Chapter 1:
Essentials of Political Research:
Alan Monroe

It is defined by the attempt to identify and test empirical generalizations. The key is empirical: it refers to facts, or the real world: that which exists and can be know through the senses – what can be seen, touched, heard, smelled.
How do you test empirical statements?
The test must be objective (free from bias, and can be duplicated)
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Generalizations: What we are looking for
We want to make statements about entire classes of objects, rather than just about individual cases.
Individual Level Analysis: Often Insufficient
How specific people may behave, whether they vote, and whether or not they are educated is of little value. But if we gather data on a large number of people from many places, and across time, we can make
generalizations about how
“ individuals ” with certain social characteristics behave politically. (3)
The objective is to explain and predict.
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Empirical and Normative Questions
Many things we think about the world are normative, rather than empirical, that is, they reflect our judgments about what should be.
Scientific method cannot deal directly with normative (non-empirical) questions.
Hence, we need to be able to distinguish between the two:
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Empirical statements: “ refers to what is or is not true and can be confirmed or disproved by sense experience.
” It can be tested.
Examples:
Bill Clinton was reelected president in 1996
Howard Dean is the Chair of the Democratic Party
…
Objective analysis of data from sensory observation can either prove or disprove an empirical statement. It can be tested.

Normative Statements : they deal with value judgments, that is, questions of what is good or bad.
Examples:
Was Bliss Clinton a good President?
Is democracy the best form of government?
Should taxes be increased?
Such questions cannot be answered objectively.
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Why cannot you answer normative questions in political science?
You can deal with normative questions/issues in scientific research, but can only do so indirectly . What you need to do is reformulate normative questions as empirical questions.
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There are two ways of doing this:
1) Change the Frame of Reference : Move from a normative judgment to a question about the normative judgments people make. (easier, but less useful)
Example:
Would it be a good idea to legalize drugs? (normative)
Do most college students favor the legalization of drugs? (Frame)
Limits on campaign spending in Congressional races should be adopted. (normative)
More Republicans favor spending limits than
Democrats. (frame)

Reformulate Normative Questions as Empirical:
2) Ask empirical questions about the assumptions behind normative judgment.
(more useful)
Example:
Would it be a good idea to legalize drugs? (normative)
Would the legalization of drugs decrease the occurrence of other crimes? (assumption)
Limits on campaign spending in Congressional races should be adopted. (normative)
Do spending limits advantage or disadvantage incumbents?
(assumption)
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Designing a Research Question:
Research should begin with a coherent question. It helps focus your thinking, research and writing.
Elements of a Good Question:
Clarity
Testability
Theoretical significance
Practical significance
Originality
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Clarity: needs to give clear direction to the research, that is, it should direct the type of research and data that will be used. But do not be too
focused. To reduce study to a particular city, or state, if you are discussing urban development, or economic policy might limit value of research. Hence the value in comparative studies.
Examples:
Why is voter turnout low in the US? (lacks direction: does not tell us where we should look, at…)
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Does same day registration increase turnout? (direct us to certain research)

Testability: have to be able to answer the question with empirical inquiry.
Theoretical Significance : answering the question should increase our general knowledge and understanding of the topic.
Hence, it is important to know what has already been written on the topic. Do a Literature Review.
Practical Significance: answering the question should have be useful to some real-life application.
Originality: does not have to be completely new question, but should at least be a new take on something.

Steps in the research process:
1) Formulate Question
2) Formulate hypotheses
3) Formulate Design
4) Research (Collect Data)
5) Analysis
6) Draw Conclusion
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Theories, Hypotheses, and Operational Definitions (17)
Building Blocks of the Research Process:
Theory: Concept 1 is related to Concept 2
Hypothesis: Variable 1 is related to Variable 2
Operational Definition: How you measure variable 1 and variable 2.

Theories, Hypotheses, and Operational Definitions (17)
Theory
It is a set of empirical generalizations about a topic. It is too general to test since it makes statements about the relationship between abstract concepts. To test a theory, it has to be brought down to more specific terms. (17)
Hypotheses
This is done by testing hypotheses, which is an empirical statement derived from a theory. They are statements about variables .

Theories, Hypotheses, and Operational Definitions (17)
Variables: (19)
They are empirical properties that can take on two or more different values .
Operational Definition
But even variables are not specific enough. Each variable in a hypothesis must have an operational definition, that is, a set of directions as to how the variable is to be observed and measured .

Types of Hypotheses (19)
Types of Hypotheses:
Univariate: making a statement about only one property or variable . (19)
Multivariate: a statement about how two or more variables are related.
Most hypotheses are multivariate and
Directional: that is, they suggest not only how the variables are related but what the direction of the relationship is. (19)
Null Hypothesis: There is in fact no relationship between the stated independent and dependent variables.

