Download
making causal inferences and ruling out rival explanations n.
Skip this Video
Loading SlideShow in 5 Seconds..
Making Causal Inferences and Ruling out Rival Explanations PowerPoint Presentation
Download Presentation
Making Causal Inferences and Ruling out Rival Explanations

Making Causal Inferences and Ruling out Rival Explanations

143 Views Download Presentation
Download Presentation

Making Causal Inferences and Ruling out Rival Explanations

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  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. + # of fire engines responding to call Fire damage in $ + + Intensity of fire Spurious Relationship (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. + Party quotas %women elected + + Political culture Spurious Relationship

  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 • Control groups • Quasi-experimental • Non-experimental designs • Statistical controls

  12. 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 O2 O3Compared 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 OO 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 O1 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.