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MPC005/ASST/TMA/2014-15

IGNOU Assignment

IGNOU MAPC material © 2015, M S Ahluwalia Psychology Learners

Research Methods in

Psychology

Solved Assignment - MAPC

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1000 words

Section A

3

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Explain the factorial design with the

help of a suitable example.

Q1.

4

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Factorial design

5

A1

Traditional research methods generally study the effect of one variable at a time, because it is statistically easier to manipulate. However, in many cases, two factors may be interdependent, and it is impractical or false to attempt to analyse them in the traditional way. By far the most common approach to including multiple independent variables in an experiment is the factorial design. In a factorial design, each level of one independent variable (which can also be called a factor) is combined with each level of the others to produce all possible combinations. Each combination, then, becomes a condition in the experiment. There is an interaction effect (or just “interaction”) when the effect of one independent variable depends on the level of another. Imagine, for example, an experiment on the effect of cell phone use (yes vs. no) and time of day (day vs. night) on driving ability. Let there be a table with 2 columns and 2 rows. The columns of the table represent cell phone use, and the rows represent time of day. The four cells of the table represent the four possible combinations or conditions: using a cell phone during the day, not using a cell phone during the day, using a cell phone at night, and not using a cell phone at night. This particular design is a 2 × 2 (read “two-by-two”) factorial design because it combines two variables, each of which has two levels. If one of the independent variables had a third level (e.g., using a handheld cell phone, using a hands-free cell phone, and not using a cell phone), then it would be a 3 × 2 factorial design, and there would be six distinct conditions. Notice that the number of possible conditions is the product of the numbers of levels. A 2 × 2 factorial design has four conditions, a 3 × 2 factorial design has six conditions, 4 × 5 factorial design would have 20 conditions, and so on. In principle, factorial designs can include any number of independent variables with any number of levels. For example, an experiment could include the type of psychotherapy (cognitive vs. behavioral), the length of the psychotherapy (2 weeks vs. 2 months), and the sex of the psychotherapist (female vs. male). This would be a 2 × 2 × 2 factorial design and would have eight conditions.

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Factorial design contd.

6

A1

In practice, it is unusual for there to be more than three independent variables with more than two or three levels each because the number of conditions can quickly become unmanageable. For example, adding a fourth independent variable with three levels (e.g., therapist experience: low vs. medium vs. high) to the current example would make it a 2 × 2 × 2 × 3 factorial design with 24 distinct conditions. Types of factorial designs In a between-subjects factorial design, all of the independent variables are manipulated between subjects. For example, all participants could be tested either while using a cell phone or while not using a cell phone and either during the day or during the night. This would mean that each participant was tested in one and only one condition. In a within-subjects factorial design, all of the independent variables are manipulated within subjects. All participants could be tested both whie using a cell phone and while not using a cell phone and both during the day and during the night. This would mean that each participant was tested in all conditions. The advantages and disadvantages of these two approaches are: · between-subjects design is conceptually simpler, avoids carryover effects, and minimizes the time and effort of each participant. · within-subjects design is more efficient for the researcher and controls extraneous participant variables. It is also possible to manipulate one independent variable between subjects and another within subjects. This is called a mixed factorial design. For example, a researcher might choose to treat cell phone use as a within-subjects factor by testing the same participants both while using a cell phone and while not using a cell phone (while counterbalancing the order of these two conditions). But he or she might choose to treat time of day as a between-subjects factor by testing each participant either during the day or during the night (perhaps because this only requires them to come in for testing once). Thus each participant in this mixed design would be tested in two of the four conditions.

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Factorial design contd.

7

A1

Regardless of whether the design is between subjects, within subjects, or mixed, the actual assignment of participants to conditions or orders of conditions is typically done randomly. Pros and Cons of Factorial Design Factorial designs are extremely useful to psychologists as a preliminary study, allowing them to judge whether there is a link between variables, whilst reducing the possibility of experimental error and confounding variables. The factorial design, as well as simplifying the process and making research cheaper, allows many levels of analysis. As well as highlighting the relationships between variables, it also allows the effects of manipulating a single variable to be isolated and analyzed singly. The main disadvantage is the difficulty of experimenting with more than two factors, or many levels. A factorial design has to be planned meticulously, as an error in one of the levels, or in the general operationalization, will jeopardize a great amount of work.

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Summary and Sources

8

A1

Factorial design research method is a mainstay of many scientific disciplines, delivering great results in the field. It enables the researcher to manipulate and control two or more independent variables simultaneously. Therefore, it enables the researcher to study the combined effect of these independent variables. There are three types of factorial designs – between subject factorial designs (all independent variables are modified between subjects), within subject factorial designs (all independent variables are modified within subjects) and mixed factorial design (some independent variables are modified within and some between subjects). While factorial design makes it cheaper to a multi-level, multi-variable analysis, the research could become extremely complicated. And, even one error could impact the results of the entire analyses. Sources: https://explorable.com/factorial-design http://www.saylor.org/site/textbooks/Introduction%20to%20Psychology.pdf

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What are the different steps

followed for conducting a scientific

research?

