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MEC-109: Research Methods in Economics

MEC-109: Research Methods in Economics

IGNOU Solved Assignment Solution for 2022-23

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Assignment Code: MEC-109/ASST/2022-23

Course Code: MEC-109

Assignment Name: Research Methods in Economics

Year: 2022-2023

Verification Status: Verified by Professor


Answer all the questions.

Section A


Answer the following questions in about 700 words each. 20x2


Q1) Explain with illustration the conflict between Inductivism and Hypothetic-Deductivism. How these two methods are useful in undertaking research in social sciences.

Ans) According to Popper, the essential purpose of philosophy is not to answer Hume's or Positivists' Induction issue. The problem of Induction can't be solved, and it doesn't need to be because science doesn't use Induction. Popper says philosophy of science must overcome Kant's problem of demarcation. Popper says scientific theories' falsifiability distinguishes science from other fields. Falsifiability separates science from pseudoscience. Science is falsifiable. Falsifiable statements are scientific.


Popper proposes a model of scientific procedure based on the hallmarks of scientific theories. His method is Hypothetico-Deductive. He says science uses Hypothetico-Deduction, not Induction. Popper opposes the inductivist view that our observations are theory-free and hence have probability 1. More crucially, he says theories are free human inventions, not observations or truths. Our scientific concepts are inventions, not observations. Since our theories are our own constructs and not based on observations, which Popper considers myths, their initial probability is zero.


The Steps of Scientific Procedure

In the Popperian system, we start with a problem, present a hypothesis as a tentative solution, try to falsify our solution by deducing test implications, and consider our solution corroborated if repeated attempts to refute it fail. Popper's theory of scientific method is called Hypothetico-Deductivism because, according to him, the essence of scientific practise consists of deducing the test implications of our hypothesis and trying to falsify the latter by showing that the former do not obtain, whereas Inductivism consists of searching for instances supporting the generalisation arrived at based on some observations and the principle of induction. Popper argues that the Hypothetico-Deductive model is better to the inductivist model.

  1. First, it honours the critical spirit of science by believing that the goal of scientific testing is to falsify our hypotheses and that our scientific theories will always be tentative. Hypothetico-Deductivists believe scientific ideas are always subject to falsification. Inductivists present scientific testing as a quest for confirming examples and scientific hypotheses as established truths, making science a safe and defensive endeavour.

  2. Popper believes science would not have advanced if it had followed the inductivist path. Suppose a scientist generalises. If he follows the inductivist message, he'll look for proof. If he finds a conflicting instance, he qualifies his generalisation by claiming it's true except in circumstances where it's unsupported.

  3. Popper's Hypothetico-Deductive perspective eliminates Hume's challenge for inductivist theory. Hume proved that induction cannot be justified logically. Science is irrational if Hume is right. Hypothetico-Deductivists believe science never uses induction.


It will be convenient if we list the main theses of Popper’s philosophy of science arranged in a manner with our list of the theses of the positivist philosophy of science:

  1. Science is superior to all other human endeavours and ideal (scientism).

  2. Science's difference, excellence, and ideal hood stem from its approach (Methodologism).

  3. All science uses the same procedure, regardless of subject (Methodological Monism).

  4. Hypothetico-Deduction is used in all natural and human sciences (Hypothetico-Deductivism).

  5. Science's characteristic is that its claims are falsifiable (falsifiability).

  6. Scientific observations are theory-dependent and not pure.

  7. Theories are human constructions, not generalisations based on 'pure observations'

  8. Observation and theory interact.

  9. Multiple theories may fit a given set of observation-statements.

  10. Our factual assessments may include value commitments and our value judgements may have cognitive substance; science is not value neutral, but value commitments can be critically analysed and are not subjective.

  11. All scientific explanations must be deductive-nomological, hence Deductive-Nomologism is valid.

  12. Science explains the observable world in terms of unobservable entities and those unobservable entities in terms of more unobservable entities. Unobservable entities are real, so our theories describe them ('Realism').

  13. Science progresses from one theory to a better one, unlike other fields. 'Better' means 'truer' More to true means "unobservable." In short, science is progressive because our successive ideas in any subject of research match reality more and more closely.

