Assignment Code: MPC-005 / ASST / TMA / 2022-23

Course Code: MPC-005

Assignment Name: Research Methods

Year: 2022 -2023

Verification Status: Verified by Professor

Note: All Questions Are Compulsory

Section A

Answer the following question in about 1000 words each. 15x3=45Marks

Q 1. Explain the factorial design with the help of a suitable example.

Ans) A design is said to have a factorial design if it makes use of two or more variables or factors in such a way that each and every possible combination of the values chosen for each variable is put to use. This type of design can be used to test a variety of hypotheses. According to Singh, a factorial design is one in which the values of two or more independent variables are manipulated in all possible combinations in order to study their independent and interactive effects on the dependent variable.

This is done in order to determine the optimal way to conduct the research. This is done so that the most effective method of carrying out the research can be determined. A factorial design is one that, according to the definition that was just provided, is one in which two or more independent variables are manipulated in every possible combination. This affords the experimenter the opportunity to investigate not only the independent effects of two or more independent variables, but also the interactive effects of these variables.

Examples of Factorial Design

In order to get a basic understanding of factorial designs, it is probably easiest to start by looking at an example. Take for example a plan in which we have an educational programme and want to test out a number of different versions of the programme to determine which one works the best. For instance, we would like to change the amount of time that each group of children spends receiving instruction so that, for example, one group spends one hour per week receiving instruction while another group spends four hours per week receiving instruction.

In addition to this, we would like to change the environment by having one group receive instruction in the classroom (most likely in one of the classroom's corners), while the other group receives instruction in a separate room. In order to complete this task, we could split up into four different groups; however, when adjusting the amount of time spent on instruction, we would either work in the main classroom or in a pull-out session.

When it comes to finding answers to these questions using factorial designs, we are not required to make any concessions. We can have it both ways if we combine each of our two conditions regarding the amount of time spent on instruction with each of our two settings. In factorial designs, an important independent variable is denoted by the term "factor." In this particular illustration, we are working with two variables: the instruction time and the setting. A factor can be broken down into multiple levels. In this particular illustration, the time allotted for instruction and the setting each have two distinct levels. A factorial design is often represented by a numbering notation, though this is not always the case.

In this particular scenario, we have a factorial arrangement known as a 2 x 2 (pronounced "two-by-two") layout. In this notation, the number of factors is indicated by the total number of numbers, while the total number of levels is indicated by the total number of number values. If I told you I had a design with a 3 x 4 factorial layout, you'd understand that it consisted of two factors, one of which had three levels, and the other of which had four.

There is no significance to the order in which the numbers appear, and we could just as easily refer to this configuration as a 4 x 3 factorial design. Multiplying through the number notation is an easy way to determine the number of unique treatment groups present in any factorial design. In this particular illustration, for instance, we have two sets of two, which equals four groups. In our notational example, 3 x 4 = 12 groups are required.

In design notation, a factorial design can also be represented as an alternative representation. Because there are multiple treatment level combinations, it is helpful to have subscripts on the treatment symbol (X). According to the figure, there are four different groups, one for each possible combination of factor levels. It is also abundantly clear that the groups were assigned based solely on the results of the posttest, and that this design only considers those results.

An educational institution is interested in determining the starting salaries of their MBA graduates. The focus of the study is on recent college graduates who have found work in the following fields: accounting, management, finance, and marketing. The researchers consider both gender and employment sector in their studies, in addition to employment sector. In this particular illustration, the starting salaries of the graduates are the dependent variables, while the type of industry in which the graduates are employed, and the graduates' gender are the independent variables. This kind of design would be known as a 4x2 factorial layout.

Researchers are interested in determining whether or not the amount of sleep an individual gets the night before a math test affects that individual's performance on the math test the following day. However, the researchers are also aware that many people find it helpful to have one or two cups of coffee first thing in the morning to get their day started.

