report writing is a crucial skill for researchers, professionals, and students alike. It involves the communication of findings, analysis, and recommendations in a clear, concise, and organized manner. This chapter will explore the essential elements of report writing, including the content and style considerations that contribute to effective reports. A report is a document that presents information in an organized format for a specific audience and purpose. Although summaries of reports may be delivered orally, complete reports are almost always in the form of written documents. In modern business scenario, reports play a major role in the progress of business. Reports are the backbone to the thinking process of the establishment and they are responsible, to a great extent, in evolving an efficient or inefficient work environment. The significance of the reports includes: •    Reports present adequate information on various aspects of the business. •    All the skills and the knowledge of the professionals are communicated through reports. •    Reports help the top line in decision making. •    A rule and balanced report also helps in problem solving. •    Reports communicate the planning, policies and other matters regarding an organization to the masses. News reports play the role of ombudsman and levy checks and balances on the establishment. One of the most common formats for presenting reports is IMRAD—introduction, methods, results, and discussion. This structure, standard for the genre, mirrors traditional publication of scientific research and summons the ethos and credibility of that discipline. Reports are not required to follow this pattern and may use alternative methods such as the problem-solution format, wherein the author first lists an issue and then details what must be done to fix the problem. Transparency and a focus on quality are keys to writing a useful report. Accuracy is also important. Faulty numbers in a financial report could lead to disastrous consequences. 16.2    Purpose and Structure of a Report Before diving into the content and style of report writing, it is important to understand the purpose and structure of a report. Reports are typically written to inform, persuade, or recommend actions based on research or evaluation findings. They generally follow a standard structure that includes an introduction, methodology, results, analysis, and conclusion. Depending on the specific requirements, additional sections such as literature review and recommendations may be included. Reports use features such as tables, graphics, pictures, voice, or specialized vocabulary in order to persuade a specific audience to undertake an action or inform the reader of the subject at hand. Some common elements of written reports include headings to indicate topics and help the reader locate relevant information quickly, and visual elements such as charts, tables and figures, which are useful for breaking up large sections of text and making complex issues more accessible. Lengthy written reports will almost always contain a table of contents, appendices, footnotes, and references. A bibliography or list of references will appear at the end of any credible report and citations are often included within the text itself. Complex terms are explained within the body of the report or listed as footnotes in order to make the report easier to follow. A short summary of the report's contents, called an abstract, may appear in the beginning so that the audience knows what the report will cover. Online reports often contain hyperlinks to internal or external sources as well. Verbal reports differ from written reports in the minutiae of their format, but they still educate or advocate for a course of action. Quality reports will be well researched and the speaker will list their sources if at all possibl 16.3    Clarity and Conciseness Clear and concise writing is essential for effective report communication. It involves using plain language, avoiding jargon or technical terms that may be unfamiliar to readers, and providing explanations or definitions when necessary. Sentences and paragraphs should be kept short and to the point, and unnecessary repetition should be avoided. Bullet points, headings, and subheadings can be used to organize information and improve readability. Elaborating on conciseness and clarity in report writing involves ensuring that your writing is clear, concise, and effectively communicates your message to the intended audience. Here are some key principles to consider: •    Clear Purpose: Clearly define the purpose and objective of your report. Understand the main message you want to convey and structure your report around it. •    Audience Understanding: Consider your target audience's background, knowledge level, and expectations. Adapt your language, tone, and level of technical detail accordingly to ensure your report is accessible and understandable. •    Organizational Structure: Use a logical and coherent structure for your report. Start with an introduction that sets the context and outlines the main points. Use headings and subheadings to guide readers through different sections. End with a conclusion that summarizes key findings and recommendations. •    Succinct Writing: Strive for concise writing by using clear and simple language. Avoid unnecessary jargon, acronyms, or technical terms unless they are essential and well-understood by your audience. Eliminate redundancies and wordiness to keep your writing concise and to the point. •    Use of Visuals: Incorporate visual aids such as graphs, tables, and charts to present complex information in a visually appealing and easily understandable format. Visuals can enhance clarity and make data more accessible. •    Logical Flow: Ensure a logical flow of ideas and information throughout the report. Present information in a logical order, linking