Workshops: Course information

en  Introduction to the Analysis of Causal Effects with EffectLite, LISREL and Mplus

Speakers: Prof. Dr. Rolf Steyer, Dipl.-Psych. Ulf Kröhne & Dipl.-Psych. MSc Steffi Pohl

Winter term 2005/2006, Workshop, Course length: 12.25 hours, Language: English, Topic: 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 Mplus" 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 analyzes a generalized multivariate analysis of variance and covariance. It creates LISREL or Mplus 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:

  1. present the theory of individual and average causal effects,
  2. show how to use EffectLite, LISREL and Mplus for this class of models, and
  3. show how to use LISREL and Mplus for individual causal effect models.


In order to view the downloadable videos, you need the RealPlayer which you can download here. The videos are hosted at the Digitale Bibliothek Thüringen Digitale Bibliothek Thüringen (dbt). The green linked videos and materials are for free. Click the green link to watch the video or download the file! In order to access all materials and videos, you need to log in.

 
Individual and Average Causal Effects (Theory)
  • 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
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
  • Several latent outcomes
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
  • Several latent outcomes and several latent covariates
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. Multitrait-multimethod analyses via ICE models


Videos


Session Topic Abstract Presenter Video-presentation with slides (Realplayer) Video (RealPlayer) Slides (PDF) & models Blackboard pictures
01 Prima facie effects: Example 1 (Simpson paradox) Abstract 01 Rolf Steyer Video with slides 01 Video 01 Causal Effects in Between-Group Experiments and Quasi-Experiments: Theory Picture 1
Picture 2
Picture 3
Picture 4
Picture 5
Picture 6
02 Prima facie effects: Example 2 (Non-orthogonal ANOVA and using EffectLite for data analysis) Abstract 02 Rolf Steyer Video with slides 02 Video 02 Causal Effects in Between-Group Experiments and Quasi-Experiments: Theory
03 Causal effects Abstract 03 Rolf Steyer Video with slides 03 Video 03 Causal Effects in Between-Group Experiments and Quasi-Experiments: Theory Picture 1
Picture 2
Picture 3
Picture 4
Picture 5
Picture 6
Picture 7
Picture 8
Picture 9
Picture 10
04 Prima facie effects and their relationship to causal effects Abstract 04 Rolf Steyer Video with slides 04 Video 04 Causal Effects in Between-Group Experiments and Quasi-Experiments: Theory
05 Latent covariates and latent outcome variables Abstract 05 Rolf Steyer Video with slides 05 Video 05
06 LISREL input and output files for the analysis with latent covariate and outcome variables Abstract 06 Rolf Steyer Video with slides 06 Video 06
07 Modelling method effects as individual causal effects Abstract 07 Rolf Steyer Video with slides 07 Video 07 Model: Unconstrained model with method factors
08 Individual and Average Causal Effects Abstract 08 Rolf Steyer & Steffi Pohl Video with slides 08 Video 08 Analysis of individual and average causal effects
Modeling method effects as individual causal effects
Picture 1
Picture 2
Picture 3
Picture 4
Picture 5
Picture 6
Picture 7
Picture 8
Picture 9
Picture 10
Picture 11
Picture 12
09 A new multitrait-multimethod model Abstract 09 Steffi Pohl Video with slides 09 Video 09 Modeling method effects as individual causal effects

Model: MTMM-ICE-Model (only the input file for Mplus & LISREL)


Material

Readings Slides Excel sheets Data sets Models

Software

EffectLite LISREL Student Version