Therefore, the researchers make the decision to investigate the relationship between the amount of sleep consumed and the amount of caffeine consumed before taking the test. The researchers then come to the conclusion that they should look at only two levels of caffeine consumption and three different amounts of sleep (4 hours, 6 hours, and 8 hours) (2 cups versus no coffee). In this particular instance, the research utilises a 3 by 2 factorial layout.

Summary

Factorial design has several important features. First, it has great flexibility for exploring or enhancing the “signal” (treatment) in our studies. Whenever we are interested in examining treatment variations, factorial designs should be strong candidates as the designs of choice. Second, factorial designs are efficient. Instead of conducting a series of independent studies we are effectively able to combine these studies into one. Finally, factorial designs are the only effective way to examine interaction effects.

Q 2. Explain the assumptions, theories, and steps of discourse analysis.

Ans) Discourse analysis is the study of how discourses - which can be found in texts and spoken language - shape the social world. Discourse analysis is a qualitative research method that examines how language is used in social contexts. Discourse analysis, which is concerned with the creation of meaning through talk and texts, provides insights into how language works to "shape and reproduce social meanings and forms of knowledge."

Assumptions of Discourse Analysis

Discourse analysis is based on three theoretical assumptions that are grounded in social constructivism, which emphasises sociocultural interactions as sources of knowledge:

1. First, scientific and positivist researchers believe that knowledge cannot be gained through pure objectivity. When conducting research, a researcher brings his or her own set of beliefs, cultural values, expectations, subjectivity, and bias into the study: A researcher recognises his or her own beliefs and recognises how his or her own personal, cultural, and historical experiences shape his or her interpretations of reality and knowledge.

2. Second, reality is constructed socially and culturally. Unlike scientific approaches, which categorise reality, ideas, or constructs (e.g., intelligence and attitudes) as naturally occurring things, social constructivist or interpretive approaches shape these categories and constructs by language, and because language is a sociocultural phenomenon, our sense of reality is socially and culturally constructed. These varied and multiple realities lead researchers to seek the complexity of perspectives rather than reducing meanings to a few categories or ideas. The goal of research, then, is to provide insights into participants' various points of view and perspectives, as well as how these points of view and perspectives are socially and historically negotiated.

3. Third, a researcher interested in social constructivism is more interested in studying language (discourse) and the role it plays in the construction of meaning and knowledge in society. As a result, the emphasis of such research is on the discursive patterns of talk in societies, their impact on the formation and reproduction of social meanings and identities, and their role in empowering and disempowering institutions and individuals.

Theories of Discourse Analysis

Discourse analysis is a qualitative research method that examines how language is used in social contexts. The three key underlying assumptions are that knowledge cannot be gained through pure objectivity, reality is socially and culturally constructed, and people emerge from social interaction. There are three types of discourse theories: modernism, structuralism, and postmodernism.

The various approaches to discourse are as follows:

1. Modernism

Modern theorists were concerned with progress and believed in the existence of natural and social laws that could be universally applied to develop knowledge and, as a result, a better understanding of society. They were preoccupied with discovering the truth and reality, and they sought to develop theories that were certain and predictable. As a result, they saw discourse as being relative to talking or speaking style, and they understood discourse to be functional.

2. Structuralism

According to structuralist theorists like Ferdinand de Saussure and Jacques Lacan, all human actions and social formations are related to language and can be understood as systems of related elements. It is the structure itself that determines the significance, meaning and function of the individual elements of a system. Saussure's theory of language emphasises the crucial role of meaning and signification in the overall structure of human life.

3. Postmodernism

Postmodern theory arose in response to the perceived limitations of the modern era. Modernist claims that there was a single theoretical approach that explained all aspects of society were rejected by postmodern theorists. They were interested in investigating the diverse experiences of individuals and groups, emphasising differences over similarities and shared experiences.

Postmodern theorists sought answers for how truths are produced and sustained rather than seeking answers for truth itself. As a result, they began analysing discourses such as texts, language, policies, and practises.