sections and paragraphs to maintain coherence. Use transitional words and phrases to guide readers from one point to another smoothly. •    Grammar and Punctuation: Pay attention to grammar, punctuation, and sentence structure to maintain clarity. Proofread your report for spelling mistakes, grammatical errors, and awkward sentence constructions. Clear and error-free writing enhances the overall quality of your report. •    Eliminate Ambiguity: Be precise and avoid ambiguity in your writing. Use specific language and provide clear definitions where necessary. Clarify any potential misunderstandings by anticipating and addressing possible questions or objections. •    Readability: Use formatting techniques such as bullet points, numbering, and subheadings to break down information and improve readability. Use appropriate font styles and sizes, spacing, and margins to enhance the visual appeal of your report. •    Editing and Revision: Finally, review your report for clarity and conciseness. Edit and revise your content to eliminate any unnecessary details, repetitions, or confusing passages. Seek feedback from peers or colleagues to ensure your report effectively conveys the intended message. By adhering to these principles, you can enhance the conciseness and clarity of your report writing, enabling your audience to understand and engage with your content more effectively. 16.4    Use of Graphics and Visual Visual aids such as graphs, charts, tables, and diagrams can greatly enhance the understanding and presentation of information in a report. They provide a visual representation of data, making it easier for readers to interpret and grasp key findings. However, it is important to ensure that the visuals are clear, labeled appropriately, and directly related to the information being presented. Captions and explanations should accompany the visuals to aid comprehension. The use of graphics and visuals in research plays a crucial role in enhancing understanding, presenting data, and communicating complex information effectively. Incorporating well-designed graphics and visuals can significantly improve the clarity and impact of your research. Here are some key benefits and considerations for using graphics and visuals in research: •    Enhancing Clarity: Graphics and visuals can simplify complex information, making it easier for readers to comprehend and interpret. By presenting data visually, you can condense large amounts of information into easily digestible formats, improving clarity and reducing cognitive load. •    Data Visualization: Graphics and visuals enable the effective presentation of data. They can transform raw data into meaningful patterns, trends, and relationships, making it easier to identify key insights. Charts, graphs, and diagrams provide visual representations that help readers grasp the significance of the data at a glance. •    Supporting Arguments: Visuals can be used to support and reinforce your research arguments and findings. Well-designed visuals can provide compelling evidence, illustrating the relationships between variables, comparing data sets, or highlighting trends. They enhance the persuasiveness and credibility of your research. •    Increasing Engagement: Visual elements in research capture readers' attention and increase engagement. Integrating relevant and visually appealing graphics can attract and retain readers' interest, helping them stay engaged with your research document. This is particularly important when presenting complex or lengthy research. •    Improving Memorability: Visuals have a higher likelihood of being remembered compared to text alone. The combination of visuals with textual information creates a more memorable experience for readers, allowing them to retain and recall key points or findings from your research more easily. •    Choosing Appropriate Visuals: Consider the type of data you are presenting and choose visuals that best represent and clarify that data. Bar charts, line graphs, pie charts, scatter plots, and tables are commonly used to present different types of data. Select visuals that align with your research goals and effectively convey the intended message. •    Design Considerations: When creating visuals, pay attention to design principles such as simplicity, clarity, and consistency. Ensure that the visual elements are well-organized, labeled clearly, and have appropriate scales or units. Use color, fonts, and other design elements purposefully to enhance readability and visual appeal. •    Proper Citation and Attribution: If you use visuals or graphics created by others, ensure that you properly attribute and cite the original source. Adhere to copyright regulations and ethical guidelines when using visuals from external sources. •    Accessibility: When incorporating graphics and visuals, consider the accessibility needs of your audience. Ensure that visuals are designed to accommodate individuals with visual impairments, such as providing alternative text descriptions or using color schemes that are accessible for color-blind readers. Remember that while graphics and visuals can enhance research, they should not replace clear and concise written explanations. Visuals should complement and reinforce the textual information, working together to convey your research effectively. 