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Experimental Design & Analysis

Experimental Design & Analysis. Introduction & Causal Inference January 23, 2006. 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:

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Experimental Design & Analysis

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  1. Experimental Design & Analysis Introduction & Causal Inference January 23, 2006 DOCTORAL SEMINAR, SPRING SEMESTER 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, 4th 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, 4th ed. Upper Saddle River, NJ: Prentice Hall. • SAS, SPSS software should be installed on your PC

  6. Evaluative Criteria • Class participation and attendance 20% • Weekly homework assignments 25% • 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 and Prediction • 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?

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

  16. Determining Causation • 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. Strengths Establish a causal relationship Advantage over correlational data Control variation Weaknesses Artificiality of setting Theory-laden assumptions Limited generalizability Experiments

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