In a Monte Carlo analysis of single-subject data, Type I and Type II error rates were compared for various statistical tests of the significance of treatment effects. Data for 5,000 subjects in each of 6 treatment effect size groups were computer simulated, and 2 types of treatment effects were simulated in the dependent variable during…

When normality does not hold, nonparametric tests represent an important data-analytic alternative to parametric tests. However, the use of nonparametric tests in educational research has been limited by the absence of easily performed tests for complex experimental designs and analyses, such as factorial designs and multiple regression analyses,…

This study investigates parameter estimation under the simple linear regression model for situations in which the underlying assumptions of ordinary least squares estimation are untenable. Classical nonparametric estimation methods are directly compared against some robust estimation methods for conditions in which varying degrees of outliers are…

The purpose of this study was to compare two non-parametric procedures, the Mantel-Haenszel (MH) procedure and the simultaneous item bias (SIB) procedure, with respect to their Type I error rates and power, and to investigate the conditions under which asymptotic distributional properties of the SIB and MH were obtained. Data were simulated to…