MB 0050 Research Methodology Contents Unit 1 An Introduction Introduction to Research Research
1
Unit 2 The Importance of Measurement in Research
13
Unit 3 Selection and Formulation of a Research Problem
23
Unit 4 Hypothesis
32
Unit 5 Research Design
46
Unit 6 Case Study Method
61
Unit 7 Sampling
66
Unit 8 Sources of Data Edition: Spring 2010 t
BKID – B1206 10 June 2010
82
Unit 9 Observation
92
Unit 10 Schedule and Questionnaire
101
Unit 11 Interviewing
108
Unit 12 Processing Data
129
Unit 13 Research Report Writing
187
Unit 14 Ethics in Research
198
Acknowledgements, Acknowledgements, References References & Suggested Readings
209
Unit 9 Observation
92
Unit 10 Schedule and Questionnaire
101
Unit 11 Interviewing
108
Unit 12 Processing Data
129
Unit 13 Research Report Writing
187
Unit 14 Ethics in Research
198
Acknowledgements, Acknowledgements, References References & Suggested Readings
209
Dean Directorate of Distance Education Sikkim Manipal University Board of Studies
Chairman HOD Management & Commerce SMU – SMU – DDE
Mr. Pankaj Khanna Director HR, Fidelity Mutual Fund
Additional Registrar SMU – SMU – DDE
Mr. Shankar Jagannathan Former Group Treasurer Wipro Technologies Limited
Controller of Examination SMU – SMU – DDE
Mr. Abraham Mathew Chief Financial Officer Infosys BPO, Bangalore
Dr. T. V. Narasimha Rao Adjunct Faculty & Advisor SMU – SMU – DDE
Ms. Sadhna Dash Ex-Senior Manager, HR Microsoft India Corporation (Pvt.) Ltd.
Prof. K. V. Varambally Director, Manipal Institute of Management, Manipal Content Preparation Team Content Writing Prof. Xavier V. K. Christ College, Bangalore
Content Modification & Review Vimala Parthasarathy Assistant Professor SMU DDE
Format Editing Ms. Shulagna Sarkar Former Lecturer, Dept. of Management & Commerce SMU DDE, Manipal
Language Editing Mr. Radhakrishna Rao Lecturer in English UPMC, Udupi
Edition Printed
: Spring 2010 : June 2010
This book is a distance education module comprising of written and compiled learning material for our students. All rights reserved. No part of this work may be reproduced in any form by any means without permission in writing from Sikkim Manipal University of Health, Medical and Technological Sciences, Gangtok, Sikkim. Printed and Published on behalf of Sikkim Manipal University of Health, Medical and Technological Sciences, Gangtok, Sikkim by Mr. Rajkumar Mascreen, GM, Manipal Universal Learning Pvt. Ltd., Manipal – 576 104. Printed at Manipal Press Limited, Manipal.
SUBJECT INTRODUCTION Research simply means a search for facts – answer to questions and solutions to problems. It is a purposive investigation. It is an organized inquiry. It seeks to find explanations to unexplained phenomenon to clarify the doubtful facts and to correct the misconceived facts. Research is a scientific endeavour. It involves scientific method. “The scientific method is a systematic step-by-step procedure following the logical processes of reasoning”. Scientific method is a means for gaining knowledge of the universe. It does not belong to any particular body of knowledge; it is universal. It does not refer to a field of specific subject of matter, but rather to a procedure or mode of investigation. Unit 1 : An Introduction to Research
Meaning of research – Purpose of research
Types of research
Significance of research in Social and Business Sciences
Unit 2 : The importance of Measurement in Research
Definition and Purpose of Measurement
Levels of Measurement
Characteristics of Good Measurement
Unit 3 : Selection and Formulation of a Research Problem
Choosing the problem
Review of literature
Formulating the problem
Criteria of a good research problem
Unit 4 : Hypothesis
Hypothesis – Meaning and Examples of hypothesis
Types of hypothesis
Testing of hypothesis
Unit 5 : Research Design
Needs of research design
Components of research design – Different research designs
Research design for studies in commerce and management.
Unit 6 : Case Study Method
Assumptions of case study method Advantages and disadvantages of case study method – Making case study effective Case study as a method of business research
Unit 7 : Sampling
Sampling procedure
Characteristics of good sample
Methods of sampling
Unit 8 : Sources of Data
Primary sources of data
Methods of collecting primary data
Secondary sources of data
Unit 9 : Observation
General characteristics of observation method
Process of observation
Use of observation in business research
Unit 10 : Schedules and Questionnaire
Process of data collection
Importance of questionnaire
Distinction between schedules and questionnaire
Unit 11 : Interview
Types of interviews
Approach to the interview
Qualities of interview
Interview techniques in business research
Unit 12 : Processing Data
Checking – Editing – Coding
Transcriptions and Tabulation
Data analysis
Unit 13 : Report Writing
Types of reports
Contents, styles of reporting
Steps in drafting reports
Editing the final draft
Evaluating the final drafts
Unit 14 : Ethics in Research
Meaning of Research Ethics
Ethical issues in the overall research process
Ethical issues in Gaining Access to Participants
Ethical issues in Data Collection
Ethical issues related to data analysis and reporting
Ethically questionable research situations
Responsibility for ethics in research
_________ _________ _________ _________
Research Methodology
Unit 1
Unit 1
An Introduction to Research
Structure: 1.1 Meaning and Definition of Research Objectives 1.1.1 Research and Scientific Method 1.1.2 Characteristics of Research 1.2 Purpose of Research 1.3 Types of Research 1.3.1 Pure Research 1.3.2 Applied Research 1.3.3 Exploratory Research 1.3.4 Descriptive Research 1.3.5 Diagnostic Study 1.3.6 Evaluation Studies 1.3.7 Action Research 1.4 Research Approaches 1.5 Significance of Research in Social and Business Sciences Self Assessment Questions I 1.6 Summary 1.7 Terminal Questions 1.8 Answers to SAQs and TQs
1.1 Meaning and Definition of Research Research simply means a search for facts – answers to questions and solutions to problems. It is a purposive investigation. It is an organized inquiry. It seeks to find explanations to unexplained phenomenon to clarify the doubtful facts and to correct the misconceived facts. The search for facts may be made through either:
Arbitrary (or unscientific) Method: It’s a method of seeking answers to question consists of imagination, opinion, blind belief or impression. E.g. it was believed that the shape of the earth was flat; a big snake swallows sun or moon causing solar or lunar eclipse. It is subjective; the finding will vary from person to person depending on his impression or imagination. It is vague and inaccurate. Or
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Scientific Method: this is a systematic rational approach to seeking facts. It eliminates the drawbacks of the arbitrary method. It is objective, precise and arrives at conclusions on the basis of verifiable evidences. Therefore, search of facts should be made by scientific method rather than by arbitrary method. Then only we may get verifiable and accurate facts. Hence research is a systematic and logical study of an issue or problem or phenomenon through scientific method.
Young defines Research as “a scientific undertaking which, by means of logical and systematic techniques ”, aims to: (a) Discover of new facts or verify and test old facts, (b) Analyze their sequences, interrelationships and causal explanations, (c) Develop new scientific tools, concepts and theories which would facilitate reliable and valid study of human behaviour. (d) Kerlinger defines research as a “systematic, controlled, empirical and critical investigation of hypothetical propositions about the presumed relations among natural phenomena ”. Objectives: After studying this lesson the students should be able to understand:
Research and scientific method
Characteristics of Research
Purpose of research
Different types of Research
Research Approaches
Significance of research in Social and Business Sciences
1.1.1 Research and Scientific Method Research is a scientific endeavour. It involves scientific method. “The scientific method is a systematic step-by-step procedure following the logical processes of reasoning”. Scientific method is a means for gaining knowledge of the universe. It does not belong to any particular body of knowledge; it is universal. It does not refer to a field of specific subject of matter, but rather to a procedure or mode of investigation.
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The scientific method is based on certain “articles of faith.” These are:
Reliance on Empirical Evidence: Truth is established on the basis of evidence. Conclusion is admitted, only when it is based on evidence. The answer to a question is not decided by intuition or imagination. Relevant data are collected through observation or experimentation. The validity and the reliability of data are checked carefully and the data are analyzed thoroughly, using appropriate methods of analysis. Use of Relevant Concepts: We experience a vast number of facts through our sense. Facts are things which actually exist. In order to deal with them, we use concepts with specific meanings. They are symbols representing the meaning that we hold. We use them in our thinking and communication. Otherwise, clarity and correct understanding cannot be achieved. Commitment of Objectivity: Objectivity is the hallmark of the scientific method. It means forming judgement upon facts unbiased by personal impressions. The conclusion should not vary from person to person. It should be the same for all persons. Ethical Neutrality: Science does not pass normal judgment on facts. It does not say that they are good or bad. According to Schrödinger “Science never imposes anything, science states. Science aims at nothing but making true and adequate statements about its object.” Generalization: In formulating a generalization, we should avoid the danger of committing the particularistic fallacy, which arises through an inclination to generalize on insufficient or incomplete and unrelated data. This can be avoided by the accumulation of a large body of data and by the employment of comparisons and control groups. Verifiability: The conclusions arrived at by a scientist should be verifiable. He must make known to others how he arrives at his conclusions. He should thus expose his own methods and conclusions to critical scrutiny. When his conclusion is tested by others under the same conditions, then it is accepted as correct. Logical reasoning process: The scientific method involves the logical process of reasoning. This reasoning process is used for drawing inference from the finding of a study or for arriving at conclusion.
