Data collection: A researcher can conduct descriptive research using specific methods like observational method, case study method, and survey method. Between these three, all primary data collection methods are covered, which provides a lot of information. This can be used for future research or even developing a hypothesis of your research object. Varied: Since the data collected is qualitative and quantitative, it gives a holistic understanding of a research topic. The information is varied, diverse, and thorough. Natural environment: Descriptive research allows for the research to be conducted in the respondent’s natural environment, which ensures that high-quality and honest data is collected. Quick to perform and cheap: As the sample size is generally large in descriptive research, the data collection is quick to conduct and is inexpensive. EXPERIMENTAL DESIGN-CAUSAL RELATIONSHIPS Experimental designs are often touted as the most “rigorous” of all research designs or, as the “gold standard” against which all other designs are judged. In one sense, they probably are. If you can implement an experimental design well (and that is a big “if” indeed), then the experiment is probably the strongest design with respect to internal validity. Why? Recall that internal validity is at the center of all causal or cause-effect inferences. When you want to determine whether some program or treatment causes some outcome or outcomes to occur, then you are interested in having strong internal validity. Essentially, you want to assess the proposition: If X, then Y Or, in more colloquial terms: If the program is given, then the outcome occurs Unfortunately, it’s not enough just to show that when the program or treatment occurs the expected outcome also happens. That’s because there may be lots of reasons, other than the program, for why you observed the outcome. To really show that there is a causal relationship, you have to simultaneously address the two propositions: If X, then Y And If not X, then not Y Or, once again more colloquially: If the program is given, then the outcome occurs 51 CU IDOL SELF LEARNING MATERIAL (SLM)
And If the program is not given, then the outcome does not occur If you are able to provide evidence for both of these propositions, then you’ve in effect isolated the program from all of the other potential causes of the outcome. You’ve shown that when the program is present the outcome occurs and when it’s not present, the outcome doesn’t occur. That points to the causal effectiveness of the program. Think of all this like a fork in the road. Down one path, you implement the program and observe the outcome. Down the other path, you don’t implement the program and the outcome doesn’t occur. But, how do we take both paths in the road in the same study? How can we be in two places at once? Ideally, what we want is to have the same conditions – the same people, context, time, and so on – and see whether when the program is given we get the outcome and when the program is not given we don’t. Obviously, we can never achieve this hypothetical situation. If we give the program to a group of people, we can’t simultaneously not give it! So, how do we get out of this apparent dilemma? Perhaps we just need to think about the problem a little differently. What if we could create two groups or contexts that are as similar as we can possibly make them? If we could be confident that the two situations are comparable, then we could administer our program in one (and see if the outcome occurs) and not give the program in the other (and see if the outcome doesn’t occur). And, if the two contexts are comparable, then this is like taking both forks in the road simultaneously! We can have our cake and eat it too, so to speak. That’s exactly what an experimental design tries to achieve. In the simplest type of experiment, we create two groups that are “equivalent” to each other. One group (the program or treatment group) gets the program and the other group (the comparison or control group) does not. In all other respects, the groups are treated the same. They have similar people, live in similar contexts, have similar backgrounds, and so on. Now, if we observe differences in outcomes between these two groups, then the differences must be due to the only thing that differs between them – that one got the program and the other didn’t. OK, so how do we create two groups that are “equivalent”? The approach used in experimental design is to assign people randomly from a common pool of people into the two groups. The experiment relies on this idea of random assignment to groups as the basis for obtaining two groups that are similar. Then, we give one the program or treatment and we don’t give it to the other. We observe the same outcomes in both groups. The key to the success of the experiment is in the random assignment. In fact, even with random assignment we never expect that the groups we create will be exactly the same. How could they be, when they are made up of different people? We rely on the idea of probability and assume that the 52 CU IDOL SELF LEARNING MATERIAL (SLM)
two groups are “probabilistically equivalent” or equivalent within known probabilistic ranges. So, if we randomly assign people to two groups, and we have enough people in our study to achieve the desired probabilistic equivalence, then we may consider the experiment to be strong in internal validity and we probably have a good shot at assessing whether the program causes the outcome(s). But there are lots of things that can go wrong. We may not have a large enough sample. Or, we may have people who refuse to participate in our study or who drop out part way through. Or, we may be challenged successfully on ethical grounds (after all, in order to use this approach we have to deny the program to some people who might be equally deserving of it as others). Or, we may get resistance from the staff in our study who would like some of their “favorite” people to get the program. Or, they mayor might insist that her daughter be put into the new program in an educational study because it may mean she’ll get better grades. The bottom line here is that experimental design is intrusive and difficult to carry out in most real world contexts. And, because an experiment is often an intrusion, you are to some extent setting up an artificial situation so that you can assess your causal relationship with high internal validity. If so, then you are limiting the degree to which you can generalize your results to real contexts where you haven’t set up an experiment. That is, you have reduced your external validity in order to achieve greater internal validity. In the end, there is just no simple answer (no matter what anyone tells you!). If the situation is right, an experiment can be a very strong design to use. But it isn’t automatically so. My own personal guess is that randomized experiments are probably appropriate in no more than 10% of the social research studies that attempt to assess causal relationships. Experimental design is a fairly complex subject in its own right. I’ve been discussing the simplest of experimental designs – a two-group program versus comparison group design. But there are lots of experimental design variations that attempt to accomplish different things or solve different problems. In this section you’ll explore the basic design and then learn some of the principles behind the major variations. CONCEPT OF INDEPENDENT & DEPENDENT VARIABLES Dependent and independent variables are variables in mathematical modeling, statistical modeling and experimental sciences. Dependent variables receive this name because, in an experiment, their values are studied under the supposition or hypothesis that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables. Independent variables, in turn, are not seen as depending on any other variable in the scope of the experiment in question; thus, even if the existing dependency is invertible (e.g., by finding the inverse function when it exists), the nomenclature is kept if the inverse dependency is not the object of study in the experiment. In this sense, some common independent variables are time, space, density, mass, fluid flow rate, and 53 CU IDOL SELF LEARNING MATERIAL (SLM)
previous values of some observed value of interest (e.g. human population size) to predict future values (the dependent variable). Of the two, it is always the dependent variable whose variation is being studied, by altering inputs, also known as regressors in a statistical context. In an experiment, any variable that the experimenter manipulates can be called an independent variable. Models and experiments test the effects that the independent variables have on the dependent variables. Sometimes, even if their influence is not of direct interest, independent variables may be included for other reasons, such as to account for their potential confounding effect. DIAGNOSTIC RESEARCH DESIGN In diagnostic design, the researcher is looking to evaluate the underlying cause of a specific topic or phenomenon. This method helps one learn more about the factors that create troublesome situations. This design has three parts of the research: Inception of the issue Diagnosis of the issue Solution for the issue Being concerned with the express characteristics and existing social problems, the diagnostic research design endeavors to find out relationship between express causes and also suggests ways and means for the solution. Thus, the diagnostic studies are concerned with discovering and testing whether certain variables are associated. Such studies may also aim at determining the frequency with which something occurs or the ways in which a phenomenon is associated with some other factors. Diagnostic studies are mostly motivated by hypotheses. A primary description of a problem serves the basis so as to relate the hypotheses with the source of the problem and only those data which form and corroborate the hypotheses are collected. As regards the objectives of diagnostic research design, it is based on such knowledge which can also be motivated or put into practice in the solution of the problem. Therefore, it is obvious that the diagnostic design is concerned with both the case as well as the treatment. Diagnostic studies seek immediate to timely solution of the causal elements. The researcher, before going through other references, endeavors to remove and solve the factors and the causes responsible for giving rise to the problem. The research design of diagnostic studies demands strict adherence to objectivity for elimination of 54 CU IDOL SELF LEARNING MATERIAL (SLM)
