The Scientific Study of Politics (POL 51)

1. The Scientific Study of Politics (POL 51) Professor B. Jones University of California, Davis

2. Today • Introduction • R…comments about. • Preliminaries/Basic Concepts

3. Political Science • Political scientists are interested in acquiring knowledge about and understanding many important political phenomena: • Many different levels of government • Many different actors

4. Political Science Political science is the application of empirical principles to the study of phenomena that are political in nature.

5. Empirical Research • Two reasons to understand how to conduct empirical research: • Citizens are confronted with empirical research daily through political news and debate. • You can use empirical research techniques to improve your own work.

6. Empirical Research • Empirical research on political phenomena can be used to • Improve understanding of and find solutions to difficult problems • Applied research • Satisfy your intellectual curiosity about the nature of political phenomena • Theoretical research

7. Empirical Research • The empirical research process of deciding • Which information will be used in an analysis • Which method will be used to conduct the analysis • Which statistic will be used to demonstrate the findings

8. Examples of Empirical Research • Political scientists study a variety of questions: • Winners and losers in politics • Who votes and who does not • Repression of human rights • Public support for U.S. foreign involvement • What questions are you interested in studying? • Find a problem!

9. Is Political Science a Science? • There are two general objections to classifying political science as a science: • Practical objections • Philosophical objections

10. Is Political Science a Science? • Practical objections: • Political behavior is extremely complex. • People can intentionally mislead researchers. • Measurement is often subjective. • Data can be difficult or impossible to attain. • Data can be “ugly” or misleading

11. Is Political Science a Science? • Philosophical objections: • The reasoning behind political behavior cannot be measured objectively. • The “facts” of political phenomena are constructed or conditioned by the observer’s perceptions, experiences, and opinions.

12. Political Science Discipline • The discipline has changed over time. • Traditional approach: • Period between 1930 and 1960—primarily described the practice of government • Empirical approach: • Followed early survey work in the 1950s—led to the widespread application of statistical methods—explanatory research

13. Political Science Discipline • The discipline has changed over time. • Normative pushback: • In response to empiricism—focused on questions of morality and policy issues that are relevant to real- world political discussions • Debate between empirical and normative research has cooled since the 1980s • To engage in modern political science requires you to understand scientific method.

14. Basic Principles • “Empirical Research” • Hypothesis Oriented • Theory Driven (Hopefully)

15. Empirically Based Research • em·pir·i·cal • 1.derived from or guided by experience or experiment. • 2.depending upon experience or observation alone, without using scientific method or theory, esp. as in medicine. • 3.provable or verifiable by experience or experiment. • Observation-based • Data are important! • Data are not created equal • Therefore, research design is important • Let’s first think about data…in general terms.

16. Good Data, Bad Data, Ugly Data • “Good” • Randomized Samples • Experiments • “Bad” • Convenience Samples • “Person-on-the-Street” Interviews • “Ugly” • Exit Polls (possibly) • “Selected Samples” • “Archival Data” (all of the above)

17. Archival Data • Government Statistics • Historical Data • All very clean data, right? • A Side-Trip to Voting Turnout • Should be easy to measure… • How do we measure turnout?

18. Turnout in America • How has turnout been historically computed? • Turnout=N Voters/VAP • VAP: “voting age population” (Now, 18+) • Problems with this? • All those 18+ years of age are not eligible to vote. • But still… • Alternative ways to compute turnout? • Turnout*=N Voters/VEP • VEP: “voting eligible population” (18+ but legally permitted to vote)

19. Turnout: Good Data?

20. Turnout Redux

21. Tale of Two States: Arizona

22. Tale of Two States: California

23. A Tale of Two Countries: U.S.A. and Australia

24. Take-away Points? • Data, even ostensibly clean data, has measurement issues we must deal with. • Know your data…US vs. Australia for example. • A study of turnout differences would be a silly study. • Have a THEORY…some grounded reason for your expectations.

25. Elements of Good Theory • Generalizability • Replicability • Transparency • Parsimony • Occam’s Razor • "when you have two competing theories which make exactly the same predictions, the one that is simpler is the better." • "We are to admit no more causes of natural things than such as are both true and sufficient to explain their appearances." (Sir Issac Newton)

26. Theorizing in Social Sciences can be a bit of Challenge! • Why do people vote? (Or not vote?) • Structural Explanations • Informational Explanations • Why do states engage in conflict? • Realist Perspective • Neorealist Perspective • Often, multiple “stories” seem consistent with known facts.

27. “Laws” are Harder to Come By • V=I x R (Ohm’s Law) • Describes the relationship between Voltage (V), Current (I), and Resistance (R) • It really is a law! • Anything like this in the social sciences?

28. Some “Laws” • Duverger’s Law: a principle which asserts that a majority voting election system naturally leads to a two-party system. (From Wikipedia) • Hotelling’s Law: in many markets it is rational for producers to make their products as similar as possible.(From Wikipedia) • Perhaps not quite the same as Ohm’s Law!

29. Hypotheses and Data • Y=f(X) • What is Y? • What is X? (…or What are the X?) • What is f()? • Hypothesis: a statement about how we think the world works. • Relates x to y.

30. Causality and Correlation • Causal explanations are desirable • “I hypothesize that x “causes” y • But are difficult to make • “Stochasticity” (The World is Probablistic!) • Correlation (“Co-Relation”) is sometimes the best we can do

31. Next Time • Theory, Hypotheses, and Measurement