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1.1.2 Characteristics of Research
It is a systematic and critical investigation into a phenomenon. It is a purposive investigation aiming at describing, interpreting and explaining a phenomenon. It adopts scientific method. It is objective and logical, applying possible test to validate the measuring tools and the conclusions reached. It is based upon observable experience or empirical evidence. Research is directed towards finding answers to pertinent questions and solutions to problems. It emphasizes the development of generalization, principles or theories. The purpose of research is not only to arrive at an answer but also to stand up the test of criticism.
1.2 Purpose of Research The objectives or purposes of research are varied. They are:
Research extends knowledge of human beings, social life and environment. The search is for answers for various types of questions: What, Where, When, How and Why of various phenomena, and enlighten us. Research brings to light information that might never be discovered fully during the ordinary course of life. Research establishes generalizations and general laws and contributes to theory building in various fields of knowledge. Research verifies and tests existing facts and theory and these help improving our knowledge and ability to handle situations and events. General laws developed through research may enable us to make reliable predictions of events yet to happen. Research aims to analyze inter-relationships between variables and to derive causal explanations: and thus enables us to have a better understanding of the world in which we live.
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Applied research aims at finding solutions to problems… socio economic problems, health problems, human relations problems in organizations and so on. Research also aims at developing new tools, concepts and theories for a better study of unknown phenomena. Research aids planning and thus contributes to national development.
1.3 Types of Research Although any typology of research is inevitably arbitrary, Research may be classified crudely according to its major intent or the methods. According to the intent , research may be classified as: 1.3.1 Pure Research It is undertaken for the sake of knowledge without any intention to apply it in practice, e.g., Einstein’s theory of relativity, Newton’s contributions, Galileo’s contribution, etc. It is also known as basic or fundamental research. It is undertaken out of intellectual curiosity or inquisitiveness. It is not necessarily problem-oriented. It aims at extension of knowledge. It may lead to either discovery of a new theory or refinement of an existing theory. It lays foundation for applied research. It offers solutions to many practical problems. It helps to find the critical factors in a practical problem. It develops many alternative solutions and thus enables us to choose the best solution. 1.3.2 Applied Research It is carried on to find solution to a real-life problem requiring an action or policy decision. It is thus problem-oriented and action-directed. It seeks an immediate and practical result, e.g., marketing research carried on for developing a news market or for studying the post-purchase experience of customers. Though the immediate purpose of an applied research is to find solutions to a practical problem, it may incidentally contribute to the development of theoretical knowledge by leading to the discovery of new facts or testing of theory or o conceptual clarity. It can put theory to the test. It may aid in conceptual clarification. It may integrate previously existing theories.
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1.3.3 Exploratory Research It is also known as formulative research. It is preliminary study of an unfamiliar problem about which the researcher has little or no knowledge. It is ill-structured and much less focused on pre-determined objectives. It usually takes the form of a pilot study. The purpose of this research may be to generate new ideas, or to increase the researcher’s familiarity with the problem or to make a precise formulation of the problem or to gather information for clarifying concepts or to determine whether it is feasible to attempt the study. Katz conceptualizes two levels of exploratory studies. “At the first level is the discovery of the significant variable in the situations; at the second, the discovery of relationships between variables.” 1.3.4 Descriptive Study It is a fact-finding investigation with adequate interpretation. It is the simplest type of research. It is more specific than an exploratory research. It aims at identifying the various characteristics of a community or institution or problem under study and also aims at a classification of the range of elements comprising the subject matter of study. It contributes to the development of a young science and useful in verifying focal concepts through empirical observation. It can highlight important methodological aspects of data collection and interpretation. The information obtained may be useful for prediction about areas of social life outside the boundaries of the research. They are valuable in providing facts needed for planning social action program. 1.3.5 Diagnostic Study It is similar to descriptive study but with a different focus. It is directed towards discovering what is happening, why it is happening and what can be done about. It aims at identifying the causes of a problem and the possible solutions for it. It may also be concerned with discovering and testing whether certain variables are associated. This type of research requires prior knowledge of the problem, its thorough formulation, clear-cut definition of the given population, adequate methods for collecting accurate information, precise measurement of variables, statistical analysis and test of significance.
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1.3.6 Evaluation Studies It is a type of applied research. It is made for assessing the effectiveness of social or economic programmes implemented or for assessing the impact of developmental projects on the development of the project area. It is thus directed to assess or appraise the quality and quantity of an activity and its performance, and to specify its attributes and conditions required for its success. It is concerned with causal relationships and is more actively guided by hypothesis. It is concerned also with change over time. 1.3.7 Action Research It is a type of evaluation study. It is a concurrent evaluation study of an action programme launched for solving a problem for improving an exiting situation. It includes six major steps: diagnosis, sharing of diagnostic information, planning, developing change programme, initiation of organizational change, implementation of participation and communication process, and post experimental evaluation. According to the methods of study, research may be classified as: 1. Experimental Research: It is designed to asses the effects of particular variables on a phenomenon by keeping the other variables constant or controlled. It aims at determining whether and in what manner variables are related to each other. 2. Analytical Study: It is a system of procedures and techniques of analysis applied to quantitative data. It may consist of a system of mathematical models or statistical techniques applicable to numerical data. Hence it is also known as the Statistical Method. It aims at testing hypothesis and specifying and interpreting relationships. 3. Historical Research: It is a study of past records and other information sources with a view to reconstructing the origin and development of an institution or a movement or a system and discovering the trends in the past. It is descriptive in nature. It is a difficult task; it must often depend upon inference and logical analysis or recorded data and indirect evidences rather than upon direct observation. 4. Survey: It is a fact-finding study. It is a method of research involving collection of data directly from a population or a sample thereof at particular time. Its purpose is to provide information, explain phenomena, to make comparisons and concerned with cause and effect relationships can be useful for making predications Sikkim Manipal University
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1.4 Research Approaches There are two main approaches to research, namely quantitative approach and qualitative approach. The quantitative approach involves the collection of quantitative data, which are put to rigorous quantitative analysis in a formal and rigid manner. This approach further includes experimental, inferential, and simulation approaches to research. Meanwhile, the qualitative approach uses the method of subjective assessment of opinions, behaviour and attitudes. Research in a situation is a function of the researcher’s impressions and insights. The r esults generated by this type of research are either in non-quantitative form or in the form which cannot be put to rigorous quantitative analysis. Usually, this approach uses techniques like depth interviews, focus group interviews, and projective techniques.
1.5 Significance of Research in Social and Business Sciences According to a famous Hudson Maxim, “All progress is born of inquiry. Doubt is often better than overconfidence, for it leads to inquiry, and inquiry leads to invention”. It brings out th e significance of research, increased amounts of which makes progress possible. Research encourages scientific and inductive thinking, besides promoting the development of logical habits of thinking and organization. The role of research in applied economics in the context of an economy or business is greatly increasing in modern times. The increasingly complex nature of government and business has raised the use of research in solving operational problems. Research assumes significant role in formulation of economic policy, for both the government and business. It provides the basis for almost all government policies of an economic system. Government budget formulation, for example, depends particularly on the analysis of needs and desires of the people, and the availability of revenues, which requires research. Research helps to formulate alternative policies, in addition to examining the consequences of these alternatives. Thus, research also facilitates the decision making of policy-makers, although in itself it is not a part of research. In the process, research also helps in the proper allocation of a country’s scare resources. Research is also necessary for collecting information on the social and economic structure of an economy to understand the process of change occurring in the country. Collection of statistical information though not a routine task, Sikkim Manipal University
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involves various research problems. Therefore, large staff of research technicians or experts is engaged by the government these days to undertake this work. Thus, research as a tool of government economic policy formulation involves three distinct stages of operation which are as follows:
Investigation of economic structure through continual compilation of facts Diagnoses of events that are taking place and the analysis of the forces underlying them; and The prognosis, i.e., the prediction of future developments
Research also assumes a significant role in solving various operational and planning problems associated with business and industry. In several ways, operations research, market research, and motivational research are vital and their results assist in taking business decisions. Market research is refers to the investigation of the structure and development of a market for the formulation of efficient policies relating to purchases, production and sales. Operational research relates to the application of logical, mathematical, and analytical techniques to find solution to business problems such as cost minimization or profit maximization, or the optimization problems. Motivational research helps to determine why people behave in the manner they do with respect to market characteristics. More specifically, it is concerned with the analyzing the motivations underlying consumer behaviour. All these researches are very useful for business and industry, which are responsible for business decision making. Research is equally important to social scientist for analyzing social relationships and seeking explanations to various social problems. It gives intellectual satisfaction of knowing things for the sake of knowledge. It also possesses practical utility for the social scientist to gain knowledge so as to be able to do something better or in a more efficient manner. This, research in social sciences is concerned with both knowledge for its own sake, and knowledge for what it can contribute to solve practical problems. Self Assessment Questions State whether the following are true or false: 1. Research is a repetitive search. 2. Applied research gives a solution to problem. Sikkim Manipal University
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3. Scientific method is systematic. 4. Objectivity is not required for all types of research. 5. Pure research is not fundamental research.