  14. Because there are no pure observations, science is not objective as positivists believed. Science is objective because its theories are testable.

  15. Science is not rational in the sense that Induction can be logically justified, as Positivists believed. Induction cannot be rationally supported; science doesn't use it. Science has institutional systems for developing critical thinking and practising falsifiability.

Q2. (a) State the various forms of regression model. For working out the GDP growth rate of Indian economy, which form of regression model would you like to use?

Ans) The various forms of regression model are:

  1. Linear Model

  2. Log-linear Model

  3. Semi-Log Model

  4. Reciprocal Model


For working out the GDP growth rate of Indian economy, using linear model would be appropriate. Example: The different features were collected from the World Bank data and were wrangled to convert them to the desired structure. Regression was used to determine the coefficients. For this purpose, all the features were scaled so that the weights obtained by fitting a regression model, corresponds to the relative importance of each feature.


The result of fitting a linear regression model on the scaled features suggested that Literacy has no impact on GDP per Capita. This result seemed weird as literacy is always associated with development. To investigate this the individual explanatory variables were plotted to understand their relationship with the dependent variable GDP per Capita.


This analysis revealed that the features were not linear which implied that a Linear regression model could not be fit on the given data as it is. The plot suggested that the maximum variation in the Y-axis was from 0 to 150,000 while maximum variation in the X-axis was from 0 to 100. So, a distinct relation could not be identified between the GDP per Capita and other variables. Therefore, log of GDP per capita was taken to make the two variations similar.


After log transformation, the variation of the X-axis and Y-axis became similar and almost a linear trend could be identified between GDP per capita and other independent variables. For example, it could be seen from literacy plot that as literacy rate increases log of GDP per capita increases and similarly the trend could be observed for other variables too. This gave motivation for using GLM(Generalized linear model). From the above plot, the link function was clear that it would be a log link function as after log transformation the trend in the data appeared linear. The shape of the above density and the fact that GDP per Capita was a positive continuous quantity suggested that Gamma distribution could be used to model the density function.


So, a GLM was fitted on the scaled data with Gamma distribution family having log link function. A linear model was also fitted by log transforming the dependent variable GDP per Capita. It is not a good practice to transform the dependent variable if the model is built for prediction purpose. But here the main task is identifying the feature importance. Also, log being a monotonically increasing function does not affect interpretability. So, a log-transformed linear model was used as a second model.



(b) Using the hypothetical date on real GDP in India for 1960-2007, we obtain the following regression results in the following table:


Rate of growth of real GDP, USA, 1960-2007.

Interpret the results.


Ans) The results are as follows:

1) There are two types of regression models:

a) Two variable regression model

b) Multi-variable regression model

2) It is showing GDP is increasing bur irregularities are also inversing.



Section B


Answer the following questions in about 400 words each. 12x5


Q1) State the common elements of interpretation and critical theory paradigm. How does Interpretivism paradigm is useful for theory building?

Ans) Critical paradigm shares similar ideas with interpretative paradigm but focuses on oppression. Social scientists must grasp people's lived experiences in context. People can sense reality and express it through words. The knower and the known define reality. Critical perspectives reveal oppressive power structures. Critical theory refers to several social science movements. As it is founded on class conflict ideology, critical theorists whose goal is to identify power-relationships among different sectors of society, it is frequently called 'ideologically orientated inquiry, neo-Marxism, materialism, the Frankfurt School and freireism.


Marxism saw class tensions in society and claimed they could be reformed only via radical transformation. Frankfurt school or the Institute for Social Research influenced critical theory. Herbert Marcuse, Theodor Adorno, Max Horkheimer, Walter Benjamin, and Erich Fromm founded it. Critical theory's ontological postulate is that matter constitutes reality. Their work is a critical response to the works of Marx, Kant, Hegel, and Weber.

  1. Historical ontology assumes an apprehend able reality. This is a reality generated and moulded by social, political, cultural, economic, ethnic, and gender-based forces that have crystallised over time into natural or genuine societal structures. Researchers assume these structures are real. Critical theorists disagree.

  2. We can't separate ourselves from what we know, which drives investigation. What can be known depends on an investigator's engagement with an object or group.


