The issue of model uncertainty has been gaining interest in education and the social sciences community over the years, and the dominant methods for handling model uncertainty are based on Bayesian inference, particularly, Bayesian model averaging. However, Bayesian model averaging assumes that the true data-generating model is within the…

While the family is a critical determinant of educational achievement, methodological difficulties and the availability of data limit estimation of the family contribution in school effectiveness models. This study uses multilevel modeling to estimate the proportion of variation in student educational achievement between families, family-level…

Background: The unprecedented access to information in the 21st century entails a deep understanding of information and communication technology (ICT)-related factors in education and their impacts on learning and teaching. The role of attitudes towards ICT is a proven factor in student achievement. However, there is no consensus about the…

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…

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…

Since the early 2000s, educational evaluation research has primarily centered on teachers', rather than schools', contributions to students' academic outcomes due to concerns that estimates of the latter were smaller, less stable, and more prone to measurement error. We argue that this disparity should be reduced. Using administrative data from…

Meta-analyses suggest that instructional quality in the classroom and the quality of teacher-student relationships (TSR) predict positive social-emotional and achievement-related outcomes. Psychoanalytic theory asserts that positive teacher-student relationships are particularly important for outcomes in classrooms with more students with severe…

Given the natural hierarchical structure in school-setting data, multilevel modeling (MLM) has been widely employed in education research using a number of different statistical software packages. The purpose of this article is to review a recent feature of Stat-JR, the statistical analysis assistants (SAAs) embedded in Stat-JR (Version 1.0.5),…

Aside from a student's personal desire to pursue higher education, a culture of high expectations in a school can have important consequences on the individual's achievement. However, the school's 'collective expectation' is affected by many contextual factors like urbanicity. Contributing to the research on urban-rural difference in education and…

This study investigated the effects of educational context variables on two types of achievement: cognitive domain and affective domain. To assess the effects of educational context variables at student and school levels, the National Assessment of Educational Achievement data were used, which were collected by the Korea Educational Development…

The social experience of transitioning to a 4-year university varies widely among students. Some attend with few or no prior contacts or acquaintances from their hometown; others attend with a large network of high school alumni. Using a sample (N = 43,240) of undergraduates spanning 7.5 years at a public university, we examine what factors…

To plan cluster-randomized trials with sufficient statistical power to detect intervention effects on student achievement, researchers need multilevel design parameters, including measures of between-classroom and between-school differences and the amounts of variance explained by covariates at the student, classroom, and school level. Previous…

The Every Student Succeeds Act requires states to identify schools with low-performing student subgroups for Targeted Support and Improvement or Additional Targeted Support and Improvement. Random differences between students' true abilities and their test scores, also called measurement error, reduce the statistical reliability of the performance…

This Snapshot highlights key findings from a study that used Bayesian stabilization to improve the reliability (long-term stability) of subgroup proficiency measures that the Pennsylvania Department of Education (PDE) uses to identify schools for Targeted Support and Improvement (TSI) or Additional Targeted Support and Improvement (ATSI). The…

The "Stabilizing Subgroup Proficiency Results to Improve the Identification of Low-Performing Schools" study used Bayesian stabilization to improve the reliability (long-term stability) of subgroup proficiency measures that the Pennsylvania Department of Education (PDE) uses to identify schools for Targeted Support and Improvement (TSI)…