Research Design:

Alan MonroeChapter 3

The Concept of Causality (31)

CasualityThe types of research designs reviewed here are all intended to test whether

one variable causes another or causes variation in another.

Three (3) Requirements of Causality (31-32)

Correlation: two things tend to occur at the same time (notsufficient to est. causation)

Examples:Whenever there is a foreign policy crisis, presidential

popularity increases.If Catholic, then more likely to oppose abortion.

Time Order: cause has to happen before the effect.

Non-spuriousness: to make sure any correlation we observebetween the independent and dependent variables is not causedby other factors.

Types of Research Designs (32)

Experimental DesignIt involves a group of subjects (units of analysis), which is divided into two

groups (randomly, to assure they are identical on the DV).

Experimental and Control GroupsThe first group is the experimental group, the second is the control group. The experimental group receives a stimulus (the Independent Variable), the control does not.

Post-TestA Post-Test is then given to both groups to test the effect (DV) of the stimulus (IV). You then compare the results.

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Experimental Design

Examples of Experimental Design (33)

Introduction to American Government Example: Does it Increase Political interest?

Hypothesis: taking course increases political interest in college students.

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Experimental: 2011 State of the Union Example

Hypothesis: watching the State of the Union address will improve the public’s opinion of how well Obama has handled the economy.

Subjects: students in a class

Pretest: before SOU give them a survey measuring their attitudes about the candidates

Post-test: did they watch the debate, and what is the strength of their preference.

Problems With Experimental Design

Problems With Experimental Design

Hard to get representative samples (hard to get accuratesample of an entire population, one solution is to reduce size ofpopulation: college students for example.)

Artificial Setting (does it test real behavior?)

Outside influences (you can never fully isolate subjects fromother variables.)

Ethical considerations (cannot mistreat or expose humans toharmful stimuli.)

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The Quasi Experimental (Natural Experiment) (37)

Quasi-Experimental It is also called the before and after test: you compare the DV (a Pretest and Posttest) before and after the IV has been applied.

Differs from experimental design in several ways:1. Groups are not assigned (we observe some happen, and then go back and sort into experimental and control groups.)

2. Requires a Pretest of DV so amount of change can be measured.…

Quasi Experimental Design

Quasi-Experimental: 2011 State of the Union Example

Hypothesis: watching the State of the Union address will improve the public’s opinion of how well Obama has handled the economy.

Subjects: students in a class

Pretest: before SOU give subjects a survey measuring their attitudes about the president’s handling of the economy.

Post-test: measure the strength of their preference after SOU.

Note: no control group. Why?

Quasi Experimental: Presidential Debate Example (38)

Hypothesis: watching the State of the Union address will improve the public’s opinion of how well Obama has handled the economy.

Subjects: students in a class

Pretest: before SOU give them a survey measuring their attitudes about the candidates

Post-test: did they watch the debate, and what is the strength of their preference.

Quasi Experimental: Presidential Debate Example (38)

Hypothesis: watching a presidential debate increases intensity of support for the candidate.

Subjects: students in a class

Pretest: before debate give them a survey measuring their attitudes about the candidates

Post-test: did they watch the debate, and what is the strength of their preference.

Meeting Conditions of Causality: Quasi Experimental (38)

Correlation: change between pretest and post-test has to besignificant (indicating IV had an effect)

Time Order: includes measure of DV before and after IV.

Non–spuriousness: effect of all outside forces is theoretically equalon all subjects. (they are all exposed to same amount of TV ads, thus any changes comes from the IV).

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Correlational Design (40)

It is very simple: collecting data on the IV and DV in order to see if there is a pattern or relationship. It is the most common design in political science.

Examples: Turnout in Urban Areas

IV: urbanizationDV: voter turnout

Operational definitions:

Urbanization: percentage of pop. Living in “urban places,” according to US Census.

Turnout: votes cast divided by voting-age population.

Correlational Design

Correlational Design: Examples

What impact does race, region or class have on voter turnout?

Race: M-C, C-ED, BW, City Voter TurnoutM-C, C-ED, WW, City Voter Turnout

Region: M-C, C-ED, BW, City Voter TurnoutM-C, C-ED, BW, Suburbs Voter Turnout

Class: M-C, C-ED, BW, City Voter TurnoutW-C, C-ED, BW, City Voter Turnout

IV: Cause DV: Effect?

Correlational Design: ExamplesWhat impact does race rate of low-birth weight babies born in the US?

Education and Class: M-C, C-ED, BW, Suburbs RLBW 1/100M-C, NC-ED, BW, Suburbs RLBW 1/100W-C, N-ED, BW, Suburbs RLBW 1/100Race and Class: M-C, C-ED, BW, Suburbs RLBW 1/100 M-C, C-ED, WW, Suburbs RLBW 1/1000W-C, C-ED, WW, Suburbs RLBW 1/500Region: M-C, C-ED, BW, B-USA RLBW 1/100M-C, C-ED, BW, NB-USA RLBW 1/1000M-C, C-ED, BW, NB-USA, US 6 months RLBW 1/100

IV: Cause DV: Effect

Meeting Conditions of Causality: Correlational Design (38)

Correlation: is directly tested between the IV and DV.

Time Order: it is weakest here: there is no consideration for the point in time when the IV and DV occurred. Have to reliable on IV that are known to exist before DV, like race, gender.

Non-spuriousness: considers control variables.…