There have been increasing calls for applied researchers to see and utilize effect sizes as the primary outcomes of their research. However, this sometimes places a methodological burden on researchers whose primary interests are substantive. Motivated by a desire to help applied researchers better report effect sizes and their confidence…

Multiple imputation is a popular method for addressing data that are presumed to be missing at random. To obtain accurate results, one's imputation model must be congenial to (appropriate for) one's intended analysis model. This article reviews and demonstrates two recent software packages, Blimp and jomo, to multiply impute data in a manner…

In many areas of sport management, individual behavior is not only driven by individual characteristics but also by higher level contextual factors. For instance, voluntary engagement in nonprofit sports clubs is influenced by organizational characteristics. Such multilevel structures should not only be considered theoretically, as in ecological…

This study examined the factors that determined the science achievement of fourth-grade students on the Trends in International Mathematics and Science Study (TIMSS) 2019 in the USA. The data were retrieved from the TIMSS international database and imported to the R program for manipulation. The EdSurvey package was used to conduct multilevel…

Background: The retention of traditionally underserved students remains a pressing problem across graduate engineering programs. Disciplinary differences in graduate engineering identity provide a lens to investigate students' experiences and can pinpoint potential opportunity structures that support or hinder progress based on social and personal…

School belonging is an important component of adolescent well-being, yet little is known about its relationship with adolescents' Information Communication Technology (ICT) use. This study aimed to examine the relationship between school belonging and various ICT use types in Australian adolescents. The sample was drawn from 14,530 Australian…

This paper drills deeper into the documented effects of the Cognitive Tutor Algebra I and ASSISTments intelligent tutoring systems by estimating their effects on specific problems. We start by describing a multilevel Rasch-type model that facilitates testing for differences in the effects between problems and precise problem-specific effect…

This study examined prior knowledge and student engagement in student performance. Log data were used to explore the distribution of final grades (i.e., weak, good, excellent final grades) occurring in an elective undergraduate course. Previous research has established behavioral and agentic engagement factors contribute to academic achievement…

Hierarchical linear modeling (HLM) has become an increasingly popular multilevel method of analyzing data among nested datasets, in particular, the effect of specialized academic programming within schools. The purpose of this methodological study is to demonstrate the use of HLM to determine the effectiveness of STEM programming in an Ohio middle…

A multilevel Rasch model using a hierarchical generalized linear model is one approach to multilevel item response theory (IRT) modeling and is referred to as a one-parameter hierarchical generalized linear logistic model (1-P HGLLM). Although it has the flexibility to model nested structure of data with covariates, the model assumes the normality…

Multiple imputation methods can generally be divided into two broad frameworks: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution, whereas FCS imputes variables one at a time from a series of univariate conditional…

Education experiments frequently assign students to treatment or control conditions within schools. Longitudinal components added in these studies (e.g., students followed over time) allow researchers to assess treatment effects in average rates of change (e.g., linear or quadratic). We provide methods for a priori power analysis in three-level…

Physics education researchers (PER) often analyze student data with single-level regression models (e.g., linear and logistic regression). However, education datasets can have hierarchical structures, such as students nested within courses, that single-level models fail to account for. The improper use of single-level models to analyze…

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),…

When data for multiple outcomes are collected in a multilevel design, researchers can select a univariate or multivariate analysis to examine group-mean differences. When correlated outcomes are incomplete, a multivariate multilevel model (MVMM) may provide greater power than univariate multilevel models (MLMs). For a two-group multilevel design…