This guide will help you navigate research in political science, with a focus on research design, methodologies, data sources, and citations.
Political science research asks empirical questions—questions that can be answered with evidence. Your project should:
Define a clear research question.
Review existing literature.
Propose a theory or hypothesis.
Select appropriate methods and data sources.
Explain how evidence will be analyzed.
The library catalog is a great place to start your research. Here are some tips to make searching more effective:
Make sure that you sign in. This allows you to see your access options for library materials, save searches, set notifications on saved searches, and save items to your favorites.
Start by identifying the main concepts and any potential synonyms or related terms. For the topic of affordable housing, you might use:
Use Boolean operators (AND, OR, NOT) to combine keywords effectively:
Use Library Catalog and Databases (JSTOR, Project MUSE, Political Science Complete, ProQuest Political Science).
Look for literature reviews (e.g., Annual Review of Political Science) to understand existing debates.
Government documents, treaties, constitutions, laws.
Speeches, interviews, archival collections.
Survey data, polling results, media coverage.
World Bank Data, UN Data, Pew Research Center, Eurobarometer, ANES (American National Election Studies).
Chicago Area Resources: archival methods and special collections UChicago Political Science methods tools
Think tanks (Brookings, RAND, Cato, Carnegie).
NGO reports.
White papers and working papers.
Before you start your search consider:
Search or browse the USA.gov departments and agency index to identify the department or agency collects data on your topic. Agencies publish data on their websites under pages titled "library," "resources," "our work," or "research."
Browse Census Bureau topics and subtopics to help find the information you need.
**Since January 20, 2025, some federal agency data and reports were removed or altered from government webpages. Please contact a librarian if you cannot find the data you are looking for.*
Qualitative approaches focus on depth over breadth. They allow you to uncover processes, meanings, and mechanisms in ways that numbers alone cannot.
Case studies & process tracing – Intensive study of one or a few cases (countries, policies, events), often with detailed historical or documentary evidence. Process tracing tracks cause-and-effect sequences within a case.
When viable: Useful for “why” and “how” questions—e.g., Why does the UN intervene in some crises but not others?
Strengths: Deep contextual knowledge; shows causal mechanisms, not just correlations.
Limitations: Hard to generalize widely; depends on rich evidence.
Ethnography & participant observation – Immersive fieldwork, where the researcher observes or participates in the daily lives of political actors.
When viable: Questions about lived experiences, identities, or practices—e.g., How do NGOs in South Africa protect immigrant children?
Strengths: Rich, ground-level insight; uncovers perspectives often missing in surveys.
Limitations: Time-intensive; may raise ethical and access challenges.
Elite interviews & focus groups – Semi-structured interviews with decision-makers, officials, or citizens; group discussions to explore attitudes.
When viable: Studying policy processes, insider decision-making, or public opinion formation—e.g., Do variations in Congressional staff salaries affect casework responsiveness?
Strengths: Access to insider knowledge; captures nuance.
Limitations: Risk of bias in self-reporting; requires careful design and consent.
Quantitative approaches emphasize breadth and generalization, drawing on large datasets and statistical techniques.
Survey design & analysis – Collecting and analyzing responses to structured questions from a sample of people.
When viable: Questions about attitudes, behavior, or perceptions—e.g., Does negative advertising increase or decrease voter turnout?
Strengths: Standardized, comparable data; can be generalized with proper sampling.
Limitations: Expensive; quality depends on question wording and response rates.
Statistical models – Using datasets to test hypotheses about correlations or causal relationships.
When viable: Macro-level questions with measurable variables
Strengths: Can analyze many cases; allows causal inference with controls.
Limitations: Risk of poor data quality, assumptions must be transparent.
Experiments – field and lab designs, including randomized control trials
Combining case studies with statistical data (Lieberman’s nested analysis).
When viable: Research that requires both broad patterns and deep causal explanation—e.g., Does female political representation improve social outcomes? (Quantitative cross-national dataset + qualitative case studies).
Strengths: Balances generalizability and depth; identifies patterns and mechanisms.
Limitations: Time- and resource-intensive.
Sequential designs: using qualitative findings to refine quantitative models.
When viable: Questions where one dataset/method cannot fully capture the phenomenon—e.g., How do stereotypes of Appalachia shape policymaking? (Content analysis of media + surveys of policy preferences).
Strengths: Triangulation improves credibility; flexible.
Limitations: Demands skill in multiple methods.
