{"title":"利用SAS PROC mix对层次线性模型进行解秘","authors":"Jianjun Wang","doi":"10.1080/00220979709601397","DOIUrl":null,"url":null,"abstract":"Abstract Hierarchical data analyses in different disciplines are reviewed to compare statistical applications of the Hierarchical Linear Model (HLM) software and the SAS MIXED procedure. Similar features of the 2 programs are illustrated through use of the SAS MIXED procedure to confirm an HLM example. The SAS is a standard statistical package with a large group of users; discussions of the shared features in statistical computing may identify options to demystify existing methods for analyzing hierarchical data.","PeriodicalId":47911,"journal":{"name":"Journal of Experimental Education","volume":"66 1","pages":"84-93"},"PeriodicalIF":2.2000,"publicationDate":"1997-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/00220979709601397","citationCount":"14","resultStr":"{\"title\":\"Using SAS PROC MIXED To Demystify the Hierarchical Linear Model\",\"authors\":\"Jianjun Wang\",\"doi\":\"10.1080/00220979709601397\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Hierarchical data analyses in different disciplines are reviewed to compare statistical applications of the Hierarchical Linear Model (HLM) software and the SAS MIXED procedure. Similar features of the 2 programs are illustrated through use of the SAS MIXED procedure to confirm an HLM example. The SAS is a standard statistical package with a large group of users; discussions of the shared features in statistical computing may identify options to demystify existing methods for analyzing hierarchical data.\",\"PeriodicalId\":47911,\"journal\":{\"name\":\"Journal of Experimental Education\",\"volume\":\"66 1\",\"pages\":\"84-93\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"1997-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/00220979709601397\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Experimental Education\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://doi.org/10.1080/00220979709601397\",\"RegionNum\":4,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Experimental Education","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1080/00220979709601397","RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
Using SAS PROC MIXED To Demystify the Hierarchical Linear Model
Abstract Hierarchical data analyses in different disciplines are reviewed to compare statistical applications of the Hierarchical Linear Model (HLM) software and the SAS MIXED procedure. Similar features of the 2 programs are illustrated through use of the SAS MIXED procedure to confirm an HLM example. The SAS is a standard statistical package with a large group of users; discussions of the shared features in statistical computing may identify options to demystify existing methods for analyzing hierarchical data.
期刊介绍:
The Journal of Experimental Education publishes theoretical, laboratory, and classroom research studies that use the range of quantitative and qualitative methodologies. Recent articles have explored the correlation between test preparation and performance, enhancing students" self-efficacy, the effects of peer collaboration among students, and arguments about statistical significance and effect size reporting. In recent issues, JXE has published examinations of statistical methodologies and editorial practices used in several educational research journals.