Modeling within-person change over time and between-person differences in change over time is a primary goal in prevention science. When modeling change in an observed score over time with multilevel or structural equation modeling approaches, each observed score counts toward the estimation of model parameters equally. However, observed scores…

This study investigates the performance of robust maximum likelihood (ML) estimators when fitting and evaluating small sample latent growth models with non-normal missing data. Results showed that the robust ML methods could be used to account for non-normality even when the sample size is very small (e.g., N < 100). Among the robust ML…

Few studies have used exploratory factor analysis (EFA) and exploratory bifactor factor analysis (EBFA) to define a baseline factor structure model checking the construct-relevant psychometric multidimensionality of student engagement. This study was conducted on a sample of 3,374 students in France, Wallonia-Brussels Federation, and Luxembourg by…

Moral psychology has been moving toward consideration of multiple kinds of moral concepts and values, such as the Ethics of Autonomy, Community, and Divinity. While these three ethics have commonly been measured qualitatively, the current study sought to validate the long and short forms of the Ethical Values Assessment (EVA), which is a…

We examine the performance of planned missing (PM) designs for correlated latent growth curve models. Using simulated data from a model where latent growth curves are fitted to two constructs over five time points, we apply three kinds of planned missingness. The first is item-level planned missingness using a three-form design at each wave such…

Utilizing planned missing data (PMD) designs (ex. 3-form surveys) enables researchers to ask participants fewer questions during the data collection process. An important question, however, is just how few participants are needed to effectively employ planned missing data designs in research studies. This article explores this question by using…

We present a new paradigm that allows simplified testing of multiparameter hypotheses in the presence of incomplete data. The proposed technique is a straight-forward procedure that combines the benefits of two powerful data analytic tools: multiple imputation and nested-model ?2 difference testing. A Monte Carlo simulation study was conducted to…

We examine longitudinal extensions of the two-method measurement design, which uses planned missingness to optimize cost-efficiency and validity of hard-to-measure constructs. These designs use a combination of two measures: a "gold standard" that is highly valid but expensive to administer, and an inexpensive (e.g., survey-based)…

Examines subjects' processing in misleading test items. Suggests that the M-power for children, when assessed behaviorally, may increase with age in a discrete manner, and have the potential to generate interval scales of measurement. In addition, suggests that, in light of the results, what statisticians often consider "error of…

Argues that rank order correlations may not be ubiquitously suitable for assessing the relations between children's behavior or characteristics at one age or in one situation, and those shown later or in another context. (HOD)