Professor B. Jones University of California, Davis

 The Nature of Research in Political Science  Hypotheses  Working Example: immigration

 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!

 Information Exposure  Implications?  Be Careful!  Don’t confuse “entertainment” with scientific research.

 Philosophers  Classical Political Theorists  Literary Figures  Ethicists  …all very important work!

 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

 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.

 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?

 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

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

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.

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

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.

 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 x k (i.e. the dependent and independent variables)  Let’s turn to a working example

 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)

 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?

 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!

 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

 “A variable that occurs prior to all other variables and that may affect other independent variables.” (i.e. other x k )  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

 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)

 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?

 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

 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.

 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!

 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

 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

 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

 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

Hypotheses  Six characteristics of a good hypothesis: 1.Should be an empirical statement that formalizes an educated guess about a phenomenon that exists in the political world 2.Should explain general rather than particular phenomena 3.Logical reason for thinking that the hypothesis might be confirmed by the data 4.Should state the direction of the relationship 5.Terms describing concepts should be consistent with the manner of testing 6.Data should be feasible to obtain and would indicate if the hypothesis is defensible

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

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.

 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?

 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)

Next time…  Theories, data, and measurement.