A GENERALIZED RASCH MODEL FOR MANIFEST PREDICTORS
A GENERALIZED RASCH MODEL FOR MANIFEST PREDICTORS
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DOI：
10.1007/bf02294492
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发表时间：
1991-12-01
期刊：
影响因子：
3
通讯作者：
ZWINDERMAN, AH
中科院分区：
文献类型：
--
作者：
ZWINDERMAN, AH
一个逻辑回归模型，建议用于估计一组显性预测因子和一个潜在特质之间的关系，假设被测量的一组卡帕二分项目。通常潜在特质模型的被试参数估计是有偏的，特别是在短测验中。因此，潜在特质和一组预测因子之间的关系不应该用回归模型来估计，在回归模型中，估计的受试者参数被用作因变量。建议直接估计潜在性状与一个或多个自变量之间的关系。讨论了Rasch模型的估计方法和检验统计量，并用模拟数据和经验数据说明了该模型。
A logistic regression model is suggested for estimating the relation between a set of manifest predictors and a latent trait assumed to be measured by a set of kappa-dichotomous items. Usually the estimated subject parameters of latent trait models are biased, especially for short tests. Therefore, the relation between a latent trait and a set of predictors should not be estimated with a regression model in which the estimated subject parameters are used as a dependent variable. Direct estimation of the relation between the latent trait and one or more independent variables is suggested instead. Estimation methods and test statistics for the Rasch model are discussed and the model is illustrated with simulated and empirical data.