any chances of personal bias or prejudice. Utmost care is taken while taking decisions regarding the variables, nature of observation to be made in the field, the type of evidence to be collected and tools of data collection. Simultaneously the research economy should not be lost sight of. Any faulty decision in these regard will result in wastage of time, energy and money. Usually the first step in such designing is accurate formulation of research problem wherein research objectives are precisely stated and principal areas of investigation are properly linked. Otherwise the investigator will find it difficult to ensure the collection of required data in a systematic manner. Simultaneously, the clarification of concepts and the operational definition of the terms should also be ensured so as to make them emendable to measurement. At the next stage certain decisions regarding collection of data are taken. In this regard, the researcher should always bear in mind the advantages and disadvantages of the method to be employed and at the same time the nature of research problem, type of data needed, degree of desired accuracy etc. should be considered. That apart, while collecting data, effort must be made to maintain objectivity to the maximum possible extent. In order to surmount the financial constraints, paucity of time, a representative sample of the research universe should be drawn so as to gather relevant information. A wide range of sampling techniques is prevalent which must be made use of, appropriately by the researchers. At the stage of analysis of data, the researcher must take proper care in placing each item in the appropriate category, tabulating of data, applying statistical computations and so on. Sufficient care must be taken to avoid potential errors due to faculty procedures of analysis of data. Advance decisions regarding the mode of tabulation, whether manual or by machine, accuracy of tabulating procedures, statistical application etc. will be of immense help in this regards. SUMMARY A research design is the set of methods and procedures used in collecting and analyzing measures of the variables specified in the problem research. The design of a study defines the study type (descriptive, correlational, semi-experimental, experimental, review, meta-analytic) and sub-type (e.g., descriptive-longitudinal case study), research problem, hypotheses, independent and dependent variables, experimental design, and, if applicable, data collection methods and a statistical analysis plan. A research design is a framework that has been created to find answers to research questions. A research faces many problems like what sample is to be taken, what method of data collection is to be used why is study being made and so on research plan or the blueprint minimize of all these problems of the research because all the decisions are taken beforehand Description research studies are those studies which are concerned with describing the characteristics 55 CU IDOL SELF LEARNING MATERIAL (SLM)
of a particular individual or of a group, Diagnostic analysis studies verify the frequency with that one thing happens or its Association with one thing else. The Research Design for descriptive and diagnostic studies have a common requirement in this the research must be able to define clearly what he wants to measure and must find adequate methods it should not be biased and should provide maximum reliability the design in such studies must be rigid and not fixable. Designing research is similar in many ways to designing buildings in architecture. In architecture, you design an architectural project by creatively suggesting ways of how your project will look like when built. In research, you design a research project by creatively and methodically suggesting ways of how your research project will proceed until it is completed or \"built\". Research design may be different from architectural design in that the former relies more on substantive information and less on artistic talents and skills. Designing research is basically deciding on the structure of the exact steps or procedures that you need to take, in order to successfully carry out the research objectives. In the \"wheel\" of the research process (see link), designing the research comes in second place following the problem identification stage. To be able to design a research, you need to have established a good research question or have formulated a good problem statement, and preferably have carried out the literature review. Although sometimes you need to draft the design of the research project to check the possibilities of conducting it before actually spending the effort on the literature review. However, conducting the literature review may assist you in revising your research design by overcoming obstacles occurring in the previous research for example. You need to know and understand the basic components of research to correctly design your research project. These components include variables (types, definitions, relationships between variables, and measuring variables), hypotheses (definition and formulation), and sampling (types and procedures). KEYWORDS Constructionism - The epistemology which assumes that there is no one absolute truth or ‘reality’ and that reality is socially constructed. It is most often used in qualitative research and the inter pretivist paradigm. Constructivism - A theory about how people learn – where they ask questions and find answers via exploration and assessment of what they already know. Constructs - Since concepts are abstract and unobservable, they need to be assigned a specifically created construct for a given research project that carries a specific meaning within that context. Content analysis - A quantitative research method used to analyses the manifest content (literal meaning) of messages in a systematic and objective manner to measure and compare their various characteristics. 56 CU IDOL SELF LEARNING MATERIAL (SLM)
LEARNING ACTIVITY 1. Prepare two or five-sentence paragraph indicating the type of research study you are going to do and justifying your choice. 2. Discuss the Exploratory Research Design. For Types And Uses UNIT END QUESTIONS (MCQ AND DESCRIPTIVE) A. Descripti ve Questions 1. Discuss Exploratory Designs 2. Describe Diagnostic Research Design 3. Explain the features of a good research design 4. State the importance of research design 5. Outline the concept of dependent & independent variables B. Multiple Choice Questions (MCQs) 1. The population census carried out by the GOI is an example of: a. Explorato ry research b. Casual research c. Descripti ve research d. Diagnostic research 2. Is it possible to apply projective techniques for exploratory investigation? a. Yes b. No 3. is defined as a research method that describes the characteristics of the population or phenomenon studied. a. Exploratory research b. Casual research c. Descriptive research d. Diagnostic research 4. is defined as a research used to investigate a problem which is not clearly defined. a. Exploratory research 57 CU IDOL SELF LEARNING MATERIAL (SLM)
b. Casual research c. Descriptive research d. Diagnostic research 5. the researcher is looking to evaluate the underlying cause of a specific topic or phenomenon. a. Exploratory research b. Casual research c. Descriptive research d. Diagnostic research Answers: 1. (c) 2. (a) 3. (c) 4. (a) 5. (d) REFERENCES Donald, R. Cooper & Pamela S. Schindler (2014). Business Research Methods. New Delhi: Tata McGraw-Hill Publishing Co. Ltd. Gupta, S.C. (2010). Fundamentals of Statistics. 6th Ed. Mumbai: HPH. Gupta, S.P. (2002). Statistical Methods. New Delhi: Sultan Chand & Sons. Beri, G.C. (2012). Business Statistics. New Delhi: Tata McGraw-Hill Publishing Co. Ltd. Zikmund. (2015). Business Research Methods. New Delhi: Cengage Learning Churchill, Gilbert A (1983) Marketing Research: Methodological Foundations, The Dryden Press, New York. Kothari C.R. (1990) Research Methodology: Methods and Technique. Wishwa Prakashan, New Delhi. Mahalotra N.K. (2002) Marketing Research: An Applied Orientation. Pearson EducationAsia. Mustafi, C.K. 1981. Statistical Methods in Managerial Decisions, Macmillan: New Delhi. Raj, D. (1968), “Sampling Theory,” McGraw-Hill Book Company, New York. Singh, D. and F.S. Chaudhary, 1986. Theory and Analysis of Sample Survey Designs, Wiley Eastern: New Delhi. Yates, E (1960), “Sampling Methods for Censuses and Surveys,” Charles Griffin & Company, Ltd., London. 58 CU IDOL SELF LEARNING MATERIAL (SLM)
UNIT-6 SAMPLING DESIGN Structure Learning Objectives Introduction Sampling design concepts Population versus sample Sampling design process Summary Keywords Learning Activity Unit End Questions (Mcq And Descriptive) References LEARNING OBJECTIVES After studying this Unit, you will be able to: State the concepts of sampling design Explain the sampling design process INTRODUCTION A sample design is a definite plan for obtaining a sample from a given population (Kothari, 1988). Sample constitutes a certain portion of the population or universe. Sampling design refers to the technique or the procedure the researcher adopts for selecting items for the sample from the population or universe. A sample design helps to decide the number of items to be included in the sample, i.e., the size of the sample. The sample design should be determined prior to data collection. There are different kinds of sample designs which a researcher can choose. Some of them are relatively more precise and easier to adopt than the others. A researcher should prepare or select a sample design, which must be reliable and suitable for the research study proposed to be undertaken. SAMPLE DESIGN CONCEPTS 1. Universe/Population: From a statistical point of view, the term ‘universe’ refers to the total of the items or units in any 59 CU IDOL SELF LEARNING MATERIAL (SLM)
field of enquiry, whereas the term ‘population’ refers to the total of items about which information is desired. The attributes that are the object of study are referred to as charac•teristics and the units possessing them are called as elementary units. The aggregate of such units is generally described as population. Thus, all units in any field of enquiry constitute universe and all ele•mentary units (on the basis of one characteristic or more) constitute population. Quite often, we do not find any difference between popu•lation and universe, and as such the two terms are taken as inter•changeable. However, a researcher must necessarily define these terms precisely. The population or universe may be finite or infinite. The popula•tion is said to be finite if it consists of a fixed number of elements so that it is possible to enumerate it in its totality. For example, the population of a city, the number of households in a village, the num•ber of workers in a factory, and the number of students in a university are the examples of finite population. The symbol ‘N’ is generally used to indicate how many elements (or items) are there in case of a finite population. An infinite population is that population in which it is theoretically impossible to observe all the elements. Thus, in an in•finite population, the number of items is infinite, i.e., we cannot have any idea about the total number of items. For example, the number of stars in the sky, sand particles at a sea beach, and pebbles in a river•bed. From a practical consideration, the term ‘infinite population’ is used for a population that cannot be enumerated in a reasonable pe•riod of time. This way we use the theoretical concept of infinite population as an approximation of a very large finite population. 2. Sampling Frame: The elementary units or the group of cluster of such units may form the basis of sampling process in which case they are called as sam•pling units. A list containing all such sampling units is known as sampling frame. The sampling frame consists of a list of items from which the sample is to be drawn. For instance, one can use telephone directory as a frame for conducting opinion survey in a city. What•ever the frame may be it should be a good representative of the popu•lation. 3. Sampling Design: A sample design is a definite plan for obtaining a sample from the sampling frame. It refers to the technique or the procedure the re•searcher would adopt in selecting some sampling units from which inferences from the population are drawn. Sampling design is determined before any data is collected. 60 CU IDOL SELF LEARNING MATERIAL (SLM)
