PSC 570

1. PSC 570 Research design Most importantly: what is it you wish to study and why?

2. Topics to be covered Inference, Collinearity • measurement error (recap) • Endogeneity • Problems of choosing cases, data sources-access-problems, dealing with the problem of too few cases • The research question and its problems • Research design: think theoretically, be explicit, choose data carefully, apply appropriate methodology.

3. The basis for research is proper research design (construction of argument, choosing variables, conceptualization, operationalization, and then interpretation and evaluation of results). We must be clear in stating whether the goal of the research is description or explanation. There will always be an element of explanation and exploration in both approaches, yet explanation dominates when you state an hypothesis and conduct your own research. There will always be uncertainty with causal inferences, but these should not lead us to avoid such inferences. • BE BOLD!

4. Generalizable inference • This is the subject of the KKV text • Generalizable inference makes the claim that the inferred causal process is true outside of the limited number of cases being investigated • Thus we must be clear about the class of circumstances or phenomena to which our inferences are supposed to apply

5. Generalizable vs. nongeneralizable inference • Nongeneralizable inference • Making an inference in a single case • Making an inference when the number of cases is less than the number of variables • Examples: • historiography (usually) • juries deciding guilt or innocence of a person

6. “Large N” research tries to find all of the observations that are relevant • in practice, this usually means “all of the data that are available” • The assumption is that the available data were randomly “censored” • meaning that the missing observations are not systematically related to the dependent variable • In “small-N” research, cases are not usually chosen at random • In choosing cases we need: • variation on the dependent variable • more cases than variables • Avoidance of (near) perfect collinearity between explanatory variables

7. Choosing cases • In both types of research, we need to think about how the selection of cases might bias our inferences • But this problem is more pronounced in “small-N” research • -------Systematic vs. random error, the former biases descriptive inference but not causal inference

8. Selection bias • One central consideration: • Most people develop their ideas by “selecting on the dependent variable” • E.g., if you want to know why war occurs, you read about wars, but then we miss cases in which wars did not occur • Selection on the dependent variable can be a big problem for inference, it can bias our inference, thus we need to include a variety of cases, ensuring variation in the dependent variable

9. Some examples of endogeneity, collinearity, and case selection • One argument states that the tariff rate in the United States and other countries is a function of: • inflation, unemployment, economic growth • partisan control of government • the party of the President • the majority party in Congress • Whether government is “divided” (Congress and the President are of different parties)

10. Endogeneity? • Suppose I argue instead that, through a process of international negotiation in the GATT, the US tariff rate is explained by the tariff rates of (e.g.) Japan and Germany, not by the domestic-level business-cycle and political variables • Thus the Japanese tariff is said to explain the US tariff • …but the US tariff explains the Japanese tariff, so there is a problem of endogeneity • Answer: look for alternative measures of the IDV

11. Research design • Research statement or question: needs to be testable, possible to evaluate • Be clear: if crap goes in→ crap comes out • Maximize leverage: explain as much as possible with as little as possible (recall multiple bivariate correlation- good for quantitative studies)

12. Study question- one sentence A thesis statement: • tells the reader how you will interpret the significance of the subject matter under discussion. • is a road map for the paper; in other words, it tells the reader what to expect from the rest of the paper. • directly answers the question asked of you. A thesis is an interpretation of a question or subject, not the subject itself. The subject (e.g. World Wars), and specific topic (e.g. the role of handguns in world wars), must offer a way to understand wars and the role of handguns that others might dispute. • is usually a single sentence somewhere in your first paragraph that presents your argument to the reader. The rest of the paper, the body of the essay, gathers and organizes evidence that will persuade the reader of the logic of your interpretation.

13. Background/justification Why is it this interesting? What has been written, investigated or proposed on this topic? What is your main thesis? (or the question you want to answer) • Timeliness and Relevance What is the justification for, and relevance of, this research? • Unit of analysis andHypothesis(es) • Data and data sources (qualitative and/or quantitative • Methodology: How will you conduct your research? • Problems Validity, replicability, data access…

14. The Qualitative Difference • “Qualitative approaches attempt to uncover meaning via analysis of non-numerical data that come from multiple sources of information including interviews, observations, audio-visual materials, and existing and researcher-developed documents.” • Movie vs. snapshot O’Connor, B. N. (2002). Qualitative case study research in business education. The Delta Pi Epsilon Journal, 44(2), 80.

15. Why Qualitative research? • The Case Studies • The research problem • Selecting the case • Data collection approaches • Trustworthiness • Data analysis and interpretation • Computer tools for data analysis • Suggestions for writing up results

16. Case studies • Case studies give depth, aims at, but never achieves, a holistic understanding -why?? -Validity and reliability depend on proper and explicit operationalization (conceptualization, and sound techniques) -Types: theory generating (exploratory) testing (illustrative), comparative (both), policy oriented process tracing (also generating/exploratory) -Techniques: interviews, surveys, original and secondary sources. Most are quantitative, yet they can be quantitative or a combination

17. Dealing with the problem of too few cases • Disaggregation • Using regional-level instead of national-level data • Divide • Get more samples (money and time allowing)

18. Suggestions for writing up any research results • Describe all data and procedures, processes, and tools used • Discuss results related to literature • Recommend future research topics and investigative methods