Multilevel modeling (MLM) is a statistical technique for analyzing clustered data. Despite its long history, the technique and accompanying computer programs are rapidly evolving. Given the complexity of multilevel models, it is crucial for researchers to provide complete and transparent descriptions of the data, statistical analyses, and results. Ten years have passed since the guidelines for reporting multilevel studies were initially published. This study reviewed new advancements in MLM and revisited the reporting practice in MLM in the past decade. A total of 301 articles from 19 journals representing different subdisciplines in education and psychology were included in the systematic review. The results showed improvement in some areas of the reporting practices, such as the number of models tested, centering of predictors, missing data treatment, software, and estimates of variance components. However, poor practices persist in terms of model specification, description of a missing mechanism, power analysis, assumption checking, model comparisons, and effect sizes. Updates on the guidelines for reporting multilevel studies and recommendations for future methodological research in MLM are presented.

Introduction: Past decades have seen a surge of applied and methodological research on meta-analysis. One methodological advancement that has gained signiicant traction is a Bayesian approach to meta-analysis. Methods: We present a non-technical introduction to Bayesian meta-analysis. This introduction re-analyzes data from a meta-analysis concerning the impact of media literacy interventions on attitudes and intentions related to risky health behaviors using a Bayesian approach. One data relate media literacy interventions to media literacy skills, and another relates media literacy interventions to attitudes and behavioral intentions towards risky health behaviors. In these examples we focus on how to conduct unconditional models via graphical and quantitative results. Further, we demonstrate how to conduct subgroup analyses using risk behavior type (drinking, sexual, or smoking). Results: We demonstrated how several meta-analytical quantities could be computed and interpreted in a Bayesian framework. This was done both graphically (plot of the marginal posterior distributions) and quantitatively (e.g., central tendency measures, highest posterior density intervals). Results also showed how analyzing efect sizes at the risk-behavior level could afect several interpretations. Conclusions: We emphasize that in no way are Bayesian methods "superior" to frequentist methods, nor that frequentist methods should be abandoned. Instead, the two approaches should be viewed as familial, each with advantages and disadvantages, but strive at a common purpose. We hope for increased use of Bayesian meta-analyses, and Bayesian methodology at large, in adolescence research. Last, all R code is provided for readers to use as a foundation for their own research.Though meta-analytic practices existed prior to the coining of the phrase by Glass (1976), recent decades have seen a surge of applied and methodological research centered on meta-analysis and research synthesis. Though the meta-analytic process has many components and purposes, a core goal is to quantitatively combine and summarize two or more studies on a speciic question or topic. This goal is completed by combining measures of efect (i.e., efect sizes), the metric of which is mainly determined by the type of data from the primary study. Looking at the past ive years of six well-cited and widely-known journals in adolescence research (

The Materials Genome Initiative (MGI) calls for the acceleration of the materials development cycle through the integration of experiments and simulations within a data-aware/enabling framework. To realize this vision, MGI recognizes the need for the creation of a new kind of workforce capable of creating and/or deploying advanced informatics tools and methods into the materials discovery/development cycle. An interdisciplinary team at Texas A&M seeks to address this challenge by creating an interdisciplinary program that goes beyond MGI in that it incorporates the discipline of engineering systems design as an essential component of the new accelerated materials development paradigm. The Data-Enabled Discovery and Development of Energy Materials (D3EM) program seeks to create an interdisciplinary graduate program at the intersection of materials science, informatics, and design. In this paper, we describe the rationale for the creation of such a program, present the pedagogical model that forms the basis of the program, and describe some of the major elements of the program.

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