Q2.

9

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Steps followed for conducting a scientific research

10

A2

Scientific research involves a systematic process that focuses on being objective and gathering a multitude of information for analysis so that the researcher can come to a conclusion. The scientific research process is a multiple-step process where the steps are interlinked with the other steps in the process. If changes are made in one step of the process, the researcher must review all the other steps to ensure that the changes are reflected throughout the process. Step 1: Identify the Problem The first step in the process is to identify a problem or develop a research question. The research problem may be something the researcher identifies as a problem or some knowledge or information that is to be developed. This serves as the focus of the study. Step 2: Forming a Hypothesis The next step of a psychological investigation is to identify an area of interest and develop a hypothesis that can then be tested. A hypothesis can be defined as an educated guess about the relationship between two or more variables. For example, a researcher might be interested in the relationship between study habits and test anxiety. They would then propose a hypothesis about how these two variables are related, such as "test anxiety decreases as a result of effective study habits." In order to form a hypothesis, you must start by collecting as many observations about something as you can. Next, it is important to evaluate these observations and look for possible causes of the problem. Create a list of possible explanations that you might want to explore. After you have developed some possible hypotheses, it is important to think of ways that you could confirm or disprove each hypothesis through experimentation.

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Steps followed for conducting a scientific research contd.

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A2

Step 3: Defining variables A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. The researcher must also define exactly what each variable is using i.e., how the variable will be manipulated and measured in the study. In our previous example, a researcher might operationally define the variable 'test anxiety' as the results on a self-report measure of anxiety experienced during an exam. The variable ‘study habits’ might be defined by the amount of studying that actually occurs as measured by time. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed. Step 4: Develop the Research Design The plan for the study is referred to as the research design. It serves as the road map for the entire study, specifying who will participate in the study; how, when, and where data will be collected; and the content of the program. It is composed of numerous decisions and considerations. It specifies all the steps that must be completed for the study. This ensures that the researcher has carefully thought through all these decisions and that he provides a step-by-step plan to be followed in the study. Step 5: Finalise research method and construct device There are two basic types of research methods—descriptive research and experimental research. 1. Descriptive Research Methods: Descriptive research such as case studies, naturalistic observations and surveys are often used when it would be impossible or difficult to conduct an experiment. 2. Experimental Research Methods: Experimental methods are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

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Steps followed for conducting a scientific research contd.

12

A2

Step 6a: Select sample and collect Data Sample selection and collection of data are critical steps in providing the information needed to answer the research question. Every study includes the collection of some type of data—whether it is from the literature or from subjects—to answer the research question. Data can be collected in the form of words on a survey, with a questionnaire, through observations, or from the literature. Once the data are collected on the variables, the researcher is ready to move to the next step of the process, which is the data analysis. Step 6b: Analyze the Data In the research design, the researcher specified how the data will be analyzed. Using statistics, the researcher analyzes the data according to the research design. The results of this analysis are then reviewed and summarized in a manner directly related to the research questions. Step 7: Interpretation and Conclusion Once a researcher has analysed the data, it is time to examine this information (interpret it) and draw conclusions about what has been found. Conclusions are drawn based on the evidence generated by analysis. Not only can analysis support (or refute) the researcher’s hypothesis; it can also be used to determine if the findings are statistically significant. When results are said to be statistically significant, it means that it is unlikely that these results are due to chance.

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Steps followed for conducting a scientific research

contd., Summary and Sources

13

A2

Step 8: Reporting and publishing the findings The final step in a psychology study is to report the findings. This is often done by writing up a description of the study and publishing the article in an academic or professional journal. The results of psychological studies can be seen in peer-reviewed journals such as Psychological Bulletin, the Journal of Social Psychology, Developmental Psychology, and many others. Why is such a detailed record of a psychological study so important? By clearly explaining the steps and procedures used throughout the study, other researchers can then replicate the results. The editorial process employed by academic and professional journals ensures that each article that is submitted undergoes a thorough peer review, which helps ensure that the study is scientifically sound. Once published, the study becomes another piece of the existing puzzle of our knowledge base on that topic. * * * Conducting research using the steps of the scientific research process requires time and effort to the planning process. However, it ensures that the results gain acceptance from the scientific community and that the research is replicable by others in the future, whether hypothesis is proved or disproved. There are eight steps in the process viz. identifying the problem, forming the hypothesis, defining variables, developing the research design, finalising research methods, selecting sample and collecting data, analysing the data, interpreting the data and drawing conclusions from it, and, reporting and publishing the results. While all steps are important and should be performed diligently, hypothesis formulation and developing the research design are of utmost criticality. Sources: http://www.humankinetics.com/excerpts/excerpts/steps-of-the-research-process http://psychology.about.com/od/researchmethods/a/form-a-hypothesis.htm http://psychology.about.com/od/researchmethods/a/data-collection.htm http://psychology.about.com/od/researchmethods/a/drawing-conclusions.htm http://psychology.about.com/od/researchmethods/a/reporting-results.htm

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Explain the assumptions, theories

and steps of discourse analysis.

