Transcript The Scientific Study of Politics (POL 51)

Professor B. Jones
University of California, Davis
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The Nature of Research in Political Science
Hypotheses
Working Example: immigration
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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!
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Information Exposure
Implications?
Be Careful!
Don’t confuse “entertainment” with scientific
research.
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Philosophers
Classical Political Theorists
Literary Figures
Ethicists
…all very important work!
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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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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.
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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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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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Research questions may originate from
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Personal observation or experience
Writings of others
Interest in some broader social theory
Practical concerns like career objectives
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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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Variables can have many different kinds of
relationships:
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Multiple independent variables usually needed
Antecedent variables
Intervening variables
An arrow diagram can map the relationships
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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.
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We can state relationships in hypotheses.
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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
Unauthorized Migrants Living in U.S. (Pew Estimates, 2005)
12
10
Number (in Millions)
10.3
8
8.4
6
5
4
3.9
2
0
1992
1996
2000
Year
2004
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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)
Number of Articles Referencing "Border" and "Immigration" in
Washington Post, Charlotte Observer, and Minneapolis Star Tribune
(1996-2005)
120
108
108
101
99
97
Number of Articles
100
80
85
60
53
57
49
42
40
41
46
28
30
20
8
25
15
8
0
1996
1997
20
19
10
5
1998
6
1999
2001
Year
34
Washington Post
17
9
2000
41
39
2002
Charlotte Observer
12
2003
17
2004
2005
Minneapolis Star Tribune
Number of Articles Referencing "Border" and "Immigration" in
Arizona Daily Star and Sacramento Bee
(1996-2005)
200
172
180
Number of Articles
160
140
130
140
112
120
109
118
112
100
100
85
80
60
67
66
36
45
40
17
31
43
32
17
27
22
20
Arizona Daily Star
Sacramento Bee
0
1996
1997
1998
1999
2000
2001
Year
2002
2003
2004
2005
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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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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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Operation Gatekeeper defined
Massive Increase in Immigration post-O.G.
“Causal Explanation”:
◦ In-flows=f(Operation Gatekeeper)
◦ Satisfied with this?
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Problems with the “explanatory story”?
◦ Time Series vs. Cross-Sectional Data
◦ Perhaps O.G. was an antecedent variable
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“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”
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O.G. shifted migration hubs
Stretched INS razor thin
Adoption of OTM category
Made migration an option to other Lat. Am.
countries
Deportable Aliens Apprehended in U.S.: Total and Mexican
2000000
Number Apprehended
(in Millions)
1800000
1600000
1400000
1200000
1000000
800000
600000
400000
200000
Total
Mexican
0
1997
1998
1999
2000
2001
2002
Fiscal Year
Source: Dept. of Homeland Security
2003
2004
OTM Apprehensions: The Top Five List
40000
36118
Number Apprehended
35000
30000
27396
27317
25000
24420
20000
16974
15000
14866
14491
Ho nduras
10000
5000
11628
9316
7036
6021
9602
7728
El Salvado r
5240
3100
0
581
B razil
8859
765
1460
2498
Guatemala
Nicaragua
2002
2003
2004
Fiscal Years 2002-2004
Source: Congressional Research Service
2005
Breakdown of 2004 Unauthorized Population (Pew Estimates)
Africa/Other, 4%
Europe/Canada, 6%
Asia, 9%
Mexican, 57%
Other Latin
America, 24%
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O.G. probably not directly connected to inflow
That is
◦ O.G.  ?  In-flow increase
◦ What “?” is would constitute your real x factor.
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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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For illustration, imagine x corresponds to
regional variables (e.g. different states,
sectors, etc.)
Causal Explanation:
◦ Regional Variation  Increased in-flows
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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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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: factors occurring “back in time.”
◦ Temporally, prior to x
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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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Statements about a relationship
◦ How does it work?
◦ In what direction are the effects?
◦ i.e. positive? negative?
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In some sense, it’s an educated guess.
Therefore, it’s inherently PROBABLISTIC
You may be wrong!
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Good Hypotheses
◦ Empirical Statements
◦ Testable: you can evaluate the relative accuracy
of the statement
◦ General statements (interesting vs. trivial)
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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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The Good
◦ Levels of law enforcement are related to in-flows of
undocumented migrants
 Where the presence of law enforcement is high, inflows will be lower
 Where the presence of law enforcement is low, inflows will be higher
◦ These illustrate “directional” hypotheses
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The Bad
◦ Immigration is a bad thing.
◦ …or immigration is a good thing.
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Normative judgments are very difficult to
evaluate.
Another example
◦ America lost the Olympics bid because of Obama
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The Ugly
◦ The desire for a better life among impoverished
Mexicans has led to an increase in undocumented
migration.
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Why “ugly”?
Another example
◦ Undocumented aliens hurt the U.S. economy
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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
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Hypotheses must specify a unit of analysis:
◦ Individuals, groups, states, organizations, etc…
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Most research uses hypotheses with one unit
of analysis.
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Definitions of concepts should be
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Otherwise, reader will not understand
concept correctly.
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Clear
Accurate
Precise
Informative
Many of the concepts used in political science
are fairly abstract—careful consideration is
necessary.
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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???
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Our working example:
◦ UOA: perhaps Border Patrol sectors
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Another example:
◦ Education and Turnout
◦ UOA? (Group vs. Individuals)
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Does the choice matter?
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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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Theories, data, and measurement.