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IGNOU MPC 005 Solved Assignment 2022-23
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Important Note – IGNOU MPC 005 Solved Assignment 2022-23 You may be aware that you need to submit your assignments before you can appear for the Term End Exams. Please remember to keep a copy of your completed assignment, just in case the one you submitted is lost in transit.
Submission Date :
- 31st March 2023 (if enrolled in the July 2022 Session)
- 30th Sept, 2023 (if enrolled in the January 2023 session).
SECTION – A
Q1.Explain the factorial design with the help of a suitable example.
Ans. The number of different treatment groups that we have in any factorial design can easily be determined by multiplying through the number notation. For instance, in our example we have 2 x 2 = 4 groups. In our notational example, we would need 3 x 4 = 12 groups. We can also depict a factorial design in design notation.
Probably the easiest way to begin understanding factorial designs is by looking at an example. Let’s imagine a design where we have an educational program where we would like to look at a variety of program variations to see which works best. For instance, we would like to vary the amount of time the children receive instruction with one group getting 1 hour of instruction per week and another getting 4 hours per week. And, we’d like to vary the setting with one group getting the instruction in-class (probably pulled off into a corner of the classroom) and the other group being pulled-out of the classroom for instruction in another room. We could think about having four separate groups to do this, but when we are varying the amount of time in instruction, what setting would we use: in-class or pull-out? And, when we were studying setting, what amount of instruction time would we use: 1 hour, 4 hours, or something else?
With factorial designs, we don’t have to compromise when answering these questions. We can have it both ways if we cross each of our two time in instruction conditions with each of our two settings. Let’s begin by doing some defining of terms. In factorial designs, a factor is a major independent variable. In this example we have two factors: time in instruction and setting. A level is a subdivision of a factor. In this example, time in instruction has two levels and setting has two levels. Sometimes we depict a factorial design with a numbering notation. In this example, we can say that we have a 2 x 2 (spoken “two-by-two) factorial design. In this notation, the number of numbers tells you how many factors there are and the number values tell you how many levels. If I said I had a 3 x 4 factorial design, you would know that I had 2 factors and that one factor had 3 levels while the other had 4. Order of the numbers makes no difference and we could just as easily term this a 4 x 3 factorial design.
Because of the treatment level combinations, it is useful to use subscripts on the treatment (X) symbol. We can see in the figure that there are four groups, one for each combination of levels of factors. It is also immediately apparent that the groups were randomly assigned and that this is a posttest-only design.
Now, let’s look at a variety of different results we might get from this simple 2 x 2 factorial design. Each of the following figures describes a different possible outcome. And each outcome is shown in table form (the 2 x 2 table with the row and column averages) and in graphic form (with each factor taking a turn on the horizontal axis). You should convince yourself that the information in the tables agrees with the information in both of the graphs. You should also convince yourself that the pair of graphs in each figure show the exact same information graphed in two different ways. The lines that are shown in the graphs are technically not necessary – they are used as a visual aid to enable you to easily track where the averages for a single level go across levels of another factor. Keep in mind that the values shown in the tables and graphs are group averages on the outcome variable of interest. In this example, the outcome might be a test of achievement in the subject being taught. We will assume that scores on this test range from 1 to 10 with higher values indicating greater achievement. You should study carefully the outcomes in each figure in order to understand the differences between these cases.
The Null Outcome
Let’s begin by looking at the “null” case. The null case is a situation where the treatments have no effect. This figure assumes that even if we didn’t give the training we could expect that students would score a 5 on average on the outcome test. You can see in this hypothetical case that all four groups score an average of 5 and therefore the row and column averages must be 5. You can’t see the lines for both levels in the graphs because one line falls right on top of the other.
The Main Effects
A main effect is an outcome that is a consistent difference between levels of a factor. For instance, we would say there’s a main effect for setting if we find a statistical difference between the averages for the in-class and pull-out groups, at all levels of time in instruction. The first figure depicts a main effect of time. For all settings, the 4 hour/week condition worked better than the 1 hour/week one. It is also possible to have a main effect for setting (and none for time).
If we could only look at main effects, factorial designs would be useful. But, because of the way we combine levels in factorial designs, they also enable us to examine the interaction effects that exist between factors. An interaction effect exists when differences on one factor depend on the level you are on another factor. It’s important to recognize that an interaction is between factors, not levels. We wouldn’t say there’s an interaction between 4 hours/week and in-class treatment. Instead, we would say that there’s an interaction between time and setting, and then we would go on to describe the specific levels involved.
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.
So far, we have only looked at a very simple 2 x 2 factorial design structure. You may want to look at some factorial design variations to get a deeper understanding of how they work. You may also want to examine how we approach the statistical analysis of factorial experimental designs.
Q2. Explain the assumptions, theories and steps of discourse analysis.
Ans. 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.
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”.
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.
- 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.
Theories of Discourse Analysis
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.
Steps of Discourse analysis
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.
- 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:
- 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. - 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.
IGNOU MPC 005 Solved Assignment 2022-23
3. What are the different steps followed for conducting a scientific research?
SECTION – B
4.Type of Quasi Experimental Designs.
5. Research Biases.
6. Distinguish between field and experimental research design.
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IGNOU MPC 005 Solved Assignment 2022-23
7. Types of questions that can be used in a survey research.
8. Strategies of interpreting data in a qualitative research.
IGNOU MPC 005 Solved Assignment 2022-23
SECTION – C
9. Difference between causal comparative and experimental research design
10. Definition of research design.
11. Significance of hypothesis formulation.
12. Meaning of reliability.
IGNOU MPC 005 Solved Assignment 2022-23
13. Method of snow ball sampling.
14. Difference between independent and dependent variable.
15. Relevance of grounded theory.
16. Meaning of ethnography.
IGNOU MPC 005 Solved Assignment 2022-23
17. Criteria for selecting a case study.
18. Concept of cross sectional survey research design.
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