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Einführung in die Analyse kausaler Effekte mit Strukturgleichungsmodellen (LISREL, Mplus)

Kursleitung: Prof. Dr. Rolf Steyer, Dr. Ivailo Partchev, Dr. Safir Yousfi

Sommersemester 2005, Workshop, Kurslänge: 25.00 Stunden, Sprache: Deutsch, Thema: Analyse kausaler Effekte

This course combines the theory of individual and average causal effects in the sense of J. Neyman and D. B. Rubin with analysis techniques of structural equation modelling. All designs and models for the analysis are developed for the purpose to learn about individual, conditional and/or average causal effects. Unlike other courses on the analysis of treatment effects, it uses structural equation modelling (with or without latent variables) instead of analysis of variance techniques, the General Linear Model or related techniques. As will be shown, this will enable us to learn not only about average and conditional effects, but, in specific models, also about individual causal effects.

This course is a synthesis of different traditions in methodology: Rubin's approach to causality, the Campbellian tradition of quasi-experimentation and internal validity, and structural equation modeling, especially latent state-trait modeling, latent change modeling and latent growth curve modeling.

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Individual and Average Causal Effects (Theory)

Motivation: The Simpson paradox
Basic concepts: Individual and average causal effects
Fixed and random effects regression
Generalization to more than two treatment conditions

Identifying the average causal effect via mean differences (Theory)

The prima facie effect
Two kinds of biases
The role of randomization
Heterogeneity of variances between treatment groups

Estimating and testing average causal effects via structural equation modeling (Applications using LISREL and Mplus)

One outcome variable, two or more treatment groups
Several outcome variables, two or more treatment groups
One latent outcome variable, two or more treatment groups
Ordinal outcome variables, two or more treatment groups

Conditional Causal Effects (Theory)

Conditional (average) causal effects
Conditional mean differences

Identifying conditional and average causal effect via conditional mean differences (Theory)

Sufficient conditions for unbiasedness of conditional mean differences
Understanding the sufficient conditions

Estimating and testing conditional and average causal effects via structural equation modeling (Applications using LISREL and Mplus)

Three kinds of covariates: manifest continuous, manifest nominal, latent continuous
Analysis of covariance, regression with interactions, nonorthogonal analysis of variance
One outcome variable, two or more treatment groups, one covariate
Several outcome variables, two or more treatment groups, one covariate
One latent outcome variable, two or more treatment groups, one covariate
Ordinal outcome variables, two or more treatment groups, one covariate

Analysis of Individual and Average Causal Effects (Theory and Applications)

A Single Group, two Pre-test and two Post-test Occasions
Two Groups, two Pre-test and two Post-test Occasions
Extensions

Aufgenommene Videos

Sitzung	Vortragender	Videopräsentation (Realplayer)	Video (RealPlayer)	Präsentation (PDF)
Teil I: 20. - 22. Mai 2005
01	Rolf Steyer	Videopräsentation 01	Video 01	AA 1 Why Conditional and Average Effects.pdf
02	Rolf Steyer	Videopräsentation 02	Video 02	AA 1 Why Conditional and Average Effects.pdf
03	Rolf Steyer	Videopräsentation 03	Video 03	AA 2 Individual and Average Causal Effects.pdf AA 3 Identifying the Average Causal Effect.pdf
04	Rolf Steyer	Videopräsentation 04	Video 04	AA 3 Identifying the Average Causal Effect.pdf
05	Rolf Steyer	Videopräsentation 05	Video 05
06	Rolf Steyer	Videopräsentation 06	Video 06
07	Rolf Steyer	Videopräsentation 07	Video 07
08	Rolf Steyer	Videopräsentation 08	Video 08	AA 4 Conditional Causal Effects.pdf
09	Rolf Steyer	Videopräsentation 09	Video 09
Teil II: 10. - 12. Juni 2005
10	Rolf Steyer	Videopräsentation 10	Video 10	AA 3 Identifying the Average Causal Effect.pdf AA 4 Conditional Causal Effects.pdf
11	Rolf Steyer	Videopräsentation 11	Video 11
12	Rolf Steyer	Videopräsentation 12	Video 12	AA 5 Analysis of Individual and Average Effects.pdf
13	Rolf Steyer	Videopräsentation 13	Video 13	AA 5 Analysis of Individual and Average Effects.pdf
14	Steffi Pohl & Safir Yousfi	Videopräsentation 14	Video 14	Analyse der Effekte von negativ formulierten Items.pdf
15	Rolf Steyer	Videopräsentation 15	Video 15	AA 6 Theory of causal regressions.pdf
16	Rolf Steyer	Videopräsentation 16	Video 16	AA 7 Latent growth curves.pdf
17	Safir Yousfi	Videopräsentation 17	Video 17
18	Rolf Steyer	Videopräsentation 18	Video 18

Material

Referenzen

Steyer, R., Gabler, S., von Davier, A., Nachtigall, C. & Buhl, T. (2000a) Causal regression models I: individual and average causal effects. Methods of Psychological Research-Online, 5, 2, 39-71. (http://www.dgps.de/fachgruppen/methoden/mpr-online/)

Steyer, R., Gabler, S., von Davier, A. & Nachtigall, C. (2000b) Causal regression models II: unconfoundedness and causal unbiasedness. Methods of Psychological Research-Online, 5, 3, 55-86. (http://www.dgps.de/fachgruppen/methoden/mpr-online/)

Steyer, R., Nachtigall, C., Wüthrich-Martone, O. & Kraus, K. (2002). Causal regression models III: covariates, conditional and unconditional average causal effects. Methods of Psychological Research-Online, 7, 1, 41-68. (http://www.dgps.de/fachgruppen/methoden/mpr-online/)

Steyer, R., Flory, F., Klein, A., Partchev, I., Yousfi, S., Müller, M. & Kröhne, U. (2004). Testing Average Effects in Regression Models with Interactions. Eingereicht.

Steyer, R. (2005). Analyzing Individual and Average Causal Effects via Structural Equation Models. Methodology European Journal of Research Methods for the Behavioral and Social Sciences, Vol.1(1), 39-54.