{"title":"Catching Up on Multilevel Modeling.","authors":"Lesa Hoffman, Ryan W Walters","doi":"10.1146/annurev-psych-020821-103525","DOIUrl":null,"url":null,"abstract":"<p><p>This review focuses on the use of multilevel models in psychology and other social sciences. We target readers who are catching up on current best practices and sources of controversy in the specification of multilevel models. We first describe common use cases for clustered, longitudinal, and cross-classified designs, as well as their combinations. Using examples from both clustered and longitudinal designs, we then address issues of centering for observed predictor variables: its use in creating interpretable fixed and random effects of predictors, its relationship to endogeneity problems (correlations between predictors and model error terms), and its translation into multivariate multilevel models (using latent-centering within multilevel structural equation models). Finally, we describe novel extensions-mixed-effects location-scale models-designed for predicting differential amounts of variability.</p>","PeriodicalId":8010,"journal":{"name":"Annual review of psychology","volume":null,"pages":null},"PeriodicalIF":23.6000,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual review of psychology","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1146/annurev-psych-020821-103525","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY","Score":null,"Total":0}
引用次数: 14
Abstract
This review focuses on the use of multilevel models in psychology and other social sciences. We target readers who are catching up on current best practices and sources of controversy in the specification of multilevel models. We first describe common use cases for clustered, longitudinal, and cross-classified designs, as well as their combinations. Using examples from both clustered and longitudinal designs, we then address issues of centering for observed predictor variables: its use in creating interpretable fixed and random effects of predictors, its relationship to endogeneity problems (correlations between predictors and model error terms), and its translation into multivariate multilevel models (using latent-centering within multilevel structural equation models). Finally, we describe novel extensions-mixed-effects location-scale models-designed for predicting differential amounts of variability.
期刊介绍：
The Annual Review of Psychology, a publication that has been available since 1950, provides comprehensive coverage of the latest advancements in psychological research. It encompasses a wide range of topics, including the biological underpinnings of human behavior, the intricacies of our senses and perception, the functioning of the mind, animal behavior and learning, human development, psychopathology, clinical and counseling psychology, social psychology, personality, environmental psychology, community psychology, and much more. In a recent development, the current volume of this esteemed journal has transitioned from a subscription-based model to an open access format as part of the Annual Reviews' Subscribe to Open initiative. As a result, all articles published in this volume are now freely accessible to the public under a Creative Commons Attribution (CC BY) license.
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