Q3.

14

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Discourse analysis

15

A3

Discourse analysis involves an ‘analysis of the ways in which discourses – which can be read in texts and talk – constitute the social world (Mason, 2006). Discourse analysis is a qualitative research method that investigates the use of language in social contexts. Concerned with the creation of meaning through talk and texts, discourse analysis provides insights into the way language works to help “shape and reproduce social meanings and forms of knowledge” (Tonkiss, 2012, p. 403). Assumptions of Discourse Analysis Grounded in social constructivism, which emphasizes the sociocultural interactions as sources of knowledge, discourse analysis is based on the three theoretical assumptions (Potter,1996) : 1. First, knowledge cannot be gained by pure objectivity as scientific and positivist researchers believe it can. A researcher brings his or her own set of beliefs, cultural values, expectations, subjectivity and bias into the study when conducting his or her research: A researcher recognizes his or her own beliefs, and acknowledges how these beliefs influenced by his or her own personal, cultural, and historical experiences shape his or her interpretations of reality and knowledge. 2. Second, reality is socially and culturally constructed. Unlike scientific approaches in which reality, ideas, or constructs (e.g. intelligence & attitudes) are categorized as naturally occurring things, in social constructivist or interpretive approaches, these categories and constructs are shaped by the language and since language is a sociocultural phenomenon, our sense of reality is socially and culturally constructed. These realities which are often varied and multiple lead researchers to look for the complexity of the views rather than reduce meanings into a few categories or ideas. The goal of research, then, is to give insights into the different views and perspectives of participants and how these views and perspectives are socially and historically negotiated. 3. Third, in social constructivism, a researcher is more interested in studying the language (discourse) and the role it plays in construction of meaning and knowledge in society. As such, the emphasis of such research is placed on the discursive patterns of talk in societies, their impact on the formation and reproduction of social meanings and identities as well as their role in empowering and disenfranchising institutions and individuals.

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Theories of Discourse Analysis

16

A3

The various approaches to discourse are as follows: Modernism Modern theorists were focused on achieving progress and believed in the existence of natural and social laws which could be used universally to develop knowledge and thus a better understanding of society. They were preoccupied with obtaining the truth and reality and sought to develop theories which contained certainty and predictability. They, therefore, viewed discourse as being relative to talking or way of talking and understood discourse to be functional. Structuralism Structuralist theorists, such as Ferdinand de Saussure and Jacques Lacan, argue that 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 highlights the decisive role of meaning and signification in structuring human life more generally. Postmodernism Following the perceived limitations of the modern era, emerged postmodern theory. Postmodern theorists rejected modernist claims that there was one theoretical approach that explained all aspects of society. They were interested in examining the variety of experience of individuals and groups and emphasized differences over similarities and common experiences. Postmodern theorists shifted away from truth seeking and instead sought answers for how truths are produced and sustained. They, therefore, embarked on analyzing discourses such as texts, language, policies and practices.

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Steps of Discourse analysis

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A3

Analysis in discourse research is highly varied and depends to some extent on the nature of the supplies that are available and how developed on the nature of the materials that are available and how developed research is on the topic or setting of interest. The following are the four stages that are overlapping but broadly distinct. 1. Generating hypotheses: The first part of the discourse research is the generation of more specific questions or hypothesis or the noticing of intriguing or troubling phenomena. It is common and productive to continue this open-ended approach to the data in group sessions where a number of researchers listen to a segment of interaction and explore different ways of understanding what is going on. 2. Coding: The building of collection. The main aim of coding is to make the analysis more straightforward by sifting relevant materials from larger corpus. It involves searching materials for some phenomena of interest and copying the instances to an archive. Often phenomena that were initially seen as disparate merge while phenomena that seemed singular become broken into different varieties. 3. Doing the Analysis: In discourse research the procedures for justification are partly separate from the procedure for arriving at analytical claims. The research will typically develop conjectures about activities through a close reading of the materials and then check the adequacy of these hypotheses through working with a corpus of coded materials. To establish the relevance of these features for the activity being done, one would do a number of things: a. Search for patterns: Looking through our corpus to see how regular pattern is. If such a pattern is not common, then our speculation will start to look weak. b. Consider next turns: In discourse work the sequential organization of interaction is a powerful resource for understanding what is going on. c. Focus on deviant cases: These might be ones in which very different question constructions were used; or where surprising next turns appeared. Such cases are analytically rich. d. Focus on other kinds of material: There is an infinite set of alternative materials that might be used for comparison.

