|Course Type||Course Code||No. Of Credits|
Semester and Year Offered:Winter Semester, every year
Course Coordinator and Team: Dr Oinam Hemlata Devi and Dr Suresh Babu
Email of course coordinator:firstname.lastname@example.org
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.
At the end of the course, students will be able to:
Brief description of modules/ Main modules:
Introduction to Study Design
Broad based or extensive data collection tools: Surveys, PRA/RRA, Mixed Methods
Positionality and bias
In-depth or intensive data: Observation based methods
In-depth or intensive data: Life histories and Case Studies
Introduction to qualitative data analysis software
Numerical and graphical representation of data
Introduction to descriptive statistics
Understand the basics of probability and statistics, including distributions, Bayesian statistics and hypothesis testing
Understand basic inferential statistics including ANOVA and regression analysis
Indicative Reading list:
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