16.5    Accuracy and Objectivity Reports should strive for accuracy and objectivity in presenting information and analysis. Claims, findings, and recommendations should be supported by evidence and data from reliable sources. Proper citation and referencing should be used to acknowledge the work of others and give credit where it is due. Avoiding personal biases, emotional language, or subjective opinions is crucial to maintaining objectivity in report writing. Accuracy and objectivity are essential principles in research that contribute to the credibility and reliability of scientific and scholarly investigations. Here's a closer look at what accuracy and objectivity entail in research: •    Accuracy: Accuracy refers to the degree of correctness, truthfulness, and precision in research. It involves ensuring that the data, information, and findings presented in a research study are free from errors, bias, or distortion. Key aspects of accuracy in research include: •    Data Collection: Collecting data accurately involves using appropriate and reliable measurement instruments, following standardized protocols, and minimizing measurement errors or biases. It also includes careful recording and documentation of data to avoid transcription or data entry errors. •    Data Analysis: Conducting rigorous and precise data analysis is crucial for accuracy. Applying appropriate statistical methods, ensuring correct calculations, and double-checking results are essential to avoid misinterpretation or incorrect conclusions. •    Reporting of Findings: Accurate reporting involves clearly and objectively presenting research findings, ensuring that the results accurately reflect the data collected and analyzed. It entails avoiding overgeneralizations or extrapolations beyond the scope of the data. •    Citing Sources: Accuracy also extends to acknowledging and citing the works of others accurately and ethically. Properly attributing sources prevents plagiarism and ensures that the information presented is trustworthy and verifiable. •    Objectivity: Objectivity in research refers to the impartiality, fairness, and lack of bias or personal opinion in the design, execution, and interpretation of research. It involves striving to approach research questions and data analysis without preconceived notions or personal prejudices. Key aspects of objectivity in research include: •    Research Design: Objectivity begins with designing research studies that minimize bias and ensure a fair representation of the phenomenon under investigation. Careful consideration of variables, controls, and sample selection helps reduce the impact of potential biases. •    Data Collection: Objectivity in data collection involves using standardized protocols and minimizing the influence of personal biases or subjective judgment. Researchers should avoid leading questions, maintain a neutral stance, and adhere to ethical guidelines. •    Data Analysis and Interpretation: Objectivity in data analysis requires researchers to follow established methodologies, use appropriate statistical techniques, and apply consistent criteria for interpretation. Being open to alternative explanations and considering multiple perspectives enhances objectivity. •    Reporting of Findings: Objectivity in reporting means presenting research findings in a balanced and unbiased manner. Researchers should clearly differentiate between empirical evidence and personal opinions. Avoiding exaggeration or overstatement of results promotes objectivity. •    Peer Review: Engaging in the peer review process, where independent experts evaluate research for its scientific rigor and objectivity, helps ensure that research meets established standards and contributes to the objectivity of the broader scientific community. Both accuracy and objectivity are essential to maintain the integrity of research. Researchers strive to adhere to these principles to provide reliable, unbiased, and credible information that can withstand scrutiny and contribute to the advancement of knowledge in their respective fields. 16.6    Organization and Coherence A well-organized report follows a logical structure that guides readers through the content seamlessly. Each section should flow smoothly into the next, maintaining a clear and coherent narrative. The introduction should provide background information, research questions, and objectives, while the conclusion should summarize key findings, draw conclusions, and present recommendations. Headings, subheadings, and transitions should be used to ensure a cohesive and structured flow of information. Start your research with a clear and concise introduction that provides an overview of the research topic, states the research question or objective, and establishes the context and significance of the study. The introduction should engage the reader and set the stage for the subsequent sections. Literature Review: The literature review section should present a comprehensive and organized review of existing research and scholarly works related to your topic. Group related studies together, highlight key themes or gaps in the literature, and provide a logical progression of ideas. Clearly state the relevance of each study to your research objectives. •    Methodology: In the methodology section, explain the research design, data collection methods, and analysis techniques used in your study. Ensure that the description is clear, concise, and provides sufficient detail for others to replicate your study if needed. Present the methods in a logical order, from data collection to analysis. •    Results: Present your research findings in a systematic and coherent manner. Organize the results logically, considering the research questions or objectives. Use appropriate headings and subheadings to guide readers through different aspects of your results. Support your findings with relevant tables, figures, or other visual aids. •    Discussion: In the discussion section, analyze and interpret your results in relation to the research objectives and existing literature. Clearly link your findings to the research questions or hypotheses. Discuss the implications, limitations, and significance of your results. Provide a coherent narrative that connects the results to the broader context of the research field. •    Conclusion: Summarize the main findings and key insights of your research in a concise and coherent manner. Restate the research