Wintersemester 2015/2016, Kurs, Sprache: Englisch, Thema: Methods of evaluation research
Topic: Analysis of conditional and average total causal treatment effects
Why we need a theory of causal effects
Concepts of probability theory
Basic ideas of the theory of causal effects
The core of the theory of causal effects
Causality conditions (sufficient conditions for unbiasedness), randomization, and covariate selection
First example: nonorthogonal analysis of variance
Intercept function, effect functions, and average effects in the example of nonorthogonal analysis of variance
Estimating intercept function, effect functions, and average effects via SPSS and its limitations
Analyzing the data of nonorthogonal analysis of variance with SPSS ANOVA (Typ I, II, III sums of squares) and why these analyses yields wrong results
The hypothesis of no treatment effects via SPSS using the R2-difference test
Analysis of the data of nonorthogonal analysis of variance with EffectLiteR
Second example: The Kirchmann study on the treatment effects on depression and its analysis with EffectLiteR
Third example: The Klauer study on the training of inductive reasoning
EffectLiteR analysis of the Klauer data with a continuous covariate
Main hypotheses in EffectLiteR and the various conditional and average treatment effects
EffectLiteR analysis of the Klauer data with a continuous covariate and a qualitative covariate
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Literature
Causal effects
Campbell, D. T. & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research on Teaching. In N. L. Gage (Ed.), Handbook of research on teaching. Chicago: Rand McNally.
West, S. G., Biesanz, J. C. & Pitts, S. C. (2000), Causal inference and generalization in field settings. Experimental and quasi-experimental designs. In H. T. Reis and C. M. Judd (eds.), Handbook of research methods in social and personality psychology. Cambridge University Press.
Steyer, R. (2003). Wahrscheinlichkeit und Regression. Berlin: Springer. (Kapitel 15 - 17)
Steyer, R. (2004). Was wollen und was können wir durch empirische Kausalforschung erfahren? In E. Erdfelder & J. Funke (Hrsg.), Allgemeine Psychologie und deduktivistische Methodologie (pp.127-147). Göttingen: Vandenhoek und Ruprecht.
Steyer, R. (2005). Analyzing Individual and Average Causal Effects via Structural Equation Models. Methodology-European Journal of Research Methods in the Behavioral and Social Sciences, 1, 39-54.
Steyer, R. & Partchev, I. (2006). Manual for EffectLite: A Program for the Uni- and Multivariate Analysis of Unconditional, Conditional and Average Mean Differences Between Groups.
Pohl, S., Steyer, R. & Kraus, K. (2008). Modelling method effects as individual causal effects. Journal of the Royal Statistical Society. Series A, 171, 41--63.
Steyer, R., Partchev, I., Kröhne, U., Nagengast, B., & Fiege, C. (in preparation). Probability and Causality.
Intercept function and conditional-effect functions in the nonorthogonal ANOVA Example
Parameterization of the intercept function and conditional-effect functions in this example
Analysis of conditional effects in the nonorthogonal ANOVA Example with the Linear Regression program of SPSS
Point estimation of the conditional effects based on this data analysis
Limitations of the analysis conditional treatment effects via Linear Regression: No standard errors of conditional effects, no average treatment effects
The distinction between fixed and stochastic regressors.
Data analysis with (nonorthogonal) ANOVA (SPSS): Type I, II, III, and IV of decomposing the sum of squares. All of them do not test the hypothesis that the average treatment effect is zero.
Hypotheses that are tested as the so-called main effects with Typ I, II, and III.
Summarizing the basic concepts and equations in the analysis of conditional and average effects.
EffectLiteR analysis of the Klauer data with a qualitative and a quantitative covariate
Model equation and linearity assumption for the regression of the outcome variable on the quantitative covariate in each cell
Meaning of the four main hypotheses in terms of (a) expected values or effects, (b) the g-functions, and (c) the coefficients of the g-functions
Adjusted expected values
Conditional effects given values of the qualitative covariate
Conditional effects given values of the qualitative covariate and the treatment variable
Conditional effects given values of the qualitative covariate and the quantitative covariate
Conditional expected values of the outcome variable under treatment and under control given values of the qualitative covariate and the quantitative covariate
EffectLiteR analysis of the Klauer data with a latent covariate and a latent outcome variable. Models of essentially tau-equivalent and tau-congeneric variables
Testing the model with a goodness-of-fit test and the RMSEA
Conditional effects given estimated values of the latent covariates
EffectLiteR analysis of the Klauer data with two latent covariates and a latent outcome variable
EffectLiteR analysis of the Klauer data with a latent covariate, a method factor, and a latent outcome variable
Methods of Evaluation Research
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