satisfaction, or an interview with a subject matter expert of quantum theory can give you in-depth information on that topic. F ocus groups: Focus group is yet another widely used method in exploratory research. In such a method a group of people is chosen and are allowed to express their insights on the topic that is being studied. Although, it is important to make sure that while choosing the individuals in a focus group they should have a common background and have comparable experiences. For example: A focus group helps a research identify the opinions of consumers if they were to buy a phone. Such a research can help the researcher understand what the consumer value while buying a phone. It may be screen size, brand value or even the dimensions. Based on which the organization can understand what are consumer buying attitudes, consumer opinions, etc. O bservations: Observation research can be qualitative observation or quantitative observation. Such a research is done to observe a person and draw the finding from their reaction to certain parameters. In such a research, there is no direct interaction with the subject. For example: An FMCG company wants to know how its consumer react to the new shape of their product. The researcher observes the customers first reaction and collects the data, which is then used to draw inferences from the collective information. b. S econdary research methods S econdary research is gathering information from previously published primary research. In such a research you gather information from sources likes case studies, magazines, newspapers, books, etc. Online research: In today’s world, this is one of the fastest ways to gather information on any topic. A lot of data is readily available on the internet and the researcher can download it whenever he needs it. An important aspect to be noted for such a research is the genuineness and authenticity of the source websites that the researcher is gathering the information from. For example: A researcher needs to find out what is the percentage of people that prefer a specific brand phone. The researcher just enters the information he needs in a search engine and gets multiple links with related information and statistics. 51 CU IDOL SELF LEARNING MATERIAL (SLM)
L iterature research: Literature research is one of the most inexpensive method used for discovering a hypothesis. There is tremendous amount of information available in libraries, online sources, or even commercial databases. Sources can include newspapers, magazines, books from library, documents from government agencies, specific topic related articles, literature, Annual reports, published statistics from research organizations and so on. However, a few things have to be kept in mind while researching from these sources. Government agencies have authentic information but sometimes may come with a nominal cost. Also, research from educational institutions is generally overlooked, but in fact educational institutions carry out more number of researches than any other entities. Furthermore, commercial sources provide information on major topics like political agendas, demographics, financial information, market trends and information, etc. For example: A company has low sales. It can be easily explored from available statistics and market literature if the problem is market related or organization related or if the topic being studied is regarding financial situation of the country, then research data can be accessed through government documents or commercial sources. C ase study research: Case study research can help a researcher with finding more information through carefully analyzing existing cases which have gone through a similar problem. Such analyses are very important and critical especially in today’s business world. The researcher just needs to make sure he analyses the case carefully in regards to all the variables present in the previous case against his own case. It is very commonly used by business organizations or social sciences sector or even in the health sector. For example: A particular orthopedic surgeon has the highest success rate for performing knee surgeries. A lot of other hospitals or doctors have taken up this case to understand and benchmark the method in which this surgeon does the procedure to increase their success rate. 5.4.2 Exploratory research: Steps to conduct a research I dentify the problem: A researcher identifies the subject of research and the problem is addressed by carrying out multiple methods to answer the questions. C reate the hypothesis: When the researcher has found out that there are no prior studies and the problem is not precisely resolved, the researcher will create a hypothesis based on the 52 CU IDOL SELF LEARNING MATERIAL (SLM)
questions obtained while identifying the problem. F urther research: Once the data has been obtained, the researcher will continue his study through descriptive investigation. Qualitative methods are used to further study the subject in detail and find out if the information is true or not. 5.4.3 Characteristics of Exploratory research T hey are not structured studies I t is usually low cost, interactive and open ended. I t will enable a researcher answer questions like what is the problem? What is the purpose of the study? And what topics could be studied? T o carry out exploratory research, generally there is no prior research done or the existing ones do not answer the problem precisely enough. I t is a time consuming research and it needs patience and has risks associated with it. T he researcher will have to go through all the information available for the particular study he is doing. T here are no set of rules to carry out the research per se, as they are flexible, broad and scattered. T he research needs to have importance or value. If the problem is not important in the industry the research carried out is ineffective. T he research should also have a few theories which can support its findings as that will make it easier for the researcher to assess it and move ahead in his study 53 CU IDOL SELF LEARNING MATERIAL (SLM)