Hypothesis
Hypothesis: Variables
(IV) Independent Variable: the cause of something
(DV) Dependent Variable: the effect
It is not always easy to determine the IV and DV.
Control Variables: when they are used the intent is to ensure their effects are excluded.
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Types of Directional Relationships: Positive/Negative
Positive: variables move in the same direction:
Example:
1. As income rises, so does voting,
2. As income drops, so does voting.
Negative (or Inverse): Variables move in opposite directions:
Example:
1. As income rises, homelessness drops.

EXAMPLES: Levels of Research: (18)
Hypothesis:
IV: Cause DV: Effect
Positive:
IV: Cause
They go up together.
They go down together.
DV: Effect

EXAMPLES: Levels of Research: (18)
Hypothesis:
IV: Cause DV: Effect
Negative:
IV: Cause
The variables move in opposite directions. They have an inverse relationship to each other .
DV: Effect

Operational Definitions (25)
Testing a hypothesis requires precise operational definitions specifying how each Variable will be measured. If a variable cannot be operationally defined, it cannot be measured.
Operational Definitions: Two Requirements:
1) It must specify what we want to know
2) And where (or how) we will get that information.
Example:
Say we want to examine ethnic diversity in Boston: What we want is how people in Boston identify ethnically, and how we would get it would be to look at the most recent US census data on Boston.

Two common Units of Analysis: (26)
Individuals: indicates either people in general, or a specific type of person (elected official, union member, etc). It can also refer to institutions, such as interest groups, corporations, political parties. What you are doing is looking at how an “ individual ” unit, a person, a party is behaving. Polls are the best source of data on people in general, whereas their can be other sources of data on specific classes of individuals. (26)
Groups: analyze group behavior, such as performance on some test. You don ’ t go down to the individual. How did Democratic state legislators vote on a particular issue, as a group? You use aggregates, as opposed to individual data points.
It is not always easy to determine the unit of analysis. Yet the choice of which unit to use is extremely important. (22)

Units of Analysis (22)
Units of Analysis: Exam Scores
Individuals: Student Score
Student: 85
Groups: Average Class Score
Class: 90
Compare to: Other Students Compare to: Other Classes
24

Units of Analysis (22)
Units of Analysis: Political Parties
Individuals: Dem. Or Rep. Party
Democrats
Republicans
Groups: Party System
Compare to: Other Parties
Amer. Party System
Compare to: Other Party Systems
25

Ecological Fallacy: (22-23)
Ecological Fallacy erroneously drawing conclusions about individuals from groups. Solution: only draw conclusion about the units of analysis from which the data is actually drawn.
Example of Ecological Fallacy: Afro-Americans and Wallace
Student found a strong positive (directional) relationship between proportion of a county that was Afro-American and those that voted for George
Wallace and assumed Afro-Americans voted for Wallace. (22-23)
In fact, virtually no minorities supported Wallace. All the student really could say is that counties with a high number of Afro-Americans voted for
Wallace. The county, not Afro-Americans was the unit of analysis.

Units of Analysis (22)
Units of Analysis: Votes for Wallace
Counties, not necessarily Black voters supported Wallace.
Individuals: Voters
Black
Black
White
Groups: County
Compare to: Other Voters
Compare to: Other Counties
Supported Wallace
27

EXAMPLES: Levels of Research: (18)
Theory: Concept 1 is related to Concept 2
Hypothesis: Variable 1 is related to Variable 2
Operational Definition:
Theory: economic development is related to political development
Hypothesis: The more industrialized a nation, the greater the level of mass political participation.
Null: There is no relationship between industrialization and mass participation.
Operational Definition: The higher percentage of manufacturing jobs ( IV ), as measured by United Nations Yearbook, the higher the percentage of people who voted ( DV ) in the last national election, according to the
Stateman
s Yearbook.
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EXAMPLES: Levels of Research: (18)
Theory: Social networking technology (Concept 1) is related to political participation (Concept 2).
Hypothesis: Facebook usage ( IV ) promotes social movement activity ( DV ) in a society by reducing the logistical and informational costs associated with mass mobilizations.
Null: There is no relationship between Facebook usage and social movement activity.
Operational Definition:
IV : Facebook Usage: Number of Pages, Friends, Chats, Hits?
DV : Social Movement Activity: March Attendance, Type of Actions, Nature of Demands, Rhetoric?
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EXAMPLES: Levels of Research: (18)
Hypothesis:
Facebook usage ( IV: Cause ) promotes Soc. Move ( DV: Effect )
Positive:
They go up together.
Facebook usage ( IV: Cause ) promotes Soc. Move ( DV: Effect )
They go down together.

