Meta-analysts often encounter missing covariate values when estimating meta-regression models. In practice, ad hoc approaches involving data deletion have been widely used. The current study investigates the performance of different methods for handling missing covariates in meta-regression, including complete-case analysis (CCA), shifting-case…

Estimation of causal effects requires making comparisons across groups of observations exposed and not exposed to a a treatment or cause (intervention, program, drug, etc). To interpret differences between groups causally we need to ensure that they have been constructed in such a way that the comparisons are "fair." This can be…

Spatial regression models provide the opportunity to analyze spatial data and spatial processes. Yet, several model specifications can be used, all assuming different types of spatial dependence. This study summarizes the most commonly used spatial regression models and offers a comparison of their performance by using Monte Carlo experiments. In…

This support document accompanies the research summary "From VET to Sustainable Employment for Aboriginal and Torres Strait Islander Peoples," which examines the contribution of vocational education and training (VET) participation to sustainable employment for this cohort of the Australian population. The purpose of this document is to…

This applied research methods report is a guide for state and local education agency policymakers and their analysts who are interested in studying teacher mobility and retention. This report is the second in a two-part set and builds on the foundational information in report 1. This report presents guidance on how to interpret differences in…

Studies analyzing clustered data sets using both multilevel models (MLMs) and ordinary least squares (OLS) regression have generally concluded that resulting point estimates, but not the standard errors, are comparable with each other. However, the accuracy of the estimates of OLS models is important to consider, as several alternative techniques…

Learning analysts often consider whether learning processes across time are related (1) to one another or (2) to learning outcomes at higher levels. For example, are a group's temporal sequences of talk (e.g., correct evaluation [right arrow] correct, new idea) during its problem solving related to its group solution? I show how to address these…

Multilevel data are very common in educational research. Hierarchical linear models/linear mixed-effects models (HLMs/LMEs) are often utilized to analyze multilevel data nowadays. This paper discusses the problems of utilizing ordinary regressions for modeling multilevel educational data, compare the data analytic results from three regression…

As a school-wide framework, Multi-Tiered Systems of Support (MTSS) relies on the prevention and early identification of students at risk of academic failure. Approaches to early identification of students in need of support include the administration of universal screening assessments and the analysis of existing student data such as attendance,…

Vocabulary knowledge is crucial to literacy development and academic success. Previous research has shown learning the meaning of a word requires encountering it in diverse informative contexts. In this work, we try to identify "nutritious" contexts for a word--contexts that help students build a rich mental representation of the word's…

Data analysis techniques can be used to analyze the pattern of data in different fields. Based on the analysis' results, it is recommended that suggestions be provided to decision making authorities. The data mining techniques can be used in educational domain to improve the outcome of the educational sectors. The authors carried out this research…

TIMSS and PIRLS assess representative samples of students at regular intervals, measuring trends in student achievement and student contexts for learning. Because individual students are not tracked over time, analysis of international large-scale assessment data is usually conducted cross-sectionally. Gustafsson (2007) proposed examining the data…

This brief examined the patterns of reading achievement using statewide data from all students (Grades 3-10) in multiple years to examine gaps based on student, school, and district characteristics. Results indicate reading achievement varied most between students within schools and that students' prior achievement was the strongest predictor of…

Modeling student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for modeling the acquisition of knowledge is Bayesian Knowledge Tracing (BKT). Various extensions to the original BKT model have been proposed, among them two novel models that unify BKT and Item Response Theory (IRT). Latent Factor Knowledge…

The purpose of this study was to investigate the extent to which professional development predicts principals' instructional leadership in order to identify whether a relationship exists between the duration of principals' participation in distinct professional development activities and their perceived practice of instructional leadership while…