Estimation of average total effects in quasi experimental designs : nonlinear contraints in structural equation models This thesis focuses on estimating and testing average total effects in quasi experimental designs based on the stochastic theory of causal effects (Steyer et al. in press). Three issues are investigated that are crucial for statistical inference about average total effects in quasi experimental designs: (1) Interactions between covariates and the treatment variable (2) stochasticity of covariates and the resulting nonlinearity of the hypothesis of no average total effect and (3) heterogeneity of residual variances. It is shown that the standard error for the average total effect estimator of the general linear model and of sample mean centered covariates is often biased if there are interactions between covariates and the treatment variable and if the covariates are stochastic. Assuming a joint distribution is necessary for developing unbiased standard errors for estimators of the average total effect. Therefore structural equation models with nonlinear constraints of estimated parameters are studied for the implementation of generalized analysis of covariance and compared to different standard error corrections in the general linear model for instance robust standard errors based on heteroscedasticity consistent estimators (White 1980) and adjusted standard errors for regression estimates (Schafer & Kang 2008).