Transcript Chapter 3 - Blogs @ Suffolk University

Research Design:
Alan Monroe
Chapter 3
The Concept of Causality (31)
Casuality
The 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 (not
sufficient 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 observe
between the independent and dependent variables is not caused
by other factors.
Types of Research Designs (32)
Experimental Design
It 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 Groups
The 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-Test
A 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 accurate
sample of an entire population, one solution is to reduce size of
population: college students for example.)
Artificial Setting (does it test real behavior?)
Outside influences (you can never fully isolate subjects from
other variables.)
Ethical considerations (cannot mistreat or expose humans to
harmful 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.
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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 be
significant (indicating IV had an effect)
Time Order: includes measure of DV before and after IV.
Non–spuriousness: effect of all outside forces is theoretically equal
on 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: urbanization
DV: 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?
IV: Cause
DV: Effect?
Race:
M-C, C-ED, BW, City
M-C, C-ED, WW, City
Voter Turnout
Voter Turnout
Region:
M-C, C-ED, BW, City
M-C, C-ED, BW, Suburbs
Voter Turnout
Voter Turnout
Class:
M-C, C-ED, BW, City
W-C, C-ED, BW, City
Voter Turnout
Voter Turnout
Correlational Design: Examples
What impact does race rate of low-birth weight babies born in the US?
IV: Cause
Education and Class:
M-C, C-ED, BW, Suburbs
M-C, NC-ED, BW, Suburbs
W-C, N-ED, BW, Suburbs
Race and Class:
M-C, C-ED, BW, Suburbs
M-C, C-ED, WW, Suburbs
W-C, C-ED, WW, Suburbs
Region:
M-C, C-ED, BW, B-USA
M-C, C-ED, BW, NB-USA
M-C, C-ED, BW, NB-USA, US 6 months
DV: Effect
RLBW 1/100
RLBW 1/100
RLBW 1/100
RLBW 1/100
RLBW 1/1000
RLBW 1/500
RLBW 1/100
RLBW 1/1000
RLBW 1/100
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.
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