objective and highlight the contributions and implications of your study. Avoid introducing new information or arguments in the conclusion section. •    Transitions and Linking Sentences: Use transitional words, phrases, and sentences to create smooth transitions between sections and paragraphs. These help readers understand the logical connections between ideas and enable them to follow the flow of your arguments easily. Linking sentences can summarize the previous section and introduce the next one, reinforcing the coherence of your research. •    Headings and Subheadings: Utilize clear and descriptive headings and subheadings to structure your research document. This allows readers to quickly navigate through different sections and locate specific information. Ensure consistency in heading styles and formatting throughout the document. •    Paragraph Structure: Maintain coherence within paragraphs by organizing your ideas logically. Begin each paragraph with a clear topic sentence that introduces the main idea or argument. Provide supporting evidence, examples, or data in a coherent manner within the paragraph. •    Readability and Formatting: Pay attention to readability and formatting aspects such as font styles, sizes, line spacing, and margins. Use appropriate formatting techniques such as bullet points, numbering, and indentation to enhance readability and highlight important information. By carefully considering organizational coherence in your research, you can effectively communicate your ideas, guide readers through your study, and present a well-structured and coherent document that is easy to understand and navigate. 16.7    Tone and Style The tone and style of a report should be professional, objective, and appropriate for the intended audience. Avoiding overly technical language or academic jargon can make the report more accessible to readers who may not have specialized knowledge. Active voice is generally preferred over passive voice, as it tends to make the writing more engaging and direct. However, passive voice may be used when the focus is on the action rather than the doer. Tone and style in research refer to the way in which information is presented and communicated in a research document. They contribute to the overall voice, professionalism, and effectiveness of the research writing. Here's a closer look at tone and style in research: •    Tone: Tone refers to the attitude, manner, or expression used to convey information in writing. It sets the overall mood and establishes the writer's perspective or approach to the 183 subject matter. In research writing, an appropriate tone is typically formal, objective, and impartial. Key considerations for tone in research include: •    Formality: Research writing generally requires a formal tone to convey credibility and professionalism. Avoid colloquial or informal language, slang, and overly casual expressions. •    Objectivity: Maintain an objective tone by presenting information in a neutral and unbiased manner. Avoid personal opinions or subjective language that may introduce bias into the research. Strive for an objective tone when discussing findings, interpretations, or conclusions. •    Clarity and Precision: Use clear and precise language to convey your ideas accurately. Ensure that your tone facilitates understanding and effectively communicates complex concepts without ambiguity. •    Confidence: Present your research with confidence and authority, demonstrating a strong command of the subject matter. However, it is important to strike a balance between confidence and humility by acknowledging limitations or areas for further research. •    Style: Style refers to the specific choices and techniques employed in writing to convey information effectively. It encompasses various aspects of language use, sentence structure, and organization. Key considerations for style in research include: •    Conciseness: Aim for clear and concise writing that avoids unnecessary repetition, wordiness, or superfluous details. Clearly express your ideas using a minimum number of words, while ensuring that the content remains complete and accurate. •    Clarity: Strive for clarity by using plain language and avoiding jargon or complex technical terms unless they are essential and well-understood by the target audience. Use straightforward sentence structures and organize ideas logically. •    Cohesion: Ensure that your writing flows smoothly and that ideas are connected logically. Use appropriate transitional words and phrases to guide readers through the research document and to establish relationships between different sections and paragraphs. •    Consistency: Maintain consistency in terms of formatting, citation style, terminology, and grammar throughout the research document. Consistency enhances readability and professionalism. •    Avoidance of Bias: Maintain an impartial and unbiased writing style. Avoid discriminatory language, stereotypes, or assumptions that may introduce bias into the research. •    Citation and Referencing: Follow the appropriate citation style consistently and accurately. Adhere to ethical guidelines for referencing and citing sources to give credit to the works of others and avoid plagiarism. It's important to note that different disciplines or academic journals may have specific style guidelines that researchers should adhere to when writing and submitting their research papers. Familiarize yourself with the requirements of your target publication or audience to ensure that your tone and style align with the expectations of the research community. 