Steps of Discourse analysis

The nature of the supplies that are available and how developed research is on the topic or setting of interest influence the nature of the supplies that are available and how developed research is on the topic or setting of interest. Discourse analysis consists of four major steps: hypothesis generation, coding, analysis, and validation.

Generating hypotheses: The first stage of discourse research is the formulation of more specific questions or hypotheses, as well as the observation of intriguing or troubling phenomena. This open-ended approach to data is common and productive in group sessions where a number of researchers listen to a segment of interaction and explore different ways of understanding what is going on.

Coding: The primary goal of coding is to simplify analysis by sifting relevant materials from a larger corpus. It entails searching materials for interesting phenomena and copying the instances to an archive. Often, phenomena that appeared disparate at first merge, while phenomena that appeared singular split into different varieties.

Doing the Analysis: The procedures for justification in discourse research are partially separate from the procedures for arriving at analytical claims. Typically, the research will develop hypotheses about activities through close reading of the materials and then test the adequacy of these hypotheses using a corpus of coded materials.

To determine the significance of these features for the activity at hand, one would do the following:

Pattern search: Examining our corpus to see how regular the patterns are. If such a pattern is not common, our speculation will appear weak.

Consider next turns: The sequential organisation of interaction in discourse work is a powerful resource for understanding what is going on.

Pay attention to outlier cases: These could be ones in which very different question constructions were used, or where unexpected next turns occurred. These types of cases are analytically rich.

Consider other types of materials: There is an infinite number of materials that could be used for comparison.

Validating the analysis: In discourse work, there is no clear distinction between validation procedures and analytical procedures; indeed, some of the analytical themes are also, differently understood, involved in validation. It is always useful in highlighting some of the key elements involved in claim validation.

Q 3. What are the different steps followed for conducting a scientific research?

Ans) If a researcher follows specific steps in conducting research, the work can be done smoothly and easily. Below are the steps:

Step-I: Identification of the Problem

The first and most important step in identifying a problem is to ask a question or identify a need that arises as a result of curiosity and for which an answer is required. The research question determines the direction of the study, and researchers must work hard to identify and articulate it.

Formulating the research problem entails two steps:

1. Thoroughly understanding the problem and

2. Rephrasing it into meaningful terms.

The primary purpose of developing a research problem is to determine what you want to learn. It is critical to assess the research problem in light of the funds, time, expertise, and knowledge at your disposal. It's also critical to identify any gaps in your knowledge of relevant disciplines, such as statistics needed for analysis.

The second step is to identify the factors that must be investigated in order to answer the question. Such factors can range from the most basic, such as a child's age or socioeconomic status, to the most complex, such as the effects of violent cartoons on a child's behaviour. The factors may include the child's age, the level of violence in the programmes, emotional arousal, facial expression, family communication patterns, and so on. The researcher reviews the literature to identify solvable problems. Literature is a body of prior research. Science involves a literature review. When a researcher reviews related research, he learns knowns and unknowns. A literature review helps eliminate duplication and guide and suggest further research.

Literature reviews serve four purposes:

1. First, it identifies conceptually and practically important and unimportant variables in the field. The literature review helps find and select study-relevant variables.

2. Second, the literature review estimates previous work and allows for meaningful extension.

3. Third, a review of the literature helps the researcher in systemising the expanding and growing body of knowledge. This facilitates in drawing useful conclusions regarding the variables under study and provides a meaningful way of their subsequent applications.

4. Fourth, a literature review helps re-define variables and determine their meanings and relationships so the researcher can build a meritorious and applicable case and context for further research. Journals, books, abstracts, indexes, and periodicals provide literature reviews. If you're not sure what journals and other resources to look at for research ideas, PsycINFO is a useful computer search engine. PsycINFO's print companion, psychological abstracts, contains abstracts from nearly all psychological research journals.

Step-II: Formulating a Hypothesis

After identifying the problem and reviewing relevant literature, the researcher forms a hypothesis, or suggested solution. Hypothesis guides any study. Hypotheses are based on previous research, theories, and personal observations and experiences. The psychologist could test one or more prepositions based on relevant theory and previous findings. These hypotheses are ideally based on a deductive theory, but they may be based on previous research.