1.6 Summary Research simply means a search for facts. The search for facts may be made through either arbitrary (or unscientific) method or scientific method. Young defines Research as “a scientific undertaking which, by means of logical and systematic techniques ”, aims to: Discover of new facts or verify and test old facts, analyze their sequences, interrelationships and causal explanations, develop new scientific tools, concepts and theories which would facilitate reliable and valid study of human behaviour. Kerlinger defines research as a “systematic, controlled, empirical and critical investigation of hypothetical propositions about the presumed relations among natural phenomena”. The scientific method is based on certain “articles of faith.” These are: 1. Reliance on empirical evidence: 2. Use of relevant concepts 3. Commitment of objectivity 4. Ethical neutrality 5. Generalization 6. Verifiability 7. Logical reasoning process Research is directed towards finding answers to pertinent questions and solutions to problems. It emphasizes the development of generalization, principles or theories. The purpose of research is not only to arrive at an answer but also to stand up the test of criticism. The purpose of research is to extend knowledge of human beings Research establishes generalizations and general laws and contributes to theory building in various fields of knowledge. Research verifies and tests existing facts and theory and these help improving our knowledge and ability to handle situations and events. General laws developed through research may enable us to make reliable predictions of events yet to happen. Research aims to analyze interrelationships between variables and to derive causal explanations: and thus enables us to have a better understanding of the world in which we live. Sikkim Manipal University
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Applied research aims at finding solutions to problems… socio -economic problems, health problems, human relations problems in organizations and so on. Research also aims at developing new tools, concepts and theories for a better study of unknown phenomena. Research aids planning and thus contributes to national development. Pure Research is undertaken for the sake of knowledge without any intention to apply it in practice. Applied Research is carried on to find solution to a real-life problem requiring an action or policy decision. It is thus problem-oriented and action-directed. Exploratory Research is also known as formulative research. It is preliminary study of an unfamiliar problem about which the researcher has little or no knowledge. Descriptive Study is a fact-finding investigation with adequate interpretation. Diagnostic Study is similar to descriptive study but with a different focus. Evaluation Studies is a type of applied research. Action Research is a type of evaluation study. The role of research in applied economics in the context of an economy or business is greatly increasing in modern times. Research also assumes a significant role in solving various operational and planning problems associated with business and industry. Research is equally important to social scientist for analyzing social relationships and seeking explanations to various social problems.
1.7 Terminal Questions 1. Define the following: i) Scientific Method iii) Applied Research v) Descriptive Study vii) Action Research
ii) Research iv) Exploratory Research vi) Diagnostic Study
2. What is the meaning of research? 3. What are the articles of faith in scientific method? 4. What are the features of research? 5
What are the purposes of research?
6
What are the types of research?
7. What is the significance of research in social and business sciences?
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1.8 Answers to SAQs and TQs SAQs 1. 2. 3. 4. 5.
True True True False False
TQs 1) i) ii) iii) iv) v) vi) vii)
Section 1.1.1 Section 1.3.3 Section 1.3.2 Section 1.3.3 Section 1.3.4 Section 1.3.5 Section 1.3.7
2) Section 1.1 3) Section 1.1.1 4) Section 1.2.2 5) Section 1.2 6) Section 1.3 7) Section 1.5
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Unit 2
The Importance of Measurement in Research
Structure: 2.1 Introduction Objectives 2.2 Definition and Purpose of Measurement 2.3 Levels of Measurement 2.4 Characteristics of Good Measurement 2.4.1 Validity 2.4.2 Reliability 2.5 Summary 2.6 Terminal Questions 2.7 Answers to SAQs and TQs
2.1 Introduction Research basically deals with the measurement of various variables. While the measurement of variables is an important stage in the research process, it is also a difficult task. This section helps to understand the concept of measurement, the need for measurement, its nature, functions and procedure. The different levels of measurement and the validity and reliability of measuring instruments will also be explained in detail. Objectives: After studying this unit, you will be able to:
Explain what is meant by measurement in research
Describe the different levels of measurement
Recognize what makes for good measurement
Distinguish between the various concepts used to describe good measurement
2.2 Definition and Purpose of Measurement Different definitions of measurement have been offered by different authors – 1. According to Stevens, measurement is “the assignment of numerals to objects or events according to rules.” Sikkim Manipal University
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A simple example of assignment of numerals according to a rule is described below – Suppose a survey is conducted to study the applicants of an MBA program and one of the objectives of the study is to find out the sex-wise break-up of applicants. In this case, we may assign the number “0” to male applicants and the number “1” to female applicants. Thus numbers may be used to label individuals, events or things. 2. Campbell defines measurement as “the assignment of numbers to represent properties.” 3. In the words of Torgerson, measurement is “th e assignment of numbers to objects to represent amounts or degrees of a property possessed by all of the objects. In research, it is necessary to distinguish between “objects” and “properties’ or characteristics of these objects. For example, a person is an object and his/her physical characteristics include height, weight, color, etc. while his or her psychological characteristics include intelligence and attitudes. The important point to remember is that the researcher is concerned with measuring properties and not the objects themselves. While physical properties may be directly observed, psychological properties such as intelligence are inferred. For example, a child’s score in an IQ test indicates his or her level of intelligence. Measurement also has several purposes –
The researcher constructs theories to explain social and psychological phenomena (e.g. labor unrest, employee satisfaction), which in turn are used to derive hypotheses or assumptions. These hypotheses can be verified statistically only by measuring the variables in the hypotheses. Measurement makes the empirical psychological phenomena easier.
description
of
social
and
Example – When conducting a study of a tribal community, measuring devices help the researcher in classifying cultural patterns and behaviors.
Measurement also makes it possible to quantify variables and use statistical techniques to analyze the data gathered. Measurement enables the researcher to classify individuals or objects and to compare them in terms of specific properties or characteristics by measuring the concerned variables.
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Examples Comparison of male and female students’ performance in college exams or of length of stay on the job of older and younger employees.
2.3 Levels of Measurement Measurement may be classified into four different levels, based on the characteristics of order, distance and origin. 1. Nominal measurement This level of measurement consists in assigning numerals or symbols to different categories of a variable. The example of male and female applicants to an MBA program mentioned earlier is an example of nominal measurement. The numerals or symbols are just labels and have no quantitative value. The number of cases under each category are counted. Nominal measurement is therefore the simplest level of measurement. It does not have characteristics such as order, distance or arithmetic origin. 2. Ordinal measurement In this level of measurement, persons or objects are assigned numerals which indicate ranks with respect to one or more properties, either in ascending or descending order. Example Individuals may be ranked according to their “socio -economic class”, which is measured by a combination of income, education, occupation and wealth. The individual with the highest score might be assigned rank 1, the next highest rank 2, and so on, or vice versa. The numbers in this level of measurement indicate only rank order and not equal distance or absolute quantities. This means that the distance between ranks 1 and 2 is not necessarily equal to the distance between ranks 2 and 3. Ordinal scales may be constructed using rank order, rating and paired comparisons. Variables that lend themselves to ordinal measurement include preferences, ratings of organizations and economic status. Statistical techniques that are commonly used to analyze ordinal scale data are the median and rank order correlation coefficients.
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3. Interval measurement This level of measurement is more powerful than the nominal and ordinal levels of measurement, since it has one additional characteristic – equality of distance. However, it does not have an origin or a true zero. This implies that it is not possible to multiply or divide the numbers on an interval scale. Example The Centigrade or Fahrenheit temperature gauge is an example of the interval level of measurement. A temperature of 50 degrees is exactly 10 degrees hotter than 40 degrees and 10 degrees cooler than 60 degrees. Since interval scales are more powerful than nominal or ordinal scales, they also lend themselves to more powerful statistical techniques, such as standard deviation, product moment correlation and “t” tests and “F” tests of significance. 4. Ratio measurement This is the highest level of measurement and is appropriate when measuring characteristics which have an absolute zero point. This level of measurement has all the three characteristics – order, distance and origin. Examples Height, weight, distance and area. Since there is a natural zero, it is possible to multiply and divide the numbers on a ratio scale. Apart from being able to use all the statistical techniques that are used with the nominal, ordinal and interval scales, techniques like the geometric mean and coefficient of variation may also be used. The main limitation of ratio measurement is that it cannot be used for characteristics such as leadership quality, happiness, satisfaction and other properties which do not have natural zero points. The different levels of measurement and their characteristics may be summed up.
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In the table below – Levels of measurement
Characteristics
Nominal
No order, distance or origin
Ordinal
Order, but no distance or origin
Interval
Both order and distance, but no origin
Ratio
Order, distance and origin
2.4 Characteristics of Good Measurement A good measurement tool must possess the following characteristics – 1. Unidimensionality – This means that the measurement scale should not measure more than one characteristic at a time. For example, a scale should measure only length and not both length and temperature at the same time. 2. Linearity – A good measurement scale should follow the straight line model. 3. Validity – This means that a measurement scale should measure what it is supposed to measure. 4. Reliability – This refers to consistency. The measurement scale should give consistent results. 5. Accuracy and Precision – The measurement scale should give an accurate and precise measure of what is being measured. 6. Simplicity – A measurement tool should not be very complicated or elaborate. 7. Practicability – The measurement tool should be easy to understand and administer. There should be proper guidelines regarding its purpose and construction procedure, so that the results of a test can be interpreted easily. Of the above characteristics, validity and reliability are the most important requirements of a measurement scale and will be explained in more detail. 2.4.1 Validity A measurement scale may be considered to be valid if it effectively measures a specific property or characteristic that it intends to measure.The question of validity does not arise in the case of measurement of physical Sikkim Manipal University
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characteristics such as length, weight and height. This is because the measurement is direct and can be done through standard measuring devices. On the other hand, the measurement of abstract characteristics such as motivation and attitudes is more indirect and therefore poses the problem of validity. In such cases, there must be some evidence to prove that the measurement scale actually measures what it is supposed to measure. Such evidence is generally gathered through the application of statistical techniques. Validity may be classified into different types, as described below. The degree of validity of each type is determined by applying logic, statistical procedures or both. 1. Content validity: This type of validity may be of two types – a) Face validity and b) Sampling validity. Face validity is determined through a subjective evaluation of a measuring scale. For example, a researcher may develop a scale to measure consumer attitudes towards a brand and pre-test the scale among a few experts. If the experts are satisfied with the scale, the researcher may conclude that the scale has face validity. However, the limitation of this type of validity is that it is determined by opinions, rather than through a statistical method. Sampling validity refers to how representative the content of the measuring instrument is. In other words, the measuring instrument’s content must be representative of the content universe of the characteristic being measured. For example, if attitude is the characteristic being measured, its content universe may comprise statements and questions indicating which aspects of attitude need to be measured. In this case, sampling validity will be determined by comparing the items in the measuring instrument with the items in the content universe. Sampling validity, like face validity, is also based on the judgment and subjective evaluation of both the researcher and outside experts. The determination of the content universe and the selection of the relevant items that are to be included in the measuring scale are both done based on the knowledge and skill of the investigator and other judges.