4. Statistic(s) and Parameter(s): A statistic is a characteristic of a sample, whereas a parameter is a characteristic of a population. Thus, when we work out certain meas•ures such as mean, median, mode, etc., from samples, they are called statistics for they describe the characteristics of a sample. But when such measures describe the characteristics of a population, they are known as parameters. For example, the population means (μ) is a pa-rameter, whereas the sample means (X) is a statistic. To obtain the es•timate of a parameter from a statistic constitutes the prime objective of sampling analysis. 5. Sampling Error: Sampling survey does imply the study of a small portion of popula•tion and as such there would naturally be a certain amount of inaccu•racy in the information collected. This inaccuracy may be termed as sampling error or error variance. In other words, sampling errors are those errors which arise on account of sampling and they generally happen to be random variations (in case of random sampling) in the sample estimates around the true population values. It can be numeri•cally described as under: Sampling error = Frame error + chance error + response error. 6. Precision: Precision is a range within which the population average (or other parameters) will lie in accordance with reliability specified in the confidence level as a percentage of the estimate ± or as a numerical quantity. For example, if the estimate is Rs. 4000 and the precision desired is ± 4 per cent, then the true value will be not less than Rs. 3840 and not more than Rs. 4160. This is the range (Rs. 3840 to Rs. 4160) within which the true answer should lie. But if we desire that the estimate should not deviate from the actual value by more than Rs. 200 in either direction, in that case the range would be Rs. 3800 to Rs. 4200. 7. Confidence Level and Significance Level: The confidence level or reliability is expected percentage of times that the actual value will fall within the stated precision limit. Thus, if we take a confidence level of 95 per cent, then we mean that there are 95 chances in 100 (or .95 in 1) that the sample results represent the true condition of the population within a specified precision range against five chances in 100 (or .05 in 1) that it does not. 61 CU IDOL SELF LEARNING MATERIAL (SLM)
Preci•sion is the range within which the answer may vary and still be ac•ceptable; confidence level indicates the likelihood that the answer will fall within that range, and the significance level indicated the likelihood that the answer will fall outside that range. It may be re•membered that if the level of confidence in 95 per cent, then the sig•nificance level will be (100-95), i.e., 5 per cent, if the confidence level is 99 per cent, the significance level is (100-99), i.e., 1 per cent, and so on. POPULATION VERSUS SAMPLE A population is the group who is the main focus of a researcher’s interest; a sample is the group from whom the researcher actually collects data. Sampling involves selecting the observations that you will analyze. To conduct sampling, a researcher starts by going where your participants are. Sampling frames can be real or imaginary. Recruitment involves informing potential participants about your study and seeking their participation. Table 6.1 POPULATION VERSUS SAMPLE 62 CU IDOL SELF LEARNING MATERIAL (SLM)
SAMPLING DESIGN PROCESS A researcher should take into consideration the following aspects while developing a sample design: 1) Type Of Universe: The first step involved in developing sample design is to clearly define the number of cases, technically known as the universe. A universe may be finite or infinite. In a finite universe the number of items is certain, whereas in the case of an infinite universe the number of items is infinite (i.e., there is no idea about the total number of items). For example, while the population of a city or the number of workers in a factory comprise finite universes, the number of stars in the sky, or throwing of a dice represent infinite universe. 2) Sampling Unit: Prior to selecting a sample, decision has to be made about the sampling unit. A sampling unit may be a geographical area like a state, district, village, etc., or a social unit like a family, religious community, school, etc., or it may also be an individual. At times, the researcher would have to choose one or more of such units for his/her study. 3) Source List: Source list is also known as the ‘sampling frame’, from which the sample is to be selected. The source list consists of names of all the items of a universe. The researcher has to prepare a source list when it is not available. The source list must be reliable, comprehensive, correct, and appropriate. It is important that the source list should be as representative of the population as possible. 4) Size Of Sample: Size of the sample refers to the number of items to be chosen from the universe to form a sample. For a researcher, this constitutes a major problem. The size of sample must be optimum. An optimum sample may be defined as the one that satisfies the requirements of representativeness, flexibility, efficiency, and reliability. While deciding the size of sample, a researcher should determine the desired precision and the acceptable confidence level for the estimate. The size of the population variance should be considered, because in the case of a larger variance generally a larger sample is required. The size of the population should be considered, as it also limits the sample size. The parameters of interest in a research study should also be considered, while deciding the sample size. Besides, costs or budgetary constraint also plays a crucial role in deciding the sample size. (A) Parameters Of Interest: The specific population parameters of interest should also be considered while determining the 63 CU IDOL SELF LEARNING MATERIAL (SLM)
sample design. For example, the researcher may want to make an estimate of the proportion of persons with certain characteristic in the population, or may be interested in knowing some average regarding the population. The population may also consist of important sub-groups about whom the researcher would like to make estimates. All such factors have strong impact on the sample design the researcher selects. (B) Budgetary Constraint: From the practical point of view, cost considerations exercise a major influence on the decisions related to not only the sample size, but also on the type of sample selected. Thus, budgetary constraint could also lead to the adoption of a non-probability sample design. (C) Sampling Procedure: Finally, the researcher should decide the type of sample or the technique to be adopted for selecting the items for a sample. This technique or procedure itself may represent the sample design. There are different sample designs from which a researcher should select one for his/her study. It is clear that the researcher should select that design which, for a given sample size and budget constraint, involves a smaller error. SUMMARY A sample is defined as a smaller set of data that a researcher chooses or selects from a larger population by using a pre-defined selection method. These elements are known as sample points, sampling units, or observations. Creating a sample is an efficient method of conducting research. In most cases, it is impossible or costly and time-consuming to research the whole population. Hence, examining the sample provides insights that the researcher can apply to the entire population. Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen. Let’s begin by covering some of the key terms in sampling like “population” and “sampling frame.” Then, because some types of sampling rely upon quantitative models, we’ll talk about some of the statistical terms used in sampling. Finally, we’ll discuss the major distinction between probability and Nonprobability sampling methods and work through the major types in each. Sampling is a technique of selecting individual members or a subset of the population to make statistical inferences from them and estimate characteristics of the whole population. Different sampling methods are widely used by researchers in market research so that they do not need to research the entire population to collect actionable insights. It is also a time- convenient and a cost-effective method and hence forms the basis of any research design. 64 CU IDOL SELF LEARNING MATERIAL (SLM)
Sampling techniques can be used in a research survey software for optimum derivation. Typically, the population for market research is enormous. Making an enumeration of the whole population is practically impossible. The sample usually represents a manageable size from this population. Researchers then collect data from these samples in the form of surveys, polls, and questionnaires, and extrapolates this data analysis to the broader community. Sampling is a process used in statistical analysis in which a predetermined number of observations are taken from a larger population. The methodology used to sample from a larger population depends on the type of analysis being performed, but it may include simple random sampling or systematic sampling. KEYWORDS Evaluation research - Research carried out to gauge the relevance, suitability and effectiveness of a specific (public relations or other) campaign or program, being implemented. It is also known as program evaluation. Exit strategy - The plan set in place to leave a field study setting at the end of the data collection. This should include debriefing, if any form of deception was involved. Explanatory studies - The researcher provides a causal explanation of ‘why it is so?’ or a functional explanation of ‘how is it so?’ for a phenomenon under study Fallacies - Wrong assumptions made in research. LEARNING ACTIVITY 1. Prepare a sample for the research which needs to be conducted to study the changing behavioural patterns in children 2. Prepare a sample for a research to be done by a NGO on the women empowerment UNIT END QUESTIONS (MCQ AND DESCRIPTIVE) 65 A. Descriptive Questions CU IDOL SELF LEARNING MATERIAL (SLM)
1. Explain the stages of the sample design process 2. Compare population & sample 3. Discuss the concepts of Population 4. Outline the concepts of sampling frame 5. State the concept of sampling design B. Multiple Choice Questions (MCQs) 1. A smaller representation of a large whole is called as a. Sample b. Case c. Unit d. Data 2. Sampling process generally consists of number of steps. a. Six b. Nine c. Seven d. Five 3. Which is the first step in sampling process? a. Defining the population b. Specifying the sampling frame c. Specifying sampling unit d. Specifying sampling method 4. A list containing all sampling units is known as a. Sampling frame b. Sampling design c. Population d. None of these 5. is determined before any data is collected. a. Sampling frame b. Sampling design c. Population d. None of these Answers: 1. (a), 2. (c) 3. (a) 4. (b) 5. (b) 66 CU IDOL SELF LEARNING MATERIAL (SLM)
REFERENCES Donald, R. Cooper & Pamela S. Schindler (2014). Business Research Methods. New Delhi: Tata McGraw-Hill Publishing Co. Ltd. Gupta, S.C. (2010). Fundamentals of Statistics. 6th Ed. Mumbai: HPH. Gupta, S. P. (2002). Statistical Methods. New Delhi: Sultan Chand & Sons. Beri, G.C. (2012). Business Statistics. New Delhi: Tata McGraw-Hill Publishing Co. Ltd. Zikmund.(2015). Business Research Methods. New Delhi: Cengage Learning Churchill, Gilbert A (1983) Marketing Research: Methodological Foundations, The Dryden Press, New York. Kothari C.R. (1990) Research Methodology: Methods and Technique. Wishwa Prakashan, NewDelhi. Mahalotra N.K. (2002) Marketing Research: An Applied Orientation. Pearson EducationAsia. Mustafi, C.K. 1981. Statistical Methods in Managerial Decisions, Macmillan: New Delhi. Raj, D. (1968), “Sampling Theory,” McGraw-Hill Book Company, New York. Singh, D. and F.S. Chaudhary, 1986. Theory and Analysis of Sample Survey Designs, Wiley Eastern: New Delhi. Yates, E (1960), “Sampling Methods for Censuses and Surveys,” Charles Griffin & Company, Ltd., London 67 CU IDOL SELF LEARNING MATERIAL (SLM)
