Professor B. JonesUniversity 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

Research questions may originate from◦ Personal observation or experience◦ Writings of others◦ Interest in some broader social theory◦ Practical concerns like career objectives

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.

Variables can have many different kinds of relationships:◦ Multiple independent variables usually needed◦ Antecedent variables◦ Intervening variables◦ An arrow diagram can map the relationships

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 xk (i.e. the dependent and independent variables)

Let’s turn to a working example

Unauthorized M igrants Liv ing in U.S. (Pew Estimates, 2005)

3.9

5

8.4

10.3

0

2

4

6

8

10

12

1992 1996 2000 2004

Year

Nu

mb

er

(in

Mil

lio

ns

)

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 M inneapolis Star Tribune

(1996-2005)

30

1510

2519

34

85

57

4942

46

8 8 5 6 9

20 1712

41

53

41

28

108

97 99 101

108

17

39

0

20

40

60

80

100

120

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Year

Nu

mb

er

of

Art

icle

s

Washington Post

Charlotte Observer

Minneapolis Star Tribune

Number of Articles Referencing "Border" and "Immigration" inArizona Daily Star and Sacramento Bee

(1996-2005)

85

45

66

3122

140

118

172

130

112 112

100109

36 3227

43

67

1717

0

20

40

60

80

100

120

140

160

180

200

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Year

Nu

mb

er

of

Art

icle

s

Arizona Daily Star

Sacramento Bee

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 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

Deportable Aliens Apprehended in U.S.: Total and Mexican

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

1800000

2000000

1997 1998 1999 2000 2001 2002 2003 2004

Fiscal YearSource: Dept. of Homeland Security

Nu

mb

er

Ap

pre

he

nd

ed

(i

n M

illi

on

s)

TotalMexican

OTM Apprehensions: The Top Five List

9316

14491

24420

36118

31005240

88597036

9602

16974

60217728

11628

14866

581 765 14602498

27396

27317

0

5000

10000

15000

20000

25000

30000

35000

40000

2002 2003 2004 2005

Fiscal Years 2002-2004Source: Congressional Research Service

Nu

mb

er

Ap

pre

he

nd

ed

Honduras

Brazil

El Salvador

Guatemala

Nicaragua

Breakdown of 2004 Unauthorized Population (Pew Estimates)

Other LatinAmerica, 24%

Asia, 9%

Europe/Canada, 6%

Africa/Other, 4%

Mexican, 57%

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

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 relationship5. Terms describing concepts should be

consistent with the manner of testing6. Data should be feasible to obtain and would

indicate if the hypothesis is defensible

Hypotheses must specify a unit of analysis:◦ Individuals, groups, states, organizations, etc…

Most research uses hypotheses with one unit of analysis.

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)

Theories, data, and measurement.