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2. Predictive validity: This type of validity refers to the extent to which one behavior can be predicted based on another, based on the association between the results yielded by the measuring instrument and the eventual outcome. Example – In the case of an admission test designed for prospective MBA students, the predictive validity of the test would be determined by the association between the scores on the test and the grade point average secured by students during the first semester of study. A statistical measure of this association – the correlation coefficient – could be computed to determine the predictive validity of the admission test. Predictive validity would be strong if the coefficient is greater than .50. One limitation of determining predictive validity using this statistical association is that the eventual outcome, in this case, the grade point average of students during the first semester, may be influenced by other “extraneous” variables or factors. In other words, the grade point average may have been influenced by other factors (e.g. extra training or coaching) and may not necessarily be linked to the score on the admission test. Therefore, predicting behavior from one situation to another is not always accurate. 3. Construct validity: A construct is a conceptual equation that is developed by the researcher based on theoretical reasoning. Various kinds of relationships may be perceived by the researcher between a variable under study and other variables. These relationships must be tested in order to determine the construct validity of a measuring instrument. The instrument may be considered to have construct validity only if the expected relationships are found to be true. When determining the validity of a particular measurement instrument, all the three types of validity discussed above should be determined. 2.4.2 Reliability This refers to the ability of a measuring scale to provide consistent and accurate results. To give a simple example, a weighing machine may be said to be reliable if the same reading is given every time the same object is weighed. Sikkim Manipal University
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There are two dimensions of reliability – stability and equivalence or nonvariability. Stability refers to consistency of results with repeated measurements of the same object, as in the weighing machine example. Non variability refers to consistency at a given point of time among different investigators and samples of items. The problem of reliability is more likely to arise with measurements in the social sciences than with measurements in the physical sciences, due to factors such as poor memory or recall of respondents, lack of clear instructions given to respondents and irrelevant contents of the measuring instrument. Reliability can be improved in three ways – 1) By reducing the external sources of variation. This in turn can be achieved by standardizing the conditions under which measurement is carried out, by employing trained investigators and by providing standard instructions. 2) By making the measuring instrument more consistent internally, through an analysis of the different items 3) By adding more number of items to the measuring instrument, in order to increase the probability of more accurate measurement. The desired level of reliability depends on the research objectives, as well as the homogeneity of the population under study. If precise estimates are required, the higher will be the desired level of accuracy. In the case of a homogeneous population, a lower level of reliability may be sufficient, since there is not much variation in the data. Reliability and validity are closely interlinked. A measuring instrument that is valid is always reliable, but the reverse is not true. That is, an instrument that is reliable is not always valid. However, an instrument that is not valid may or may not be reliable and an instrument that is not reliable is never valid. Self Assessment Questions Are the following statements true or false? 1. Research is concerned with the measurement of objects. 2. A person’s emotions may be directly observed. 3. The most powerful level of measurement is ratio measurement.
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Unit 2
4. Linearity means that the measuring scale should not measure more than one characteristic at a time. 5. The problem of extraneous variables arises in the case of construct validity. 6. Validity is determined mainly by predictive validity. 7. Validity and reliability do not always go together. 8. Different research situations require different levels of reliability.
2.4 Summary Measurement is an important concept in research and is a difficult task. It refers to the assignment of numerals to objects in order to measure the characteristics or properties of objects . Measurement facilitates the verification of hypotheses, helps to quantify variables, makes data suitable for statistical analysis and enables comparison between objects in terms of specific characteristics. Measurement may be classified into four different levels, based on three characteristics – order, distance and origin. The lowest level of measurement is nominal measurement and involves assigning numerals or labels to different categories of a variable. The next level is ordinal measurement in which objects are rank ordered with respect to a specific characteristic. The interval level of measurement has the characteristics of order, distance and equality of interval but no origin. The highest level of measurement is ratio measurement which is suitable for measuring properties which have an absolute zero point. It permits the use of advanced statistical techniques to analyze the data. The characteristics of good measurement are uni-dimensionality, linearity, validity, reliability, accuracy, precision, simplicity and practicability. Validity refers to how effective an instrument is in measuring a property which it intends to measure. There are three types of validity – content validity, predictive validity and construct validity. Content validity may be of two types – face validity and sampling validity. Face validity is determined by a subjective evaluation of a measuring scale. Sampling validity refers to the extent to which the measuring instrument’s content is representative of the content universe of the characteristic being Sikkim Manipal University
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measured. The main limitation of content validity is that it is determined in a subjective manner, rather than through a statistical method. Predictive validity of a measuring instrument refers to the extent to which it may be used to predict a particular behavior, based on another behavior. Construct validity of a measuring instrument is determined by testing the relationships between the variables in the study and other variables. Reliability of a measuring instrument refers to its ability to provide consistent and accurate results with repeated measurements. Reliability and validity are closely associated. An instrument that is valid is also reliable, but not vice versa.
2.6 Terminal Questions 1. Differentiate between nominal, ordinal, interval and ratio scales, with an example of each. 2. What is meant by validity? How does it differ from reliability and what are its types? 3. What are the purposes of measurement in social science research?
2.7 Answers to SAQs and TQs SAQs 1. 2. 3. 4. 5. 6. 7. 8.
F F T F F F T T
TQs 1. Refer 2.3 2. Refer 2.4.1, 2.4.2 3. Refer 2.2
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Unit 3
Unit 3
Selection and Formulation of a Research Problem
Structure: 3.1 Meaning of Research Problem Objectives 3.2 Choosing the Problem 3.3 Review of Literature 3.4 Formulating the Problem 3.4.1 Internal Criteria 3.4.2 External Criteria 3.5 Objective of Formulating the Problem 3.6 Techniques involved in Formulating the Problem 3.7 Criteria of Good Research Problem Self Assessment Questions I 3.8 Summary 3.9 Terminal Questions 3.10 Answers to SAQs and TQs
3.1 Meaning of Research Problem Research really begins when the researcher experiences some difficulty, i.e., a problem demanding a solution within the subject-are of his discipline. This general area of interest, however, defines only the range of subjectmatter within which the researcher would see and pose a specific problem for research. Personal values play an important role in the selection of a topic for research. Social conditions do often shape the preference of investigators in a subtle and imperceptible way. The formulation of the topic into a research problem is, really speaking the first step in a scientific enquiry. A problem in simple words is some difficulty experienced by the researcher in a theoretical or practical situation. Solving this difficulty is the task of research. R. L. Ackoffs analysis affords considerable guidance in identifying problem for research. He visualizes five components of a problem. 1) Research-consumer: There must be an individual or a group which experiences some difficulty. Sikkim Manipal University
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2) Research-consumer’s Objectives: The r esearch-consumer must have available, alternative means for achieving the objectives he desires. 3) Alternative Means to Meet the Objectives: The research-consumer must have available, alternative means for achieving the objectives he desires. 4) Doubt in Regard to Selection of Alternatives: The existence of alternative courses of action in not enough; in order to experience a problem, the research consumer must have some doubt as to which alternative to select. 5) There must be One or More Environments to which the Difficulty or Problem Pertains: A change in environment may produce or remove a problem. A research-consumer may have doubts as to which will be the most efficient means in one environment but would have no such doubt in another. Objectives: After studying this unit you should be able to understand:
The meaning of Research Problem
Choosing the problem
Review of Literature
Criteria for formulating the problem
Objective of Formulating the Problem
Techniques involved in Formulating the Problem
Criteria of Good Research Problem
3.2 Choosing the Problem 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; it self is a problem. It is least amenable to formal methodological treatment. Vision, an imaginative insight, plays an important role in this process. One with a critical, curious and imaginative mind and is sensitive to practical problems could easily identify problems for study. The sources from which one may be able to identify research problems or develop problems awareness are:
Review of literature
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Academic experience
Daily experience
Exposure to field situations
Consultations
Brain storming
Research
Intuition
Unit 3
3.3 Review of Literature Frequently, an exploratory study is concerned with an area of subject matter in which explicit hypothesis have not yet been formulated. The researcher’s task then is to review the available material with an eye on the possibilities of developing hypothesis from it. In some areas of the subject matter, hypothesis may have been stated by previous research workers. The researcher has to take stock of these various hypotheses with a view to evaluating their usefulness for further research and to consider whether they suggest any new hypothesis. Sociological journals, economic reviews, the bulletin of abstracts of current social sciences research, directory of doctoral dissertation accepted by universities etc afford a rich store of valuable clues. In addition to these general sources, some governmental agencies and voluntary organizations publish listings of summaries of research in their special fields of service. Professional organizations, research groups and voluntary organizations are a constant source of information about unpublished works in their special fields.
