{"title":"赶上多层次建模。","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":23,"journal":{"name":"ACS Photonics","volume":" ","pages":"659-689"},"PeriodicalIF":6.5000,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"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\":23,\"journal\":{\"name\":\"ACS Photonics\",\"volume\":\" \",\"pages\":\"659-689\"},\"PeriodicalIF\":6.5000,\"publicationDate\":\"2022-01-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Photonics\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1146/annurev-psych-020821-103525\",\"RegionNum\":1,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Photonics","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1146/annurev-psych-020821-103525","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
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:
Published as soon as accepted and summarized in monthly issues, ACS Photonics will publish Research Articles, Letters, Perspectives, and Reviews, to encompass the full scope of published research in this field.