S uch a research usually produces qualitative data, however in certain cases quantitative data can be generalized for a larger sample through use of surveys and experiments. 5.4.4 Advantages of Exploratory research T he researcher has a lot of flexibility and can adapt to changes as the research progresses. I t is usually low cost. I t helps lay the foundation of a research, which can lead to further research. I t enables the researcher understand at an early stage, if the topic is worth investing the time and resources and if it is worth pursuing. I t can assist other researchers to find out possible causes for the problem, which can be further studied in detail to find out, which of them is the most likely cause for the problem. 5.4.5 Disadvantages of Exploratory research E ven though it can point you in the right direction towards what is the answer, it is usually inconclusive. T he main disadvantage of exploratory research is that they provide qualitative data. Interpretation of such information can be judgmental and biased. M ost of the times, exploratory research involves a smaller sample, hence the results cannot be accurately interpreted for a generalized population. M any a times, if the data is being collected through secondary research, then there is a chance of that data being old and is not updated. 54 CU IDOL SELF LEARNING MATERIAL (SLM)
5.4.6 Importance of Exploratory research Exploratory research is carried out when a topic needs to be understood in depth, especially if it hasn’t been done before. The goal of such a research is to explore the problem and around it and not actually derive a conclusion from it. Such kind of research will enable a researcher to set a strong foundation for exploring his ideas, choosing the right research design and finding variables that actually are important for the analysis. Most importantly, such a research can help organizations or researchers save up a lot of time and resources, as it will enable the researcher to know if it worth pursuing. 5.5 DESCRIPTIVE RESEARCH DESIGNS - CONCEPT, TYPES AND USES Descriptive research is defined as a research method that describes the characteristics of the population or phenomenon studied. This methodology focuses more on the “what” of the research subject than the “why” of the research subject. The descriptive research method primarily focuses on describing the nature of a demographic segment, without focusing on “why” a particular phenomenon occurs. In other words, it “describes” the subject of the research, without covering “why” it happens. For example, an apparel brand that wants to understand the fashion purchasing trends among New York buyers will conduct a demographic survey of this region, gather population data and then conduct descriptive research on this demographic segment. The study will then uncover details on “what is the purchasing pattern of New York buyers,” but not cover any investigative information about “why” the pattern exits. Because for the apparel brand trying to break into this market, understanding the nature of their market is the study’s objective. 5.5.1 Characteristics of descriptive research The term descriptive research then refers to research questions, design of the study, and data analysis conducted on that topic. We call it an observational research method because none of the research study variables are influenced in any capacity. Some distinctive characteristics of descriptive research are: Q uantitative research: Descriptive research is a quantitative research method that attempts to collect quantifiable information for statistical analysis of the population sample. It is a popular market research tool that allows us to collect and describe the demographic segment’s nature. 55 CU IDOL SELF LEARNING MATERIAL (SLM)
U ncontrolled variables: In descriptive research, none of the variables are influenced in any way. This uses observational methods to conduct the research. Hence, the nature of the variables or their behavior is not in the hands of the researcher. C ross-sectional studies: Descriptive research is generally a cross-sectional study where different sections belonging to the same group are studied. The basis for further research: Researchers further research the data collected and analyzed from descriptive research using different research techniques. The data can also help point towards the types of research methods used for the subsequent research. 5.5. 2 Applications of descriptive research with examples A descriptive research method can be used in multiple ways and for various reasons. Before getting into any survey, though, the survey goals and survey design are crucial. Despite following these steps, there is no way to know if one will meet the research outcome. How to use descriptive research? To understand the end objective of research goals, below are some ways organizations currently use descriptive research today: D efine respondent characteristics: The aim of using close-ended questions is to draw concrete conclusions about the respondents. This could be the need to derive patterns, traits, and behaviors of the respondents. It could also be to understand from a respondent, their attitude, or opinion about the phenomenon. For example, understanding from millennials the hours per week they spend on browsing the internet. All this information helps the organization researching to make informed business decisions. M easure data trends: Researchers measure data trends over time with a descriptive research design’s statistical capabilities. Consider if an apparel company researches different demographics like age groups from 24-35 and 36-45 on a new range launch of autumn wear. If one of those groups doesn’t take too well to the new launch, it provides insight into what clothes are like and what is not. The brand drops the clothes and apparel that customers don’t like. C onduct comparisons: Organizations also use a descriptive research design to understand how different groups respond to a specific product or service. For example, an apparel brand 56 CU IDOL SELF LEARNING MATERIAL (SLM)
