Jihoon Ryu

Applications of latent transition analysis (LTA) have emerged since the early 1990s, with numerous scientific findings being published in many areas, including social and behavioral sciences, education, and public health. Although LTA is effective as a statistical analytic tool for a person-centered model using longitudinal data, model building in LTA has often been subjective and confusing for applied researchers. To fill this gap in the literature, we review the components of LTA, recommend a

Involvement in bullying and victimization has been mostly studied using cross-sectional data from 1 time point. As such, much of our understanding of bullying and victimization has not captured the dynamic experiences of youth over time. To examine the change of latent statuses in bullying and victimization, we applied latent transition analysis examining self-reported bullying involvement from 1,180 students in 5th through 9th grades across 3 time points. We identified unobserved heterogeneous

Model building or model selection with linear mixed models (LMMs) is complicated by the presence of both fixed effects and random effects. The fixed effects structure and random effects structure are codependent, so selection of one influences the other. Most presentations of LMM in psychology and education are based on a multilevel or hierarchical approach in which the variance-covariance matrix of the random effects is assumed to be positive definite with nonzero values for the variances. When

To examine the experiences of victimization and bullying among gifted students and their general education peers, we applied a latent transition analysis with longitudinal data from 299 gifted and 689 general education students (fifth to ninth graders). We identified 4 latent statuses for victimization (4.8%–5.2%, frequent victims; 7.4%–12.2%, frequent relational victims; 28.7%–35.8%, occasional victims; 46.8%–59.2%, infrequent victims) and 3 latent statuses for perpetration (3.9%–5.6%, frequent

The 2011 Trends in International Mathematics and Science Study shows average mathematics scores of U.S. fourth graders are lower than children in many Asian countries. There are questions about differences in mathematics skills at younger ages. This study examines differences in score growth for High-, Average-, and Low-performing children in two U.S. states and one city in China. The samples are not representative of site populations and are different in socioeconomic status (SES). Test of Earl

Abstract Many states have scaled up School‐Wide Positive Behavioral Interventions and Supports (SW‐PBIS) with the goal of improving student behavior and academic outcomes. Although the effects of SW‐PBIS on behavioral and discipline outcomes have been promising, the findings for academic achievement have been inconclusive and are often limited to cross‐sectional data. This paper examined the longitudinal effect of SW‐PBIS on student behavioral problems and academic achievement growth in elementa

Abstract Background Capturing measures of students’ attitudes toward science has long been a focus within the field of science education. The resulting interest has led to the development of many instruments over the years. There is considerable disagreement about how attitudes should be measured, and especially whether students’ attitudes toward science can or should be measured unidimensionally, or whether separate attitude dimensions or subscales should be considered. When it is agreed upon t

Generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling (SEM). GSCA regards weighted composites or components of indicators as proxies for latent variables and estimates model parameter via least squares without resorting to a distributional assumption such as multivariate normality of indicators. As with other SEM approaches, model evaluation is a crucial procedure in GSCA that is used to examine whether a hypothesized model is consistent w

This longitudinal study examined the influence of prekindergarten teacher characteristics and classroom instructional processes during mathematical activities on the growth of mathematics learning scores in prekindergarten, kindergarten, and first grade. Participants attended state-funded and Head Start prekindergarten programs. Mathematical performance was measured in fall and spring in prekindergarten and spring in kindergarten and first grade using the Test of Early Mathematics Ability–3 (TEM

Fuzzy clustering has been broadly applied to classify data into K clusters by assigning membership probabilities of each data point close to K centroids. Such a function has been applied into characterizing the clusters associated with a statistical model such as structural equation modeling. The characteristics identified by the statistical model further define the clusters as heterogeneous groups selected from a population. Recently, such statistical model has been formulated as fuzzy clusterw

As in cross sectional studies, longitudinal studies involve non-Gaussian data such as binomial, Poisson, gamma, and inverse-Gaussian distributions, and multivariate exponential families. A number of statistical tools have thus been developed to deal with non-Gaussian longitudinal data, including analytic techniques to estimate parameters in both fixed and random effects models. However, as yet growth modeling with non-Gaussian data is somewhat limited when considering the transformed expectation

Applications of growth mixture modeling have become widespread in the fields of medicine, public health, and the social sciences for modeling linear and nonlinear patterns of change in longitudinal data with presumed heterogeneity with respect to latent group membership. However, in contrast to linear approaches, there has been relatively less focus on methods for modeling nonlinear change. We introduce a nonlinear mixture modeling approach for estimating change trajectories that rely on the use