# ☰Workshops: Kursinformationen

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)
• 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)
1. A Single Group, two Pre-test and two Post-test Occasions
2. Two Groups, two Pre-test and two Post-test Occasions
3. Extensions

### Aufgenommene Videos

Sitzung
Vortragender
Videopräsentation (Realplayer)
Video (RealPlayer)
Präsentation (PDF)
Teil I: 20. - 22. Mai 2005
01
Rolf Steyer
02
Rolf Steyer
03
Rolf Steyer
04
Rolf Steyer
05
Rolf Steyer
06
Rolf Steyer

07
Rolf Steyer

08
Rolf Steyer
09
Rolf Steyer
Teil II: 10. - 12. Juni 2005
10
Rolf Steyer
11
Rolf Steyer
12
Rolf Steyer
13
Rolf Steyer
14
Steffi Pohl &
Safir Yousfi
15
Rolf Steyer
16
Rolf Steyer
17
Safir Yousfi
18
Rolf Steyer

### Material

Slides
AA 1 Why Conditional and Average Effects.pdf
AA 2 Individual and Average Causal Effects.pdf
AA 3 Identifying Average Causal Effect.pdf
AA 4 Conditional Causal Effects.pdf
AA 5 Analysis of Individual and Average Effects.pdf
AA 6 Theory of causal regressions.pdf
AA 7 Latent growth curves.pdf
Analyse der Effekte von negativ formulierten Items.pdf

Analyzing Individual and Average Causal Effects Printed version.pdf
Testing average effects Methodology Version 8.pdf

Excel sheets
Excelsheet Ben March 4.xls
Excelsheet mit Kovariate Ben April 14.xls
Excelsheet zum bedingten Effekt von Benjamin Nagengast Jan 05.xls
Excelsheet zum Bias Nur Bias1 Dez 04.xls
Excelsheet zum Bias Nur Bias1 Dez 04a.xls
Excelsheet zum Bias Nur Bias2 Dez 04.xls
Excelsheet zum Bias Randomized Dez 04.xls
Excelsheet zum Bias von Benjamin Nagengast Dezember 04 mit Abhaengigkeit.xls
Excelsheet zum Bias von Benjamin Nagengast Dezember 04.xls
Excelsheet zum Bias von Benjamin Nagengast Oktober 04 mit Abhaengigkeit.xls
Excelsheet zum Bias von Benjamin Nagengast Oktober 04 mit Unabhaengigkeit.xls
Excelsheet zur Unkonfundiertheit von Benjamin Nagengast Oktober 04.xls

Data sets with Syntax
ICE Modelle mit simulierten Daten.zip
Analyse der Effekte negativ formulierter Items.ZIP
Causal Growth Curve Models.ZIP
Causal Randomized LST-Model.ZIP

EffectLite
http://www.statlite.com/

### 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.