Longitudinal models of individual growth typically emphasize between-person predictors of change but ignore how growth may vary "within" persons because each person contributes only one point at each time to the model. In contrast, modeling growth with multi-item assessments allows evaluation of how relative item performance may shift…

Longitudinal models typically emphasize between-person predictors of change but ignore how growth varies "within" persons because each person contributes only one data point at each time. In contrast, modeling growth with multi-item assessments allows evaluation of how relative item performance may shift over time. While traditionally…

The importance of sample size, although widely discussed in the literature on structural equation modeling (SEM), has not been widely recognized among applied SEM researchers. To narrow this gap, we focus on second language testing and learning studies and examine the following: (a) Is the sample size sufficient in terms of precision and power of…

This paper reports a set of Monte Carlo simulations designed to evaluate the main claims made by Laufer and Nation about the Lexical Frequency Profile (LFP). Laufer and Nation claim that the LFP is a sensitive and reliable tool for assessing productive vocabulary in L2 speakers, and they suggest it might have a serious role to play in diagnostic…

To determine the accuracy of simulated data sets, an investigation was conducted of the effects of item sampling plans in the application of multiple matrix sampling using both simulated and empirical data sets. Although results were similar, empirical data results were more precise. (SLD)

Probabilistic models with more than one latent variable are designed to report profiles of skills or cognitive attributes. Testing programs want to offer additional information beyond what a single test score can provide using these skill profiles. Many recent approaches to skill profile models are limited to dichotomous data and have made use of…