Multilevel modeling is a statistical approach to analyze hierarchical data, which consist of individual observations nested within clusters. Bayesian methods is a well-known, sometimes better, alternative of Maximum likelihood methods for fitting multilevel models. Lack of user-friendly and computationally efficient software packages or programs…

This article presents a latent class model for multilevel data to identify latent subgroups and estimate heterogeneous treatment effects. Unlike sequential approaches that partition data first and then estimate average treatment effects (ATEs) within classes, we employ a Bayesian procedure to jointly estimate mixing probability, selection, and…

Due to the increased use of scientific models and modelling in K-12 education, there is a need to uncover its effects on students over time. Prior research has shown that the use of scientific modelling in K-12 classes is associated with improved conceptual knowledge and problem-solving skills. However, few studies have explicitly tested the…

Many national and international educational data collection programs offer researchers opportunities to investigate contextual effects related to student performance. In those programs, schools are often used in the first-stage sampling process and students are randomly drawn from selected schools. However, the "incidental" dependence of…

Lord's Paradox occurs when a continuous covariate is statistically controlled for and the relationship between a continuous outcome and group status indicator changes in both magnitude and direction. This phenomenon poses a challenge to the notion of evidence-based policy, where data are supposed to be self-evident. We examined 50 effect size…

School effectiveness is a topic of interest addressed by numerous research projects focused on clarifying which variables contribute to the explanation of educational performance. This research aims to find out to what extent social, cultural, and academic variables at the student and school levels, as perceived by families, influence performance,…

In line with assumptions made by the control-value theory of academic emotions, it was hypothesized that the association between the classroom social environment, in terms of students' perceptions of their teachers' interpersonal behaviour, and students' academic emotions was partially mediated by students' achievement goals. The present study…

As the economic landscape changes, a college degree has become increasingly necessary for securing employment in an information-based society. Student engagement is an important factor in predicting and preventing high school dropout, and improving student outcomes. Although the relationship between secondary school engagement and high school…

Most college students work for pay while enrolled, however theoretical and methodological concerns with the extant literature make it nearly impossible to understand how employment affects students. Responding to those shortcomings, I designed a validation study with robust statistical methods to examine relationships between student employment…

The methodology of single-case experimental designs (SCED) has been expanding its efforts toward rigorous design tactics to address a variety of research questions related to intervention effectiveness. Effect size indicators appropriate to quantify the magnitude and the direction of interventions have been recommended and intensively studied for…

Intersectional approaches have become increasingly important for explaining educational inequalities because they help to improve our understanding of how individual experiences are shaped by simultaneous membership in multiple social categories that are associated with interconnected systems of power, privilege, and oppression. For years, there…

We recently developed a method called Meta-STEPP based on the fixed-effects meta-analytic approach to explore treatment effect heterogeneity across a continuous covariate for individual time-to-event data arising from multiple clinical trials. Meta-STEPP forms overlapping subpopulation windows (meta-windows) along a continuous covariate of…

The Bayesian information criterion (BIC) can be useful for model selection within multilevel modeling studies. However, the formula for BIC requires a value for N, which is unclear in multilevel models, since N is observed in at least two levels. The present study uses simulated data to evaluate the rate of false positives and power when using a…

This study used a propensity score approach to estimate treatment effects in a multilevel setting. The propensity score approach involves the estimation of propensity scores for covariate balancing and the estimation of treatment effects. This study aimed at understanding how propensity scores estimated through a simple logistic regression compare…

Complex data structures are ubiquitous in psychological research, especially in educational settings. In the context of randomized controlled trials, students are nested in classrooms but may be cross-classified by other units, such as small groups. Furthermore, in many cases only some students may be nested within a unit while other students may…