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Methods of Evaluation Research
Kursleitung: Prof. Dr. Rolf Steyer 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 R^{2}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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Causal effects
 Campbell, D. T. & Stanley, J. C. (1963). Experimental and QuasiExperimental 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 quasiexperimental 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.127147). Göttingen: Vandenhoek und Ruprecht.
 Steyer, R. (2005). Analyzing Individual and Average Causal Effects via Structural Equation Models. MethodologyEuropean Journal of Research Methods in the Behavioral and Social Sciences, 1, 3954.
 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, 4163.
 Steyer, R., Partchev, I., Kröhne, U., Nagengast, B., & Fiege, C. (in preparation). Probability and Causality.
 Steyer. R. and Nagel, W. (2017). Probability and conditional expectation: Fundamentals for the empirical sciences. Chichester: Wiley.
Download of the relevant examination / lecture relevant literature (only for students of the lectures)
Date 
Topic 
Video 
Präsentation (PDF) and other material 
20151019 
Why we need a theory of causal effects
 Example Joe and Ann with selfselection
 Random experiment
 Set of possible outcomes of a random experiment
 Event
 Probability of an event
 Conditional probability of an event
 Random variable
 Expectation of a discrete random variable
 Conditional expectation of a discrete random variable

Video (Stream; Windows Media Player)
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Slides (updated 20151026)
Blackboard sketches

20151026 
Basic Ideas of the theory of total causal effects
 Kirchmann example
 Individual total causal Effect
 Average total causal Effect

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Probability and Conditional Expectation
Materials (updated 20151027)
Blackboard sketches

20151102 
The core of the theory of total causal effects
 Covariate
 The random experiment (the empirical phenomenon) considered
 Examples in which one of the causality conditions for E(YX,Z) is satisfied
 Unbiasedness of E(YX,Z) and E^{X=x}(YZ)
 Implications unbiasedness for the identification of conditional and average causal total effects.

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Slides (updated 20151110)

20151109 
 Four causality conditions for E(YX,Z)
 The experimental design technique of conditional randomization
 Covariate selection based on the causality conditions
 The example of nonorthogonal analysis of variance: Conditional and average total treatment effects

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Dataset
Blackboard sketch 01
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20151116 
 Intercept function and conditionaleffect functions in the nonorthogonal ANOVA Example
 Parameterization of the intercept function and conditionaleffect 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

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SPSSOutput
Blackboard sketches

20151123 
 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 socalled main effects with Typ I, II, and III.
 Summarizing the basic concepts and equations in the analysis of conditional and average effects.

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Slides
SPSSOutput
Blackboard sketches

20151130 
 R^{2}Difference test for the hypothesis "There are no treatmenteffects"
 Point estimate for the average treatment effects in SPSS
 Analysis of the nonorthodata with EffectLiteR
 Interpretation of the first results of this EffectLiteRAnalysis: Average effects and conditional effects
 A first analysis of the Kirchmanndata set with EffectLiteR

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Materials
Blackboard sketches

20151207 
 Conditional effects in the example of nonorthogonal Anova (continued)
 Conditional effects given a treatment condition
 Analysis of the Klauer data with a continuous covariate (pretest CFT) and an outcome variable (posttest CFT)
 Assuming linearity of the gfunctions
 Interpretation of the main hypotheses in terms of the gfunktions and the gammacoefficients
 Conditional effect given a pretest score

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Slides
Dataset
Blackboard sketches

20151214 
 Checking linearity of the gfunctions with EffectLiteR
 Testing linearity of the gfunctions with SPSS (R^{2}difference test)
 (X=x)conditional treatment effects
 When should we consider average effects and when (X=x)conditional treatment effects?
 Basic idea and assumptions in the EffectLiteR analysis with qualitative and quantitative covariates

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Materials
Blackboard sketches

20160104 
 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 gfunctions, and (c) the coefficients of the gfunctions
 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

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Dataset
Blackboard sketch 01
Blackboard sketch 02

20160111 
 EffectLiteR analysis of the Klauer data: one qualitative and one quantitative covariate (continued)
 Histograms of the dependent variable in the cells
 Regression of the dependent variable on the continuous covariate in each cell of the design
 Scattergram of the conditional effects and the continuous covariate
 Reaggregation of the conditional effects in the context of the theory of causal effects
 EffectLiteR analysis of the Klauer data: one qualitative and two quantitative covariates
 Basic concepts and models of classical test theory

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Slides 1
Slides 2
Steyer, R., Mayer, A., Geiser, C., & Cole, D. (2015). A Theory of States and Traits—Revised. Annual Review of Clinical Psychology, 11, 7198.
Blackboard sketches

20160118 
 EffectLiteR analysis of the Klauer data with a latent covariate and a latent outcome variable. Models of essentially tauequivalent and taucongeneric variables
 Testing the model with a goodnessoffit 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

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Blackboard sketches

20160125 
 Method factor as an additional latent covariate (continued)
 Method factor with a reference method and method factor with a common factor
 An EffectLiteR analysis with a qualitative and a latent covariate
 Under which circumstances should we use a model with a latent instead of a manifest covariate?

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Blackboard sketch 01
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20160201 
 Combining the theory of causal Effects with EffectLiteR Analyses
 TrueOutcomeVariables for total effects
 Atomic causal total effects
 Average causal total effects
 Conditional causal total effects
 Unbiasedness of the conditional expectations E^{X=x}(YZ)
 Implications of Unbiasedness
 Two sufficient conditions for unbiasedness
 Testing the sufficient conditions for unbiasedness

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Slides
SPSSOutput
Blackboard sketch 01
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20160208 

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