{"title":"解释多面Rasch模型中的卡方统计。","authors":"R E Schumacker, M E Lunz","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>The different chi-square statistics reported in the many-faceted Rasch model analysis are presented and interpreted. In addition, other chi-square summary values are computed and presented for interpretation of facets. The chi-square values are useful for determining: (1) the significance of a facet in the Rasch model; (2) the significant contribution of facet main and interaction effects; (3) differences among facet elements; and (4) identifying the specific facet interaction adjustments to the subjects' calibrated logit ability measure.</p>","PeriodicalId":79673,"journal":{"name":"Journal of outcome measurement","volume":"1 3","pages":"239-57"},"PeriodicalIF":0.0000,"publicationDate":"1997-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Interpreting the chi-square statistics reported in the many-faceted Rasch model.\",\"authors\":\"R E Schumacker, M E Lunz\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The different chi-square statistics reported in the many-faceted Rasch model analysis are presented and interpreted. In addition, other chi-square summary values are computed and presented for interpretation of facets. The chi-square values are useful for determining: (1) the significance of a facet in the Rasch model; (2) the significant contribution of facet main and interaction effects; (3) differences among facet elements; and (4) identifying the specific facet interaction adjustments to the subjects' calibrated logit ability measure.</p>\",\"PeriodicalId\":79673,\"journal\":{\"name\":\"Journal of outcome measurement\",\"volume\":\"1 3\",\"pages\":\"239-57\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of outcome measurement\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of outcome measurement","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Interpreting the chi-square statistics reported in the many-faceted Rasch model.
The different chi-square statistics reported in the many-faceted Rasch model analysis are presented and interpreted. In addition, other chi-square summary values are computed and presented for interpretation of facets. The chi-square values are useful for determining: (1) the significance of a facet in the Rasch model; (2) the significant contribution of facet main and interaction effects; (3) differences among facet elements; and (4) identifying the specific facet interaction adjustments to the subjects' calibrated logit ability measure.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
