This article shows how a multivariate approximate degrees of freedom procedure based on the Welch-James procedure as simplified by S. Johansen (1980) can be applied to the analysis of repeated measures designs without assuming covariance homogeneity. A Monte Carlo study illustrates the approach. (SLD)

Proposes a procedure for generating multivariate nonnormal distributions. The procedure, an extension of the Fleishman power method (A. Fleishman, 1978), generates the average value of intercorrelations much closer to population parameters than competing procedures for skewed and heavy tailed distributions and small sample sizes. Reports Monte…

Studied whether moving average or autoregressive moving average models fit two longitudinal data sets previously thought to possess quasi-simplex structures better than the quasi-simplex, one-factor, or autoregressive models. Results of a Monte Carlo study show the importance of evaluating the fit, propriety, and parsimony of models before one…

Extends the basic Markov chain Monte Carlo (MCMC) strategy of R. Patz and B. Junker (1999) for Bayesian inference in complex Item Response Theory settings to address issues such as nonresponse, designed missingness, multiple raters, guessing behaviors, and partial credit (polytomous) test items. Applies the MCMC method to data from the National…

Strategies to avoid the penalties of extra variation described by J. Cornfield and reduced degrees of freedom in community-level trials were compared in Monte Carlo simulations. The three conditions necessary to ensure nominal Type I and Type II error rates are detailed. (SLD)

The authors used a Monte Carlo simulation to examine how the violation of the exchangeability assumption affects empirical Type I error rates of the LMH randomization test (J. R. Levin, L. A. Marascuilo, & L. J. Hubert, 1978). Simulation results showed that the LMH test is not always an appropriate technique for analyzing systematic designs when…

This manuscript introduces a new Bayesian finite mixture methodology for the joint clustering of row and column stimuli/objects associated with two-mode asymmetric proximity, dominance, or profile data. That is, common clusters are derived which partition both the row and column stimuli/objects simultaneously into the same derived set of clusters.…

In this article, a maximum likelihood (ML) approach for analyzing a rather general two-level structural equation model is developed for hierarchically structured data that are very common in educational and/or behavioral research. The proposed two-level model can accommodate nonlinear causal relations among latent variables as well as effects…

A simulation study was conducted to examine the effect of item parceling on confirmatory factor analysis parameter estimates and their standard errors at different levels of sample size, number of indicators per factor, size of factor structure/pattern coefficients, magnitude of interfactor correlations, and variations in item-level data…

Differential item functioning (DIF) continues to receive attention both in applied and methodological studies. Because DIF can be an indicator of irrelevant variance that can influence test scores, continuing to evaluate and improve the accuracy of detection methods is an essential step in gathering score validity evidence. Methods for detecting…

This paper presents the development of the fusion model skills diagnosis system (fusion model system), which can help integrate standardized testing into the learning process with both skills-level examinee parameters for modeling examinee skill mastery and skills-level item parameters, giving information about the diagnostic power of the test.…

A Monte Carlo study was conducted using the Statistical Analysis System IML computer program to compare the multivariate analysis of variance (MANOVA) simultaneous test procedures of Roy's Greatest Root, the Pillai-Bartlett trace, the Hotelling-Lawley trace, and Wilks' lambda, in terms of power and Type I error under various conditions, including…

The test of homogeneity developed by L. V. Hedges (1982) for the fixed effects model is frequently used in quantitative meta-analyses to test whether effect sizes are equal. Despite its widespread use, evidence of the behavior of this test for the less-than-ideal case of small study sample sizes paired with large numbers of studies is…

In order to obtain conditional maximum likelihood estimates, the so-called conditioning estimates have to be calculated. In this paper a method is examined that does not calculate these constants exactly, but approximates them using Monte Carlo Markov Chains. As an example, the method is applied to the conditional estimation of both item and…

Monte Carlo studies investigated effects of within-group covariance structure on subgroup recovery by several widely used hierarchical clustering methods. In Study 1, subgroup size, within-group correlation, within-group variance, and distance between subgroup centroids were manipulated. All clustering methods were strongly affected by…