16.8    Proofreading and Editing Proofreading and editing are essential steps in the report writing process. They help eliminate grammatical errors, spelling mistakes, and inconsistencies in language or formatting. It is recommended to take a break between writing and proofreading to approach the report with fresh eyes. Reading the report aloud can also help identify awkward sentence structures or areas that need improvement. Peer review or seeking feedback from colleagues can provide valuable insights for further enhancing the quality of the report. Elaborating on proofreading and editing in research is crucial for ensuring the accuracy, clarity, and professionalism of your work. It involves carefully reviewing and revising your research document to identify and correct errors, improve the overall quality of writing, and enhance the readability and coherence of your work. Here are some key steps to consider: •    Take a Break: Before starting the proofreading process, take a break from your research document. This break allows you to approach the text with a fresh perspective, making it easier to spot errors and inconsistencies. •    Review for Overall Structure: Begin by reviewing the overall structure and organization of your research. Check if the introduction, literature review, methodology, results, discussion, and conclusion are logically connected and flow smoothly. Ensure that each section addresses its purpose and contributes to the research objectives. •    Check Grammar and Spelling: Carefully review the document for grammar and spelling errors. Pay attention to subject-verb agreement, verb tenses, sentence structure, punctuation, and capitalization. Use grammar and spell-check tools, but be cautious as they may not catch all mistakes. •    Verify Accuracy and Consistency: Check the accuracy and consistency of the information presented in your research. Verify that data, references, citations, and quotations are correctly cited and formatted according to the required citation style (e.g., APA, MLA). Cross-check all facts, figures, and statistics to ensure they are accurate and up-to-date. •    Improve Clarity and Coherence: Read through your research document to ensure that your ideas are clearly and coherently presented. Check for any ambiguous or unclear sentences, awkward phrasing, or convoluted language. Simplify complex ideas, rephrase confusing sentences, and provide additional explanations where needed. •    Formatting and Style: Verify that your research document adheres to the required formatting guidelines. Check font styles and sizes, line spacing, margins, and page numbering. Ensure consistency in headings, subheadings, and citation formatting throughout the document. •    Proofread Citations and References: Carefully review your citations and references to ensure they are accurately formatted and correspond to the sources cited in the text. Verify the completeness of the reference list, including author names, publication dates, titles, and other required information. •    Read Aloud: Reading your research document aloud can help you identify grammatical errors, awkward phrasing, and inconsistencies more easily. Pay attention to the flow and rhythm of the sentences, and listen for clarity and coherence. •    Seek Feedback: Consider asking a colleague, mentor, or a professional editor to review your research document. Fresh eyes can often catch errors or provide valuable suggestions for improvement. •    Final Review: After making the necessary revisions based on the proofreading and editing process, perform a final review of your research document to ensure all changes have been incorporated correctly. Double-check formatting, citations, and references before finalizing the document. By thoroughly proofreading and editing your research, you can enhance the quality and professionalism of your work, improve its clarity and coherence, and ensure that it meets the highest standards of academic or scientific writing. 16.9    Self-Check Exercise 1.    What do you understand by Report Writing? Discuss about features of a good report writing. 2.    Describe important steps in report writing. 3.    Discuss about different types contents and style of report writing. 16.10    Summary This chapter has discussed the content and style considerations in report writing. An effective report presents information clearly, concisely, and objectively. It utilizes graphics and visuals to enhance understanding, maintains accuracy and objectivity, and ensures organization and coherence. The tone and style of the report should be professional and appropriate for the intended audience. Through careful attention to these elements, researchers and professionals can produce high-quality reports that effectively communicate their findings and recommendations. 16.11    Glossary Design – a plan or drawing produced to show the look and function or other object before it is made.Variable- not consistent or having a fixed pattern Experimental- based on untested ideas or technique and not yet established or finalized. 16.12    Answer to Self-Check Exercise (a) See 16.1 & 16.3 (b) See 16.4 (c) See 16.6 16.13    Terminal Questions a. Critically examine the report writing, content and style? 16.14    Suggested Readings •    Kothari. C.R., &. Garg, Gaurav Research Methodology: Method and Techniques, New Age International Publishers. New Delhi. 2018. •    Sharma. R.D. Research Methods in Social Sciences, National Book Organization, New Delhi, 1986. •    Young, Pauline. Scientific Social Surveys and Research, Prentice Hall Publishers, 1984 Dicknson, Me Graw. Political and Social Inquiry, Wiley, Watson, 1976. •  Lilian, Conen. Statistical Methods for Social Sciences, New Age Publisher, New Delhi, 1983. •    Moser, C.A. Survey Methods in Social Investigation, The Mac Millan Co, New York, 1958. •    Cohen, Maurice R., & Earnest Nagai. An Introduction to Logic and Scientific Method, Harcout Brace and Work Inc, New York, 1944. •    Festinger, L., & Deniel, Katz., Research Methods in the Behavioural Sciences, Holt Rinehart, New York, 1953. •    Mills C. Wright, The Sociological Imagination, Oxford University Press, London, 1959. 188