MA in Liberal Arts

This Subject Includes

• Course No: HS 763
• Course: MA in Liberal Arts
• Semester: I
• Title: Research Methods in Political Science
• Stream: Political Science

• Preamble: This course is designed to introduce students to the fundamental research tools and techniques used in political analysis. The objective of the course is to provide an overview of the key concepts, research designs and methods used to explore political phenomena in a variety of settings. The course will enable students to consider the nature of research, different approaches to research designs, the appropriate choice of methodology and different ways in which methodology is linked to key concepts and theoretical issues in the major subfields of political science such as comparative politics, area studies, political theory and international relations. The first part of the course deals with a variety of methodological tools that are extensively used in the literature, including observation, process-tracing, documentary evidence, surveys, case studies, participatory research, qualitative comparative analysis, content analysis and ethics of research in social sciences. Students will also be briefly introduced to ways of conducting research by integrating qualitative tools and computer-assisted qualitative methods. The second part is aimed at training students to prepare dissertation proposals and write academic papers in the sub-field. Course Content: The ‘science’ issue in Political Science: key philosophical issues, concepts and approaches, positivist research, interpretive research, post-positivist approaches; Methods of data collection: documentary evidence, ethnography, focused group interviews, observation, in-depth interviews, survey research, art of asking questions, sampling strategies, selecting study populations, participatory research tools, process tracing, mixed methods, power relations, ethical issues in fieldwork; Case selection methods: fieldwork setting, case studies, single case studies, small n-comparisons & large N comparisons, comparative methods; Data analysis: transcribing field notes, data measurement, content analysis; causality, descriptive accounts, reliability, the