期刊
MULTIVARIATE BEHAVIORAL RESEARCH
卷 54, 期 1, 页码 85-99出版社
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/00273171.2018.1491381
关键词
Item response theory; Bayesian statistics; Bayesian item response theory; linking; test equating
类别
资金
- National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health [5U01AR052171]
- Department of Education, NIDRR [H133B031129]
There are a growing number of item response theory (IRT) studies that calibrate different patient-reported outcome (PRO) measures, such as anxiety, depression, physical function, and pain, on common, instrument-independent metrics. In the case of depression, it has been reported that there are considerable mean score differences when scoring on a common metric from different, previously linked instruments. Ideally, those estimates should be the same. We investigated to what extent those differences are influenced by different scoring methods that take into account several levels of uncertainty, such as measurement error (through plausible value imputation) and item parameter uncertainty (through full Bayesian IRT modeling). Depression estimates from different instruments were more similar, and their corresponding confidence/credible intervals were larger when plausible value imputation or Bayesian modeling was used, compared to the direct use of expected a posteriori (EAP) estimates. Furthermore, we explored the use of Bayesian IRT models to update item parameters based on newly collected data.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据