Currently, dynamic structural equation modeling (DSEM) and residual DSEM (RDSEM) are commonly used in testing intensive longitudinal data (ILD). Researchers are interested in ILD mediation models, but their analyses are challenging. The present paper mathematically derived, empirically compared, and step-by-step demonstrated three types (i.e.,…

The social relations model (SRM) is very often used in psychology to examine the components, determinants, and consequences of interpersonal judgments and behaviors that arise in social groups. The standard SRM was developed to analyze cross-sectional data. Based on a recently suggested integration of the SRM with structural equation models (SEM)…

Intensive longitudinal data has been widely used to examine reciprocal or causal relations between variables. However, these variables may not be temporally aligned. This study examined the consequences and solutions of the problem of temporal misalignment in intensive longitudinal data based on dynamic structural equation models. First the impact…

There has been extensive research into the underrepresentation of minoritized students in STEM disciplines since the 1990s with limited success in improving the representation of Black women in math-intensive STEM fields. This dissertation aims to address how the guiding tenets of critical quantitative (QuantCrit) methods work when used with…

Measuring change in an educational or psychological construct over time is often achieved by repeatedly administering the same items to the same examinees over time and fitting a second-order latent growth curve model. However, latent growth modeling with full information maximum likelihood (FIML) estimation becomes computationally challenging…

Longitudinal research is often interested in identifying correlates of heterogeneity in change. This paper compares three approaches for doing so: the mixed-effects model (latent growth curve model), the growth mixture model, and structural equation model trees. Each method is described, with special focus given to how each structures…

One major challenge of longitudinal data analysis is to find an appropriate statistical model that corresponds to the theory of change and the research questions at hand. In the present article, we argue that "continuous-time models" are well suited to study the continuously developing constructs of primary interest in the education…

Longitudinal data analysis has received widespread interest throughout educational, behavioral, and social science research, with latent growth curve modeling currently being one of the most popular methods of analysis. Despite the popularity of latent growth curve modeling, limited attention has been directed toward understanding the issues of…

In Switzerland, every student graduating from grammar school can begin to study at a university. This leads to high dropout rates. Although students' motivation is considered a strong predictor of performance, the development of motivation during students' transition from high school to university has rarely been investigated. Additionally, little…

This case study uses structural equation modeling to examine the predictive validity of the Read Aloud Profile-Together, a measure of the distinct behaviors of parents and children during shared book reading, in relation to preschool children's early reading competency. Using secondary data analysis, this case study includes 800 parent-child pairs…

This study conducted secondary analysis of data from the National Longitudinal Transition Study-2 (NLTS2) to examine the degree to which student, family, and school constructs predicted self-determination outcomes. Multi-group structural equation modeling was used to examine predictive relationships between 5 student, 4 family, and 7 school…

This study conducted secondary analysis of data from the National Longitudinal Transition Study-2 (NLTS2) to examine the degree to which student, family, and school constructs predicted self-determination outcomes. Multi-group structural equation modeling was used to examine predictive relationships between 5 students, 4 family, and 7 school…

Longitudinal data analysis in education is the study growth over time. A longitudinal study is one in which repeated observations of the same variables are recorded for the same individuals over a period of time. This type of research is known by many names (e.g., time series analysis or repeated measures design), each of which can imply subtle…

Panel studies, in which the same subjects are repeatedly observed at multiple time points, are among the most popular longitudinal designs in psychology. Meanwhile, there exists a wide range of different methods to analyze such data, with autoregressive and cross-lagged models being 2 of the most well known representatives. Unfortunately, in these…

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)…