Recent advances in genomics make it possible to predict individual differences in education from polygenic scores that are person-specific aggregates of inherited DNA differences. Here, we systematically reviewed and meta-analyzed the strength of these DNA-based predictions for educational attainment (e.g., years spent in full-time education) and…

Measurement invariance (MI) describes the equivalence of measurement models of a construct across groups or time. When comparing latent means, MI is often stated as a prerequisite of meaningful group comparisons. The most common way to investigate MI is multi-group confirmatory factor analysis (MG-CFA). Although numerous guides exist, a recent…

Mediation analysis is an important statistical tool in many research fields, where the joint significance test is widely utilized for examining mediation effects. Nevertheless, the limitation of this mediation testing method stems from its conservative Type I error, which reduces its statistical power and imposes certain constraints on its…

Random forests are a nonparametric machine learning method, which is currently gaining popularity in the behavioral sciences. Despite random forests' potential advantages over more conventional statistical methods, a remaining question is how reliably informative predictor variables can be identified by means of random forests. The present study…

The world's move to a global economy has an impact on the high rate of student academic failure. Higher education, as the affected party, is considered crucial in reducing student academic failure. This study aims to construct a prediction (predictive model) that can forecast students' time to graduation in developing countries such as Indonesia,…

Study success in Higher Education is of primary importance in the European policy agenda. Yet, given the diverse educational landscape across countries and institutions, more coordinated action is needed to gain a more solid knowledge of the dropout phenomenon. This study aims to gain a better insight into students' dropout based on an integrated…

In sample survey analysis, accurate population mean estimation is an important task, but traditional approaches frequently ignore the intricacies of real-world data, leading to biassed results. In order to handle uncertainties, indeterminacies, and ambiguity, this work presents an innovative approach based on neutrosophic statistics. We proposed…

Knowledge of results (KR), particularly its informational role, has often been regarded as redundant for learning interception-like tasks, such as coincidence-anticipation timing tasks. However, it is possible that the KR's guiding effect might be detrimental to motor adaptation, instead of only redundant, leading to a dependency on KR and…

Caregivers play an outsized role in shaping early life experiences and development, but we often lack mechanistic insight into "how" exactly caregiver behavior scaffolds the neurodevelopment of specific learning processes. Here, we capitalized on the fact that caregivers differ in how predictable their behavior is to ask if infants'…

Lagged dependent variables (LDVs) are often used as predictors in ordinary least squares (OLS) models in the social sciences. Although several estimators are commonly employed, little is known about their relative merits in the presence of classical measurement error and different longitudinal processes. We assess the performance of four commonly…

Assessing student mastery is often done by using exams. Inevitably, some students will complete remediation, which may include exam retakes. This method provides students an additional opportunity to take an exam that assesses the same objectives as the original exam, while using different questions. Although this form of remediation increases…

Student mobility that occurs within a school year may be especially disruptive for student outcomes, yet little is known regarding the predictors of within-year mobility. In particular, research has yet to comprehensively examine the role of student achievement in predicting within-year student mobility. Thus, we sought to understand this link by…

This study highlights the role and importance of advanced, machine learning-driven predictive models in enhancing the accuracy and timeliness of identifying students at-risk of negative academic outcomes in data-driven Early Warning Systems (EWS). K-12 school districts have, at best, 13 years to prepare students for adulthood and success. They…

Motivation is a key factor for child development, but very few studies have examined child and family predictors of both child task and perceived motivation. Thus, the three aims of this 6-month longitudinal study in preschoolers with global developmental delays (GDD) were to explore: 1) differences between task and perceived motivation in…

We evaluate the feasibility of developing predictive models of rater behavior, that is, "rater-specific" models for predicting the scores produced by a rater under operational conditions. In the present study, the dependent variable is the score assigned to essays by a rater, and the predictors are linguistic attributes of the essays…