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Steps of Discourse analysis contd., Summary and

Sources

18

A3

4. Validating the analysis: There is no clear cut distinction between validation procedures and analytical procedures in discourse work; indeed some of the analytical themes are also differently understood, involved in validation. It is always useful in highlighting some of the major elements involved in validating claims. * * * Discourse analysis is a qualitative research method that investigates the use of language in social contexts. The three key underlying assumptions are knowledge cannot be gained by pure objectivity, reality is socially and culturally constructed and people are the result of social interaction. There are three different views of discourse viz. Modernism, Structuralism and Post-modernism. Discourse analysis is composed of four major steps – generating hypothesis, coding, doing the analysis and validating the analysis. Sources: http://en.wikipedia.org/wiki/Discourse_analysis http://en.wikiversity.org/wiki/Discourse_analysis http://discourseanalysis-interviews.weebly.com/discourse-analysis.html http://en.wikipedia.org/wiki/Discourse

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400 words

Section B

19

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Type of Quasi Experimental

Designs.

Q4.

20

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Types of Quasi Experimental Designs

21

A4

A quasi-experiment is one that applies an experimental interpretation to results that do not meet all the requirements of a true experiment. It means when the situation is such that the experimenter has some control over the manipulation of independent variables but fails to arrange for the other basic requirement of a true experiment, which is, creating equivalent groups. The various types of Quasi-experimental designs are listed below: Time-series design When a control group or comparison group cannot be included in an experiment because of the situation in which the experiments is being conducted, but the experimenter wants to have a design which may exercise a better control over the extraneous variables time series design is used. Equivalent time –samples design It is an extension of the time-series design with the repeated introduction of the treatment or the experimental variable. Like the time series design, a single group is used and the group is exposed to repeated treatments in some systematic way. Non-equivalent control group design Except for random assignment of subjects to the experimental and the control conditions, which occur in case of the pretest-posttest control group design, this is identical to it. Counterbalanced design In counterbalanced design experimental control is achieved by randomly applying experimental treatments. Such designs are called crossover designs, switch-over designs and rotation experiments. A counterbalancing design in which four treatments have been randomly given to four groups on four different occasions.

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Types of Quasi Experimental Designs contd.

22

A4

Separate-Sample Pretest-Posttest Design It is suited to situations where experimenter cannot assign treatments to all subjects at a time. Hence, he is forced to select a sample and administer the treatment. Then, again another sample is taken and the same treatment is repeated. Patched-up design The experimenter starts with an inadequate design and then, adds some features so that recurrent factors producing invalidity may be maximally controlled. The patched-up design, shown below is a combination of two different pre-experimental designs, neither of which is adequate in itself, but which become adequate when combined. Longitudinal design In longitudinal design the researcher usually measures a group of subjects in order to observe the effect of passage of time. In fact, such designs are confounded by extraneous events that occur during the study and they may not generalize over time. Cross-section design Cross-sectional design is a between subjects quasi-experiment in which the researcher observes the subjects at different ages or at different points in temporal sequence. Thus, the researcher may select a cross-section of ages, testing the vocabulary of a group of 5 yr olds, another group of 6 yr olds and so on. Cohort design In a cohort design the researcher conducts a longitudinal study of several groups, each from a different generation.

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Summary and Sources

23

A4

There are a number of different techniques of quasi-experimental research some of which have been discussed above. Depending on the particular situation and factors like data availability, a researcher may choose the technique that appears to be best. Sources: Tests, Measurements and Research Methods in Behavioural Sciences, AK Singh

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Research Biases.

Q5.

24

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Research Biases

25

A5

Bias is the distortion of results by a variable. Some of the common biases that may impact the results of a research study are: Sampling Bias Sampling bias occurs when the sample studied in an experiment does not correctly represent the population the researcher wants to draw conclusions about. Example: A psychologist wants to study the eating habits of a population of New Yorkers who are between eighteen and forty-five years old. Because she can’t study the entire group she’d have to take a sample. However, she can generalize her results to the whole population only if her sample is representative of the population. If this is not the case, her sample will reflect sampling bias. Subject Bias Research subjects’ expectations can affect and change the subjects’ behavior, resulting in subject bias. Such a bias can manifest itself in two ways: • Placebo effect: It is the effect on a subject receiving a fake drug or treatment. Placebo effects occur when subjects believe they are getting a real drug or treatment even though they are not. A single-blind experiment is an experiment in which the subjects don’t know whether they are receiving a real or fake drug or treatment. Single-blind experiments help to reduce placebo effects. • Social desirability bias: It is the tendency of some research subjects to describe themselves in socially approved ways. It can affect self-report data or information people give about themselves in surveys.

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Research Biases contd.

