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 Bad 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 Why “ugly”? Another example
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.