Critical Theory is multi-disciplinary. It finds its applications in anthropology, economics, art criticism, education, history, psychology, political science, sociology, and theology. An interesting question is why and where to apply critical theory paradigm in research. Critical theory paradigm should be applied where the objective of the researcher is to analyse the power structures in a set-up or social problems arising of such structures.


Interpretive Paradigm

Interpretive research assumes that our knowledge of reality is gained only through social constructions such as language, consciousness, shared meanings, documents, tools, and other artifacts. As the term indicates, interpretive paradigm looks for understanding of a particular context. Interpretivists believe that it is important to understand the context in which research is conducted for proper interpretation of the data.


The interest of interpretivists lies not in the generation of a new theory, but to judge or evaluate and refine interpretive theories. Researchers using an interpretive approach aim to uncover meaning towards a better understanding of the issues involved. The underlying ontological assumption of interpretive paradigm is subjectivism as here reality is viewed as socially constructed and interpreted. Epistemological assumption is that knowledge of reality is obtained from the accounts that social actors provide.


Criteria for Interpretive Case study aiming at Theory Building:

Q2) Distinguish between Mono Method Research and Mixed Method Research. State the rationale for use of mixed methods research in social sciences.

Ans) A study that uses only one methodology, be it quantitative or qualitative, is referred to as monomethod. Typically, a quantitative study analyses information utilising numerical data that is in numerical form. The mono method refers to the employment of a single research strategy for a particular topic. And a mixed method research study/project is one that combines at least one qualitative and at least one quantitative component relating to measurement scale, tools of data collecting, or data processing approach.


The Rationale for Mixed Methods Research

In recent years, mixed methods research has become extremely popular in the social, behavioural, and related sciences. The claims made by mixed method researchers, the rise in publications on the subject, and the inclusion of mixed methods designs in textbooks all demonstrate the growing popularity of this type of study.


The following reasons are advanced in favour of integrating qualitative and quantitative methodologies:

  1. Quantitative and qualitative research have merits and disadvantages. Researchers can offset their weaknesses by combining them.

  2. Mixed methods designs utilise quantitative and qualitative methodologies to provide a thorough account of the researcher's area of interest.

  3. Quantitative and qualitative methodologies answer study problems. Mixed techniques can provide complementary perspectives on the same phenomenon or relationship. The two strands of the hybrid study have similar research questions.

  4. Research can be expanded using mixed techniques. One strand's questions come from another's inferences, or one strand supplies a hypothesis for the next.

  5. Mixed techniques can be used to evaluate one approach's inferences. Exploratory and explanatory/confirmatory inquiries may help.

  6. Divergent pictures of the same phenomenon can be obtained using mixed approaches. Compare and contrast these divergent findings.

  7. Quantitative research explains social structures, but qualitative research explains process.

  8. Mixed methods research involves utilising qualitative approaches to generate hypotheses and quantitative ways to test them.

  9. Mixed methods research combines researchers' and participants' viewpoints through quantitative and qualitative research to show links between factors and meaning among participants.


Q3) What is cluster analysis? Which steps are involved in cluster analysis? Give illustration.

Ans) Classes, or conceptually significant collections of things with similar features, are crucial to how individuals interpret and represent the world. Using a technique known as cluster analysis, related items or variables are grouped together based on their measured properties. This is a crucial multi-variate analytic technique. We group the observations after gathering the data and then give each group a label.


Cluster analysis is a more archaic technique because no presumptions are made about the number of groups or the structure of the groups. According to similarities or distances, groups are formed. Similarity measures or data from which similarities can be computed are needed as inputs. The main practical use of cluster analysis is to determine whether the researcher is knowledgeable enough about the issue to discern between "good" groupings and "poor" groupings. This can be achieved by listing all feasible groupings and choosing the "best" ones for additional research.


Steps Involved in Cluster Analysis

The following five broad steps are involved in conducting cluster analysis:

  1. Measuring the relevant variables that can be used, both quantitative and categorical.

  2. Establishing a similarity matrix for a suitable comparison of similarity, distances are used to gauge how similar or dissimilar two variable samples are. We create a matrix known as the (dis)similarity matrix to assess the degree of (dis)similarity. Each entry of the (dis)similarity matrix is calculated using the formal metric for distance. Different metrics can be used, each of which is appropriate for the specific type of data set.

