A Monte Carlo study considers the use of meta analysis with the Solomon four-group design. Experiment-wise Type I error properties and the relative power properties of Stouffer's Z in the Solomon four-group design are explored. Obstacles to conducting meta analysis in the Solomon design are discussed. (SLD)

This article considers the impact of missing data arising from balanced incomplete block (BIB) spiraled designs on the chi-square goodness-of-fit test in factor analysis. The new approach is shown to outperform the pairwise available case method for continuous variables and to be comparatively better for dichotomous variables. (SLD)

A Monte Carlo experiment was conducted to investigate the performance of bootstrap methods in normal theory maximum likelihood factor analysis when the distributional assumption was satisfied or unsatisfied. Problems arising with the use of bootstrap methods are highlighted. (SLD)

Monte Carlo simulations were used to examine three cases using analyses of variance (ANOVAs) sequentially. Simulation results show that Type I error rates increase when using ANOVAs in this sequential fashion, and the detrimental effect is greatest in situations in which researchers would most likely use ANOVAs sequentially. (SLD)

Suggest practical procedure for estimating number of subjects that need to be screened to obtain sample of fixed size that meets multiple correlated criteria. Procedure described is based on fact that least-squares regression provides good quadratic fit for Monte Carlo estimates of multivariate probabilities when they are plotted as function of…

Provides a comparison of centered and raw-score analyses in least squares regression. The two methods are demonstrated with constructed data in a Monte Carlo study to be equivalent, yielding identical hypothesis tests associated with the moderation effect and regression equations that are functionally equivalent. (SLD)

Effects of item parameters on ability estimation were investigated through Monte Carlo studies using the Expected-A-Posteriori estimation. Results show a significant effect of item discriminating parameter on standard error of ability estimation. As the discriminating parameter increases, the standard error decreases. (SLD)

Studied incorporating adjusted model fit information into the root mean square error of approximation fit index (RMSEA). Monte Carlo simulation results show that incorporating robust information into the RMSEA may yield improved performance for assessing model fit under nonnormal data situations. (SLD)

Used Monte Carlo simulation to compare the relative measurement efficiency of polytomous modeling and dichotomous modeling under different scoring schemes and termination criteria. Results suggest that polytomous computerized adaptive testing (CAT) yields marginal gains over dichotomous CAT when termination criteria are more stringent. Discusses…

This article considers the problem of power and sample size calculations for normal outcomes within the framework of multivariate linear models. The emphasis is placed on the practical situation that not only the values of response variables for each subject are just available after the observations are made, but also the levels of explanatory…

Through Monte Carlo simulation, small sample methods for evaluating overall data-model fit in structural equation modeling were explored. Type I error behavior and power were examined using maximum likelihood (ML), Satorra-Bentler scaled and adjusted (SB; Satorra & Bentler, 1988, 1994), residual-based (Browne, 1984), and asymptotically…

The meta-analytic random effects model assumes that the variability in effect size estimates drawn from a set of studies can be decomposed into two parts: heterogeneity due to random population effects and sampling variance. In this context, the usual goal is to estimate the central tendency and the amount of heterogeneity in the population effect…

There is an increasing use of Markov chain Monte Carlo (MCMC) algorithms for fitting statistical models in psychometrics, especially in situations where the traditional estimation techniques are very difficult to apply. One of the disadvantages of using an MCMC algorithm is that it is not straightforward to determine the convergence of the…

Multilevel analysis is a useful technique for analyzing longitudinal data. To describe a person's development across time, the quality of the estimates of the random coefficients, which relate time to individual changes in a relevant dependent variable, is of importance. The present study compares three estimators of the random coefficients: the…

Monte Carlo computational experiments reveal that the ability to discriminate between first- and second-order kinetics from least-squares analysis of time-dependent concentration data is better than implied in earlier discussions of the problem. The problem is rendered as simple as possible by assuming that the order must be either 1 or 2 and that…