1. Making Causal Inferences and Ruling out Rival Explanations
29 February

2. 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?

3. Hypothesized relationship:
%Women elected in National Parliaments
Party rules gender quotas

4. 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

5. Establishing Causation:
Co-variation
Time – (x occurs before y)
Consistent with other evidence
Rule out rival explanations
Example – spurious relationship

6. 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

7. Spurious Relationship
Fire damage in $
# of fire engines responding to call +
Intensity of fire +
(the third variable problem)

8. Alternative explanations:
Electoral System
Political Culture
%Women elected in National Parliaments
Women’s Labor Force Participation
Party rules - quotas
Access to educational opportunities
Women’s Political Resources
% of women candidates standing for election

9. Spurious Relationship
%women elected
Party quotas +
Political culture +

10. When choosing a research design?
When and how to make observations:
Internal Validity
Ability to establish causality
External Validity
Ability to generalize

11. Types of Designs: Experimental designs Quasi-experimental
Control groups
Quasi-experimental
Non-experimental designs
Statistical controls

12. Manipulation of an independent variable
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

13. These basic characteristics effectively solve the two basic problems in nonexperimental (correlational) research:
The directionality problem
The third variable problem

14. 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

15. 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.

16. Problems? Random assignment might be difficult in this case.
Turn to quasi-experiments when randomization not possible

17. To Review - One Group Post-Test Only Design
X O
The simplest and the weakest possible design:
Lack of a pretest prevents assessment of change
Lack of a control group prevents threats from being ruled out.

18. Threats to Internal Validity
Selection Threats
Maturation
History
Testing
Instrumentation
Regression
Note: Experimental designs control for these
Party adopts quotas  % women elected
X O

19. 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 O O3 Compared to norms or expectations, only O2 should be unusual.

20. Post-Test Only Design with Nonequivalent Groups
X O O
Threats:
Selection

21. 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

22. 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.

23. O O O O O O O O O X O

24. Untreated Control Group Design with Pretest and Posttest
O1 X O2
O O2
Can compare change within groups and across groups
Expect change in treatment group to be greater
Selection still a threat

25. 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.