creates a survey asking general questions that measure the brand’s image. The same study also asks demographic questions like age, income, gender, geographical location, etc. This consumer research helps the organization understand what aspects of the brand appeal to the population and what aspects do not. It also helps make product or marketing fixes or even creates a new product line to cater to high growth potential groups. V alidate existing conditions: Researchers widely use descriptive research to help ascertain the research object’s prevailing conditions and underlying patterns. Due to the non-invasive research method and the use of quantitative observation and some aspects of qualitative observation, researchers observe each variable and conduct an in-depth analysis. Researchers also use it to validate any existing conditions that may be prevalent in a population. C onduct research at different times: The analysis can be conducted at different periods to ascertain any similarities or differences. This also allows any number of variables to be evaluated. For verification, studies on prevailing conditions can also be repeated to draw trends. Descriptive research methods There are three distinctive methods to conduct descriptive research. They are: a. O bservational method The observational method is the most effective method to conduct this research, and researchers make use of both quantitative and qualitative observations. A quantitative observation is the objective collection of data, which is primarily focused on numbers and values. It suggests “associated with, of or depicted in terms of a quantity.” Results of quantitative observation are derived using statistical and numerical analysis methods. It implies observation of any entity associated with a numeric value such as age, shape, weight, volume, scale, etc. For example, the researcher can track if current customers will refer the brand using a simple Net Promoter Score question. Qualitative observation doesn’t involve measurements or numbers but instead just monitoring characteristics. In this case, the researcher observes the respondents from a distance. Since the respondents are in a comfortable environment, the characteristics observed are natural and effective. In a descriptive research design, the researcher can choose to be either a complete observer, an observer as a participant, a participant as an observer, or a full participant. For example, in a 57 CU IDOL SELF LEARNING MATERIAL (SLM)
supermarket, a researcher can from afar monitor and track the customers’ selection and purchasing trends. This offers a more in-depth insight into the purchasing experience of the customer. b. C ase study method Case studies involve in-depth research and study of individuals or groups. Case studies lead to a hypothesis and widen a further scope of studying a phenomenon. However, case studies should not be used to determine cause and effect as they can’t make accurate predictions because there could be a bias on the researcher’s part. The other reason why case studies are not a reliable way of conducting descriptive research is that there could be an atypical respondent in the survey. Describing them leads to weak generalizations and moving away from external validity. c. S urvey research In survey research, respondents answer through surveys or questionnaires or polls. They are a popular market research tool to collect feedback from respondents. A study to gather useful data should have the right survey questions. It should be a balanced mix of open-ended questions and close ended-questions. The survey method can be conducted online or offline, making it the go-to option for descriptive research where the sample size is enormous. Examples of descriptive research Some examples of descriptive research are: A specialty food group launching a new range of barbecue rubs would like to understand what flavors of rubs are favored by different people. To understand the preferred flavor palette, they conduct this type of research study using various methods like observational methods in supermarkets. By also surveying while collecting in-depth demographic information, offers insights about the preference of different markets. This can also help tailor make the rubs and spreads to various preferred meats in that demographic. Conducting this type of research helps the organization tweak their business model and amplify marketing in core markets. Another example of where this research can be used is if a school district wishes to evaluate teachers’ attitudes about using technology in the classroom. By conducting surveys and observing their comfortableness using technology through observational methods, the researcher can gauge what they can help understand if a full-fledged implementation can face an issue. This also helps in understanding if the students are impacted in any way with this change. Some other problems and research questions that can lead to descriptive research are: 58 CU IDOL SELF LEARNING MATERIAL (SLM)
