{"title":"运用SAS PROC IRT进行多维项目反应理论分析","authors":"Ki Cole, Insu Paek","doi":"10.1080/15366367.2021.1976090","DOIUrl":null,"url":null,"abstract":"ABSTRACT Statistical Analysis Software (SAS) is a widely used tool for data management analysis across a variety of fields. The procedure for item response theory (PROC IRT) is one to perform unidimensional and multidimensional item response theory (IRT) analysis for dichotomous and polytomous data. This review provides a summary of the features of PROC IRT specifically for multidimensional data with examples provided for simple structure data, complex structure data, and bifactor data. Instructive examples for dichotomous data (using the Rasch and 2-parameter logistic models) and polytomous data (using the graded response model) are given. Explanations of the syntax are also presented.","PeriodicalId":46596,"journal":{"name":"Measurement-Interdisciplinary Research and Perspectives","volume":"13 1","pages":"49 - 55"},"PeriodicalIF":0.6000,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Using SAS PROC IRT for Multidimensional Item Response Theory Analysis\",\"authors\":\"Ki Cole, Insu Paek\",\"doi\":\"10.1080/15366367.2021.1976090\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Statistical Analysis Software (SAS) is a widely used tool for data management analysis across a variety of fields. The procedure for item response theory (PROC IRT) is one to perform unidimensional and multidimensional item response theory (IRT) analysis for dichotomous and polytomous data. This review provides a summary of the features of PROC IRT specifically for multidimensional data with examples provided for simple structure data, complex structure data, and bifactor data. Instructive examples for dichotomous data (using the Rasch and 2-parameter logistic models) and polytomous data (using the graded response model) are given. Explanations of the syntax are also presented.\",\"PeriodicalId\":46596,\"journal\":{\"name\":\"Measurement-Interdisciplinary Research and Perspectives\",\"volume\":\"13 1\",\"pages\":\"49 - 55\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2022-01-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Measurement-Interdisciplinary Research and Perspectives\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/15366367.2021.1976090\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"SOCIAL SCIENCES, INTERDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement-Interdisciplinary Research and Perspectives","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/15366367.2021.1976090","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"SOCIAL SCIENCES, INTERDISCIPLINARY","Score":null,"Total":0}
Using SAS PROC IRT for Multidimensional Item Response Theory Analysis
ABSTRACT Statistical Analysis Software (SAS) is a widely used tool for data management analysis across a variety of fields. The procedure for item response theory (PROC IRT) is one to perform unidimensional and multidimensional item response theory (IRT) analysis for dichotomous and polytomous data. This review provides a summary of the features of PROC IRT specifically for multidimensional data with examples provided for simple structure data, complex structure data, and bifactor data. Instructive examples for dichotomous data (using the Rasch and 2-parameter logistic models) and polytomous data (using the graded response model) are given. Explanations of the syntax are also presented.