A Monte Carlo study is reported that shows the comparative performance of alternative approaches under deviations from their respective assumptions in the case of structural equation models with latent variables with attention restricted to point estimates of model parameters. The conditional polychoric correlations method is shown most robust…

Compared the quality of item parameter estimates for marginal maximum likelihood (MML) and Markov Chain Monte Carlo (MCMC) with the nominal response model using simulation. The quality of item parameter recovery was nearly identical for MML and MCMC, and both methods tended to produce good estimates. (SLD)

Examines the ways in which level-1 outliers can impact the estimation of fixed effects and random effects in hierarchical models (HMs). Also outlines and illustrates the use of Markov Chain Monte Carlo algorithms for conducting sensitivity analyses under "t" level-1 assumptions, including algorithms for settings in which the degrees of…

The use of Monte Carlo methods demonstrated that a formula presented by M. G. Kendall for estimating Pearson's rho from tau is somewhat more accurate than a formula presented by K. Pearson for estimating Pearson's rho from a Spearman's rho coefficient. Implications for meta-analysis of correlations are discussed. (SLD)

Outlines costs and benefits associated with different cross-validation strategies; in particular the way in which the study design affects the cost and benefits of different types of cross-validation. Suggests that the choice between empirical estimation methods and formula estimates involves a trade-off between accuracy and simplicity. (JAC)

The purpose of this study was to examine the recovery of item parameters in simulated Automatic Item Generation (AIG) conditions, using Markov chain Monte Carlo (MCMC) estimation methods to attempt to recover the generating distributions. To do this, variability in item and ability parameters was manipulated. Realistic AIG conditions were…

A Monte Carlo study compared the accuracy of different estimates of the standard error of correlations corrected for restriction in range. The procedure suggested by P. Bobko and A. Rieck (1980) generated the most accurate estimates of the standard error. Aspects of accuracy are discussed. (SLD)

Illustrates that sampling variance can be very large when a three-wave quasi simplex model is used to obtain reliability estimates. Also shows that, for the reliability parameter to be identified, the model assumes a Markov process. These problems are evaluated with both real and Monte Carlo data. (SLD)

Whether "more is ever too much" for the number of indicators (p) per factor (p/f) in confirmatory factor analysis (CFA) was studied by varying sample size (N) from 50 to 1,000 and p/f from 2 to 12 items per factor in 30,000 Monte Carlo simulations. For all sample sizes, solution behavior steadily improved (more proper solutions and more…

Multiple linear regression is used to model the effects of violating statistical assumptions on the likelihood of making a Type I error. This procedure is illustrated for the student's t-test (for independent groups) using data from previous Monte Carlo studies in which the actual alpha levels associated with violations of the normality…

Monte Carlo methods were used to evaluate an EM algorithm used for the correction of missing data in latent class analysis. Findings regarding bias in parameter estimates suggest practical limits for the utility of the EM algorithm in terms of sample size and nonresponse rate. (SLD)

Monte Carlo methods were used to investigate the effect of misspecification of the second level in a two-level hierarchical linear model (HLM). Sample composition, heterogeneity of the group size, level of intraclass correlation, and correlation between second-level predictors were manipulated. Each of 20 generated data sets was analyzed nine…

That the jackknifing technique is superior to traditional techniques for assessing the external validity of statistical results of discriminant analysis is defended. Traditional approaches assessed include: (1) the empirical method, in which the discriminant function coefficients (DFCs) obtained in a given analysis are applied to predict group…

Supporting the use of separate analyses of measurement and structural portions of latent or mixed manifest and latent variable models, limited information (single equation) procedures are presented for estimating structural parameters. These procedures are recommended for testing specific causal hypotheses and locating specific structural model…

Monte Carlo methods are used to explore the accuracy of a method for establishing confidence intervals for variance component estimates in generalizability studies. Previous research has shown that variance component estimation errors due to sampling are often larger than suspected. (Author/CM)