M arket researchers want to observe the habits of consumers. A company wants to evaluate the morale of its staff. A school district wants to understand if students will access online lessons rather than textbooks. T o understand if its wellness programs enhance the overall health of the employees. 5.5. 3 Advantages of descriptive research Some of the significant advantages of descriptive research are: D ata 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. V aried: Since the data collected is qualitative and quantitative, it gives a holistic understanding of a research topic. The information is varied, diverse, and thorough. N atural 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. Q uick to perform and cheap: As the sample size is generally large in descriptive research, the data collection is quick to conduct and is inexpensive. 5.6 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 59 CU IDOL SELF LEARNING MATERIAL (SLM)
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 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 60 CU IDOL SELF LEARNING MATERIAL (SLM)
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 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, 61 CU IDOL SELF LEARNING MATERIAL (SLM)
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. 5.7 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 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. 5.8 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. 62 CU IDOL SELF LEARNING MATERIAL (SLM)
This design has three parts of the research: I nception of the issue D iagnosis of the issue S olution 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 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. 63 CU IDOL SELF LEARNING MATERIAL (SLM)
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. 5.9 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 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 64 CU IDOL SELF LEARNING MATERIAL (SLM)
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). 5.10 KEYWORDS C onstructionism - 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. C onstructivism - A theory about how people learn – where they ask questions and find answers via exploration and assessment of what they already know. C onstructs - 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. C ontent 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. 65 CU IDOL SELF LEARNING MATERIAL (SLM)
5.11 LEARNING ACTIVITY 1. P repare two or five-sentence paragraph indicating the type of research study you are going to do and justifying your choice. _________________________________________________________________________________ _________________________________________________________________________________ 2. D iscuss the Exploratory Research Design. For Types And Uses _________________________________________________________________________________ _________________________________________________________________________________ 5.12 UNIT END QUESTIONS (MCQ AND DESCRIPTIVE) A. Descripti ve Questions Discuss 1. Describe Exploratory Designs Explain 2. State the Diagnostic Research Design Outline 3. the features of a good research design 4. importance of research design 5. the concept of dependent & independent variables B. Multiple Choice Questions (MCQs) The 1. Explorato population census carried out by the GOI is an example of: Casual a. Descripti ry research b. 66 research c. ve research CU IDOL SELF LEARNING MATERIAL (SLM)
d. Diagnosti c 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. Explorato ry research b. Casual research c. Descripti ve research d. Diagnosti c research 4. ________ _____ is defined as a research used to investigate a problem which is not clearly defined. a. Explorato ry research b. Casual research c. Descripti ve research d. Diagnosti c research 5. ________ ___ the researcher is looking to evaluate the underlying cause of a specific topic or phenomenon. a. Explorato ry research b. Casual research 67 CU IDOL SELF LEARNING MATERIAL (SLM)
c. Descripti ve research Diagnosti d. c research Answers: (c) 1. 3. (c) 4. (a) 5. (d) 2. (a) 5.13 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 Education Asia. 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. 68 CU IDOL SELF LEARNING MATERIAL (SLM)
Yates, E (1960), “Sampling Methods for Censuses and Surveys,” Charles Griffin & Company, Ltd., London. 69 CU IDOL SELF LEARNING MATERIAL (SLM)
UNIT-6 SAMPLING DESIGN Structure 6.0. Learning Objectives 6.1. Introduction 6.2. Sampling design concepts 6.3. Population versus sample 6.4. Sampling design process 6.5. Summary 6.6. Keywords 6.7. Learning Activity 6.8. Unit End Questions (Mcq And Descriptive) 6.9. References 6.0 LEARNING OBJECTIVES After studying this Unit, you will be able to: State the concepts of sampling design Explain the sampling design process 6.1 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. 6.2 SAMPLE DESIGN CONCEPTS 1. U niverse/Population: From a statistical point of view, the term ‘universe’ refers to the total of the items or units in any 70 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. S ampling 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. S ampling 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. 71 CU IDOL SELF LEARNING MATERIAL (SLM)
4. S tatistic(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 parameter, 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. S ampling 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. P recision: 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. C onfidence 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. 72 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. 6.3 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. S ampling involves selecting the observations that you will analyze. T o conduct sampling, a researcher starts by going where your participants are. S ampling frames can be real or imaginary. R ecruitment involves informing potential participants about your study and seeking their participation. 73 CU IDOL SELF LEARNING MATERIAL (SLM)