EXAMPLES: Levels of Research: (18)
Hypothesis:
Facebook usage ( IV: Cause ) promotes Soc. Move ( DV: Effect )
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Negative:
The variables move in opposite directions. They have an inverse relationship to each other .
Facebook usage ( IV: Cause ) promotes Soc. Move ( DV: Effect )

EXAMPLES: Levels of Research: (18)
Theory: Economic status effects political participation.
Hypothesis: The higher a person ’ s income, the more likely they are to vote.
Null: There is no relationship between income and voting rates.
Operational Definition: The higher someone ’ s income ( IV ) is, as determined by a poll, the more likely they are to say they vote ( DV ), on the same poll.
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Examples of Units of Analysis and IV and DV:
Hypothesis: The better the state of the economy, the greater the proportion of votes received by the party of the president.
Independent Variable: State of the Economy
Dependent Variable: proportion of votes
Unit of Analysis: Elections
Hypothesis: The more negative the advertising in a Senatorial campaign, the lower the turnout rate.
Independent Variable: negativity of ads
Dependent Variable: turnout
Unit of Analysis: US states (elections)

Examples of Units of Analysis and IV and DV:
Hypothesis: Media attention is necessary for a candidate to succeed in a primary election.
Independent Variable: media attention
Dependent Variable: electoral success
Unit of Analysis: elections
Hypothesis: Southern states have less party competition than Northern states.
Independent Variable: region
Dependent Variable: party competition
Unit of Analysis: states

Review: Levels of Analysis
Theory: Concept 1 is related to Concept 2
Hypothesis: Variable 1 (IV) is related to Variable 2 (DV)
Operational Definition:
IV: Definition of Cause
DV: Definition of Effect

Review: Levels of Analysis
Theory: Inequality and Democracy
Hypothesis: Inequality (IV) adversely effects Democracy (DV).
Operational Definitions:
IV: (Inequality): Income Levels
DV: (Democracy): Voting
IV: (Inequality): Political Contributions
DV: (Democracy): Representation
IV: (Inequality): Racial Density of City
DV: (Democracy): Rate of Government Response

Review: Levels of Analysis
Theory: SSM and 2004 Election
Hypothesis: State bans on SSM (IV) aided Bush ’ s reelection (DV).
Operational Definitions:
IV: (SSM): States with SSM bans on the ballot
DV: (Bush Reelection): Republican Voting % by state
Selection Bias: Variation on DV
DV: (Bush Reelect): Rep. Voting % by state with Ban
DV: (Bush Reelect): Rep. Voting % by state without Ban

Review: Levels of Analysis
Theory: US Culture and Foreign Policy
Hypothesis : A Culture of Unilateralism (IV) shapes US Foreign Policy (DV).
Operational Definitions:
IV: (Unilateralism): Policy/Rhetoric of White House
DV: (US Foreign Policy): Number of US Allies In Iraq
Selection Bias: Variation on DV
DV: (US Foreign Policy): Number of US Allies In Iraq
DV: (US Foreign Policy): Number of US Allies In
Afghanistan

Review: Levels of Analysis
Theory: Gambling and Economic Development
Hypothesis: Legalized Gambling (IV) will increase state revenues (DV).
Operational Definitions:
IV: (Gambling): Casino and Slot Machines Licenses
DV: (State Revenue): Increase in State Taxes
Selection Bias: Variation on DV
DV: (State Revenue): Increase in State Taxes in States with Gambling
DV: (State Revenue): Increase in State Taxes in States without Gambling

Review: Levels of Analysis
Theory: Women and Politics
Hypothesis: Gender (IV) had a defining effect on Nancy Pelosi ’ s election as Speaker of the House of Representatives. (DV).
Operational Definitions:
IV: (Gender): Gender of Candidate
DV: (Leadership): Likelihood Women are Elected as a
Leader.

Essay Outlines:
Introduction:
Topic: Inequality and Democracy
Question: How does Inequality affect Democracy?
Thesis: (Hypothesis): Inequality (IV) adversely effects Democracy
(DV).
Literature Review: What have other said about the topic?
Argument/Analysis: (Operational Definitions):
How do you plan to test/demonstrate your argument:
IV: (Inequality): Racial Density of City
DV: (Democracy): Rate of Government Response

Essay Outlines:
Introduction:
Topic: SSM and 2004 Election
Question: How did State bans on SSM impact the Bush ’ s reelection?
Thesis: (Hypothesis): State bans on SSM (IV) aided Bush ’ s reelection
(DV).
Literature Review: What have other said about the topic?
Argument/Analysis: (Operational Definitions):
How do you plan to test/demonstrate your argument:
IV: (SSM): States with SSM bans on the ballot
DV: (Bush Reelect): Rep. Voting % by state with Ban
DV: (Bush Reelect): Rep. Voting % by state without Ban

Essay Outlines:
Introduction:
Topic: US Culture and Foreign Policy
Question: Is a Culture of Unilateralism shaping US Foreign Policy?
Thesis: (Hypothesis): A Culture of Unilateralism (IV) is shapes US Foreign Policy (DV).
Literature Review: What have other said about the topic?
Argument/Analysis: (Operational Definitions):
How do you plan to test/demonstrate your argument:
IV: (Unilateralism): Policy/Rhetoric of White House
DV: (US Foreign Policy): Number of US Allies In Iraq
DV: (US Foreign Policy): Number of US Allies In Afghanistan