Hypothesis is a tentative statement indicating a relationship between variables. It's declared. Consider how rewards affect learning. You analysed past research and found a positive relationship between the two variables. Create a testable statement from this idea. Here's a possible hypothesis: Rewardees need fewer trials to learn the lesson than unrewardees. For unbiased research, a hypothesis must be formed before data collection. After data collection, no hypothesis should be made.

Step-III: Identifying, Manipulating and Controlling Variables

Variable is a term used in psychological literature when discussing the hypothesis. Experimenter-manipulated, controlled, and observed characteristics are variables. At the outset, you must distinguish between the dependent, independent, and extraneous variables. The dependent variable is what the experiment predicts. The dependent variable changes as the experimenter changes the independent variables. The independent variable is manipulated by the experimenter to determine its relationship to observed phenomena.

A dependent variable's extraneous variable is uncontrolled. The experimenter isn't interested in extraneous variable changes, so he tries to control it. Relevant variable is extraneous variable. Operationally defining a variable makes it clear, precise, and easy to communicate. An operational definition specifies a variable's operations. Measurement requires operational definition. Complex psychological variables pose measurement challenges. Psychologists love operational definitions. In their studies, they often use verbal, behavioural, and psychological variables to specify operations and allow quantification.

Step-IV: Formulating a Research Design

A research design is the researcher's blueprint for testing the relationship between the dependent and independent variables. The experimental design chosen depends on the purpose of the research, the variables to be controlled and manipulated, and the experimental conditions. The main purpose of experimental design is to help the researcher manipulate the independent variables freely and control the extraneous variables so that the experimental change is due only to the experimental variable.

Research designs explain how to answer research questions. Your research design explains its logic. A research design should include logistics, measurement procedures, a sampling strategy, an analysis frame, and a time frame. Choosing the right research design is crucial for valid findings, comparisons, and conclusions in any investigation. Incorrect design can produce misleading results. The research design is used to evaluate empirical research. Valid, workable, and manageable research designs must be chosen.

Step-V: Data Analysis and their Interpretation

After observations, data are analysed using quantitative/statistical and qualitative methods. Scientific method requires careful data analysis. The analysis interprets the data and draws conclusions about the problem and hypotheses of the study. Data analysis can be done using univariate analysis, bivariate analysis, and multivariate analysis, which involves more than two characteristics.

Statistical analysis uses parametric or non-parametric statistics depending on the data and experiment. Statistical analysis is used to reject the null hypothesis and accept the alternative. After observations, data are analysed using quantitative/statistical and qualitative methods. Scientific method requires careful data analysis. The analysis interprets the data and draws conclusions about the problem and hypotheses of the study. Data analysis can be done using univariate analysis, bivariate analysis, and multivariate analysis, which involves more than two characteristics.

Statistical analysis uses parametric or non-parametric statistics depending on the data and experiment. Statistical analysis is used to reject the null hypothesis and accept the alternative.

Step-VI: Drawing Conclusions

Following analysis, the investigator draws conclusions. The researcher wants to make a statement about the research problem without doing his research. The researcher generalises any conclusion. This phase accepts or rejects hypotheses. The study's conclusions are related to the hypotheses' underlying theory or research. New findings may require modifying the original theory.

Step-VII: Preparation of Report and Publication

Most research ends here. All research steps are clearly documented. This report details what you've done, found, and concluded. You'll know how to write your report if you understand the process. This helps readers understand and use the study. It enables replication. Publication in scientific journals or books and in the public domain spreads research.

Section B

Answer the following questions in about 400 words each. 5x5=25Marks

Q 4. Type of Quasi Experimental Designs.

Ans) Many types of quasi experimental designs have specific applications. Some important quasi-experimental designs are:

1. Non-Equivalent Group, Posttest only Design

Non-equivalent, posttest-only designs involve giving an outcome measure to two groups or a program/treatment group and a comparison. One group of students may use a whole language programme while the other uses phonetics. After 12 weeks, a reading comprehension test can determine which programme is better.

