Reporting Practice in Multilevel Modeling: A Revisit After 10 Years:

Published on Feb 6, 2021in Review of Educational Research
· DOI :10.3102/0034654321991229
Wen Luo20
Estimated H-index: 20
(A&M: Texas A&M University),
Haoran Li (A&M: Texas A&M University)+ 3 AuthorsBrandie Semma2
Estimated H-index: 2
(A&M: Texas A&M University)
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 complex...
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Mixed-effects multilevel models are often used to investigate cross-level interactions, a specific type of context effect that may be understood as an upper-level variable moderating the association between a lower-level predictor and the outcome. We argue that multilevel models involving cross-level interactions should always include random slopes on the lower-level components of those interactions. Failure to do so will usually result in severely anti-conservative statistical inference. We ill...
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The brms package allows R users to easily specify a wide range of Bayesian single-level and multilevel models, which are fitted with the probabilistic programming language Stan behind the scenes. Several response distributions are supported, of which all parameters (e.g., location, scale, and shape) can be predicted at the same time thus allowing for distributional regression. Non-linear relationships may be specified using non-linear predictor terms or semi-parametric approaches such as splines...
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: In this article we address a number of important issues that arise in the analysis of nonindependent data. Such data are common in studies in which predictors vary within "units" (e.g., within-subjects, within-classrooms). Most researchers analyze categorical within-unit predictors with repeated-measures ANOVAs, but continuous within-unit predictors with linear mixed-effects models (LMEMs). We show that both types of predictor variables can be analyzed within the LMEM framework. We discuss des...
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Mediation analyses have provided a critical platform to assess the validity of theories of action across a wide range of disciplines. Despite widespread interest and development in these analyses, literature guiding the design of mediation studies has been largely unavailable. Like studies focused on the detection of a total or main effect, an important design consideration is the statistical power to detect indirect effects if they exist. Understanding the sensitivity to detect indirect effects...
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Mixed-effects models are increasingly used in many areas of applied science. Despite their popularity, there is virtually no systematic approach for examining the homogeneity of the random-effects ...
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