1. DOCTORAL SEMINAR, SPRING SEMESTER 2006 Experimental Design & Analysis Introduction & Causal Inference January 23, 2006

2. Course Introduction The goal of this course is to provide doctoral students the concepts and tools needed to investigate research questions using experimental methods This course focuses on:  Causal inference and validity  Experimental design  Data analysis and use of SAS, SPSS

3. Course Texts We will be using three texts:  Bechtel, W. (1988). Philosophy of Science: An Overview for Cognitive Science. Hillsdale, NJ: Lawrence Erlbaum Associates.  Keppel, G. and Wickens, T.D. (2004). Design and Analysis: A Researcher’s Handbook, 4 th ed. Upper Saddle River, NJ: Prentice Hall.  Shadish, W.R., Cook, T.D., & Campbell, D.T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston: Houghton Mifflin.

4. Discussion Readings Readings from journal articles will be the basis of class discussion  Submit written one-page summary  Prepare discussion questions

5. Other Resources Cody, R. and Smith, J. (1997). Applied Statistics and the SAS Programming Language, 4 th ed. Upper Saddle River, NJ: Prentice Hall. SAS, SPSS software should be installed on your PC

6. Evaluative Criteria Class participation and attendance20% Weekly homework assignments25% Midterm exam (March 20, 2006)25% Final project*30%  Design of experiment, hypotheses  Data collection  Analysis  Presentation of hypotheses, findings *HRC approval necessary

7. What is Science? Epistemological traditions  Mysticism  Rationality  Logical Positivists  Post-Positivists Scientific method

8. Mysticism Operates without intellectual effort or sensory processing  Arrive at knowledge or belief through non- rational means, such as through faith or feeling

9. Rationalism Knowledge is developed through reasoning  Syllogisms  Proofs Logical rules are followed to arrive at an acceptable conclusion

10. Modus Ponens All crows are black (the major premise) This is a crow (the minor premise) Therefore, this crow is black (the conclusion)

11. Modus Tollens If it rains, the game will be canceled (the major premise) The game was not canceled (the minor premise) Therefore, it did not rain (the conclusion)

12. Empiricism Knowledge through observation, experience through our senses  Naïve empiricism: anything that cannot be directly observed does not exist  Sophisticated empiricism: we can observe phenomena indirectly through direct observation of the impact on other things

13. Goals of Scientific Research To describe behavior To predict behavior To determine the causes of behavior To understand or explain behavior

14. Description begins with careful observation Prediction based on observation of regularity of phenomena  Two events are systematically related to one another, it becomes possible to make prediction  Illusory correlations? Description and Prediction

15. Accurate prediction does not imply accurate causal attribution  Predict rain when it is cloudy  Predict light turns on when switch is flipped Determining Causation

16. Cause and effect are distinct entities  When these are the same, relationship is tautological Cause and effect covary  Probabilistic relationship Cause precedes effect Elimination of rival explanations

17. Power of Explanation Researchers seek to discover what is the nature of the relationship that causes an effect or phenomenon and allows prediction

18. Exploring Relationships Breast-fed babies have higher IQs than formula- fed babies Plastic surgery patients are more likely to commit suicide Violence on TV increases rate of crime among youth School breakfast programs lead to improved academic performance

19. Distinctions of Importance Correlation vs. causation vs. confounds Independent vs. dependent variable Construct vs. variable Treatment group vs. control group Causal description vs. causal explanation Randomized vs. quasi experiments Natural experiment vs. observation Falsification vs. confirmation

20. Experiments Strengths  Establish a causal relationship  Advantage over correlational data  Control variation Weaknesses  Artificiality of setting  Theory-laden assumptions  Limited generalizability