3.4 Formulating the Problem The selection of one appropriate researchable problem out of the identified problems requires evaluation of those alternatives against certain criteria, which may be grouped into: 3.4.1 Internal Criteria Internal Criteria consists of: 1) 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 Sikkim Manipal University
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the researcher’s educational background, experience, outlook and sensitivity. 2) Researcher’s competence: A mere interest in a problem will not do. The researcher must be competent to plan and carry out a study of the problem. He must have the ability to grasp and deal with int. he must possess adequate knowledge of the subject-matter, relevant methodology and statistical procedures. 3) Researcher ’s own resource: In the case of a research to be done by a researcher on his won, consideration of his own financial resource is pertinent. If it is beyond his means, he will not be able to complete the work, unless he gets some external financial support. Time resource is more important than finance. Research is a time-consuming process; hence it should be properly utilized. 3.4.2 External Criteria 1) Research-ability of the problem: The problem should be researchable, i.e., amendable for finding answers to the questions involved in it through scientific method. To be researchable a question must be one for which observation or other data collection in the real world can provide the answer. 2) 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 significance should be considered. An important and urgent problem should be given priority over an unimportant one. 3) Novelty of the problem: The problem must have novelty. There is no use of wasting one’s time and energy 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. 4) 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.
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5) Facilities: Research requires certain facilities such as well-equipped library facility, suitable and competent guidance, data analysis facility, etc. Hence the availability of the facilities relevant to the problem must be considered. 6) 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 academic research and sponsored research. 7) 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 talent persons are not attracted to research projects. Each identified problem must be evaluated in terms of the above internal and external criteria and the most appropriate one may be selected by a research scholar.
3.5 Objective of Formulating the Problem A problem well put is half-solved. The primary task of research is collection of relevant data and the analysis of data for finding answers to the research questions. The proper performance of this task depends upon the identification of exact data and information required for the study. The formulation serves this purpose. The clear and accurate statement of the problem, the development of the conceptual model, the definition of the objectives of the study, the setting of investigative questions, the formulation of hypothesis to be tested and the operational definition of concepts and the delimitation of the study determine the exact data needs of the study. Once the exact data requirement is known, the researcher can plan and execute the other steps without any waste of time and energy. Thus formulation gives a direction and a specific focus to the research effort. It helps to delimit the field of enquiry by singling out the pertinent facts from a vast ocean of facts and thus saves the researcher from becoming lost in a welter of irrelevancies. It prevents a blind search and indiscriminate gathering of data which may later prove irrelevant to the problem under study. It helps in determining the methods to be adopted for sampling and collection of data. Sikkim Manipal University
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3.6 Techniques involved in Formulating Problem The problem selected for research may initially be a vague topic. The question to be studied or the problem to be solved 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 problem selected. The process of defining a problem includes: 1. Developing title: The title should be carefully worded. It should indicate the core of the study, reflect the real intention of the researcher, and show on what is the focus e.g., “Financing sm all-scale industries by commercial banks.” This shows that the focus is on commercial banks and not on small-scale industries. On the other hand, if the title is “The Financial Problem of Small-scale industries”, the focus is on small -scale industries. 2. Building a conceptual model: On the basis of our theoretical knowledge of the phenomenon under study, the nature of the phenomenon, its properties / elements and their inter-relations should be identified and structured into a framework. This conceptual model gives an exact idea of the research problem and shows its various properties and variables to be studied. It serves as a basis for the formulation of the objectives of the study, on the hypothesis to be tested. In order to workout a conceptual model we must make a careful and critical study of the available literature on the subject-matter of the selected research problem. It is for this reason; a researcher is expected to select a problem for research in his field of specialization. Without adequate background knowledge, a researcher cannot grasp and comprehend the nature of the research problem. 3. Define the Objective of the Study: The objectives refer to the questions to be answered through the study. They indicate what we are trying to get through the study. The objectives are derived from the conceptual model. They state which elements in the conceptual modelwhich levels of, which kinds of cases, which properties, and which connections among properties – are to be investigated, but it is the conceptual model that defines, describes, and states the assumptions underlying these elements. The objectives may aim at description or Sikkim Manipal University
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explanation or analysis of causal relationship between variables, and indicate the expected results or outcome of the study. The objectives may be specified in the form of either the statements or the questions.
3.7 Criteria of Good Research Problem Horton and Hunt have given following characteristics of scientific research: 1. Verifiable evidence: That is factual observations which other observers can see and check. 2. Accuracy: That is describing what really exists. It means truth or correctness of a statement or describing things exactly as they are and avoiding jumping to unwarranted conclusions either by exaggeration or fantasizing. 3. Precision: That is making it as exact as necessary, or giving exact number or measurement. This avoids colourful literature and vague meanings. 4. Systematization: That is attempting to find all the relevant data, or collecting data in a systematic and organized way so that the conclusions drawn are reliable. Data based on casual recollections are generally incomplete and give unreliable judgments and conclusions. 5. Objectivity: That is free being from all biases and vested interests. It means observation is unaffected by the observer’s values, beliefs and preferences to the extent possible and he is able to see and accept facts as they are, not as he might wish them to be. 6. Recording: That is jotting down complete details as quickly as possible. Since human memory is fallible, all data collected are recorded. 7. Controlling conditions: That is controlling all variables except one and then attempting to examine what happens when that variable is varied. This is the basic technique in all scientific experimentation – allowing one variable to vary while holding all other variables constant. 8. Training investigators: That is imparting necessary knowledge to investigators to make them understand what to look for, how to interpret in and avoid inaccurate data collection.
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Self Assessment Question I 1. ––––––––––––––––– is the first step in research. 2. Journals are ––––––––––––– of research problems. 3. Internal criteria of research problem consist of –––––– and –––––– .
3.8 Summary Research really begins when the researcher experiences some difficulty, i.e., a problem demanding a solution within the subject-are of his discipline. The formulation of the topic into a research problem is, really speaking the first step in a scientific enquiry. The selection of one appropriate researchable problem out of the identified problems requires evaluation of those alternatives against certain criteria, which may be grouped into internal criteria and external criteria. A problem well put is half-solved. The primary task of research is collection of relevant data and the analysis of data for finding answers to the research questions. The problem selected for research may initially be a vague topic. The process of defining a problem includes:
Developing title
Building a conceptual model
Define the Objective of the Study
Horton and Hunt have given following characteristics of scientific research:
Verifiable evidence
Accuracy
Precision
Systematization
Objectivity
Recording
Controlling conditions
3.9 Terminal Questions 1. How is a research problem formulated? 2. What are the sources from which one may be able to identify research problems? 3. Why literature survey is important in research? Sikkim Manipal University
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4. What is the classification of research problems? 5. What are the criteria of good research problem?
3.10 Answers to SAQs and TQs SAQs 1. Selection of a problem 2. Sources of problem 3. Researcher’s interest and competence TQs 1. 2. 3. 4. 5.
Section Section Section Section Section
4 3.3 3.3 3.6 3.7
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Unit 4
Unit 4
Hypothesis
Structure: 4.1 Introduction Objectives 4.2 Meaning and Examples of Hypothesis 4.2.1 Criteria for constructing of hypothesis 4.2.2 Nature of Hypothesis 4.2.3 The need for having Hypothesis 4.2.4 Characteristics of good hypothesis 4.3 Types of hypothesis 4.3.1 Null Hypothesis and alternative hypothesis 4.4 Concepts of Hypothesis 4.4.1 The level of Significance 4.4.2 Decision rule of testing hypothesis 4.4.3 Type I and Type II Errors 4.4.4 Two Tailed and One Tailed Test 4.5 Procedures for testing hypothesis 4.5.1 Making formal statement 4.5.2 Selecting a significant level 4.5.3 Deciding the distribution to use 4.5.4 Selecting a Random Sample and computing am approximate value 4.5.5 Calculation of Probability 4.5.6 Comparing the Probability 4.6 Testing of Hypothesis 4.6.1 Important Parametric Tests Self Assessment Questions 4.7 Summary 4.8 Terminal Questions 4.9 Answers to SAQs and TQs
4.1 Introduction A hypothesis is an assumption about relations between variables. It is a tentative explanation of the research problem or a guess about the research outcome. Before starting the research, the researcher has a rather general, Sikkim Manipal University
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diffused, even confused notion of the problem. It may take long time for the researcher to say what questions he had been seeking answers to. Hence, an adequate statement about the research problem is very important. What is a good problem statement? It is an interrogative statement that asks: what relationship exists between two or more variables? It then further asks questions like: Is A related to B or not? How are A and B related to C? Is A related to B under conditions X and Y? Proposing a statement pertaining to relationship between A and B is called a hypothesis. Objectives: After studying this lesson you should be able to understand:
Meaning and Examples of Hypothesis
Criteria for constructing of hypothesis
Nature of Hypothesis
the need for having Hypothesis
Characteristics of good hypothesis
Types of hypothesis
Null Hypothesis and alternative hypothesis
Concepts of Hypothesis
The level of Significance
Decision rule of testing hypothesis
Type I and Type II Errors
Two Tailed and One Tailed Test
Procedures for Testing hypothesis
Testing of Hypothesis
4.2 Meaning and Examples of Hypothesis According to Theodorson and Theodorson, “a hypothesis is a tentative statement asserting a relationship between certain facts. Kerlinger describes it as “a conjectural statement of the relationship between two or more variables”. Black and Champion have described it as “a tentative statement about something, the validity of which is usually unknown”. This statement is intended to be tested empirically and is either verified or rejected. It the statement is not sufficiently established, it is not considered a scientific law. In other words, a hypothesis carries clear implications for testing the stated relationship, i.e., it contains variables that are measurable and specifying Sikkim Manipal University
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how they are related. A statement that lacks variables or that does not explain how the variables are related to each other is no hypothesis in scientific sense. 4.2.1 Criteria for Hypothesis Construction Hypothesis is never formulated in the form of a question. The standards to be met in formulating a hypothesis:
It should be empirically testable, whether it is right or wrong.