UNIT-7 UNDERSTANDING OF QUALITATIVE AND QUANTITATIVE RESEARCH Structure Learning Objectives Introduction Difference between Qualitative and Quantitative research Concept of generalization, replication Merging the two approaches Summary Keywords Learning Activity Unit End Questions (Mcq And Descriptive) References LEARNING OBJECTIVES After studying this Unit, you will be able to: Discuss the qualitative research Outline the difference between qualitative and quantitative research Explain the quantitative research INTRODUCTION Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions. This type of research can be used to establish generalizable facts about a topic. Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research is expressed in words. It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood. Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories. 68 CU IDOL SELF LEARNING MATERIAL (SLM)
DIFFERENCE BETWEEN QUALITATIVE AND QUANTITATIVE RESEARCH Quantitative Research Qualitative Research Focuses on testing theories and Focuses on exploring ideas and hypotheses formulating a theory or hypothesis Analyzed through math and statistical analysis Analyzed by summarizing, categorizing and interpreting Mainly expressed in numbers, graphs and tables Mainly expressed in words Requires many respondents Requires few respondents Closed (multiple choice) questions Open-ended questions Key terms: testing, measurement, Key terms: understanding, objectivity, replicability context, complexity, subjectivity CONCEPT OF GENERALIZATION, REPLICATION Generalization, which is an act of reasoning that involves drawing broad inferences from particular observations, is widely-acknowledged as a quality standard in quantitative research, but is more controversial in qualitative research. The goal of most qualitative studies is not to generalize but rather to provide a rich, contextualized understanding of some aspect of human experience through the intensive study of particular cases. Yet, in an environment where evidence for improving practice is held in high esteem, generalization in relation to knowledge claims merits careful attention by both qualitative and quantitative researchers. Issues relating to generalization are, however, often ignored or misrepresented by both groups of researchers. Three models of generalization, as proposed in a seminal article by Firestone, are discussed in this paper: classic sample-to-population (statistical) generalization, analytic generalization, and case-to-case transfer (transferability). Suggestions for enhancing the capacity for generalization in terms of all three models are offered. The suggestions cover such issues as planned replication, sampling strategies, systematic reviews, reflexivity and higher-order conceptualization, thick description, mixed methods research, and the RE-AIM framework within pragmatic trials. Replication is difficult to apply to qualitative studies in so far as it means recreating the exact conditions of the original study — a condition that is often impossible in the real world. The key 69 CU IDOL SELF LEARNING MATERIAL (SLM)
question then becomes: “how close to the original must a replication be to validate an original experiment?” (Searle, 2018) This question is particularly important because of the widespread belief that only quantitative research is replicable. Leppink (2017) writes: “Unfortunately, the heuristic of equating a qualitative–quantitative distinction with that of a multiple–single truths distinction is closely linked with the popular belief that replication research has relevance for quantitative research only. In fact, the usefulness of replication research has not rarely been narrowed down even further to repeating randomized controlled experiments.” (Leppink, 2017) MERGING THE TWO APPROACHES While you can combine qualitative and quantitative methods at various points—data collection or data analysis, for example—we typically use the following three research designs (also called topologies). a. Explanatory Sequential Design An explanatory sequential design emphasizes quantitative analysis, which we follow with interviews or observation (qualitative measures) to help explain the quant findings. Figure 7.1 Explanatory Sequential Design For example, we conducted a large comparative branding study with an internet retailer on attitudes toward the shopping experience on five mobile websites. After statistical analysis and cross-tabbing on experience levels to gauge brand attitudes, we came up with topics to further explore. We then recruited a new set of 16 participants for 1-on-1 sessions in which participants interacted with the sites used earlier and discussed their attitudes toward those sites. This enabled us to look more closely into trends we observed in the larger sample. In this study we used a new set of 16 participants; you can also use a subset of participants from the first survey phase 70 CU IDOL SELF LEARNING MATERIAL (SLM)
and dig deeper into any interesting patterns. To remember, the explanatory sequential design, think of qual explaining quant. b. Exploratory Sequential Design An exploratory sequential design starts with the qualitative research and then uses insights gained to frame the design and analysis of the subsequent quantitative component. Figure 7.2 Exploratory Sequential Design For example, to develop a new questionnaire, start with a qualitative phase where you interview participants and identify phrases, questions, or terms used to help derive the items used. We used this approach to develop the SUPR-Q. Exploratory sequential design lends itself well to usability testing. We often start with 5 to 10 participants in a classic think-aloud, moderated usability test. This exposes problem areas for which to create new tasks and survey questions, which in turn helps us refine our understanding of customer attitudes. We then launch a larger-scale, unmoderated study to get a better idea of the magnitude of the problems in the larger customer population. To remember the exploratory sequential approach, think of qual to enable research questions followed by quant for validation. c. Convergent Parallel Design If you collect qualitative data and quantitative data simultaneously and independently, and if you then analyze the results, you’re executing a convergent parallel design. In the analysis phase, you often give equal weight to the quant and qual data—you look to compare and contrast the results to look for patterns or contradictions. For example, one team may conduct ethnographic research at customer locations while another launches a survey to a set of global customers on the same product experience. The teams then converge and compile the findings to generate insights. 71 CU IDOL SELF LEARNING MATERIAL (SLM)
Figure 7.3 Convergent Parallel Design SUMMARY Ultimately, whether to pursue a qualitative or a quantitative study approach is up to you – however, be sure to base your decision on the nature of your project and the kind of information you seek in the context of your study and the resources available to you. Quantitative research is statistical: it has numbers attached to it, like averages, percentages or quotas. Qualitative research uses non-statistical methods. For example, you might perform a study and find that 50% of a district’s students dislike their teachers. The quantity (50%) makes it quantitative research. A follow up qualitative study could interview a small percentage of those students to find out why. The answers are free-form and don’t have numbers associated with them, so that makes them qualitative. Qualitative research (QR) is way to gain a deeper understanding of an event, organization or culture. Depending on what type of phenomenon you are studying, QR can give you a broad understanding of events, data about human groups, and broad patterns behind events and people. While traditional lab-based research looks for a specific “something” in the testing environment, qualitative research allows the meaning, themes, or data to emerge from the study. Qualitative research uses non-statistical methods to gain understanding about a population. In other words, you’re not dealing with the numbers you’d find in quantitative research. For example, let’s say your research project was to answer the question “Why do people buy fast food?”. Instead of a survey (which can usually be analyzed with math), you might use in- depth interviews to gain a deeper understanding of people’s motives. Another major difference between qualitative and quantitative research is that QR is usually performed in a natural setting (As opposed to a lab). Quantitative research method relies on the methods of natural sciences, that develops hard facts and numerical data. it establishes the cause-and-effect relationship between two variables using different 72 CU IDOL SELF LEARNING MATERIAL (SLM)
statistical, computational, and statistical methods. As the results are accurately and precisely measured, this research method is also termed as “Empirical Research”. This type of research is generally used to establish the generalized facts about the particular topic. This type of research is usually done by using surveys, experiments, and so on. KEYWORDS Exclusion criteria- characteristics that disqualify a person from being included in a sample Inclusion criteria- the characteristics a person must possess in order to be included in a sample Population- the cluster of people about whom a researcher is most interested Recruitment- the process by which the researcher informs potential participants about the study and attempts to get them to participate Sample-the group of people you successfully recruit from your sampling frame to participate in your study Sampling frame- a real or hypothetical list of people from which a researcher will draw her sample LEARNING ACTIVITY 1. Give examples of qualitative research 2. Differentiate between Qualitative and Quantitative Research UNIT END QUESTIONS (MCQ AND DESCRIPTIVE) A. Descriptive Questions 1. Compare quantitative & qualitative research 2. Explain the Explanatory Sequential Design 3. Discuss the Exploratory Sequential Design B. Multiple Choice Questions (MCQs) 73 1) Uniting various qualitative methods with quantitative methods can be called as…….. a. Coalesce b. Triangulation CU IDOL SELF LEARNING MATERIAL (SLM)
c. Bipartite d. Impassive 2) Which method can be applicable for collecting qualitative data? a. Artifacts (Visual) b. People c. Media products ( Textual, Visual and sensory) d. All of these 3) design emphasizes quantitative analysis, which we follow with interviews or observation (qualitative measures) to help explain the quant findings a. Explanatory b. Exploratory c. Convergent Parallel d. None of these 4) design starts with the qualitative research and then uses insights gained to frame the design and analysis of the subsequent quantitative component. a. Explanatory b. Exploratory c. Convergent Parallel d. None of these 5) If you collect qualitative data and quantitative data simultaneously and independently, and if you then analyse the results, you’re executing a design. a. Explanatory b. Exploratory c. Convergent Parallel d. None of these Answers: 1. (b) 2. (d) 3. (a) 4. (b) 5. (c) 74 CU IDOL SELF LEARNING MATERIAL (SLM)
REFERENCES Donald, R. Cooper & Pamela S. Schindler (2014). Business Research Methods. New Delhi: Tata McGraw-Hill Publishing Co. Ltd. Gupta, S.C. (2010). Fundamentals of Statistics. 6th Ed. Mumbai: HPH. Gupta, S. P. (2002). Statistical Methods. New Delhi: Sultan Chand & Sons. Beri, G. C. (2012). Business Statistics. New Delhi: Tata McGraw-Hill Publishing Co. Ltd. Zikmund. (2015). Business Research Methods. New Delhi: Cengage Learning Abrams, M.A., Social Surveys and Social Action, London: Heinemann, 1951. Arthur, Maurice, Philosophy of Scientific Investigation, Baltimore: John Hopkins University Press, 1943. RS. Bhardwaj, Business Statistics, Excel Books, New Delhi, 2008. S.N. Murthy and U. Bhojanna, Business Research Methods, Excel Books, 2007. 75 CU IDOL SELF LEARNING MATERIAL (SLM)