26

A5

Experimenter Bias Experimenter bias occurs when researchers’ preferences or expectations influence the outcome of their research. In these cases, researchers see what they want to see rather than what is actually there. It includes Expectancy Bias, Personal Bias and Observer Bias. A method called the double-blind procedure can help experimenters prevent this bias from occurring. In a double-blind procedure, neither the experimenter nor the subject knows which subjects come from the experimental group and which come from the control group. Following is a list of various biases in psychology by category: 1) Cognitive bias: Confirmation bias, Negative bias, Gender bias, Anchoring bias, Memory bias, Overconfidence effect, Positive outcome bias, Optimism bias, Attentional bias. 2) Social bias: Actor-observer bias, hindsight bias, Egocentric bias, Notational bias, Outgroup homogeneity bias, Projection bias, Self-serving bias, Trait ascription bias, cultural bias, correspondence bias. 3) Research bias: Social desirability bias, Measurement bias, Experimental bias, Design bias, Quantitative research bias, Qualitative research bias, Selection bias, Systematic bias, Choice-supportive bias Confirmation bias, Congruence bias, Distinction bias, Information bias, Omission bias, Outcome bias, Status quo bias, Unit bias, Zero-risk bias, Subject bias.

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Summary and Sources

27

A5

When the result of a research is distorted due to some factor we say it is biased. Research biases are of many types – the common ones are sample bias, subject bias (includes placebo effect and social desirability effect) and experimenter bias (includes expectancy bias and observer bias). There are various other kinds of biases as listed above. Some of the ways to control biases include simple-blind and double-blind experiments. Sources: http://www.psychwiki.com/wiki/What_are_the_types_of_bias%3F http://www.sparknotes.com/psychology/psych101/researchmethods/section3.rhtml

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Distinguish between field and

experimental research design.

Q6.

28

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Difference between field and experimental research

29

A6

An experiment is a systematic way of proving or disproving a hypothesis and used to determine a cause and effect relationship between the subject and its environment. Key differences between experimental research and field study are: Setting Experimental research is done in a closed setting or in a highly controlled environment. This lets researchers conduct full replication of the experiment in other laboratories. Field experiment, on the other hand, takes place in the “real” world (natural settings). Validity Laboratory experiments provide the ability to control the environment and confounding variables resulting in good internal validity. Due to the artificial nature of laboratory experiments the results may not be a true reflection of the wider population, i.e., external validity is lower. Field experiments take place in the participant’s natural environment and consequently are easier to generalise (higher external validity). However, this comes at the cost of internal validity as less control may lead to confounding variables impacting the dependent variable. Reliability By standardising experimental procedures the results of experimental research are more likely to be reliable. Random allocation of participants involves randomly assigning the sample to either to a controlled or experimental group, allowing for a direct comparison of conditions. This can reduce the impact of individual differences and also make it easier to investigate whether the IV directly impacts the DV.

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Difference between field and experimental research

contd.

30

A6

Procedure Experimental research involves random allocation of homogeneous sample to a controlled or experimental group. The experimental group is then subject to variation in independent variable, and the effects are observed. Field experiments do not provide this opportunity. Similar groups are taken and one set is exposed to intervention. The effect on dependent variable is then observed for both. Variables Experimental research: controlling independent variables results in a precise measurement of behaviours and identical conditions for participants. Field experiments can experience inconsistencies with the control group and often have many confounding variables to eliminate. Knowledge of experiment Experimental research: subjects are typically aware of the experiment, even if details are not available to them. The control condition consists of a placebo, which may affect the results. In field experiment, it is possible to conduct a blind study - subjects aren’t aware they are being scrutinized. Uses Social psychologists prefer conducting tests in the field to study subjects’ relationships with their natural surroundings. Experiments in neuro-psychological studies, on the other hand, are better conducted in a laboratory, where a lesser number of set variables are needed to yield a credible result.

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Difference between field and experimental research

contd., Summary and Sources

31

A6

Ethical viewpoint Laboratory experiments are more ethical in comparison to field experiments. For example, a study conducted by Loftus and Palmer (1974) showed participants a film of a car accident to investigate the impact of eyewitness testimony. Participants were not viewing the footage first hand and therefore would find the study less distressing, consequently making the research more ethical. * * * There are various differences between field and experimental research and the researcher should choose based on what suits the situation best. Sources: https://carlymorris.wordpress.com/2012/02/05/laboratory-experiments-vs-field-experiments/ http://www.ehow.com/info_12089230_difference-between-lab-field-experiments.html

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Types of questions that can be

used in a survey research.

Q7.

32

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Types of questions that can be used in a survey

research

33

A7

Questionnaires can be an effective means of measuring the behaviour, attitudes, preferences, opinions and intentions of relatively large numbers of subjects more cheaply and quickly than other methods. Survey questions can be divided into two broad types: 1) Closed questions: They structure the answer by allowing only answers which fit into categories that have been decided in advance by the researcher. a) Dichotomous Questions: Questions having two possible responses such as Yes/No, True/False or Agree/Disagree. b) Questions Based on Level of Measurement: The level of measurement decides how to interpret the data from that variable and the statistical analyses appropriate to use. Following are the various levels of measurement:

i) In nominal measurement the numerical values just "name" the attribute uniquely. No ordering of the cases is implied. Ex: we measure occupation using a nominal question. The number, next to each response has no meaning except as a placeholder for that response. ii) In ordinal measurement the attributes can be rank-ordered. Here, distances between attributes do not have any meaning. Ex: We might ask respondents to rank order their preferences for presidential candidates by putting a 1, 2, 3 or 4 next to the candidate, where 1 is the respondent's first choice. iii) In interval measurement the distance between attributes does have meaning.

