期刊
EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT
卷 83, 期 6, 页码 1202-1228出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/00131644221140941
关键词
item response theory; scale linking; missing data; common item nonequivalent group design
This study evaluates the effects of six different approaches to handling missing data on the accuracy of scale linking based on item response theory. The results show that imputing with a response function, multiple imputation, and full information likelihood produce less errors in scale linking accuracy, while listwise deletion is associated with the most errors.
For large-scale assessments, data are often collected with missing responses. Despite the wide use of item response theory (IRT) in many testing programs, however, the existing literature offers little insight into the effectiveness of various approaches to handling missing responses in the context of scale linking. Scale linking is commonly used in large-scale assessments to maintain scale comparability over multiple forms of a test. Under a common-item nonequivalent group design (CINEG), missing data that occur to common items potentially influence the linking coefficients and, consequently, may affect scale comparability, test validity, and reliability. The objective of this study was to evaluate the effect of six missing data handling approaches, including listwise deletion (LWD), treating missing data as incorrect responses (IN), corrected item mean imputation (CM), imputing with a response function (RF), multiple imputation (MI), and full information likelihood information (FIML), on IRT scale linking accuracy when missing data occur to common items. Under a set of simulation conditions, the relative performance of the six missing data treatment methods under two missing mechanisms was explored. Results showed that RF, MI, and FIML produced less errors for conducting scale linking whereas LWD was associated with the most errors regardless of various testing conditions.
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