Performed a simulation study to demonstrate the dramatic impact of capitalization on estimation errors on ability estimation in adaptive testing. Discusses four different strategies to minimize the likelihood of capitalization in computerized adaptive testing. (SLD)

Proposes a new multistage adaptive-testing procedure that factors the discrimination parameter (alpha) into the item-selection process. Simulation studies indicate that the new strategy results in tests that are well-balanced, with respect to item exposure, and efficient. (SLD)

Examined whether the item response theory (IRT) true-score equating method is more adversely affected by the presence of multidimensionality than two conventional equating methods, linear and equipercentile equating. Results of two simulation studies suggest that the IRT method performs as well as the conventional methods when the correlation…

Proposed an approximate chi square statistic based on the nonlinear factor representation of R. McDonald (1967) and investigated it with simulated data. The approximate chi square statistics had good control over Type I errors when unidimensional data were generated and displayed good power in identifying the two-dimensional data. (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)

Describes trends in technologies for electronic learning: developments in wireless that enable mobile learning, virtual universities, and simulations that involve emotionally charged situations. (SK)

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)

Three developmental connectionist models simulate a purported shift from "featural" to "correlational" processing in infant categorization (models: Gureckis & Love, 2004/this issue; Shultz & Cohen, 2004/this issue; Westermann & Mareschal, 2004/this issue; empirical data: Cohen & Arthur, 2003; Younger, 1985; Younger & Cohen, 1986). In this article,…

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 are able to recognise familiar faces easily across large variations in image quality, though our ability to match unfamiliar faces is strikingly poor. Here we ask how the representation of a face changes as we become familiar with it. We use a simple image-averaging technique to derive abstract representations of known faces. Using Principal…

There are certain simple rotations of objects that most people cannot reason about accurately. Reliable gaps in the understanding of a fundamental physical domain raise the question of how learning to reason in that domain might proceed. Using virtual reality techniques, this project investigated the nature of learning to reason across the domain…

Interactions between (multiple indicator) latent variables are rarely used because of implementation complexity and competing strategies. Based on 4 simulation studies, the traditional constrained approach performed more poorly than did 3 new approaches-unconstrained, generalized appended product indicator, and quasi-maximum-likelihood (QML). The…

Confirmatory factor analysis (CFA) is widely used for examining hypothesized relations among ordinal variables (e.g., Likert-type items). A theoretically appropriate method fits the CFA model to polychoric correlations using either weighted least squares (WLS) or robust WLS. Importantly, this approach assumes that a continuous, normal latent…