Courses: Course information

  Methods of Evaluation Research

Speakers: Prof. Dr. Rolf Steyer

Winter term 2015/2016, Course, Language: English, Topic: Methods of evaluation research

Topic: Analysis of conditional and average total causal treatment effects

1. Why we need a theory of causal effects
2. Concepts of probability theory
3. Basic ideas of the theory of causal effects
4. The core of the theory of causal effects
5. Causality conditions (sufficient conditions for unbiasedness), randomization, and covariate selection
6. First example: nonorthogonal analysis of variance
7. Intercept function, effect functions, and average effects in the example of nonorthogonal analysis of variance
8. Estimating intercept function, effect functions, and average effects via SPSS and its limitations
9. Analyzing the data of nonorthogonal analysis of variance with SPSS ANOVA (Typ I, II, III sums of squares) and why these analyses yields wrong results
10. The hypothesis of no treatment effects via SPSS using the R²-difference test
11. Analysis of the data of nonorthogonal analysis of variance with EffectLiteR
12. Second example: The Kirchmann study on the treatment effects on depression and its analysis with EffectLiteR
13. Third example: The Klauer study on the training of inductive reasoning
14. EffectLiteR analysis of the Klauer data with a continuous covariate
15. Main hypotheses in EffectLiteR and the various conditional and average treatment effects
16. EffectLiteR analysis of the Klauer data with a continuous covariate and a qualitative covariate

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Literature

Causal effects

• Campbell, D. T. & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research on Teaching. In N. L. Gage (Ed.), Handbook of research on teaching. Chicago: Rand McNally.
• West, S. G., Biesanz, J. C. & Pitts, S. C. (2000), Causal inference and generalization in field settings. Experimental and quasi-experimental designs. In H. T. Reis and C. M. Judd (eds.), Handbook of research methods in social and personality psychology. Cambridge University Press.
• Steyer, R. (2003). Wahrscheinlichkeit und Regression. Berlin: Springer. (Kapitel 15 - 17)
• Steyer, R. (2004). Was wollen und was können wir durch empirische Kausalforschung erfahren? In E. Erdfelder & J. Funke (Hrsg.), Allgemeine Psychologie und deduktivistische Methodologie (pp.127-147). Göttingen: Vandenhoek und Ruprecht.
• Steyer, R. (2005). Analyzing Individual and Average Causal Effects via Structural Equa­tion Models. Methodology-European Journal of Research Methods in the Behavio­ral and Social Sciences, 1, 39-54.
• Steyer, R. & Partchev, I. (2006). Manual for EffectLite: A Program for the Uni- and Multivariate Analysis of Unconditional, Conditional and Average Mean Differences Between Groups.
• Pohl, S., Steyer, R. & Kraus, K. (2008). Modelling method effects as individual causal effects. Journal of the Royal Statistical Society. Series A, 171, 41--63.
• Steyer, R., Partchev, I., Kröhne, U., Nagengast, B., & Fiege, C. (in preparation). Probability and Causality.
• Steyer. R. and Nagel, W. (2017). Probability and conditional expectation: Fundamentals for the empirical sciences. Chichester: Wiley.

Download of the relevant examination / lecture relevant literature (only for students of the lectures)