Table 6.1 POPULATION VERSUS SAMPLE 6.4 SAMPLING DESIGN PROCESS A researcher should take into consideration the following aspects while developing a sample design: 1) T ype 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) S ampling 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 74 CU IDOL SELF LEARNING MATERIAL (SLM)
choose one or more of such units for his/her study. 3) S ource 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) S ize 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) P arameters Of Interest: The specific population parameters of interest should also be considered while determining the 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) B udgetary 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) S 75 CU IDOL SELF LEARNING MATERIAL (SLM)
ampling 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. 6.5 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. 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. 76 CU IDOL SELF LEARNING MATERIAL (SLM)
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. 6.6 KEYWORDS Evaluatio n 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. Explanat ory 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. 6.7 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 _________________________________________________________________________________ _________________________________________________________________________________ 6.8 UNIT END QUESTIONS (MCQ AND DESCRIPTIVE) A. Descripti ve Questions 77 CU IDOL SELF LEARNING MATERIAL (SLM)
1. Explain the stages of the sample design process Compare Discuss 2. Outline population & sample State the 3. Multiple the concepts of Population A smaller Sample 4. Case the concepts of sampling frame Unit Data 5. Sampling concept of sampling design Six Nine B. Seven Choice Questions (MCQs) Five 1. Which is representation of a large whole is called as Defining a. Specifyin Specifyin b. c. 78 d. 2. process generally consists of ------ number of steps. a. b. c. d. 3. the first step in sampling process? a. the population b. g the sampling frame c. g sampling unit CU IDOL SELF LEARNING MATERIAL (SLM)
d. Specifyin g sampling method A list 4. Sampling containing all sampling units is known as _______________ Sampling a. Populatio frame None of b. design ________ c. Sampling n Sampling d. Populatio these None of 5. 2. (c) ___ is determined before any data is collected. a. Donald, frame Business b. Gupta, design c. 79 n d. these Answers: 1. (a), 3. (a) 4. (b) 5. (b) 6.9 REFERENCES R. Cooper & Pamela S. Schindler (2014). Research Methods. New Delhi: Tata McGraw-Hill Publishing Co. Ltd. S.C. (2010). Fundamentals of Statistics. 6th Ed. Mumbai: HPH. CU IDOL SELF LEARNING MATERIAL (SLM)
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 Education Asia. 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 80 CU IDOL SELF LEARNING MATERIAL (SLM)
UNIT-7 UNDERSTANDING OF QUALITATIVE AND QUANTITATIVE RESEARCH Structure 7.0. Learning Objectives 7.1. Introduction 7.2. Difference between Qualitative and Quantitative research 7.3. Concept of generalization, replication 7.4. Merging the two approaches 7.5. Summary 7.6. Keywords 7.7. Learning Activity 7.8. Unit End Questions (Mcq And Descriptive) 7.9. References 7.0 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 7.1 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. 81 CU IDOL SELF LEARNING MATERIAL (SLM)
7.2 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 Analyzed by summarizing, analysis categorizing and interpreting Mainly expressed in numbers, graphs and Mainly expressed in words tables 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 7.3 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 82 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) 7.4 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. E xplanatory 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 83 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. E xploratory 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. C onvergent 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. 84 CU IDOL SELF LEARNING MATERIAL (SLM)
Figure 7.3 Convergent Parallel Design 7.5 SUMMARY U ltimately, 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. Q ualitative 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. Q ualitative 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 85 CU IDOL SELF LEARNING MATERIAL (SLM)
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). Q uantitative 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 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. 7.6 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 Populatio n- the cluster of people about whom a researcher is most interested Recruitm ent- 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 7.7 LEARNING ACTIVITY 1. Give examples of qualitative research _________________________________________________________________________________ _________________________________________________________________________________ 86 CU IDOL SELF LEARNING MATERIAL (SLM)
2. Differenti ate between Qualitative and Quantitative Research _________________________________________________________________________________ _________________________________________________________________________________ 7.8 UNIT END QUESTIONS (MCQ AND DESCRIPTIVE) A. Descripti ve Questions Compare Explain 1. Discuss quantitative & qualitative research 2. the Explanatory Sequential Design 3. the Exploratory Sequential Design B. Multiple Choice Questions (MCQs) 1) Uniting various qualitative methods with quantitative methods can be called as…….. a. Coalesce b. Triangula tion c. Bipartite d. Impassiv e 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 87 CU IDOL SELF LEARNING MATERIAL (SLM)