UNIT-8 COLLECTION OF DATA Structure Learning Objectives Introduction Tools and techniques of data collection Primary and secondary data Methods of Collecting Primary Data Methods of Collecting Secondary Data Summary Keywords Learning Activity Unit End Questions (Mcq And Descriptive) References LEARNING OBJECTIVES After studying this Unit, you will be able to: Explain different methods of data collection Discuss the tools used for data collection State the difference between primary & secondary data INTRODUCTION It is important for a researcher to know the sources of data which he requires for different purposes. Data are nothing but the information. There are two sources of information or data they are - Primary and Secondary data. The data are name after the source. Primary data refers to the data collected for the first time, whereas secondary data refers to the data that have already been collected and used earlier by somebody or some agency. For example, the statistics collected by the Government of India relating to the population is primary data for the Government of India since it has been collected for the first time. Later when the same data are used by a researcher for his study of a particular problem, then the same data become the secondary data for the researcher. Both the sources of information have their merits and demerits. The selection of a particular source depends upon the 76 CU IDOL SELF LEARNING MATERIAL (SLM)
(a) purpose and scope of enquiry, (b) availability of time, (c) availability of finance, (d) accuracy required, (e) statistical tools to be used, (f) sources of information (data), and (g) Method of data collection. TOOLS AND TECHNIQUES OF DATA COLLECTION Data collection tools refer to the devices/instruments used to collect data, such as a paper questionnaire or computer-assisted interviewing system. Case Studies, Checklists, Interviews, Observation sometimes, and Surveys or Questionnaires are all tools used to collect data. It is important to decide the tools for data collection because research is carried out in different ways and for different purposes. The objective behind data collection is to capture quality evidence that allows analysis to lead to the formulation of convincing and credible answers to the questions that have been posed. The objective behind data collection is to capture quality evidence that allows analysis to lead to the formulation of convincing and credible answers to the questions that have been posed - Click to Tweet Here are 7 top data collection methods and tools for Academic, Opinion or Product Research The following are the top 7 data collection methods for Academic, Opinion-based or product research. Also discussed in detail is the nature, pros and cons of each one. At the end of this segment, you will be best informed about which method best suits your research. 1. INTERVIEW An interview is a face-to-face conversation between two individuals with the sole purpose of collecting relevant information to satisfy a research purpose. Interviews are of different types namely; Structured, Semi-structured and unstructured with each having a slight variation from the other. Use this interview consent form template to let interviewee give you consent to use data gotten from your interviews for investigative research purpose. Structured Interviews - Simply put, it is a verbally administered questionnaire. In terms of depth, it is surface level and is usually completed within a short period. For speed and efficiency, it is highly recommendable, but it lacks depth. 77 CU IDOL SELF LEARNING MATERIAL (SLM)
Semi-structured Interviews - In this method, there subsist several key questions which cover the scope of the areas to be explored. It allows a little more leeway for the researcher to explore the subject matter. Unstructured Interviews - It is an in-depth interview that allows the researcher to collect a wide range of information with a purpose. An advantage of this method is the freedom it gives a researcher to combine structure with flexibility even though it is more time- consuming. Pros In-depth information Freedom of flexibility Accurate data. Cons Time-consuming Expensive to collect. What are the best Data Collection Tools for Interviews? For collecting data through interviews, here are a few tools you can use to easily collect data. 2. Audio Recorder An audio recorder is used for recording sound on disc, tape, or film. Audio information can meet the needs of a wide range of people, as well as provide alternatives to print data collection tools. 3. Digital Camera An advantage of a digital camera is that it can be used for transmitting those images to a monitor screen when the need arises. 4. Camcorder A camcorder is used for collecting data through interviews. It provides a combination of both an audio recorder and a video camera. The data provided is qualitative in nature and allows the respondents to answer questions asked exhaustively. If you need to collect sensitive information during an interview, a camcorder might not work for you as you would need to maintain your subject’s privacy. 5. QUESTIONNAIRES This is the process of collecting data through an instrument consisting of a series of questions and 78 CU IDOL SELF LEARNING MATERIAL (SLM)
prompts to receive a response from individuals it is administered to. Questionnaires are designed to collect data from a group. For clarity, it is important to note that a questionnaire isn't a survey, rather it forms a part of it. A survey is a process of data gathering involving a variety of data collection methods, including a questionnaire. On a questionnaire, there are three kinds of questions used. They are; fixed-alternative, scale, and open-ended. With each of the questions tailored to the nature and scope of the research. Pros Can be administered in large numbers and is cost-effective. It can be used to compare and contrast previous research to measure change. Easy to visualize and analyze. Questionnaires offer actionable data. Respondent identity is protected. Questionnaires can cover all areas of a topic. Relatively inexpensive. Cons Answers may be dishonest or the respondents lose interest midway. Questionnaires can't produce qualitative data. Questions might be left unanswered. Respondents may have a hidden agenda. Paper Questionnaire A paper questionnaire is a data collection tool consisting of a series of questions and/or prompts for the purpose of gathering information from respondents. Mostly designed for statistical analysis of the responses, they can also be used as a form of data collection. 1. REPORTING By definition, data reporting is the process of gathering and submitting data to be further subjected to analysis. The key aspect of data reporting is reporting accurate data because of inaccurate data reporting leads to uninformed decision making. 79 CU IDOL SELF LEARNING MATERIAL (SLM)
Pros Informed decision making. Easily accessible. Cons Self-reported answers may be exaggerated. The results may be affected by bias. Respondents may be too shy to give out all the details. Inaccurate reports will lead to uninformed decisions. What are the best Data Collection Tools for Reporting? Reporting tools enable you to extract and present data in charts, tables, and other visualizations so users can find useful information. You could source data for reporting from Non-Governmental Organizations (NGO) reports, newspapers, website articles, hospital records. a. NGO Reports Contained in NGO reports is an in-depth and comprehensive report on the activities carried out by the NGO, covering areas such as business and human rights. The information contained in these reports are research-specific and forms an acceptable academic base towards collecting data. NGOs often focus on development projects which are organized to promote particular causes. b. Newspapers Newspaper data are relatively easy to collect and are sometimes the only continuously available source of event data. Even though there is a problem of bias in newspaper data, it is still a valid tool in collecting data for Reporting. c. Website Articles Gathering and using data contained in website articles is also another tool for data collection. Collecting data from web articles is a quicker and less expensive data collection Two major disadvantages of using this data reporting method are biases inherent in the data collection process and possible security/confidentiality concerns. d. Hospital Care records Health care involves a diverse set of public and private data collection systems, including health surveys, administrative enrollment and billing records, and medical records, used by various entities, including hospitals, CHCs, physicians, and health plans. The data provided is clear, unbiased and 80 CU IDOL SELF LEARNING MATERIAL (SLM)
accurate, but must be obtained under the legal means as medical data is kept with the strictest regulations. 2. EXISTING DATA This is the introduction of new investigative questions in addition to/other than the ones originally used when the data was initially gathered. It involves adding measurement to a study or research. An example would be sourcing data from an archive. Pros Accuracy is very high. Easily accessible information. Cons Problems with evaluation. Difficulty in understanding. What are the Best Data Collection Tools for Existing Data? The concept of Existing data means that data is collected from existing sources to investigate research questions other than those for which the data were originally gathered. Tools to collect existing data include: Research Journals - Unlike newspapers and magazines, research journals are intended for an academic or technical audience, not general readers. A journal is a scholarly publication containing articles written by researchers, professors, and other experts. Surveys - A survey is a data collection tool for gathering information from a sample population, with the intention of generalizing the results to a larger population. Surveys have a variety of purposes and can be carried out in many ways depending on the objectives to be achieved. 3. OBSERVATION This is a data collection method by which information on a phenomenon is gathered through observation. The nature of the observation could be accomplished either as a complete observer, an observer as a participant, a participant as an observer or as a complete participant. This method is a key base of formulating a hypothesis. Pros Easy to administer. There subsists a greater accuracy with results. 81 CU IDOL SELF LEARNING MATERIAL (SLM)
It is a universally accepted practice. It diffuses the situation of an unwillingness of respondents to administer a report. It is appropriate for certain situations. Cons Some phenomena aren’t open to observation. It cannot be relied upon. Bias may arise. It is expensive to administer. Its validity cannot be predicted accurately. What are the best Data Collection Tools for Observation? Observation involves the active acquisition of information from a primary source. Observation can also involve the perception and recording of data via the use of scientific instruments. The best tools for Observation are: Checklists - state specific criteria, allow users to gather information and make judgments about what they should know in relation to the outcomes. They offer systematic ways of collecting data about specific behaviors, knowledge, and skills. Direct observation - This is an observational study method of collecting evaluative information. The evaluator watches the subject in his or her usual environment without altering that environment. 4. FOCUS GROUPS The opposite of quantitative research which involves numerical based data, this data collection method focuses more on qualitative research. It falls under the primary category for data based on the feelings and opinions of the respondents. This research involves asking open-ended questions to a group of individuals usually ranging from 6-10 people, to provide feedback. Pros Information obtained is usually very detailed. Cost-effective when compared to one-on-one interviews. It reflects speed and efficiency in the supply of results. 82 CU IDOL SELF LEARNING MATERIAL (SLM)
