The Scientific Study of Politics POL 51

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The Scientific Study of Politics (POL 51)

• Professor B. Jones
• University of California, Davis

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Today

• The Nature of Research in Political Science
• Hypotheses
• Working Example immigration

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Approaches to Research

• Normative
• Value Judgments
• What ought to be?
• The Problem?
• Normative conclusions often passed off as
  causally inferred or scientifically derived
• But its 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!

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Pundits and Entertainers

• Information Exposure
• Implications?
• Be Careful!
• Dont confuse entertainment with scientific
  research.

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True Normative Theorists

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

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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

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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 interestingbut hard to
  answer small questions may be trivial.

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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?

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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

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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

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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.

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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

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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.

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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)
• Lets turn to a working example

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Immigration yn undocumented
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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)

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Fun with Numbers
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And More Fun
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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?

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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!

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Immigration and Operation Gatekeeper

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

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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.-------gtIncrease 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

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Always Helpful to Look at Data
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And More Data
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And Still More Data
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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)

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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?

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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

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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.

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Hypotheses

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

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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

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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

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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

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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

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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

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Hypotheses

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

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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 abstractcareful consideration is
  necessary.

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Hypotheses and Data

• If its testable, youll need data.
• But which data?
• Units of Analysis
• Defined as the level upon which youll
  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?

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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)

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Next time

• Theories, data, and measurement.