2. Non-Equivalent Control Group Design

This design compares a control and experimental group. The groups are assigned for convenience, not randomization. This design's problem is comparing experimental and control group results. We're studying the effect of special training programmes on 10th graders' GPAs. The school wouldn't let the experimenter regroup classes for a random sample.

3. The Separate Pretest -Posttest Sample Design

The basic idea in this design is that the people we use for the pretest are not the

same as the people we use for posttest. Here non-equivalence exists not only between the organisations but that is within organisation the pre and post groups are non-equivalent.

4. The Double Pre-Test Design

This is a valid quasi-experimental design. In pre-post non-equivalent group design, non-equivalent groups may be different before the programme is given, and we may attribute post-test differences to the programme. The pre-test helps assess pre-program similarity, but it doesn't tell us if the groups change at similar rates.

5. The Switching Replications Design

Switching Replications is a strong quasi-experimental design. It may improve external validity or generalisability because it allows two independent programme implementations. Two groups and three measurement phases are used. Both groups are pretested, one is given the programme, and both are post-tested.

6. Mixed Factorial Design with one Non-Manipulated Variable

Experiments explain this design. Edmund Keogh and Gerke Witt hypothesise that caffeine affects men's and women's pain perception differently. In two sessions, 25 men and 25 women participated. In one session, participants drank caffeinated coffee and in the other, decaf. In both sessions, participants put their non-dominant hand in ice water to find the pair.

7. Interrupted Time-Series Designs

These research designs compare the same group over time by analysing data trends before and after experimental manipulation. One group of subjects is pre- and post-tested at different intervals. In a time, series design, the number of pre- and post-tests can vary from one to many to determine long-term treatment effects. Sometimes tests are spaced out to assess treatment over time.

8. Multiple Time Series Design

In this design, one group is treated and the other is the control group. Complex settings with many events and trends can affect behaviour. Adding a comparison group with the same series of measures, but not exposed to the treatment being studied, can clarify the relationship between the treatment and any change in behavioural measures.

Q 5. Research Biases.

Ans) When conducting research, one of the most difficult challenges is to maintain objectivity and avoid being influenced by one's own biases. Because they are shaped by your perspectives and ideals, the vast majority of your thoughts and convictions are probably tainted with some form of prejudice. It has been discovered that people's perceptions of data collected can be distorted due to a variety of biases.

People can be swayed to accept a particular perspective on the world by factors outside of themselves, such as their culture or the media. Estimation and evaluation procedures can be skewed by personal bias if the estimator or evaluator is influenced by their own beliefs, characteristics, or experiences in the past.

The phenomenon known as observer bias occurs when certain events are interpreted as meaningful by some people but not by other people. It is essential to keep in mind that the researchers themselves were influenced by the cultures and societies of their upbringing. Additionally, it is possible that they were made aware of particular gender role expectations. All of these contextual elements have the potential to influence how researchers observe and make sense of the happenings in their own lives.

The effect of expectation bias on observations of behaviour can be seen in the way that it encourages reactions to the events that are being observed. If researchers go into a project with preconceived notions about the results they will uncover, it's possible that they will only see what they anticipate finding rather than maintaining an objective stance. Unfortuitously, if one is not aware of the possibility of expectancy bias, it may appear as though the observed events are being "discovered," rather than created by the observer's expectations. This is because expectancy bias is a form of confirmation bias.

When people have a strong desire to believe a treatment is successful, they are more likely to be affected by placebo biases. For instance, a large number of individuals may assert that they feel better after consuming a placebo, such as a sugar pill. The desire for a drug or therapeutic method to work may be enough to achieve the desired result in situations where the outcome involves a subjective judgement about results, such as how well a person feels or whether the pain has been reduced or relieved. In these instances, the outcome involves a subject's ability to evaluate the results.

Q 6. Distinguish between field and experimental research design.