Cons Lacking depth in covering the nitty-gritty of a subject matter. Bias might still be evident. Requires interviewer training The researcher has very little control over the outcome. Afew vocal voices can drown out the rest. Difficulty in assembling an all-inclusive group. What are the best Data Collection Tools for Focus Groups? A focus group is a data collection method that is tightly facilitated and structured around a set of questions. The purpose of the meeting is to extract from the participants' detailed responses to these questions. The best tools for tackling Focus groups are: Two-Way - One group watches another group answer the questions posed by the moderator. After listening to what the other group has to offer, the group that listens are able to facilitate more discussion and could potentially draw different conclusions. Dueling-Moderator - There are two moderators who play the devil’s advocate. The main positive of the dueling-moderator focus group is to facilitate new ideas by introducing new ways of thinking and varying viewpoints. 5. COMBINATION RESEARCH This method of data collection encompasses the use of innovative methods to enhance participation to both individuals and groups. Also under the primary category, it is a combination of Interviews and Focus Groups while collecting qualitative data. This method is key when addressing sensitive subjects. Pros Encourage participants to give responses. It stimulates a deeper connection between participants. The relative anonymity of respondents increases participation. It improves the richness of the data collected. 83 CU IDOL SELF LEARNING MATERIAL (SLM)
Cons It costs the most out of all the top 7. It's the most time-consuming. What are the best Data Collection Tools for Combination Research? The Combination Research method involves two or more data collection methods, for instance, interviews as well as questionnaires or a combination of semi-structured telephone interviews and focus groups. The best tools for combination research are: Online Survey - The two tools combined here are online interviews and the use of questionnaires. This is a questionnaire that the target audience can complete over the Internet. It is timely, effective and efficient. Especially since the data to be collected is quantitative in nature. Dual-Moderator - The two tools combined here are focus groups and structured questionnaires. The structured questioners give a direction as to where the research is headed while two moderators take charge of proceedings. Whilst one ensures the focus group session progresses smoothly, the other makes sure that the topics in question are all covered. Dual- moderator focus groups typically result in a more productive session and essentially leads to an optimum collection of data. PRIMARY AND SECONDARY DATA Methods of Collecting Primary Data: Primary data may be obtained by applying any of the following methods: Direct Personal Interviews. Indirect Oral Interviews. Information from Correspondents. Mailed Questionnaire Methods. Schedule Sent Through Enumerators. 1. Direct Personal Interviews: A face to face contact is made with the informants (persons from whom the information is to be obtained) under this method of collecting data. The interviewer asks them questions pertaining to the 84 CU IDOL SELF LEARNING MATERIAL (SLM)
survey and collects the desired information. Thus, if a person wants to collect data about the working conditions of the workers of the Tata Iron and Steel Company, Jamshedpur, he would go to the factory, contact the workers and obtain the desired information. The information collected in this manner is first hand and also original in character. There are many merits and demerits of this method, which are discussed as under: Merits: 1. Most often respondents are happy to pass on the information required from them when contacted personally and thus response is encouraging. 2. The information collected through this method is normally more accurate because interviewer can clear doubts of the informants about certain questions and thus obtain correct information. In case the interviewer apprehends that the informant is not giving accurate information, he may cross- examine him and thereby try to obtain the information. 3. This method also provides the scope for getting supplementary information from the informant, because while interviewing it is possible to ask some supplementary questions which may be of greater use later 4. There might be some questions which the interviewer would find difficult to ask directly, but with some tactfulness, he can mingle such questions with others and get the desired information. He can twist the questions keeping in mind the informant’s reaction. Precisely, a delicate situation can usually he handled more effectively by a personal interview than by other survey techniques. 5. The interviewer can adjust the language according to the status and educational level of the person interviewed, and thereby can avoid inconvenience and misinterpretation on the part of the informant. Demerits: 1. This method can prove to be expensive if the number of informants is large and the area is widely spread. 2. There is a greater chance of personal bias and prejudice under this method as compared to other methods. 3. The interviewers have to be thoroughly trained and experienced; otherwise they may not be able to obtain the desired information. Untrained or poorly trained interviewers may spoil the entire work. 4. This method is more time taking as compared to others. This is because interviews can be held only at the convenience of the informants. Thus, if information is to be obtained from the 85 CU IDOL SELF LEARNING MATERIAL (SLM)
working members of households, interviews will have to be held in the evening or on week end. Even during evening only an hour or two can be used for interviews and hence, the work may have to be continued for a long time, or a large number of people may have to be employed which may involve huge expenses. Though there are some demerits in this method of data collection still we cannot say that it is not useful. The matter of fact is that this method is suitable for intensive rather than extensive field surveys. Hence, it should be used only in those cases where intensive study of a limited field is desired. In the present time of extreme advancement in the communication system, the investigator instead of going personally and conducting a face to face interview may also obtain information over telephone. A good number of surveys are being conducted every day by newspapers and television channels by sending the reply either by e-mail or SMS. This method has become very popular nowadays as it is less expensive and the response is extremely quick. But this method suffers from some serious defects, such as (a) those who own a phone or a television only can be approached by this method, (b) only few questions can be asked over phone or through television, (c) the respondents may give a vague and reckless answers because answers on phone or through SMS would have to be very short. 2. Indirect Oral Interviews: Under this method of data collection, the investigator contacts third parties generally called ‘witnesses’ who are capable of supplying necessary information. This method is generally adopted when the information to be obtained is of a complex nature and informants are not inclined to respond if approached directly. For example, when the researcher is trying to obtain data on drug addiction or the habit of taking liquor, there is high probability that the addicted person will not provide the desired data and hence will disturb the whole research process. In this situation taking the help of such persons or agencies or the neighbors who know them well becomes necessary. Since these people know the person well, they can provide the desired data. Enquiry Committees and Commissions appointed by the Government generally adopt this method to get people’s views and all possible details of the facts related to the enquiry. Though this method is very popular, its correctness depends upon a number of factors such as 1. The person or persons or agency whose help is solicited must be of proven integrity; otherwise any bias or prejudice on their part will not bring out the correct information and the whole process of research will become useless. 2. The ability of the interviewers to draw information from witnesses by means of appropriate questions and cross-examination. 3. It might happen that because of bribery, nepotism or certain other reasons those who are collecting the information give it such a twist that correct conclusions are not arrived at. 86 CU IDOL SELF LEARNING MATERIAL (SLM)
Therefore, for the success of this method it is necessary that the evidence of one person alone is not relied upon. Views from other persons and related agencies should also be ascertained to find the real position .Utmost care must be exercised in the selection of these persons because it is on their views that the final conclusions are reached. 3. Information from Correspondents: The investigator appoints local agents or correspondents in different places to collect information under this method. These correspondents collect and transmit the information to the central office where data are processed. This method is generally adopted by newspaper agencies. Correspondents who are posted at different places supply information relating to such events as accidents, riots, strikes, etc., to the head office. The correspondents are generally paid staff or sometimes they may be honorary correspondents also. This method is also adopted generally by the government departments in such cases where regular information is to be collected from a wide area. For example, in the construction of a wholesale price index numbers regular information is obtained from correspondents appointed in different areas. The biggest advantage of this method is that, it is cheap and appropriate for extensive investigation. But a word of caution is that it may not always ensure accurate results because of the personal prejudice and bias of the correspondents. As stated earlier, this method is suitable and adopted in those cases where the information is to be obtained at regular intervals from a wide area. 4. Mailed Questionnaire Method: Under this method, a list of questions pertaining to the survey which is known as ‘Questionnaire’ is prepared and sent to the various informants by post. Sometimes the researcher himself too contacts the respondents and gets the responses related to various questions in the questionnaire. The questionnaire contains questions and provides space for answers. A request is made to the informants through a covering letter to fill up the questionnaire and send it back within a specified time. The questionnaire studies can be classified on the basis of: i. The degree to which the questionnaire is formalized or structured. ii. The disguise or lack of disguise of the questionnaire and iii. The communication method used. When no formal questionnaire is used, interviewers adapt their questioning to each interview as it progresses. They might even try to elicit responses by indirect methods, such as showing pictures on which the respondent comments. When a researcher follows a prescribed sequence of questions, it is referred to as structured study. On the other hand, when no prescribed sequence of questions exists, the study is non-structured. When questionnaires are constructed in such a way that the objective is clear to the respondents then these questionnaires are known as non- disguised; on the other hand, when the objective is not clear, 87 CU IDOL SELF LEARNING MATERIAL (SLM)