It should be specific and precise.
The statements in the hypothesis should not be contradictory.
It should specify variables between which the relationship is to be established. It should describe one issue only.
4.2.2 Nature of Hypothesis A scientifically justified hypothesis must meet the following criteria:
It must accurately reflect the relevant sociological fact. It must not be in contradiction with approved relevant statements of other scientific disciplines. It must consider the experience of other researchers.
4.2.3 The Need for having Working Hypothesis
A hypothesis gives a definite point to the investigation, and it guides the direction on the study. A hypothesis specifies the sources of data, which shall be studied, and in what context they shall be studied. It determines the data needs. A hypothesis suggests which type of research is likely to be most appropriate.
It determines the most appropriate technique of analysis.
A hypothesis contributes to the development of theory
4.2.4 Characteristics of Good Hypothesis 1. Conceptual Clarity 2. Specificity 3. Testability 4. Availability of Techniques 5. Theoretical relevance Sikkim Manipal University
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6. Consistency 7. Objectivity 8. Simplicity
4.3 Types of Hypothesis There are many kinds of hypothesis the researcher has to be working with. One type of hypothesis asserts that something is the case in a given instance; that a particular object, person or situation has particular characteristics. Another type of hypothesis deals with the frequency of occurrence or of association among variables; this type of hypothesis may state that X is associated with Y. A certain Y proportion of items e.g. urbanism tends to be accompanied by mental disease or than something are greater or lesser than some other thing in specific settings. Yet another type of hypothesis asserts that a particular characteristics is one of the factors which determine another characteristic, i.e. X is the producer of Y. hypothesis of this type are called causal hypothesis. 4.3.1 Null Hypothesis and Alternative Hypothesis In the context of statistical analysis, we often talk null and alternative hypothesis. If we are to compare method A with method B about its superiority and if we proceed on the assumption that both methods are equally good, then this assumption is termed as null hypothesis. As against this, we may think that the method A is superior, it is alternative hypothesis. Symbolically presented as: Null hypothesis = H 0 and Alternative hypothesis = H a Suppose we want to test the hypothesis that the population mean is equal to the hypothesis mean (µ H 0) = 100. Then we would say that the null hypotheses are that the population mean is equal to the hypothesized mean 100 and symbolical we can express as: H 0: µ= µ H0=100 If our sample results do not support these null hypotheses, we should conclude that something else is true. What we conclude rejecting the null hypothesis is known as alternative hypothesis. If we accept H 0, then we are rejecting Ha and if we reject H 0, then we are accepting H a. For H0: µ= µ H0=100, we may consider three possible alternative hypotheses as follows:
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Alternative Hypothesis
To be read as follows
Ha: µ≠µ H0
(The alternative hypothesis is that the population mean is not equal to 100 i.e., it may be more or less 100)
Ha: µ>µ H0
(The alternative hypothesis is that population mean is greater than 100)
the
Ha: µ< µ H0
(The alternative hypothesis is population mean is less than 100)
the
that
The null hypothesis and the alternative hypothesis are chosen before the sample is drawn (the researcher must avoid the error of deriving hypothesis from the data he collects and testing the hypothesis from the same data). In the choice of null hypothesis, the following considerations are usually kept in view:
Alternative hypothesis is usually the one which wishes to prove and the null hypothesis are ones that wish to disprove. Thus a null hypothesis represents the hypothesis we are trying to reject, the alternative hypothesis represents all other possibilities. If the rejection of a certain hypothesis when it is actually true involves great risk, it is taken as null hypothesis because then the probability of rejecting it when it is true is α (the level of significance) which is chosen very small. Null hypothesis should always be specific hypothesis i.e., it should not state about or approximately a certain value. Generally, in hypothesis testing we proceed on the basis of null hypothesis, keeping the alternative hypothesis in view. Why so? The answer is that on assumption that null hypothesis is true, one can assign the probabilities to different possible sample results, but this cannot be done if we proceed with alternative hypothesis. Hence the use of null hypothesis (at times also known as statistical hypothesis) is quite frequent.
4.4 Concepts of Hypothesis Testing Basic concepts in the context of testing of hypothesis need to be explained. Sikkim Manipal University
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4.4.1 The Level of Significance This is a very important concept in the context of hypothesis testing. It is always some percentage (usually 5%) which should be chosen with great care, thought and reason. In case we take the significance level at 5%, then this implies that H 0 will be rejected when the sampling result (i.e., observed evidence) has a less than 0.05 probability of occurring if H 0 is true. In other words, the 5% level of significance means that researcher is willing to take as much as 5% risk rejecting the null hypothesis when it (H 0) happens to be true. Thus the significance level is the maximum value of the probability of rejecting H0 when it is true and is usually determined in advance before testing the Decision Rule of Test of Hypothesis: Given a hypothesis H 0 and an alternative hypothesis H 0 we make rule which is known as decision rule according to which we accept H 0 (i.e., reject H a) or reject H0 (i.e., accept a). For instance, if (H 0 is that a certain lot is good (there are very few defective items in it) against H a that the lot is not good (there are many defective items in it), that we must decide the number of items to be tested and the criterion for accepting or rejecting the hypothesis. We might test 10 items in the lot and plan our decision saying that if there are none or only 1 defective item among the 10, we will accept H 0 otherwise we will reject H0 (or accept H a). This sort of basis is known as decision rule. Type I & Type II Errors In the context of testing of hypothesis there are basically two types of errors that researchers make. We may reject H 0 when H0 is true & we may accept H0 when it is not true. The former is known as Type I & the later is known as Type II. In other words, Type I error mean rejection of hypothesis which should have been accepted & Type II error means accepting of hypothesis which should have been rejected. Type I error is donated by α (alpha), also called as level of significance of test; and Type II error is donated by β(beta). Decision Accept H0
Reject H0
H0 (true)
Correct decision
Type I error (α error)
Ho (false)
Type II error (β error)
Correct decision
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The probability of Type I error is usually determined in advance and is understood as the level of significance of testing the hypothesis. If type I error is fixed at 5%, it means there are about chances in 100 that we will reject H0 when H0 is true. We can control type I error just by fixing it at a lower level. For instance, if we fix it at 1%, we will say that the maximum probability of committing type I error would only be 0.01. But with a fixed sample size, n when we try to reduce type I error, the probability of committing type II error increases. Both types of errors can not be reduced simultaneously. There is a trade-off in business situations, decision-makers decide the appropriate level of type I error by examining the costs of penalties attached to both types of errors. If type I error involves time & trouble of reworking a batch of chemicals that should have been accepted, where as type II error means taking a chance that an entire group of users of this chemicals compound will be poisoned, then in such a situation one should prefer a type I error to a type II error means taking a chance that an entire group of users of this chemicals compound will be poisoned, then in such a situation one should prefer a type II error. As a result one must set very high level for typ e I error in one‟s testing techniques of a given hypothesis. Hence, in testing of hypothesis, one must make all possible effort to strike an adequate balance between Type I & Type II error. 4.4.2 Two Tailed Test & One Tailed Test In the context of hypothesis testing these two terms are quite important and must be clearly understood. A two-tailed test rejects the null hypothesis if, say, the sample mean is significantly higher or lower than the hypnotized value of the mean of the population. Such a test inappropriate when we haveH0: µ= µ H 0 and Ha: µ≠µ H0 which may µ>µ H 0 or µ<µ H0. If significance level is % and the two-tailed test to be applied, the probability of the rejection area will be 0.05 (equally split on both tails of curve as 0.025) and that of the acceptance region will be 0.95. If we take µ = 100 and if our sample mean deviates significantly from µ, in that case we shall accept the null hypothesis. But there are situations when only one-tailed test is considered appropriate. A one-tailed test would be used when we are to test, say, whether the population mean in either lower than or higher than some hypothesized value.
