SMABS 2004 Jena University
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European Association of Methodology

Department of methodology and evaluation research

Jena University

Contributions: Abstract

Reviewing the definitions of true-score variables in Classical Test Theory and of latent trait variables in Latent State-Trait Theory it is shown that individual causal effects can be defined as differences between true-scores in a treatment and a control condition, respectively. This invokes that the design and analysis techniques of latent variable modelling can be utilized for models in which individual causal effects are the values of latent variables. Specifically, designs and methods of data analysis are presented which yield not only (a) estimates of the average causal effect of a treatment variable on a response variable in the sense of Rubins approach to causality, but also (b) estimates of the variance of the individual causal effects and (c) of the covariance between pretest and individual causal effects. It is shown how to include exogenous variables in the analysis that (d) explain the interindividual differences in the individual causal effects of the treatment variable on the response variable. All this is based on a specific design without random assignment of units to the treatment conditions, assessing pretests and introducing some additional assumptions which, however, can be tested in the analysis as well.