3) ________ ___ design emphasizes quantitative analysis, which we follow with interviews or observation (qualitative measures) to help explain the quant findings a. Explanat ory b. Explorato ry c. Converge nt 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. Explanat ory b. Explorato ry c. Converge nt 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. Explanat ory b. Explorato ry c. Converge nt Parallel d. None of these Answers: 88 CU IDOL SELF LEARNING MATERIAL (SLM)
1. (b) 2. (d) 3. (a) 4. (b) 5. (c) 7.9 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. 89 CU IDOL SELF LEARNING MATERIAL (SLM)
UNIT-8 COLLECTION OF DATA Structure Learning Introducti 8.0. Tools Objectives Prima 8.1. Summary on Keyword Learning 8.2. Unit End and techniques of data collection Reference 8.3. ry and secondary data 8.3.1. Methods of Collecting Primary Data 8.3.2. Methods of Collecting Secondary Data 8.4. 8.5. s 8.6. Activity 8.7. Questions (Mcq And Descriptive) 8.8. s 8.0 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 8.1 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 90 CU IDOL SELF LEARNING MATERIAL (SLM)
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 (a) p urpose and scope of enquiry, (b) a vailability of time, (c) a vailability of finance, (d) a ccuracy required, (e) s tatistical tools to be used, (f) s ources of information (data), and (g) M ethod of data collection. 8.2 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 91 CU IDOL SELF LEARNING MATERIAL (SLM)
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. I NTERVIEW 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. S tructured 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. S emi-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. U nstructured 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 I n-depth information F reedom of flexibility A ccurate data. 92 CU IDOL SELF LEARNING MATERIAL (SLM)
Cons T ime-consuming E xpensive to collect. W hat are the best Data Collection Tools for Interviews? F or collecting data through interviews, here are a few tools you can use to easily collect data. 2. A udio 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. D igital 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. C amcorder 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. Q UESTIONNAIRES This 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. Questionnaires are designed to collect data from a group. 93 CU IDOL SELF LEARNING MATERIAL (SLM)
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 C an be administered in large numbers and is cost-effective. I t can be used to compare and contrast previous research to measure change. E asy to visualize and analyze. Q uestionnaires offer actionable data. R espondent identity is protected. Q uestionnaires can cover all areas of a topic. R elatively 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 94 CU IDOL SELF LEARNING MATERIAL (SLM)
responses, they can also be used as a form of data collection. 6. R EPORTING 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. Pros I nformed decision making. E asily accessible. Cons S elf-reported answers may be exaggerated. T he results may be affected by bias. R espondents may be too shy to give out all the details. I naccurate 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. N GO 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 95 CU IDOL SELF LEARNING MATERIAL (SLM)
often focus on development projects which are organized to promote particular causes. b. N ewspapers 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. W ebsite 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. H ospital 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 accurate, but must be obtained under the legal means as medical data is kept with the strictest regulations. 7. E XISTING 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 A ccuracy is very high. E asily accessible information. Cons 96 CU IDOL SELF LEARNING MATERIAL (SLM)
P roblems with evaluation. D ifficulty 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: R esearch 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. S urveys - 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. 8. O BSERVATION 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 E asy to administer. T here subsists a greater accuracy with results. I t is a universally accepted practice. 97 CU IDOL SELF LEARNING MATERIAL (SLM)
I t diffuses the situation of an unwillingness of respondents to administer a report. I t is appropriate for certain situations. Cons S ome phenomena aren’t open to observation. I t cannot be relied upon. B ias may arise. I t is expensive to administer. I ts 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: C hecklists - 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. D irect 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. 9. F OCUS GROUPS 98 CU IDOL SELF LEARNING MATERIAL (SLM)
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 I nformation obtained is usually very detailed. C ost-effective when compared to one-on-one interviews. I t reflects speed and efficiency in the supply of results. Cons L acking depth in covering the nitty-gritty of a subject matter. B ias might still be evident. R equires interviewer training T he researcher has very little control over the outcome. A few vocal voices can drown out the rest. D ifficulty 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: T 99 CU IDOL SELF LEARNING MATERIAL (SLM)
wo-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. D ueling-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. 10. C OMBINATION 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 E ncourage participants to give responses. I t stimulates a deeper connection between participants. T he relative anonymity of respondents increases participation. I t improves the richness of the data collected. Cons I t costs the most out of all the top 7. I t'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 100 CU IDOL SELF LEARNING MATERIAL (SLM)
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