(1) Traditional 1-to-5 rating (or 1-to-7, or 1-to-9, etc.)/Likert response scale: Here, we ask an opinion question on a 1-to-5 bipolar scale (there is a neutral point and the two ends of the scale are at opposite positions of the opinion). (2) Semantic differential: Here, an object is assessed by the respondent on a set of bipolar adjective pairs (using 5-point rating scale). (3) Cumulative or Guttman scale: Here, the respondent checks each item with which they agree. The items themselves are constructed so that they are cumulative.

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Types of questions that can be used in a survey

research contd., Summary and Sources

34

A7

Filter or Contingency Questions Sometimes you have to ask the respondent one question in order to determine if they are qualified or experienced enough to answer a subsequent one. This requires using a filter or contingency question. 2) Open Questions: They enable the respondent to answer in as much detail as they like in their own words. Example: “can you tell me how happy you feel right now?” They provide a rich source of qualitative information (i.e. more descriptive than numerical) as there is no restriction to the response. These give no pre-set answer options and instead allow the respondents to put down exactly what they like in their own words. Used for complex questions that cannot be answered in simple categories. However, they are harder to analyse and make comparisons from. * * * Survey research uses open and closed questions to collect quantitative and qualitative data. The closed questions can be categorised as Dichotomous questions or by level of measurement into nominal, ordinal, interval and ratio scales. Filter questions are used to determine if respondent is eligible to answer the following questions. Sources: http://www.socialresearchmethods.net/kb/questype.php http://www.simplypsychology.org/questionnaires.html

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Strategies of interpreting data in a

qualitative research.

Q8.

35

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Strategies of interpreting data in a qualitative research

36

A8

In interpreting qualitative research data the idea is to examine the meaningful and symbolic content within it. This can be done using a deductive (use questions to group data) or inductive approach (look for relationships within the data). Analyzing qualitative data is an eclectic process. There is no single, accepted approach. It involves a simultaneous process while you are also collecting data and the phases are also iterative. The first step involves organizing and transcribing the data. The collected data should be properly managed so that there is no loss of any manner. The second step involves exploring and coding the data. Coding is the process of segmenting and labeling text to form descriptions and broad themes in the data. This is an inductive process of narrowing data into a few themes. The third step involves building descriptions and themes. Description is a detailed rendering of people, places or events in a setting in qualitative research. It is easiest to start the analysis after the initial reading and coding of the data. Themes could be of the following types: 1. Ordinary themes: themes a researcher expects to find. 2. Unexpected themes: themes that are a surprise and were not expected 3. Hard-to-classify themes: themes that contain ideas that do not easily fit into one theme or that overlap with several themes. 4. Major and minor themes: themes that represent the major ideas and the minor, secondary ideas in a database.

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Strategies of interpreting data in a qualitative research

contd.

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A8

The next step involves reporting and representing findings. The data can be represented in the form of: 1. Matrices (including demographic tables) 2. Narratives (including dialogues) 3. Flow charts The next step is the interpretation of findings. Interpretation in qualitative research means that the researcher steps back and forms some larger meaning about the phenomenon based on personal views, comparisons with past studies, or both. This involves the following steps: 1. Summarize findings: general recap of the major findings 2. Convey personal reflections: personal reflections about the meaning of the data 3. Make comparisons to the literature: compare qualitative findings with the literature, or combine personal views with (psychological) concepts or ideas. 4. Offer limitations and suggestions for future research: suggest possible limitations or weaknesses of the study and make recommendations for future research. The final step is to validate the accuracy of the findings. The researcher determines the accuracy or credibility of the findings through the following strategies: 1. Triangulation 2. Member checking 3. External audit

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Summary and Sources

38

A8

Interpreting the data is a critical component of any research project. It can be approached in two ways – the deductive approach where use the data to answer your questions or inductive approach where you look for patterns and relationships within data. The process followed has six steps viz. organizing and transcribing the data, exploring and coding the data, building descriptions and themes, reporting and representing findings, interpretation of findings and to validate the accuracy of the findings. Sources: http://www.slideshare.net/mbakdos/pdu-211-research-methods-analyzing-interpreting-qualitative-data?qid=a39d27c2-fcdc-417a-a526-3f0602e1372c&v=default&b=&from_search=1 http://onlineqda.hud.ac.uk/Intro_QDA/what_is_qda.php