3. Use a clustering algorithm to create one or more clusters.

4. Evaluating the grouping that was obtained (s).

5. Applying a substantial interpretation to the clustering(s).


Q4) What is the difference between case study method and survey method?

Ans) Data gathering is a flexible and exciting process; especially when you use surveys. There are different survey methods that allow you to collect relevant information from research participants or the people who have access to the required data.


Case Study Method

Business economists analyse the behaviour of enterprises and organisations using the case study technique. The researcher can narrow their focus to one company or organisation and create a comprehensive case history of that particular entity. A complete case study will take into account the opinions of all of the stakeholders because there are several actors in a corporation. To get information from diverse stakeholders, including managers, workers, etc., you can apply a range of ways.


A schedule or a questionnaire may be used to interview the personnel. The interview technique can be used to compile a history of the company and an organogram outlining the duties and responsibilities of each employee. The organization's records that are now accessible can be examined to do a content analysis. This gives a thorough picture of the business. The case study approach is thorough, idiographic, and inductive.


Survey Method

A survey method is a procedure, instrument, or technique you might use to interview a predetermined group of people in order to collect data for your project. Typically, it makes it easier for participants in the research to communicate with the individual or group conducting the study. Depending on the type of study you're conducting and the kind of data you ultimately want to collect, survey methodologies might be either qualitative or quantitative.


Types of Survey Methods


Interviews: A survey research technique called an interview involves the researcher facilitating a conversation with a research subject in order to collect pertinent data. This discussion may take place in person during a face-to-face interview, virtually over the phone, or through platforms for video and audio conferencing.


Surveys: A survey is a mechanism for gathering data in which participants respond to a series of structured questions based on their knowledge and experiences. You can access data from a predetermined group of respondents using this common method of data collection during research.


Observation: The method of observation involves keeping an eye on the actions and behaviours of the research participants as they interact with one another and their surroundings.


Focus Groups: An open discussion with a small group of carefully chosen participants who contribute valuable data to research is known as a focus group. The chosen participants are a sample of your research population, and they need to represent the various subpopulations within it.

Q5) Writer short note on any three of the following: (4x3=12)


i. Systematic sampling and stratified sampling


Stratified Sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample. To use this sampling method, you divide the population into subgroups based on the relevant characteristic. Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.


Systematic Sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

ii. Interval scale and Ratio scale as measurement scale technique


Interval Scale

All the traits of the nominal and ordinal scales of measurement are present in the interval scale. Equivalent distances on the interval scale represent equal distances in the characteristics of the thing being measured. As an illustration, a scale might be used to represent student grades using the attributes 0 to 10, 10 to 20, 20 to 30, 30 to 40, 40 to 50, and so on. Each range's midpoint is equally apart from the others. Interval data is the term for information derived from an interval scale. Arithmetic mean, standard deviation, Karl Pearson's coefficient of correlation, and tests like the t-test and f-test are suitable measurements to use with this scale. The interval scale does not allow us to apply the coefficient of variation.

Ratio Scale

The nominal, ordinal, and interval scales' features are all present in the ratio scale. Additionally, it has a conceptually significant zero point when the characteristic being measured is completely absent. In contrast to social sciences, physical sciences frequently use ratio scales. Height, weight, distance, and other measurements are examples of ratio scales. Geometric and harmonic means are both valid indicators of central trends on this scale.


iii. Research Design and Research Method

Ans) Research design is a plan to answer your research question. A research method is a strategy used to implement that plan. Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively. The research methodologies are distinct from the research design. The research design is a logical framework for an investigation, as opposed to the research methodologies, which consist of procedures for data gathering and analysis.


There is nothing inherent in any research design that mandates a specific approach to data gathering. The rationale of the research plan is unaffected by the method of data collection. Poor evaluation of designs results from conflating research techniques and designs. Comparing cross sectional designs to questionnaires, for instance, or case studies to participant observation leads to an evaluation of research designs based on the method's strengths and weaknesses rather than on their capacity to reach relatively unambiguous conclusions or choose among competing plausible hypotheses.

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