Speakers: Prof. Dr. Rolf Steyer
Summer term 2009, Workshop, Course length: 11.50 hours, Language: English, Topic: Analysis of causal effects
This course is an introduction to the stochastic theory of causality (Steyer et al. Springer: 2010), which is a generalization of the theory of causal effects in the tradition of J. Neyman and D. B. Rubin. All designs and models for the analysis are developed for the purpose of learning about conditional and/or average causal effects, in some cases also about individual causal effects. Unlike other courses on the analysis of treatment effects, it uses structural equation modeling (with or without latent variables) instead of analysis of variance techniques, the General Linear Model or related techniques.
This course is a synthesis of different traditions in methodology: Rubin's approach to causality, the Campbellian tradition of quasiexperimentation and internal validity, and structural equation modeling, especially latent statetrait modeling, latent change modeling and latent growth curve modeling.
Although this workshop does not require experience and knowledge in structural equation modelling (SEM), we do not recommend this workshop as a first introduction to SEM, if the motivation is to have an introduction into SEM. For this purpose, I rather suggest our course "Introduction to latent variable modeling with Structural Equation Models using Mplus" held in April 2009 at the University of Jena. This and other courses are still available in the internet and on DVDs at: http://www.metheval.unijena.de/courses.
The course, "Introduction to the Analysis of Causal Effects with EffectLite, LISREL and/or Mplus" aims at those students and researchers that are interested in data analysis in experimental and quasiexperimental studies involving covariates such as one or several pretests, a discrete treatment variable, and one or several outcome variables.
EffectLite is a program developed by Rolf Steyer and Ivailo Partchev that will be provided to all participants. It analyzes a generalized multivariate analysis of variance and covariance. It creates LISREL and/or Mplus input files, reads and interprets the results, computes some statistics, and produces an output file containing the results that most important for the analysis of causal effects. In the univariate case, EffectLite does not assume homogeneity of variances of the outcome variable between groups. In the multivariate case with two or more outcome variables it does not assume homogeneity of covariance matrices of the outcome variables between groups. Furthermore, it allows analyzing mean differences and adjusted means differences  aimed at estimating causal effects  between groups with respect to
The covariate(s) may also be qualitative (blocking factors). In this case, we estimate and test average effects for nonorthogonal analysis of variance designs, provided that the covariates are specified as qualitative indicator variables. If the covariates fulfil certain assumptions, the program estimates and tests the conditional and average causal effects.
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Session  Videopresentation with slides  

Wednesday, August 05, 2009 

Session 01 
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Session 02 
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Session 03 
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Session 04 
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Thursday, August 06, 2009 

Session 05 
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Session 06 
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Session 07 
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Session 08 
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In Lisrelversions which had compiled before August, 29, 2007 there were a bug in the FIML option for multiple group analyses. Here you can get an updated version of the executable for the student edition of Lisrel 8.8 without this bug. If you wanted to have the updated executable for the full version of Lisrel, please contact the technical support from Scientific Software International, Inc.