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50 words

Section C

39

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Difference between causal comparative and

experimental research design

40

A9

Causal Comparative or Ex Post Facto Research Design: This research design attempts to explore cause and affect relationships where causes already exist and cannot be manipulated. It uses what already exists and looks backward to explain why. Experimental Research Design: This design is most appropriate in controlled settings such as laboratories. The design assumes random assignment of subjects and random assignment to groups. It attempts to explore cause and affect relationships where causes can be manipulated to produce different kinds of effects. Because of the requirement of random assignment, this design can be difficult to execute in the real world (non-laboratory) setting. The differences in causal-comparison and experimental studies are: CCS: individuals are not randomly selected but selected because they belong to groups ES: individuals are randomly selected and assigned to two (or more) groups CCS: the researcher cannot manipulate the independent variable ES: the researcher manipulates the independent variable CCS: the independent variable has already occurred and cannot be manipulated ES: the researcher manipulates the independent variable to determine its effects CCS: the random sample is selected from two already-existing populations ES: the random sample is selected from a single population * * * Sources: http://www83.homepage.villanova.edu/richard.jacobs/EDU%208603/lessons/causal.ppt http://www.dissertation-statistics.com/research-designs.html

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Definition of research design.

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A10

The term research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. A research design is the arrangement of conditions for collection and evaluation of data in a fashion which is designed to combine relevance to the research purpose with economy in process. The function of a research design is to ensure that the evidence obtained enables us to answer the initial question as unambiguously as possible. Key elements of a good research design include: a. sources and kinds of information strongly related to the research problem. b. strategy indicating which method is going to be employed for collecting and analysing the data. c. the time and cost budgets because most research is done under these two constraints. * * * Sources: http://universalteacher.com/1/elements-of-research-design/ http://www.nyu.edu/pages/classes/bkg/methods/005847ch1.pdf http://libguides.usc.edu/print_content.php?pid=83009&sid=818072

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Significance of hypothesis formulation

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A11

Hypothesis formulation has a very important role in research. Following are the various reasons behind it: 1. It provides a tentative explanation of phenomena and facilitates the extension of knowledge in an area. 2. It provides the investigator with a relational statement that is directly testable in a research study. 3. It provides direction to the research by setting a goal to achieve – proving or disproving the hypothesis. 4. It provides a framework for reporting conclusions of the study. 5. It could be considered as the working instrument of theory. Hypotheses can be deduced from theory and from other hypotheses. 6. It could be tested and shown to be probably supported or not supported, apart from man’s own values and opinions. * * * Sources: http://www.public.asu.edu/~kroel/www500/HYPOTHESIS%20Fri.pdf

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Meaning of reliability.

43

A12

Reliability refers to the repeatability of findings. It refers to consistency: if the results of a test or measurement are reliable, a person should receive a similar score if tested on different occasions.. Ex: When people take a vocabulary test two times, their scores on the two occasions should be very similar. If so, the test can then be described as reliable. Reliability takes the following forms: 1. Test-retest reliability: Similar result obtained when the same test administered twice over a period of time to a group of individuals. 2. Parallel forms reliability: Similar result obtained when different versions of an assessment tool are administered to the same group of individuals. 3. Inter-rater reliability: Similar results are obtained when different judges or raters administer the test. 4. Internal consistency reliability: It is used to evaluate the degree to which different test items that probe the same construct produce similar results. * * * Sources: https://www.uni.edu/chfasoa/reliabilityandvalidity.htm http://psychology.ucdavis.edu/faculty_sites/sommerb/sommerdemo/intro/validity.htm

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Method of snow ball sampling.

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A13

Some populations that we are interested in studying can be hard-to-reach and/or hidden. These include populations such as drug addicts, homeless people, AIDS/HIV patients, prostitutes etc. The populations can be hard-to-reach and/or hidden due to social stigma, illicit or illegal behaviours, or other traits that makes them atypical and/or socially marginalized. Snowball sampling is a type of non-probability sampling technique i.e., the technique is based on the judgement of the researcher, and can be used to gain access to such populations. To create a snowball sample, there are two steps: 1. First, we need to try and find one or more units from the population we are studying. Ex: If we want to study students that take drugs, the aim is to start with whatever small number students, who are willing to participate, even if it’s one or two units. 2. Using these units to find further units and so on until the sample size is met. Ex: The researcher asks the students who participated in the research to help identify other students that may be willing to take part. In this respect, the initial students help to identify additional units that will make up our sample. * * * Source: http://dissertation.laerd.com/snowball-sampling.php

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Difference between independent and dependent variable

45

A14

All experiments examine some kind of variable(s). A variable is not only something that we measure, but also something that we can manipulate and something we can control for. An independent variable, sometimes called an experimental or predictor variable, is a variable that is being manipulated in an experiment in order to observe the effect on a dependent variable, sometimes called an outcome variable. The dependent variable is simply a variable that is dependent on an independent variable(s). Example: A tutor wants to know why some students perform better than others in a Maths test. She thinks that it might be because of two reasons: (1) some students spend more time revising for their test; and (2) some students are naturally more intelligent than others. She decides to investigate the effect of revision time and intelligence on the test performance of the 100 students. The variables for the study would be: • Dependent Variable: Test Marks (from 0 to 100) • Independent Variables: Revision time (in hours) Intelligence (IQ score) * * * Sources: https://statistics.laerd.com/statistical-guides/types-of-variable.php