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4.5 Procedure for Testing Hypothesis To test a hypothesis means to tell (on the basis of the data researcher has collected) whether or not the hypothesis seems to be valid. In hypothesis testing the main question is: whether the null hypothesis or not to accept the null hypothesis? Procedure for hypothesis testing refers to all those steps that we undertake for making a choice between the two actions i.e., rejection and acceptance of a null hypothesis. The various steps involved in hypothesis testing are stated below: 4.5.1 Making a Formal Statement The step consists in making a formal statement of the null hypothesis (H o) and also of the alternative hypothesis (H a). This means that hypothesis should clearly state, considering the nature of the research problem. For instance, Mr. Mohan of the Civil Engineering Department wants to test the load bearing capacity of an old bridge which must be more than 10 tons, in that case he can state his hypothesis as under: Null hypothesis H O: µ =10 tons Alternative hypothesis H a: µ >10 tons Take another example. The average score in an aptitude test administered at the national level is 80. To evaluate a state‟s education system, the average score of 100 of the state‟s students selected on the random basis was 75. The state wants to know if there is a significance difference between the local scores and the national scores. In such a situation the hypothesis may be state as under: Null hypothesis H O: µ =80 Alternative hypothesis H a: µ ≠ 80 The formulation of hypothesis is an important step which must be accomplished with due care in accordance with the object and nature of the problem under consideration. It also indicates whether we should use a tailed test or a two tailed test. If H a is of the type greater than, we use alone tailed test, but when H a is of the type “whether greater or smaller” then we use a two-tailed test. 4.5.2 Selecting a Significant Level The hypothesis is tested on a pre-determined level of significance and such the same should have specified. Generally, in practice, either 5% level or Sikkim Manipal University
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1% level is adopted for the purpose. The factors that affect the level of significance are:
The magnitude of the difference between sample ;
The size of the sample;
The variability of measurements within samples;
Whether the hypothesis is directional or non – directional (A directional hypothesis is one which predicts the direction of the difference between, say, means). In brief, the level of significance must be adequate in the context of the purpose and nature of enquiry.
4.5.3 Deciding the Distribution to Use After deciding the level of significance, the next step in hypothesis testing is to determine the appropriate sampling distribution. The choice generally remains between distribution and the t distribution. The rules for selecting the correct distribution are similar to those which we have stated earlier in the context of estimation. 4.5.4 Selecting A Random Sample & Computing An Appropriate Value Another step is to select a random sample(S) and compute an appropriate value from the sample data concerning the test statistic utilizing the relevant distribution. In other words, draw a sample to furnish empirical data. 4.5.5 Calculation of the Probability One has then to calculate the probability that the sample result would diverge as widely as it has from expectations, if the null hypothesis were in fact true. 4.5.6 Comparing the Probability Yet another step consists in comparing the probability thus calculated with the specified value for α, the significance level. If the calculated probability is equal to smaller than α value in case of one tailed test (and α/2 in case of two-tailed test), then reject the null hypothesis (i.e. accept the alternative hypothesis), but if the probability is greater then accept the null hypothesis. In case we reject H 0 we run a risk of (at most level of significance) committing an error of type I, but if we accept H 0, then we run some risk of committing error type II.
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Flow Diagram for Testing Hypothesis State H0 as well as Ha
Specify the level of significance
Decide the correct sampling distribution
Sample a random sample and workout an appropriate value
Calculate the probability that sample result would diverge as widely as it has form expectations, if H 0 were true
Is this probability equal to or smaller than α value in case of one-tailed test and α/2 in case of two -tailed test
Run the risk of some risk of
Reject H0
Accept H0
Run
committing type II error
4.6 Testing of Hypothesis The hypothesis testing determines the validity of the assumption (technically described as null hypothesis) with a view to choose between the conflicting hypotheses about the value of the population hypothesis about the value of the population of a population parameter. Hypothesis testing helps to secede on the basis of a sample data, whether a hypothesis about the population is likely to be true or false. Statisticians have developed several
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tests of hypothesis (also known as tests of significance) for the purpose of testing of hypothesis which can be classified as:
Parametric tests or standard tests of hypothesis ;
Non Parametric test or distribution – free test of the hypothesis.
Parametric tests usually assume certain properties of the parent population from which we draw samples. Assumption like observations come from a normal population, sample size is large, assumptions about the population parameters like mean, variants etc must hold good before parametric test can be used. But there are situation when the researcher cannot or does not want to make assumptions. In such situations we use statistical methods for testing hypothesis which are called non parametric tests because such tests do not depend on any assumption about the parameters of parent population. Besides, most non-parametric test assumes only nominal or original data, where as parametric test require measurement equivalent to at least an interval scale. As a result non-parametric test needs more observation than a parametric test to achieve the same size of Type I & Type II error. 4.6.1 Important Parametric Tests The important parametric tests are:
z-test
t-test
x2-test
f-test
All these tests are based on the assumption of normality i.e., the source of data is considered to be normally distributed. In some cases the population may not be normally distributed, yet the test will be applicable on account of the fact that we mostly deal with samples and the sampling distributions closely approach normal distributions. Z-test is based on the normal probability distribution and is used for judging the significance of several statistical measures, particularly the mean. The relevant test statistic is worked out and compared with its probable value (to be read from the table showing area under normal curve) at a specified level of significance for judging the significance of the measure concerned. This is a most frequently used test in research studies. This test is used even when binomial distribution or t-distribution is applicable on the presumption that Sikkim Manipal University
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such a distribution tends to approximate normal distribution as „n‟ becomes larger. Z-test is generally used for comparing the mean of a sample to some hypothesis mean for the population in case of large sample, or when population variance is known as z-test is also used for judging the significance of difference between means to of two independent samples in case of large samples or when population variance is known z-test is generally used for comparing the sample proportion to a theoretical value of population proportion or for judging the difference in proportions of two independent samples when happens to be large. Besides, this test may be used for judging the significance of median, mode, co-efficient of correlation and several other measures T-test is based on t-distribution and is considered an appropriate test for judging the significance of sample mean or for judging significance of difference between the two means of the two samples in case of samples when population variance is not known (in which case we use variance of the sample as an estimate the population variance). In case two samples are related, we use paired t-test (difference test) for judging the significance of their mean of difference between the two related samples. It can also be used for judging the significance of co-efficient of simple and partial correlations. The relevant test statistic, t, is calculated from the sample data and then compared with its probable value based on t-distribution at a specified level of significance for concerning degrees of freedom for accepting or rejecting the null hypothesis it may be noted that t-test applies only in case of small sample when population variance is unknown. X2-test is based on chi-square distribution and as a parametric test is used for comparing a sample variance to a theoretical population variance is unknown. F-test is based on f-distribution and is used to compare the variance of the two-independent samples. This test is also used in the context of variance (ANOVA) for judging the significance of more than two sample means at one and the same time. It is also used for judging the significance of multiple correlation coefficients. Test statistic, f, is calculated and compared with its probable value for accepting or rejecting the H 0.
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Self Assessment Questions Fill in the Blanks 1. –––––––––- is a negative statement. 2. Type II error is –––––––––––. 3. –––––––––– is tentative statement.
4.7 Summary A hypothesis is an assumption about relations between variables. It is a tentative explanation of the research problem or a guess about the research outcome. Before starting the research, the researcher has a rather general, diffused, even confused notion of the problem. A hypothesis gives a definite point to the investigation, and it guides the direction on the study. A hypothesis specifies the sources of data, which shall be studied, and in what context they shall be studied. In the context of hypothesis testing these two terms are quite important and must be clearly understood. A two-tailed test rejects the null hypothesis if, say, the sample mean is significantly higher or lower than the hypnotized value of the mean of the population. The hypothesis is tested on a pre-determined level of significance and such the same should have specified. Generally, in practice, either 5% level or 1% level is adopted for the purpose. After deciding the level of significance, the next step in hypothesis testing is to determine the appropriate sampling distribution. The hypothesis testing determines the validity of the assumption (technically described as null hypothesis) with a view to choose between the conflicting hypotheses about the value of the population of a population parameter. Z-test is based on the normal probability distribution and is used for judging the significance of several statistical measures, particularly the mean. The relevant test statistic is worked out and compared with its probable value (to be read from the table showing area under normal curve) at a specified level of significance for judging the significance of the measure concerned. This is a most frequently used test in research studies. T-test is based on t-distribution and is considered an appropriate test for judging the significance of sample mean or for judging significance of difference between the two means of the two samples in case of samples when population variance is not known (in which case we use variance of the sample as an estimate of the population variance). X 2-test is based on Sikkim Manipal University
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chi-square distribution and as a parametric test is used for comparing a sample variance to a theoretical population variance is unknown. F-test is based on f-distribution and is used to compare the variance of the twoindependent samples.
4.8 Terminal Questions 1. 2 3. 3. 4. 5. 6. 7. 8.
What is the meaning of Hypothesis? What are the criteria for Hypothesis Construction? What is the need for having Working Hypothesis? What are the characteristics of Good Hypothesis? What are the types of Hypothesis? What is Type I & Type II Errors? What are Two Tailed Test & One Tailed Test? What are the procedure and Flow Diagram for Testing Hypothesis? Which are the important Parametric Tests?
4.9 Answers to SAQs and TQs SAQs 1. Null hypothesis 2. Accepting a statement that is false 3. Hypothesis TQs 1. 2. 3. 4. 5. 6. 7. 8. 9.