Ans) Experimentation is commonly used in science subjects such as physics, chemistry, medicine, and biology. One independent variable and one dependent variable are required for the experiment. It is critical in experimental research to manipulate the independent variable and observe the effect of manipulation on the dependent variable. The laboratory completely controls all other extraneous factors.

Field Experiments

Field experiments, on the other hand, are experiments conducted in real-world settings. Extraneous factors cannot be controlled in this setting because it is a natural setting, and there is no way to control any factor as completely as in laboratory experiments. As a result, in field experiments, we take two groups that are matched for a variety of factors such as age, gender, education, socioeconomic status, and so on. Both of these groups are in real-life settings and thus are subjected to similar extraneous variables, allowing the experimenter to compare the effects of his manipulation on one group to the effects of no intervention on the other group.

The table below compares lab and field experiments:

Q 7. Types of questions that can be used in survey research.

Ans) The type of questions chosen affects the entire research project. Structured and unstructured survey questions are common. Explained below:

1. Structured Questions

Structured questions are closed survey questions that elicit fast, precise answers while reducing participant thinking. These questions reduce the researcher's workload because the answers are easy to analyse. Multiple structured questions can reveal a lot of information quickly while still allowing for an in-depth survey.

Structured questions have a predetermined format. Structured questions include:

a) Dichotomous questions

Belonging to the closed-ended family of questions, dichotomous questions are ones that only offer two possible answers, which are typically presented to survey takers in the following format – Yes or No, True or False, Agree or Disagree and Fair or Unfair. By narrowing down the answer options that are available in this way, dichotomous questions are a great way of clarifying opinion or understanding on something, with recipients providing answers that are absolute – either one way or another.

b) Level of measurement-based questions

Levels of measurement, also called scales of measurement, tell you how precisely variables are recorded. In scientific research, a variable is anything that can take on different values across your data set (e.g., height or test scores). Not to be mentioned that three basic levels of measurement are: nominal (based on names, classification of persons, objects, and groups), ordinal (based on ranks and preferences) and interval (based on ratings) measurements.

c) Filter or Contingency Questions

Questions that are limited to a subset of respondents for whom they are relevant are called "contingency questions." Relevancy is sometimes based on a respondent characteristic such as gender or age. For example, it is typical to ask only women of childbearing age if they are currently pregnant; conversely, only men are asked if they have ever had a prostate cancer screening examination. Other times, questions are asked only of those that engage in a certain activity or hold a certain opinion about an issue. When a question is framed in such a way that it is followed by succeeding questions, which are sub parts of the main question, such types of question design is known as filter or contingency questions.

2. Unstructured Questions

Unstructured questions are used in qualitative research and face-to-face interviews because conversation flows more naturally. Open-ended questions can be used in phone interviews. Unstructured questions are used in interviews when the researcher does not prepare a list of questions and the series of questions depends on the subject's response or they ask questions in an informal setting. To get needed information, the researcher should silently probe, verbally encourage, ask for clarification, and show empathy for the respondent.

Q 8. Strategies of interpreting data in qualitative research.

Ans) Analysis, also known as interpretation, is the process of deconstructing and reconstructing evidence through purposeful interrogation and critical thinking about data in order to produce a meaningful interpretation and relevant understanding in response to the questions asked or that arise during the investigation process. To begin developing answers to our research questions, we need systematic, rigorous, and transparent data manipulation methods when analysing qualitative data. We must also keep meticulous records of the steps we took to conduct our analysis in order to communicate this process to readers and reviewers.

The data can be interpreted and summarised in numerous ways. These are some examples: Only a few of them exist, and they are as follows:

1. Making a final list;

2. Creating elaborate narratives; and

3. Using matrices.

The researcher may choose to summarise and code the labelled categories before preparing a final list of findings, which can be further explained in the subsequent stage of the report-writing process.

Elaborate Narratives

Based on the data gathered through interviews, recordings, and discussion, the researcher can also give meaning to the findings or elaborate on them.