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Relevance of grounded theory

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A15

In research using grounded theory, researchers start with the data and develop a theory or an interpretation that is “grounded in” those data. They do this in stages. 1. First, they identify ideas that are repeated throughout the data. 2. Then they organize these ideas into a smaller number of broader themes. 3. Finally, they write a theoretical narrative—an interpretation—of the data in terms of the themes that they have identified. Following factors make the grounded theory relevant in Psychological research: • Inductive research: The theory development based on actual data gathered through qualitative research despite the fact that events are processed and interpreted through the eyes of both participant and researcher. • Good reliability and validity: The grounding of theory in data tends to make it more reflective of practical situations than speculatively derived theory (Glaser and Strauss, 1967). • Opportunity to discover new facts: The researcher does not enter the field guided by a predefined theoretical formulation. Therefore, there is an opportunity to discover new facts that may be reflected in the data and study their causality. * * * Sources: Research Methods in the Social Sciences, Bridget Somekh and Cathy Lewin (Click for eBook) http://www.saylor.org/site/textbooks/Research%20Methods%20in%20Psychology.pdf

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Meaning of ethnography.

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A16

“Ethnographic writing” entails the close study of a local community, culture, group, or activity. It is the study of the history, customs, myths, traditions and CULTURE of ethnic groups. Such groups may have in common the same GENE pool, nationality, religion, language or any permutation of these factors. Ethnography is generally carried out by researchers who do not belong to the ethnic community under study but who spend a considerable amount of time within the ethnic community and usually speak the local language. Ethnographers seek to uncover how cultural practices take shape, drawing on an interdisciplinary array of qualitative fieldwork and/or primary research methods—participatory involvement, observation, photography, mapping exercises, interviews, note taking, focus group discussions and then move to analysis, interpretation, and triangulation. Of particular interest to psychologists are: • Clinical ethnography • Person-centered ethnography an approach within Psychological anthropology Two related terms are: Ethnology: The attempt to analyse and understand the ethnic group patterns of behavior described by Ethnography. Ethnomethodology: it is the attempt to focus on people's daily lives and examine their unquestioned assumptions about the social world. Sources: The concise dictionary of Psychology, David Statt (Click for eBook) http://psychology.wikia.com/wiki/Ethnography Encyclopedia of Applied Psychology (Click for eBook) http://psychology.gmu.edu/courses/2408/course_sections/18139

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Criteria for selecting a case study

48

A17

Case study analysis focuses on a small number of cases expected to provide insight into a causal relationship across a larger population of cases. A case selection based on representativeness may not generate revealing insights. Researchers, therefore, prefer information-oriented sampling, as opposed to random sampling, for selecting case-study subjects. Cases can be selected based on three criteria: 1. Key cases: The case might be given and studied with an intrinsic interest in the case itself or the circumstances surrounding it. The researcher has no interest in generalising the findings. He focuses on understanding the case. If findings are generalised, it is done by audiences through “naturalistic generalisation”. 2. Purposefully or analytically selected case: A case may be purposefully selected in virtue of being, for instance, information-rich, critical, revelatory, unique, or extreme (Stake 1995, Patton 1990). In this case, then there may be an interest in generalising the findings. 3. Local knowledge cases: Cases are chosen because of researchers' in-depth local knowledge. This means researchers are in a position to “soak and poke” (Fenno, 1986), and thereby to offer reasoned lines of explanation based on this rich knowledge of setting and circumstances. * * * Sources: http://www.kenbenoit.net/courses/principles/Gerring_Case_Study_Research_Ch5.pdf http://www.psyking.net/htmlobj-3839/case_study_methodology-_rolf_johansson_ver_2.pdf http://en.wikipedia.org/wiki/Case_study

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Concept of cross sectional survey research design.

49

A18

It involves the collection of quantitative data on at least two variables at one point in time (or over a short period) and from a number of cases. The data is used to look for patterns of association or relationships either in the group as a whole (all cases) or in subgroups sharing characteristics or attributes (females or males for example). Advantages of cross-sectional studies 1. Relatively inexpensive and takes up little time to conduct; 2. Can estimate prevalence of outcome of interest as sample is usually taken from the whole population; 3. Many outcomes and risk factors can be assessed; 4. Useful for public health planning, understanding disease aetiology and for the generation of hypotheses; 5. There is no loss to follow-up. Disadvantages of cross-sectional studies 1. Difficult to make causal inference in simple statistical tests of relationships but causal inferences can be made using sophisticated techniques such as regression analyses. 2. Only a snapshot: situation may provide differing results if another time-frame had been chosen; 3. Prevalence-incidence bias (Neyman bias): a risk factor causing death will be under-represented among those with the disease. * * * Sources: http://www.nature.com/ebd/journal/v7/n1/full/6400375a.html Research Methods in the Social Sciences, Bridget Somekh and Cathy Lewin (Click for eBook)

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