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Introduction to the Analysis of Causal Effects with EffectLite, LISREL and M*plus*

Kursleitung: Prof. Dr. Rolf Steyer, Dipl.-Psych. Norman Rose

Sommersemester 2007, Workshop, Kurslänge: 11.75 Stunden, Sprache: Englisch, Thema: Analysis of causal effects

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.

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 we rather suggest our workshop "Erste Einführung in die Analyse von Strukturgleichungsmodellen mit LISREL" held in German in November 2005. Similar introduction in English is: "Analysis of Structural Equation Models with Lisrel 8.54" held in March 2004. (These workshops are still available in the internet and on DVDs).

The present workshop, "Introduction to the Analysis of Causal Effects with EffectLite, LISREL and M*plus*" aims at those interested in data analysis in experimental and quasi-experimental 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 Prof. Dr. R. Steyer and his colleagues, which will be provided to all participants in the workshop. It analyzes a generalized multivariate analysis of variance and covariance. It creates LISREL or M*plus* input files, reads and interprets the results, computes some statistics, and produces an output file containing the most important results. EffectLite does not assume homogeneity of variances (in the univariate case with a single outcome variable) or covariance matrices (in the multivariate case with two or more outcome variables) of the outcome variables between groups. Furthermore, it allows analyzing mean differences between groups with respect to (a) several manifest outcome variables, (b) one or more latent outcome variables, and (c) a mixture of the two kinds of outcome variables. The results include estimates of conditional and average effects with respect to several manifest covariates or with respect to one or more latent covariates, and a mixture of the two kinds of covariates. The covariate(s) may also be qualitative. In this case we estimate and test average effects for non-orthogonal 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 average causal effects.

In the workshop we will:

- present the theory of individual and average causal effects,
- show how to use EffectLite, LISREL and M
*plus*for this class of models, and - show how to use LISREL and M
*plus*for individual causal effect models.

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

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

- 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
- Several latent outcomes

- Conditional (average) causal effects
- Conditional mean differences

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

- 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
- Several latent outcomes and several latent covariates

- A Single Group, two Pre-test and two Post-test Occasions
- Two Groups, two Pre-test and two Post-test Occasions
- Multitrait-multimethod analyses via ICE models

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Monday, August 27, 2007 |
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Tuesday, August 28, 2007 |
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- Steyer, R., Partchev, I., Kröhne, U., Nagengast, B., & Fiege, C. (2007). Causal Effects in Between-Group Experiments and Quasi-Experiments: Theory. Book in preparation.
- Steyer, R., Partchev, I., Kröhne, U., Nagengast, B., & Fiege, C. (2007). Causal Effects in Between-Group Experiments and Quasi-Experiments: Theory. Book in preparation. Basic Concepts and Definitions.
- 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.
- 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.
- 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.
- Steyer, R., Flory, F., Klein, A., Partchev, I., Yousfi, S., Müller, M. & Kröhne, U. (submitted). Testing Average Effects in Regression Models with Interactions.

- Steyer, R. (2005). Analyzing Individual and Average Causal Effects via Structural Equation Models. In: Methodology European Journal of Research Methods for the Behavioral and Social Sciences, 1, 39-54.
- Pohl S. , Steyer R., (submitted). Modelling Method Effects as Individual Treatment Effects.

*A short excursus concerning true change models*- Steyer, R., Eid, M. und Schwenkmezger, P. (1997). Modeling true intraindividual change: True change as a latent variable. Methods of Psychological Research-Online, 2, 21-33.
- Steyer, R., Partchev, I. & Shanahan, M. (2000). Modeling True Intra-Individual Change in Structural Equation Models: The Case of Poverty and Children's Psychosocial Adjustment. In: Little, T. D., Schnabel, K. U., & Baumert, J. (Eds.), Modeling longitudinal and multiple-group data: Practical issues, applied approaches, and specific examples (pp. 109-126). Hillsdale, NJ: Erlbaum