the questionnaire is a disguised one. On the basis of these two classifications, four types of studies can be distinguished: 1. Non-disguised structured, 2. Non-disguised non-structured, 3. Disguised structured and 4. Disguised non-structured. There are certain merits and demerits of this method of data collection which are discussed below: Merits: 1. Questionnaire method of data collection can be easily adopted where the field of investigation is very vast and the informants are spread over a wide geographical area. 2. This method is relatively cheap and expeditious provided the informants respond in time. 3. This method has proved to be superior when compared to other methods like personal interviews or telephone method. This is because when questions pertaining to personal nature or the ones requiring reaction by the family are put forth to the informants, there is a chance for them to be embarrassed in answering them. Demerits: 1. This method can be adopted only where the informants are literates so that they can understand written questions and lend the answers in writing. 2. It involves some uncertainty about the response. Co-operation on the part of informants may be difficult to presume. 3. The information provided by the informants may not be correct and it may be difficult to verify the accuracy. However, by following the guidelines given below, this method can be made more effective: The questionnaires should be made in such a manner that they do not become an undue burden on the respondents; otherwise the respondents may not return them back. i. Prepaid postage stamp should be affixed ii. The sample should be large iii. It should be adopted in such enquiries where it is expected that the respondents would return the questionnaire because of their own interest in the enquiry. 88 CU IDOL SELF LEARNING MATERIAL (SLM)
iv. It should be preferred in such enquiries where there could be a legal compulsion to provide the information. 5. Schedules Sent Through Enumerators: Another method of data collection is sending schedules through the enumerators or interviewers. The enumerators contact the informants, get replies to the questions contained in a schedule and fill them in their own handwriting in the questionnaire form. There is difference between questionnaire and schedule. Questionnaire refers to a device for securing answers to questions by using a form which the respondent fills in himself, whereas schedule is the name usually applied to a set of questions which are asked in a face-to face situation with another person. This method is free from most of the limitations of the mailed questionnaire method. Merits: The main merits or advantages of this method are listed below: 1. It can be adopted in those cases where informants are illiterate. 2. There is very little scope of non-response as the enumerators go personally to obtain the information. 3. The information received is more reliable as the accuracy of statements can be checked by supplementary questions wherever necessary. This method too like others is not free from defects or limitations. The main limitations are listed below: Demerits: 1. In comparison to other methods of collecting primary data, this method is quite costly as enumerators are generally paid persons. 2. The success of the method depends largely upon the training imparted to the enumerators. 3. Interviewing is a very skilled work and it requires experience and training. Many statisticians have the tendency to neglect this extremely important part of the data collecting process and this result in bad interviews. Without good interviewing most of the information collected may be of doubtful value. 4. Interviewing is not only a skilled work but it also requires a great degree of politeness and thus the way the enumerators conduct the interview would affect the data collected. When questions are asked by a number of different interviewers, it is possible that variations in the personalities of the interviewers will cause variation in the answers obtained. This variation will not be 89 CU IDOL SELF LEARNING MATERIAL (SLM)
obvious. Hence, every effort must be made to remove as much of variation as possible due to different interviewers. Methods of Collecting Secondary Data: As stated earlier, secondary data are those data which have already been collected and analyzed by some earlier agency for its own use, and later the same data are used by a different agency. According to W.A.Neiswanger, “A primary source is a publication in which the data are published by the same authority which gathered and analyzed them. A secondary source is a publication, reporting the data which was gathered by other authorities and for which others are responsible.” Sources of Secondary Data: The various sources of secondary data can be divided into two broad categories: 1. Published sources, and 2. Unpublished sources. 1. Published Sources: The governmental, international and local agencies publish statistical data, and chief among them are explained below: (a) International Publications: There are some international institutions and bodies like I.M.F, I.B.R.D, I.C.A.F.E and U.N.O who publish regular and occasional reports on economic and statistical matters. (b) Official Publications of Central and State Governments: Several departments of the Central and State Governments regularly publish reports on a number of subjects. They gather additional information. Some of the important publications are: The Reserve Bank of India Bulletin, Census of India, Statistical Abstracts of States, Agricultural Statistics of India, Indian Trade Journal, etc. (c) Semi-Official Publications: Semi-Government institutions like Municipal Corporations, District Boards, Panchayats, etc. Publish reports relating to different matters of public concern. (d) Publications of Research Institutions: Indian Statistical Institute (I.S.I), Indian Council of Agricultural Research (I.C.A.R), Indian Agricultural Statistics Research Institute (I.A.S.R.I), etc. Publish the findings of their research 90 CU IDOL SELF LEARNING MATERIAL (SLM)
programmes. (e) Publications of various Commercial and Financial Institutions (f) Reports of various Committees and Commissions appointed by the Government as the Raj Committee’s Report on Agricultural Taxation, Wanchoo Committee’s Report on Taxation and Black Money, etc. Are also important sources of secondary data. (g) Journals and News Papers: Journals and News Papers are very important and powerful source of secondary data. Current and important materials on statistics and socio-economic problems can be obtained from journals and newspapers like Economic Times, Commerce, Capital, Indian Finance, Monthly Statistics of trade etc. 2. Unpublished Sources: Unpublished data can be obtained from many unpublished sources like records maintained by various government and private offices, the theses of the numerous research scholars in the universities or institutions etc. Precautions in the Use of Secondary Data: Since secondary data have already been obtained, it is highly desirable that a proper scrutiny of such data is made before they are used by the investigator. In fact the user has to be extra-cautious while using secondary data. In this context Prof. Bowley rightly points out that “Secondary data should not be accepted at their face value.” The reason being that data may be erroneous in many respects due to bias, inadequate size of the sample, substitution, errors of definition, arithmetical errors etc. Even if there is no error such data may not be suitable and adequate for the purpose of the enquiry. Prof. Simon Kuznet’s view in this regard is also of great importance. According to him, “the degree of reliability of secondary source is to be assessed from the source, the compiler and his capacity to produce correct statistics and the users also, for the most part, tend to accept a series particularly one issued by a government agency at its face value without enquiring its reliability”. Therefore, before using the secondary data the investigators should consider the following factors: Choice between Primary and Secondary Data: As we have already seen, there are a lot of differences in the methods of collecting Primary and Secondary data. Primary data which is to be collected originally involves an entire scheme of plan starting with the definitions of various terms used, units to be employed, type of enquiry to be conducted, extent of accuracy aimed at etc. For the collection of secondary data, a mere compilation of the existing data would be sufficient. A proper choice between the type of data needed for any particular statistical investigation is to be made after taking into consideration the nature, objective 91 CU IDOL SELF LEARNING MATERIAL (SLM)
and scope of the enquiry; the time and the finances at the disposal of the agency; the degree of precision aimed at and the status of the agency (whether government- state or central-or private institution of an individual). In using the secondary data, it is best to obtain the data from the primary source as far as possible. By doing so, we would at least save ourselves from the errors of transcription which might have inadvertently crept in the secondary source. Moreover, the primary source will also provide us with detailed discussion about the terminology used, statistical units employed, size of the sample and the technique of sampling (if sampling method was used), methods of data collection and analysis of results and we can ascertain ourselves if these would suit our purpose. Now-a-days in a large number of statistical enquiries, secondary data are generally used because fairly reliable published data on a large number of diverse fields are now available in the publications of governments, private organizations and research institutions, agencies, periodicals and magazines etc. In fact, primary data are collected only if there do not exist any secondary data suited to the investigation under study. In some of the investigations both primary as well as secondary data may be used. SUMMARY In Statistics, the data collection is a process of gathering information from all the relevant sources to find a solution to the research problem. It helps to evaluate the outcome of the problem. The data collection methods allow a person to conclude an answer to the relevant question. Most of the organizations use data collection methods to make assumptions about future probabilities and trends. Once the data is collected, it is necessary to undergo the data organization process. The main sources of the data collections methods are “Data”. A data can be classified into two types, namely primary data and the secondary data. The primary importance of data collection in any research or business process is that it helps to determine many important things about the company, particularly the performance. So, the data collection process plays an important role in all the streams. Depends on the type of data, the data collection method is divided into two categories namely, Primary Data Collection methods Secondary Data Collection methods There are two types of data, primary and secondary. Data which are collected first hand are called Primary data and data which have already been collected and used by somebody are called Secondary data. There are two methods of collecting data: (a) Survey method or total enumeration method and (b) Sample method. When a researcher goes for investigating all the units of the subject, it is called as survey method. On the other hand if he/she resorts to investigating only a few units of the subject and gives the result on the basis of that, it is known as sample survey method. There are different sources of collecting Primary and Secondary data. Some of the important sources of 92 CU IDOL SELF LEARNING MATERIAL (SLM)
