Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…

The effects of some restraints not included in the classical assumptions of the F- and H-Test (e.g., correlation of mean and sample size) were examined in a simulation design. Also simulated was a situation in which two assumptions were not met simultaneously. (Author/BW)

The present study compares the robustness of two different one way fixed-effects analysis of covariance (ANCOVA) models to investigate whether the model which uses a test statistic incorporating estimates of separate unequal regression slopes is more robust than the conventional model which assumes the slopes are equal. (Author/BW)