Making Causal Inferences and Ruling out Rival Explanations
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Transcript of Making Causal Inferences and Ruling out Rival Explanations
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Making Causal Inferences and Ruling out Rival Explanations
29 February
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Questions?
• How do we know that X is causing Y?• Did X have any effect on Y?
– If X had not happened would Y have changed anyway?
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Hypothesized relationship:
%Women elected in National Parliaments
Party rules gender quotas
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Questions?
• How do we know that party quotas causing changes in %women elected?
• Standard Design– Party adopts quotas % women elected
X O Where X = treatment and O = observation
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Establishing Causation:
• Co-variation• Time – (x occurs before y)• Consistent with other evidence• Rule out rival explanations
– Example – spurious relationship
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Spurious Relationship
a relationship in which two variables that are not causally linked appear to be so because a third variables in influencing both of them
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Spurious Relationship
Fire damage in $# of fire engines responding to call
+
Intensity of fire
+ +
(the third variable problem)
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Alternative explanations:
%Women elected in National Parliaments
Electoral System
Political Culture
Women’s Labor Force Participation
Access to educational opportunities
Women’s Political Resources
% of women candidates standing for election
Party rules - quotas
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Spurious Relationship
%women electedParty quotas +
Political culture
+ +
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When choosing a research design?
• When and how to make observations:• Internal Validity
– Ability to establish causality
• External Validity– Ability to generalize
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Types of Designs:
• Experimental designs• Control groups
• Quasi-experimental• Non-experimental designs
• Statistical controls
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Experiments come in a wide variety of apparent types but all share three basic characteristics:
• Random assignment
• Manipulation of an independent variable
• Control over other potential sources of systematic variance X O1
R
O2
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These basic characteristics effectively solve the two basic problems in nonexperimental (correlational) research:
•The directionality problem
•The third variable problem
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Random Assignment
Random assignment means that assignment to experimental conditions is determined by chance.
Participants have a equal probabilities of being assigned to a treatment or control group.
This insures that any pre-existing characteristics that participants bring with them to the study are distributed equally among the experimental groups . . . in the long run.
Treatment group = (equivalent to) Control group
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Think about example of party quotas a % women elected:
Randomly assign countries to two groups: treatment and control
Theoretically should end up with two groups that have equivalent distributions on all other “third variables” (i.e. culture, % women in labour force, etc.)
Have one group adopt quotas
Observe % women elected, treatment group expected to have higher average for % women elected.
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Problems?
• Random assignment might be difficult in this case.
• Turn to quasi-experiments when randomization not possible
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To Review - One Group Post-Test Only Design
X O
The simplest and the weakest possible design:
(a) Lack of a pretest prevents assessment of change
(b) Lack of a control group prevents threats from being ruled out.
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Threats to Internal Validity
• Selection Threats • Maturation• History• Testing• Instrumentation• Regression• Note: Experimental designs
control for these
Party adopts quotas % women elected
X O
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One Group Post-Test Only Design
X O
Without changing the basic nature of this design, it can be improved considerably by adding additional outcome measures:
O1
X O2
O3
Compared to norms or expectations, only O2 should be unusual.
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Post-Test Only Design with Nonequivalent Groups
X O
O
Threats:
Selection
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One-Group Pretest Post-Test Design
O X O
This very common applied design is susceptible to all threats to within-groups comparisons:
• History
• Maturation
• Testing
• Regression
• Instrumentation
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One-Group Pretest Post-Test Design
O X O
One powerful modification is to add pretests:
O O O O O X O
Maturation threats can now be examined and their influence separated from treatment effects.
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O O O O O O O O O X O
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Untreated Control Group Design with Pretest and Posttest
O1 X O2
O1 O2
Can compare change within groups and across groups
Expect change in treatment group to be greater
Selection still a threat
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Conclusions:
• Experiments best for internal validity• May not be good on external validity• In non-experimental designs, use statistical
controls (hold constant all possible “third” variables.