Bifactor latent structures were introduced over 70 years ago, but only recently has bifactor modeling been rediscovered as an effective approach to modeling "construct-relevant" multidimensionality in a set of ordered categorical item responses. I begin by describing the Schmid-Leiman bifactor procedure (Schmid & Leiman, 1957) and highlight its…

Literature documents that the judgments people hold about themselves, their life, and their future are important ingredients of their psychological functioning and well-being and are commonly related to each other. In this article, results from a longitudinal study (N = 298, 45% males) are presented. Using an integrative Latent Curve, Latent…

In this article, we directly question the common practice in growth mixture model (GMM) applications that exclusively rely on the fitting model without covariates for GMM class enumeration. We provide theoretical and simulation evidence to demonstrate that exclusion of covariates from GMM class enumeration could be problematic in many cases. Based…

The application of multidimensional item response theory models to repeated observations has demonstrated great promise in developmental research. It allows researchers to take into consideration both the characteristics of item response and measurement error in longitudinal trajectory analysis, which improves the reliability and validity of the…

Using an approach to the analysis of missing data, this study investigated developmental trends in alcohol, marijuana, and cigarette use among 750 adolescents across 5 years using multiple-group latent growth modeling. Latent variable structural equation modeling and missing data approaches to studying developmental change are explored. (SLD)

There is evidence to suggest that the effects of behavioral interventions may be limited to specific types of individuals, but methods for evaluating such outcomes have not been fully developed. This study proposes the use of finite mixture models to evaluate whether interventions, and, specifically, group randomized trials, impact participants…

Demonstrates the use of a general model for latent variable growth analysis that takes into account cluster sampling. Multilevel Latent Growth Modeling was used to analyze longitudinal and multilevel data for adolescent and parent substance use measured at four annual time points for 435 families. (SLD)

Developed procedures for constructing a retrospective person-period data set from cross-sectional data and discusses modeling strategies for estimating multilevel discrete-time event history models. Applied the methods to the analysis of cigarette use by 1,979 urban adolescents. Results show the influence of the racial composition of the…

Longitudinal data from a school-based alcohol-prevention trial involving 385 girls and 353 boys were used to show how a model using individual growth modeling and a covariance structure approach can investigate relationships among individual changes in two domains (positive and negative alcohol expectancies). Advantages of the approach are…

Several competing dynamic models were applied to adolescent substance abuse using a cohort of 3,580 participants in a longitudinal study beginning in grade five. Evidence was found for an intrinsic self-regulating mechanism in adolescent smoking and drinking. Support for a dampened linear oscillator model is discussed. (SLD)

The latent trait-state model for estimating stable and changing components of depressive symptomatology in adolescents was studied by investigating the factorial structure of the Center for Epidemiological Studies Depression Scale on 4 occasions with 805 high school students. Findings are discussed with regard to depressive mood fluctuations among…