Section Section Section Section Section Section Section Section Section
4.1 4.2.1 4.2.3 4.2.4 4.3 4.4.3 4.4.4 4.5 4.6
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Unit 5
Research Design
Structure: 5.1 Meaning Objectives 5.2 Needs of Research Design 5.2.1 Characteristics of a Good Research Design 5.3 Components of Research Design 5.3.1 Experimental and Non-experimental Hypothesis Testing Research 5.4 Different Research Designs 5.5 Research Design for Studies in Commerce and Management 5.5.1 Research Design in Case of Exploratory Research Studies 5.5.2 Research Design in case of Descriptive and Diagnostic Research Studies 5.5.3 Research Design in case of Hypothesis testing Research Studies 5.5.4 Principles of Experimental Designs 5.5.5 Important Experimental Designs 5.5.6 Formal Experimental Designs Self Assessment Questions 5.6 Summary 5.7 Terminal Questions 5.8 Answers to SAQs and TQs
5.1 Meaning of Research Design The research designer understandably cannot hold all his decisions in his head. Even if he could, he would have difficulty in understanding how these are inter-related. Therefore, he records his decisions on paper or record disc by using relevant symbols or concepts. Such a symbolic construction may be called the research design or model. A research design is a logical and systematic plan prepared for directing a research study. It specifies the objectives of the study, the methodology and techniques to be adopted for achieving the objectives. It constitutes the blue print for the collection, measurement and analysis of data. It is the plan, structure and strategy of investigation conceived so as to obtain answers to research questions. The Sikkim Manipal University
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plan is the overall scheme or program of research. A research design is the program that guides the investigator in the process of collecting, analyzing and interpreting observations. It provides a systematic plan of procedure for the researcher to follow elltiz, Jahoda and Destsch and Cook describe, “A research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure.” Objectives: After studying this lesson you should be able to understand:
Needs of Research Design
Characteristics of a Good Research Design
Components of Research Design
Experimental and Non-experimental Hypothesis Testing Research
Different Research Designs
Research Design for Studies in Commerce and Management
Research Design in Case of Exploratory Research Studies
Research Design in case of Descriptive and Diagnostic Research Studies
Research Design in case of Hypothesis testing Research Studies
Principles of Experimental Designs
Important Experimental Designs
Formal Experimental Designs
5.2 Needs of Research Design The need for the methodologically designed research: a. In many a research inquiry, the researcher has no idea as to how accurate the results of his study ought to be in order to be useful. Where such is the case, the researcher has to determine how much inaccuracy may be tolerated. In a quite few cases he may be in a position to know how much inaccuracy his method of research will produce. In either case he should design his research if he wants to assure himself of useful results. b. In many research projects, the time consumed in trying to ascertain what the data mean after they have been collected is much greater than the Sikkim Manipal University
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time taken to design a research which yields data whose meaning is known as they are collected. c. The idealized design is concerned with specifying the optimum research procedure that could be followed were there no practical restrictions. 5.2.1 Characteristics of a Good Research Design 1. It is a series of guide posts to keep one going in the right direction. 2. It reduces wastage of time and cost. 3. It encourages co-ordination and effective organization. 4. It is a tentative plan which undergoes modifications, as circumstances demand, when the study progresses, new aspects, new conditions and new relationships come to light and insight into the study deepens. 5. It has to be geared to the availability of data and the cooperation of the informants. 6. It has also to be kept within the manageable limits
5.3 Components of Research Design It is important to be familiar with the important concepts relating to research design. They are: 1. Dependent and Independent variables: A magnitude that varies is known as a variable. The concept may assume different quantitative values, like height, weight, income, etc. Qualitative variables are not quantifiable in the strictest sense of objectivity. However, the qualitative phenomena may also be quantified in terms of the presence or absence of the attribute considered. Phenomena that assume different values quantitatively even in decimal points are known as „continuous variables‟. But, all variables need not be continuous. Values that can be expressed only in integer values are called „non -continuous variables‟. In statistical ter m, they are also known as „discrete variable‟. For example, age is a continuous variable; where as the number of children is a noncontinuous variable. When changes in one variable depends upon the changes in one or more other variables, it is known as a dependent or endogenous variable, and the variables that cause the changes in the dependent variable are known as the independent or explanatory or exogenous variables. For example, if demand depends upon price, then demand is a dependent variable, while price is the independent variable. Sikkim Manipal University
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And if, more variables determine demand, like income and prices of substitute commodity, then demand also depends upon them in addition to the own price. Then, demand is a dependent variable which is determined by the independent variables like own price, income and price of substitute. 2. Extraneous variable: The independent variables which are not directly related to the purpose of the study but affect the dependent variable are known as extraneous variables. For instance, assume that a researcher wants to test the hypothesis that there is relationship between children‟s school performance and their self-concepts, in which case the latter is an independent variable and the former, the dependent variable. In this context, intelligence may also influence the school performance. However, since it is not directly related to the purpose of the study undertaken by the researcher, it would be known as an extraneous variable. The influence caused by the extraneous variable on the dependent variable is technically called as an „experimental error‟. Therefore, a research study should always be framed in such a manner that the dependent variable completely influences the change in the independent variable and any other extraneous variable or variables. 3. Control: One of the most important features of a good research design is to minimize the effect of extraneous variable. Technically, the term control is used when a researcher designs the study in such a manner that it minimizes the effects of extraneous independent variables. The term control is used in experimental research to reflect the restrain in experimental conditions. 4. Confounded relationship: The relationship between dependent and independent variables is said to be confounded by an extraneous variable, when the dependent variable is not free from its effects.
Research hypothesis: When a prediction or a hypothesized relationship is tested by adopting scientific methods, it is known as research hypothesis. The research hypothesis is a predictive statement which relates a dependent variable and an independent variable. Generally, a research hypothesis must consist of at least one dependent variable and one independent variable. Whereas, the relationships that are assumed but not be tested are predictive
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statements that are not to be objectively verified are not classified as research hypothesis.
Experimental and control groups: When a group is exposed to usual conditions in an experimental hypothesis-testing research, it is known as „control group‟. On the other hand, when the group is exposed to certain new or special condition, it is known as an „experimental group‟. In the afore -mentioned example, the Group A can be called a control group and the Group B an experimental one. If both the groups A and B are exposed to some special feature, then both the groups may be called as „experimental groups‟. A research design may include only the experimental group or the both experimental and control groups together. Treatments: Treatments are referred to the different conditions to which the experimental and control groups are subject to. In the example considered, the two treatments are the parents with regular earnings and those with no regular earnings. Likewise, if a research study attempts to examine through an experiment regarding the comparative impacts of three different types of fertilizers on the yield of rice crop, then the three types of fertilizers would be treated as the three treatments. Experiment: An experiment refers to the process of verifying the truth of a statistical hypothesis relating to a given research problem. For instance, experiment may be conducted to examine the yield of a certain new variety of rice crop developed. Further, Experiments may be categorized into two types namely, absolute experiment and comparative experiment. If a researcher wishes to determine the impact of a chemical fertilizer on the yield of a particular variety of rice crop, then it is known as absolute experiment. Meanwhile, if the researcher wishes to determine the impact of chemical fertilizer as compared to the impact of bio-fertilizer, then the experiment is known as a comparative experiment. Experiment unit: Experimental units refer to the predetermined plots, characteristics or the blocks, to which the different treatments are applied. It is worth mentioning here that such experimental units must be selected with great caution.
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5.3.1 Experimental and Non-Experimental Hypothesis Testing Research When the objective of a research is to test a research hypothesis, it is known as a hypothesis-testing research. Such research may be in the nature of experimental design or non-experimental design. A research in which the independent variable is manipulated is known as „experimental hypothesis-testing research‟, where as a research in which the independent variable is not manipulated is termed as „non -experimental hypothesistesting research‟. E.g., assume that a researcher wants to examine whether family income influences the social attendance of a group of students, by calculating the coefficient of correlation between the two variables. Such an example is known as a non-experimental hypothesis-testing research, because the independent variable family income is not manipulated. Again assume that the researcher randomly selects 150 students from a group of students who pay their school fees regularly and them classifies them into tow sub-groups by randomly including 75 in Group A, whose parents have regular earning, and 75 in group B, whose parents do not have regular earning. And that at the end of the study, the researcher conducts a test on each group in order to examine the effects of regular earnings of the parents on the school attendance of the student. Such a study is an example of experimental hypothesis-testing research, because in this particular study the independent variable regular earnings of the parents have been manipulated
5.4 Different Research Designs There are a number of crucial research choices, various writers advance different classification schemes, some of which are: 1. Experimental, historical and inferential designs (American Marketing Association). 2. Exploratory, descriptive and causal designs (Selltiz, Jahoda, Deutsch and Cook). 3. Experimental, and expost fact (Kerlinger) 4. Historical method, and case and clinical studies (Goode and Scates) 5. Sample surveys, field studies, experiments in field settings, and laboratory experiments (Festinger and Katz) 6. Exploratory, descriptive and experimental studies (Body and Westfall) Sikkim Manipal University
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7. Exploratory, descriptive and casual (Green and Tull) 8. Experimental, „quasi-experimental designs‟ (Nachmias and Nachmias) 9. True experimental, quasi-experimental and non-experimental designs (Smith). 10. Experimental, pre-experimental, quasi-experimental designs and Survey Research (Kidder and Judd). These different categorizations exist, because „research design‟ is a complex concept. In fact, there are different perspectives from which any given study can be viewed. They are: 1. The degree of formulation of the problem (the study may be exploratory or formalized) 2. The topical scope-breadth and depth-of the study(a case or a statistical study) 3. The research environment: field setting or laboratory (survey, laboratory experiment) 4. The time dimension(one-time or longitudinal) 5. The mode of data collection (observational or survey) 6. The manipulation of the variables under study (experimental or expost facto) 7. The nature of the relationship among variables (descriptive or causal)
5.5 Research Design for Studies in Commerce and Management The various research designs are: 5.5.1 Research design in case of exploratory research studies Exploratory research studies are also termed as formulative research studies. The main purpose of such studies is that of formulating a problem for more precise investigation or of developing the working hypothesis from an operational point of view. The major emphasis in such studies is on the discovery of ideas and insights. As such the research design appropriate for such studies must be flexible enough to provide opportunity for considering different aspects of a problem under study. Inbuilt flexibility in research design is needed because the research problem, broadly defined initially, is transformed into one with more precise meaning in exploratory studies, which fact may necessitate changes in the research procedure for gathering relevant data. Generally, the following three methods in the context of research design for such studies are talked about: Sikkim Manipal University
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