Use of Matrices

A matrix is a type of chart that looks like a cross table and is filled with words. Matrixes may be used by researchers to compare several different groups or data sets on significant variables presented in key words.

If a researcher wants to compare the number of girls and boys at a school who use a cosmetic product made by a specific company, he might present the data in the form of the matrix below so that he can make the comparison.

Table: Matrix indicating age group and gender:

Flow chart

A flow chart is a diagram that contains boxes that contain variables and arrows that indicate the relationships between these variables. The data can be represented in the same way that we analysed the number of boys and girls of various age groups who used the product in the preceding example:

Figure: Matrix on introduction of a cosmetic product among students of different age groups

Section C

Answer the following in about 50 words each. 10x3=30Marks

Q 9. Difference between causal comparative and experimental research design

Ans) In experimental design, researchers randomly select a sample and divide it into groups. The study involves treating groups. In causal comparative design, participants were already assigned to groups before the study began. In experimental research, the researcher manipulates the independent variable, whereas in causal comparative research, the groups are already different on the independent variable.

Q 10. Definition of research design.

Ans) When conducting research, a definite pattern or plan of action is followed throughout the procedure, i.e., from problem identification to report preparation and presentation. The term "research design" refers to this specific pattern or plan of action. It is a road map that directs the researcher's data collection and analysis. In other words, research design serves as a guideline that must be followed throughout the research process.

Q 11. Significance of hypothesis formulation.

Ans) Science relies on hypotheses. If a simple, brief, and clear scientific hypothesis is formulated, the researcher can proceed. Its research utility and importance are below. Goode and Hatt say that without a hypothesis, research is unfocused and random. The results aren't clear facts. Hypothesis formulation links theory and investigation to increase knowledge.

Q 12. Meaning of reliability.

Ans) The concept of repeatability states that any significant results must be repeatable. Reliability is an essential component for determining the overall validity of a scientific experiment and strengthening the results. The consistency of your measurement, or the degree to which an instrument measures the same way each time it is used under the same conditions with the same subjects, is referred to as reliability.

Q 13. Method of snowball sampling.

Ans) This sampling method entails a primary data source nominating additional potential data source who will be able to participate in the research studies. The snowball sampling method is entirely based on referrals, which is how a researcher generates a sample. As a result, this method is also known as the chain-referral sampling method.

Q 14. Difference between independent and dependent variable.

Ans) The term "independent variable" refers to a variable that represents the hypothesised "cause," and it is a variable that is precisely controlled by the experimenter and is independent of what the participant does.

The dependent variable is a variable whose values ultimately depend on the values of the independent variable. It is a variable that represents the effect that is being hypothesised.

Q 15. Relevance of grounded theory.

Ans) Grounded theory is a systematic methodology that has been widely used in qualitative social science research. The methodology entails the development of hypotheses and theories through data collection and analysis. Inductive reasoning is used to develop grounded theory. The methodology differs from the traditional hypothetico-deductive model used in scientific research.

Q 16. Meaning of ethnography.

Ans) Ethnography is a subfield of anthropology that focuses on the systematic study of individual cultures. Ethnography investigates cultural phenomena from the perspective of the study's subject. Ethnography is another type of social research that entails examining the behaviour of participants in a given social situation and comprehending the group members' interpretation of such behaviour.

Q 17. Criteria for selecting a case study.

Ans) We frequently use information-oriented sampling to select cases for case studies. Our cases are based solely on this information, which is mostly based on extreme or typical cases. The average case is not always the most information rich. A typical or extreme case reveals more information because it activates more basic mechanisms and more actors in the situation under study.

Q 18. Concept of cross-sectional survey research design.

Ans) A cross-sectional study is a type of observational study in which data is collected from a specific population at a specific point in time. Cross-sectional research studies are a type of descriptive research that collects data from different groups. This type of research cannot be used to define cause and effect relationships between variables because it is a snapshot in time. Furthermore, in cross-sectional studies, researchers do not influence the variables of the study but simply observe them.IN