Research Methodology II

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Course TypeCourse CodeNo. Of Credits
Foundation CoreSHE2ED2024

Semester and Year Offered:Winter Semester, every year

Course Coordinator and Team: Dr Oinam Hemlata Devi and Dr Suresh Babu

Email of course coordinator:hemlata[at]aud[dot]ac[dot]in

Pre-requisites: None

Course Objectives/Description:

The module on Qualitative Methods deals with the fundamentals of field research, the planning and logistics of various types of field research and the research process. Specific data collection methods and techniques will include observation, interview, questionnaire, case study, life history, documentary, ethnography, RRA/PRA/PLA. Hands-on training in qualitative data analysis, writing up research reports and proposals will be the backbone of the course. Students are required to undertake field studies based on one area or population for understanding the applicability of methods and techniques.

The module on Quantitative Methods has the standard structure of a course on basic statistics (a first course) covering descriptive statistics, probability, bivariate analysis and inference. The distinctiveness of the course is in its orientation. The relative emphasis is on finding out with (or making sense of) data rather than on formal model testing and estimation. As an approach to statistics it aims to learn from data by visualizing them and, hence, it relies mainly on graphical methods to assist thinking with data. It is less formal than the theory and practice of statistical inference (hypothesis testing), more playful perhaps, and more focused on conceptualising with data foreshadowed by theory on the subject of enquiry. The objective is to provide practical guidance to use data description as a tool for discovery, i.e. getting ideas from the data (hypotheses seeking). The mathematical threshold is kept fairly modest – familiarity with high school mathematics is the minimum requirement.

Learning Objectives:

  • To understand the methods and techniques of various research interest in a holistic approach.
  • Will give the students a very comprehensive understanding of it so that they can incorporate it in other theoretical aspects of understanding an issue.
  • To independently develop and test simple hypotheses based on real-life primary or secondary data relevant to environment/development.
  • To introduce to specialised Software for preliminary data graphing and analysis

Course Outcomes:

At the end of the course, students will be able to:

  1. Critically evaluate ideas, evidences and experiences, existing gaps of research and different perspectives across the discipline of social and ecological sciences in the area of environment and Development to design and carry out a researchable problem with the formulation of objectives/hypothesis.
  2. Develop and test simple hypotheses based on primary and secondary data.
  3. Formulate research questions based on literature reviews, observed realities in the field.
  4. Think critically and analyse discourses of positionality, subjectivity or objectivity from different epistemological traditions.
  5. Apply competencies of specific methods and techniques acquired to understand and address real world problems and issues.
  6. Analyse, interpret and draw conclusions from qualitative/quantitative data or both.
  7. Use specialised software such as SPSS, R, Microsoft excel etc. for qualitative and quantitative data analysis.
  8. Develop robust study designs around specific research themes and questions relevant to environment and development.
  9. Work in teams, peer learning and sensible atmosphere that accommodates people from different social groups and cultural background with dignity.
  10. Conduct ethical research with sensitivity and empathy towards human and non-human subjects of investigation.
  11. Pursue careful field based inquiry into the big questions of justice, well-being and sustainability in local, empirical contexts.

Brief description of modules/ Main modules:

Module No.


Module 1

Introduction to Study Design

Module 2

Broad based or extensive data collection tools: Surveys, PRA/RRA, Mixed Methods

Module 3

Positionality and bias

Module 4

In-depth or intensive data: Observation based methods

Module 5

In-depth or intensive data: Life histories and Case Studies

Module 6

Introduction to qualitative data analysis software

Module 7

Numerical and graphical representation of data

Module 8

Introduction to descriptive statistics

Modules 9-11

Understand the basics of probability and statistics, including distributions, Bayesian statistics and hypothesis testing

Module 12

Understand basic inferential statistics including ANOVA and regression analysis


Indicative Reading list:

  • Chambers, R. (1997). Whose reality counts? Putting the first last. London: Intermediate Technology.
  • Chambers, R. (2003). The best of both worlds. In R. Kanbur (Ed.), Q-Squared: Qualitative and quantitative poverty appraisal (pp. 34–45). Delhi: Permanent Black.
  • Edmondson, A. & D. Druve. (1996). Advanced Biology Statistics. Oxford University Press.
  • Einspruch, E.L. (2005). An introductory Guide to SPSS for Windows (2nd ed.). Sage Publications.
  • Kothari, Uma. (2001). Power, Knowledge and Social Control in Participatory Development. In Bill Cooke and Uma Kothari (Eds.). Participation: The New Tyranny? (pp.139–52) 1st ed. London: Zed Books.
  • Neil A. Weiss. (1993). Elementary Statistics. Addison-Wesley Publishing Company.
  • Sarantakos, Sotirios. (2005). Social Research. 3rd ed. Palgrave Macmillan.
  • Singh, Y.K. (2006). Fundamental of Research Methodology and Statistics (pp. 147-160). New Delhi: New Age International Pubishers.

Assessment details with weights:

This course will have continuous assessment in the form of classroom-based activities, take-home assignments, field based activities and an end-term examination.



Weightage in percentage


Qualitative: Continuous assessments



Quantitative: Continuous assessments