The Scientific Study of Politics (POL 51)

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

2. Today • The Nature of Research in Political Science • Hypotheses • Working Example: immigration

3. Approaches to Research • Normative • Value Judgments • What ought to be? • The Problem? • Normative conclusions often passed off as causally inferred or scientifically derived • But it’s difficult to sustain inference if derived solely by normative judgment • Also, they way we want the world to work may cloud our understanding of it!

4. Pundits and Entertainers • Information Exposure • Implications? • Be Careful! • Don’t confuse “entertainment” with scientific research.

5. True Normative Theorists • Philosophers • Classical Political Theorists • Literary Figures • Ethicists • …all very important work!

6. Positive Approaches • Purports to account for “what is” • Empirically based • Grounded in scientific method • Often mathematical in its treatment • Important “names” • Harold Gosnell, Charles Merriam, William Riker

7. Proposing Questions/Positing Relationships • Always much harder than you may think • The “relationship” posed undergirds your “research question.” • It connects y to x. • Big vs. Small Questions • Big questions may be interesting…but hard to answer; small questions may be trivial.

8. Some Interesting Kinds of Questions • Why do democratic states tend to not engage each other in conflict? • Do Supreme Court justices vote ideologically? • How did the 1965 VRA effect congressional redistricting? • Did 19c. changes to the ballot effect how members of Congress behave? • Does electoral system variability impact the behavior of legislators?

9. Formulating Questions • Spend Time! • Quickly derived questions will be trivial (usually)… • And very hard to answer/study • My experience: students are way too broad in the kinds of questions they ask

10. Choosing a Research Question • Research questions may originate from • Personal observation or experience • Writings of others • Interest in some broader social theory • Practical concerns like career objectives

11. Specifying an Explanation • How are two or more variables related? • A variable is a concept with variation. • An independent variable is thought to influence, affect, or cause variation in another variable. • A dependent variable is thought to depend upon or be caused by variation in an independent variable.

12. Specifying an Explanation • Variables can have many different kinds of relationships: • Multiple independent variables usually needed • Antecedent variables • Intervening variables • An arrow diagram can map the relationships

14. Specifying an Explanation • Causal relationships are the most interesting. • A causal relationhip has three components: • X and Y covary. • The change in X precedes the change in Y. • Covariation between X and Y is not a coincidence or spurious. • We can state relationships in hypotheses.

15. Deriving/Positing Explanations • The research question puts boundaries on the problem: • Why did illegal immigration increase in the mid 90s/2000s? • The explanation leads you to think of y and the xk (i.e. the dependent and independent variables) • Let’s turn to a working example

16. Immigration: y=n undocumented

17. Other Choices? • Attitudes of Americans toward Immigration? • The number of anti-immigrant protests/rallies? • Court/congressional action on immigration? • Legislation dealing w/immigration? • Hate crimes? • News coverage? (Look at some data)

18. Fun with Numbers

19. And More Fun

20. The Causal Explanation • What are the factors increasing undocumented migration? • These are your x factors. • Possible suspects • Crushing poverty in Mexico and Latin America? • Willingness of American firms to hire undocumented workers? • Terrorism? • State policies promoting migration? • Lax enforcement among U.S. agencies?

21. Causal Explanation • In fact, all of these probably had an impact. • The problem? What kinds of variables are these? • Antecedent vs. Intervening Variables • Getting the explanatory story straight can be difficult!

22. Immigration and Operation Gatekeeper • Operation Gatekeeper defined • Massive Increase in Immigration post-O.G. • “Causal Explanation”: • In-flows=f(Operation Gatekeeper) • Satisfied with this? • Problems with the “explanatory story”? • Time Series vs. Cross-Sectional Data • Perhaps O.G. was an antecedent variable

23. The Concept of an Antecedent Variable • “A variable that occurs prior to all other variables and that may affect other independent variables.” (i.e. other xk) • O.G.------->Increase of Migrants • Suppose Operation Gatekeeper did not have a “direct effect” on in-migration? • “Hidden Effects” • O.G. shifted migration hubs • Stretched INS razor thin • Adoption of OTM category • Made migration an option to other Lat. Am. countries

