Uses computer simulation to study the effects on parametric and nonparametric statistical tests when assumptions of normality and homogeneity of variance are violated. Results reveal that nonparametric methods are not always acceptable substitutes for parametric methods in research studies when parametric assumptions are not satisfied. (SLD)

The power functions of Student t tests performed on initial scores, ordinary ranks, 3 kinds of modular ranks, and dichotomies were investigated for 1 normal and 3 nonnormal distributions using 2 samples of 26 simulated scores each. Advantages of extending the rank transformation concept are discussed. (SLD)

It is argued that outlier-prone distributions reduce the power of nonparametric tests, but power can be restored through procedures usually associated with parametric tests. Computer simulation is used to show how an outlier detection and downweighting procedure augments the power of the t-test and the Wilcoxon-Mann-Whitney test. (SLD)

The nonparametric Mokken model of test data was compared with parametric models using simulated data through latent class analysis. It is demonstrated that latent class analysis provides a consistent comparison of item response models. (SLD)

Kernel smoothing methods for nonparametric item characteristic curve estimation are reviewed. A simulation with 500 examinees and real data from 3,000 records of the Graduate Record Examination illustrate the rapidity of kernel smoothing. Even when population curves are three-parameter logistic, simulation suggests no loss of efficiency. (SLD)

A modified "F" test is derived that includes a correction for nonindependence of between-groups and within-groups sample values in analysis of variance (ANOVA) designs. Computer simulations based on normal and nonnormal distributions illustrate the usefulness of the approach, which was more powerful than conventional within-subjects…

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…

Use of parallel analysis (PA), a selection rule for the number-of-factors problem, is investigated from the viewpoint of permutation assessment through a Monte Carlo simulation. Results reveal advantages and limitations of PA. Tables of sample eigenvalues are included. (SLD)

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…

A procedure--based on item response theory--for testing the hypothesis of unidimensionality of the latent space is proposed. Use of the procedure is supported by an asymptotic theory and a Monte Carlo simulation study. The procedure tests for unidimensionality in test construction and/or compares two tests. (SLD)

A detailed investigation is provided of Stout's statistical procedure (the computer program DIMTEST) for testing the hypothesis that an essentially unidimensional latent trait model fits observed binary item response data from a psychological test. Three refinements achieve greater power. The revised approach is validated using real data sets.…

The validity of the assumptions under which the ordinal true score test theory was derived was examined using (1) simulation based on classical test theory; (2) a long empirical test with data from 321 sixth graders; and (3) an extensive simulation with 480 datasets based on the 3-parameter model. (SLD)

The difference between item bias and item impact and the way they relate to item validity are discussed from a multidimensional item response theory perspective. The Mantel-Haenszel procedure and the Simultaneous Item Bias strategy are used in a Monte Carlo study to illustrate detection of item bias. (SLD)