Primary data are—Direct Personal Interviews, Indirect Oral Interviews, Information from Correspondents, Mailed questionnaire method, Schedules sent through enumerators and so on. Though all these sources or methods of Primary data have their relative merits and demerits, a researcher should use a particular method with lot of care. There are basically two sources of collecting secondary data- (a) Published sources and (b) Unpublished sources. Published sources are like publications of different government and semi-government departments, research institutions and agencies etc. Whereas unpublished sources are like records maintained by different government departments and unpublished theses of different universities etc. Editing of secondary data is necessary for different purposes as – editing for completeness, editing for consistency, editing for accuracy and editing for homogeneity. It is always a tough task for the researcher to choose between primary and secondary data. Though primary data are more authentic and accurate, time, money and labor involved in obtaining these more often prompt the researcher to go for the secondary data. There are certain amount of doubt about its authenticity and suitability, but after the arrival of many government and semi government agencies and some private institutions in the field of data collection, most of the apprehensions in the mind of the researcher have been removed. KEYWORDS Field notes - A researcher’s notes on the observations made at the setting during a field study Field study - A qualitative data collection method borrowed from anthropology, also known as field observation. It is carried out in the natural setting where the phenomenon takes place. Framing - The way the messages of a discourse are regulated and controlled, that shapes how the message is interpreted. Functiona lism - Research carried under the functionalist paradigm examines why people behave the way they do and assumes it is because people know the consequences and uses (functions) of their behaviour or actions. It is also known as administrative research and is linked to the Columbia School. LEARNING ACTIVITY 1. Explain the various tools & techniques you would use to collect the data for the reason in fall in sales. 2. Difference between methods of data collection in primary & secondary data 93 CU IDOL SELF LEARNING MATERIAL (SLM)
UNIT END QUESTIONS (MCQ AND DESCRIPTIVE) A. Descriptive Questions 1. Explain the various tools & techniques of data collection in detail 2. What is primary and secondary data? 3. List down methods for collecting primary data. 4. Explain the interview method 5. Describe questionnaire method used in research. B. Multiple Choice Questions (MCQs) 1. Accurate watching & noting of phenomena as they occur in nature with regard to cause & effect or mutual relations is --- a. Observation b. Experiment c. Research d. Survey 2. Observation does not include------- 94 a. Listening b. Speaking c. Watching d. Reading 3. Observation does not include the process of— a. Sensation b. Attention c. Discussion d. Perception CU IDOL SELF LEARNING MATERIAL (SLM)
4. Which of the following is the published source of secondary data a. Journals b. Newspapers c. Reports d. All of these 5. ____ is the process of collecting data through an instrument consisting of a series of questions and prompts to receive a response from individuals it is administered to a. Survey b. Interview c. Questionnaire d. Observation Answers: 1. (a), 2. (b) 3. (c) REFERENCES Donald, R. Cooper & Pamela S. Schindler (2014). Business Research Methods. New Delhi: Tata McGraw-Hill Publishing Co. Ltd. Gupta, S.C. (2010). Fundamentals of Statistics. 6th Ed. Mumbai: HPH. Gupta, S. P. (2002). Statistical Methods. New Delhi: Sultan Chand & Sons. Beri, G. C. (2012). Business Statistics. New Delhi: Tata McGraw-Hill Publishing Co. Ltd. Zikmund. (2015). Business Research Methods. New Delhi: Cengage Learning Churchill, Gilbert A (1983) Marketing Research: Methodological Foundations, The Dryden Press, New York. Kothari C.R. (1990) Research Methodology: Methods and Technique. Wishwa Prakashan, New Delhi. Mahalotra N.K. (2002) Marketing Research: An Applied Orientation. Pearson EducationAsia. Mustafi, C.K. 1981. Statistical Methods in Managerial Decisions, Macmillan: New Delhi. 95 CU IDOL SELF LEARNING MATERIAL (SLM)
UNIT-9 SCALING Structure Learning Objectives Introduction Meaning of scaling Important scaling techniques Rating and ranking scales Rating Scale Ranking Scale Scale construction techniques Summary Keywords Learning Activity Unit End Questions (Mcq And Descriptive) References LEARNING OBJECTIVES After studying this Unit, you will be able to: Explain the different scaling techniques Discuss different types of scales used in research INTRODUCTION Scaling is considered as the extension of measurement. What is Measurement? The measurement is the process of assigning numbers or symbol to the characteristics of the object as per the specified rules. Here, the researcher assigns numbers, not to the object, but to its characteristics such as perceptions, attitudes, preferences, and other relevant traits. In research, usually, the numbers are assigned to the qualitative traits of the object because the quantitative data helps in statistical analysis of the resulting data and further facilitates the communication of measurement rules and results. MEANING OF SCALING Scaling is the procedure of measuring and assigning the objects to the numbers according to the specified rules. In other words, the process of locating the measured objects on the continuum, a continuous sequence of numbers to which the objects are assigned is called as scaling. 96 CU IDOL SELF LEARNING MATERIAL (SLM)
All the scales used in scaling techniques can be explained in terms of four basic characteristics., Viz. Description, Order, Distance, and origin. These characteristics collectively define the Levels of Measurement of scale. The level of measurement indicates that what properties of an object are measured or not measured by the scale. IMPORTANT SCALING TECHNIQUES The researchers have identified many scaling techniques; today, we will discuss some of the most common scales used by business organizations, researchers, economists, experts, etc. These techniques can be classified as primary scaling techniques and other scaling techniques. Let us now study each of these methods in-depth below: A. Primary Scaling Techniques The major four scales used in statistics for market research consist of the following: Figure 9.1 Important scaling techniques a. Nominal Scale Nominal scales are adopted for non-quantitative (containing no numerical implication) labelling variables which are unique and different from one another. 97 CU IDOL SELF LEARNING MATERIAL (SLM)
Types of Nominal Scales: Dichotomous: A nominal scale that has only two labels is called ‘dichotomous’; for example, Yes/No. Nominal with Order: The labels on a nominal scale arranged in an ascending or descending order is termed as ‘nominal with order’; for example, Excellent, Good, Average, Poor, Worst. Nominal without Order: Such nominal scale which has no sequence, is called ‘nominal without order’; for example, Black, White. b. Ordinal Scale The ordinal scale functions on the concept of the relative position of the objects or labels based on the individual’s choice or preference. For example, At Amazon.in, every product has a customer review section where the buyers rate the listed product according to their buying experience, product features, quality, usage, etc. The ratings so provided are as follows: Star – Excellent Star – Good Star – Average 2Star – Poor 1 Star – Worst c. Interval Scale An interval scale is also called a cardinal scale which is the numerical labelling with the same difference among the consecutive measurement units. With the help of this scaling technique, researchers can obtain a better comparison between the objects. For example; A survey conducted by an automobile company to know the number of vehicles owned by the people living in a particular area who can be its prospective customers in future. It adopted the interval scaling technique for the purpose and provided the units as 1, 2, 3, 4, 5, 6 to select from. In the scale mentioned above, every unit has the same difference, i.e., 1, whether it is between 2 and 3 or between 4 and 5. d. Ratio Scale One of the most superior measurement techniques is the ratio scale. Similar to an interval scale, a ratio scale is an abstract number system. It allows measurement at proper intervals, order, categorization and distance, with an added property of originating from a fixed zero point. Here, the 98 CU IDOL SELF LEARNING MATERIAL (SLM)
comparison can be made in terms of the acquired ratio. For example, A health product manufacturing company surveyed to identify the level of obesity in a particular locality. It released the following survey questionnaire: Select a category to which your weight belongs to: Less than 40 kilograms 40-59 Kilograms 60-79 Kilograms 80-99 Kilograms 100-119 Kilograms 120 Kilograms and more The following table will better clarify the difference between all the four primary scaling techniques: PARTICULAR NOMINAL ORDINAL INTERVAL RATIO Characteristics SCALE SCALE SCALE SCALE Description, Description Order Distance Order, Distance and Sequential Not Applicable Applicable Applicable Origin Arrangement Applicable Not Applicable Not Applicable Fixed Zero Point Not Applicable Not Applicable Not Applicable Applicable Applicable Multiplication and Not Applicable Not Applicable Applicable Division Applicable Non-Measurable Measurable Addition and Not Applicable Measurable Not Applicable Applicable Subtraction Applicable Applicable Applicable Applicable Applicable Applicable Difference Non-Measurable Applicable between Variables Mean Not Applicable Median Not Applicable Mode Applicable 99 CU IDOL SELF LEARNING MATERIAL (SLM)
Other Scaling Techniques Scaling of objects can be used for a comparative study between more than one objects (products, services, brands, events, etc.). Or can be individually carried out to understand the consumer’s behavior and response towards a particular object. Following are the two categories under which other scaling techniques are placed based on their comparability: Figure 9.2 IMPORTANT SCALING TECHNIQUES a. Comparative Scales For comparing two or more variables, a comparative scale is used by the respondents. Following are the different types of comparative scaling techniques: b. Paired Comparison A paired comparison symbolizes two variables from which the respondent needs to select one. This technique is mainly used at the time of product testing, to facilitate the consumers with a comparative analysis of the two major products in the market. To compare more than two objects say comparing P, Q and R, one can first compare P with Q 100 CU IDOL SELF LEARNING MATERIAL (SLM)
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