Understanding the Fundamentals of Qualitative Data Analysis

Session Report
Sameeran Galagali

The IMPRI Impact and Policy Research Institute, New Delhi is hosting a Four-Month Online Immersive Public Policy Qualitative Participatory Action Research Fieldwork Certificate Fellowship Cohort 2.0 from December 2023 to April 2024. The fellowship programme aims at providing participants a comprehensive experiential knowledge of action research fieldwork, and the nuances of data collection and analysis involved in qualitative research. The fellowship program goes about with periodic expert lectures, participants’ fieldwork, updates on participants’ research in every research thus making the fellowship a highly immersive and rigorous.

The Day 6 of the fellowship programme focused on giving the participants an overview of the fundamentals of qualitative data analysis. The session was flagged with Prof. Vibhuti Patel’s opening remarks. Prof. Patel gave a brief overview of the progress done by participants thus far, and also mentioned that they are now at that juncture of research which involves analysis of the data collected so far.

The expert for the day was Prof. Utpal Kumar De, Professor and former Head, Department of Economics at North Eastern Hill University (NEHU), Shillong and distinguished senior visiting professor IMPRI. Prof. De uncovered and nuances of data collection, analysis, quantifying the qualitative data, the various methods of analyzing qualitative data, models, and ways of preventing fault lines in the analysis.

Defining Variables:

Prof. De opened his deliberation with identifying the diverse occupational backgrounds that the participants come from, and how different models and methods need to be adopted to suit each participant’s research problem. In this light, he explained the difference between qualitative and quantitative variables; that quantitative variables are those which are cardinal or are expressed in direct numerical terms, and qualitative variables are categorical variables. The latter is also called as a dummy variable, because they are quantified for analysis purposes in the course of research. This established the basic understanding for the rest of the session.

He further clarified the basic tenets of qualitative variables by citing two examples. One of the demand for woolen clothes, and the factors such as age, income, area of residence, sex of consumers and the season responsible for affecting this demand; second of the visiting decision made by citizens to a particular park, and the factors affecting this decision. In both cases, the demand as well as decision of the stakeholders is the dependent or the target variable, and the factors influencing the dependent variable are the independent variables.

Qualitative Data Analysis in Economics:

Prof. De further explained methods of qualitative data analysis through the visiting decision example. He explained what are binary variables in qualitative data, and how it is necessary to quantify the responses of the survey into two categories in order to identify the dependent or target variables. Thus, on the left-hand side, there will be a binary category, and on the right-hand side will be the factors or variables affecting the independent binary variable. The variables on the right-hand side may be either qualitative or quantitative. Prof. De added that finding the logical relationship between the dependent/target variable and the independent variables is of utmost importance and accordingly the decision to include and exclude variables should be taken.

To further illustrate this concept, Prof. De cited an example from his own research experience of the study of ‘Health Status of children under the age of 5 in slum areas of India.’ During this research, the main objective was to find solutions to the stunted growth of the target population. Therefore, in order to suit the research objective, he converted all the categories of ailments such as obesity, malnutrition, BMI etc., into two: stunted growth and not stunted growth, thus quantifying the existing qualitative data into a binary variable.

He also cited an example of quantitative binary variable from macroeconomic theory; interest rate as the function of investments in the economy. Prof. De thus gave illustrations on binary dependent variable from both, quantitative as well as qualitative research questions.

Regression Analysis in Qualitative Research:

Prof. De also introduced the participants to regression analysis in qualitative research. He first introduced the participants to the concept of a Response Variable (which synonymously used for a dependent variable), by citing the example of a medical testing, where the probability of a patients getting cured by a medicine is tested. He also explained what are the various research fields where response variable is employed.

Further, he explained the how the research must be carried out carefully when the target variable might be quantitative such as income of the respondents, and the independent variables might be qualitative such as social status, a few categories from the independent variable must be eliminated to suit the research problem and research objective, and moreover, to avoid a dummy variable trap situation.  

In this light, cited a lucid example by showing the relation between individuals owning houses (target dummy variable), and this ownership being affected by their incomes (quantitative dependent variable), and how the probability of house ownership changes with changing income levels. In this context, in the last segment of his deliberation, he introduced the participants to various models of regression analysis such as logistic regression model, probit regression model, tobit regression model, and multi-variate regression model.

Chair’s concluding remarks and Participants’ Interaction:

The chair for the session. Prof. Vibhuti Patel, in her concluding remarks summed up Prof. De’s lecture and highlighted the importance of quantifying qualitative research to ensure the representability of the qualitative research, and how understanding the convergence between the qualitative and quantitative research methods is necessary for social scientists to ensure the credibility of their research.

In the last segment of the session, there was an interactive session held by Prof. De and Prof. Patel with the participants wherein the participants asked questions regarding the session, updated the experts regarding their research progress, clarified their doubts on drawing the appropriate questions tactically, procuring responses, and co-relating the responses properly in order to analyze their findings.

Conclusion:

The session proved to be very insightful and useful for the participants. The deliberation on quantifying qualitative data, introduction to regression models, understanding of binary variables amongst others proved to be helpful for the participants to analyze the data collected for their research by them so far.  

Acknowledgement: This article was published by Sameeran Galagali, a Research Intern at IMPRI.

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