24. Always Helpful to Look at Data

25. And More Data

26. And Still More Data

27. What do we learn? • O.G. probably not directly connected to in-flow • That is • O.G.  ?  In-flow increase • What “?” is would constitute your real x factor. • Other things learned from data? • Terrorism explanations simply do not account for increases in y. • Perhaps the problem extends beyond Mexico • América (Brazilian telenovela)

28. The Concept of an Intervening Variable • For illustration, imagine x corresponds to regional variables (e.g. different states, sectors, etc.) • Causal Explanation: • Regional Variation  Increased in-flows • Does this model make sense? …maybe • Southern border much more difficult than Northern. • Tucson/Yuma sectors the toughest of all. • The real question: what is it about region that elicits this effect?

29. Intervening Variables • Suppose law enforcement varied across regions: some sectors are tougher than others. • New Model: Region  Law Enforcement -Increased in-flows • Here, law enforcement acts as an intervening variable. • Classic example: education and voting • Education may induce feelings of civic duty • Thus: education  civic duty  voting

30. Antecedents and Intervenors: Summing Up • Antecedents: factors occurring “back in time.” • Temporally, prior to x • Intervening Variables: occurring “closer in time.” • Their relationship is related to x • Law enforcement is connected to region. • Civic duty is connected to education.

31. Hypotheses • Statements about a relationship • How does it work? • In what direction are the effects? • i.e. positive? negative? • In some sense, it’s an educated guess. • Therefore, it’s inherently PROBABLISTIC • You may be wrong!

32. Hypotheses • Good Hypotheses • Empirical Statements • Testable: you can evaluate the relative accuracy of the statement • General statements (interesting vs. trivial) • Bad Hypotheses • Normative Statements (Why?) • Not testable: impossible to bring data to bear on your statement • Non-general: the triviality problem

33. Some Examples • The Good • Levels of law enforcement are related to in-flows of undocumented migrants • Where the presence of law enforcement is high, in-flows will be lower • Where the presence of law enforcement is low, in-flows will be higher • These illustrate “directional” hypotheses

34. Some Examples • The Bad • Immigration is a bad thing. • …or immigration is a good thing. • Normative judgments are very difficult to evaluate. • Another example • America lost the Olympics bid because of Obama

35. Some Examples • The Ugly • The desire for a better life among impoverished Mexicans has led to an increase in undocumented migration. • Why “ugly”? • Another example • Undocumented aliens hurt the U.S. economy

36. Hypotheses • Six characteristics of a good hypothesis: • Should be an empirical statement that formalizes an educated guess about a phenomenon that exists in the political world • Should explain general rather than particular phenomena • Logical reason for thinking that the hypothesis might be confirmed by the data • Should state the direction of the relationship • Terms describing concepts should be consistent with the manner of testing • Data should be feasible to obtain and would indicate if the hypothesis is defensible

37. Hypotheses • Hypotheses must specify a unit of analysis: • Individuals, groups, states, organizations, etc… • Most research uses hypotheses with one unit of analysis.

38. Hypotheses • Definitions of concepts should be • Clear • Accurate • Precise • Informative • Otherwise, reader will not understand concept correctly. • Many of the concepts used in political science are fairly abstract—careful consideration is necessary.

39. Hypotheses and Data • If it’s testable, you’ll need data. • But which data? • Units of Analysis • Defined as the level upon which you’ll collect/analyze data • Countries, regions, individuals??? • Our working example: • UOA: perhaps Border Patrol sectors • Another example: • Education and Turnout • UOA? (Group vs. Individuals) • Does the choice matter?

40. Ecological Fallacy • Yes! Beware the Ecological Fallacy • Quick definition: conclusions about individuals are based on aggregated data (or group-level data) • History • Phrase coined by William Robinson (1950) • Literacy and immigration • Found literacy rate was positively correlated with percentage of people born outside the U.S. (r=.53) • However, at the individual level, he found immigrants were less literate than native born. (r=-.11)

41. Next time… Theories, data, and measurement.