Proposes the use of nonnested tests for the two stage least squares (2SLS) estimator of latent variable models to discriminate between scales. Compares the finite sample performance of these tests to structural equation modeling information-based criteria. Presents practical recommendations based on the Monte Carlo analysis. (SLD)

Studied, through Monte Carlo simulation, the conditions for which analysis of covariance (ANCOVA) does not maintain adequate Type I error rates and power and evaluated some alternative tests. Discusses differences in ANCOVA robustness for balanced and unbalanced designs. (SLD)

Compared the Type I error rate and the power to detect differences in slopes and additive treatment effects of analysis of covariance (ANCOVA) and randomized block designs through a Monte Carlo simulation. Results show that the more powerful option in almost all simulations for tests of both slope and means was ANCOVA. (SLD)

Studied the effects of sample size on the cumulative distribution of selected fit indices using Monte Carlo simulation. Generally, the comparative fit index exhibited very stable patterns and was less influenced by sample size or data types than were other fit indices. (SLD)

Studied the maximum likelihood estimation of unknown parameters in a general LISREL-type model with mixed polytomous and continuous data through Monte Carlo simulation. Proposes a model selection procedure for obtaining good models for the underlying substantive theory and discusses the effectiveness of the proposed model. (SLD)

The authors of the paper on growth mixture modelling (GMM) give a description of GMM and related techniques as applied to antisocial behaviour. They bring up the important issue of choice of model within the general framework of mixture modelling, especially the choice between latent class growth analysis (LCGA) techniques developed by Nagin and…

We consider power calculation in structural equation modeling with data missing completely at random (MCAR). Muth?n and Muth?n (2002) recently demonstrated how power calculations with data MCAR can be carried out by means of a Monte Carlo study. Here we show that the method of Satorra and Saris (1985), which is based on the nonnull distribution of…

Lazraq and Cleroux (Psychometrika, 2002, 411-419) proposed a test for identifying the number of significant components in redundancy analysis. This test, however, is ill-conceived. A major problem is that it regards each redundancy component as if it were a single observed predictor variable, which cannot be justified except for the rare…

The latent trait-state-error model (TSE) and the latent state-trait model with autoregression (LST-AR) represent creative structural equation methods for examining the longitudinal structure of psychological constructs. Application of these models has been somewhat limited by empirical or conceptual problems. In the present study, Monte Carlo…

Scale indeterminacy in analysis of differential item functioning (DIF) within the framework of item response theory can be resolved by imposing 3 anchor item methods: the equal-mean-difficulty method, the all-other anchor item method, and the constant anchor item method. In this article, applicability and limitations of these 3 methods are…

In recent years, confirmatory factor analytic (CFA) techniques have become the most common method of testing for measurement equivalence/invariance (ME/I). However, no study has simulated data with known differences to determine how well these CFA techniques perform. This study utilizes data with a variety of known simulated differences in factor…

Multilevel modeling is an increasingly popular technique for analyzing hierarchical data. This article addresses the problem of predicting a future observable y[subscript *j] in the jth group of a hierarchical data set. Three prediction rules are considered and several analytical results on the relative performance of these prediction rules are…

Monte Carlo methodologies are frequently applied to study the sampling distribution of the estimated proficiency level in adaptive testing. These methods eliminate real situational constraints. However, these Monte Carlo methodologies are not currently supported by the available software programs, and when these programs are available, their…

In this article, a nonlinear structural equation model is introduced and a quasi-maximum likelihood method for simultaneous estimation and testing of multiple nonlinear effects is developed. The focus of the new methodology lies on efficiency, robustness, and computational practicability. Monte-Carlo studies indicate that the method is highly…

A Monte Carlo study compared the usefulness of six variable weighting methods for cluster analysis. Data were 100 bivariate observations from 2 subgroups, generated according to a finite normal mixture model. Subgroup size, within-group correlation, within-group variance, and distance between subgroup centroids were manipulated. Of the clustering…