{"title":"使用PARSCALE对评级尺度模型进行参数恢复。","authors":"G A French, B G Dodd","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>The purpose of the present study was to investigate item and trait parameter recovery for Andrich's rating scale model using the PARSCALE computer program. The four factors upon which the simulated data matrices varied were (a) the distribution of the scale values for the items (skewed or uniform), (b) the number of category response options (4 or 5), (c) the distribution of known trait levels (normal or skewed), and (d) the sample size (60, 125, 250, 500, or 1,000). Each condition was replicated 10 times resulting in 400 data matrices. Accurate item and trait parameter estimates were obtained for all sample sizes examined. As expected, sample size seemed to have little influence on the recovery of trait parameters but did influence item parameter recovery. The distribution of known trait levels did not seriously impact the item parameter recovery. It was concluded that Andrich's rating scale model allows for the use of considerably smaller calibration samples than are typically recommended for other polytomous IRT models.</p>","PeriodicalId":79673,"journal":{"name":"Journal of outcome measurement","volume":"3 2","pages":"176-99"},"PeriodicalIF":0.0000,"publicationDate":"1999-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Parameter recovery for the rating scale model using PARSCALE.\",\"authors\":\"G A French, B G Dodd\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The purpose of the present study was to investigate item and trait parameter recovery for Andrich's rating scale model using the PARSCALE computer program. The four factors upon which the simulated data matrices varied were (a) the distribution of the scale values for the items (skewed or uniform), (b) the number of category response options (4 or 5), (c) the distribution of known trait levels (normal or skewed), and (d) the sample size (60, 125, 250, 500, or 1,000). Each condition was replicated 10 times resulting in 400 data matrices. Accurate item and trait parameter estimates were obtained for all sample sizes examined. As expected, sample size seemed to have little influence on the recovery of trait parameters but did influence item parameter recovery. The distribution of known trait levels did not seriously impact the item parameter recovery. It was concluded that Andrich's rating scale model allows for the use of considerably smaller calibration samples than are typically recommended for other polytomous IRT models.</p>\",\"PeriodicalId\":79673,\"journal\":{\"name\":\"Journal of outcome measurement\",\"volume\":\"3 2\",\"pages\":\"176-99\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-01-01\",\"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}
Parameter recovery for the rating scale model using PARSCALE.
The purpose of the present study was to investigate item and trait parameter recovery for Andrich's rating scale model using the PARSCALE computer program. The four factors upon which the simulated data matrices varied were (a) the distribution of the scale values for the items (skewed or uniform), (b) the number of category response options (4 or 5), (c) the distribution of known trait levels (normal or skewed), and (d) the sample size (60, 125, 250, 500, or 1,000). Each condition was replicated 10 times resulting in 400 data matrices. Accurate item and trait parameter estimates were obtained for all sample sizes examined. As expected, sample size seemed to have little influence on the recovery of trait parameters but did influence item parameter recovery. The distribution of known trait levels did not seriously impact the item parameter recovery. It was concluded that Andrich's rating scale model allows for the use of considerably smaller calibration samples than are typically recommended for other polytomous IRT models.