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首页> 外文期刊>Journal of applied measurement >A Monte Carlo Study of the Impact of Missing Data and Differential Item Functioning on Theta Estimates from Two Polytomous Rasch Family Models
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A Monte Carlo Study of the Impact of Missing Data and Differential Item Functioning on Theta Estimates from Two Polytomous Rasch Family Models

机译：蒙特卡洛研究的缺失数据和微分项功能对两个多角Rasch族模型的Theta估计的影响

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摘要

This paper examines the impact of differential item functioning (DIF), missing item values, and different methods for handling missing item values on theta estimates with data simulated from the partial credit model and Andrich's rating scale model. Both Rasch family models are commonly used when obtaining an estimate of a respondent's attitude. The degree of missing data, DIF magnitude, and the percentage of DIF items were varied in MCAR data conditions in which the focal group was 10% of the total population. Four methods for handling missing data were compared: complete-case analysis, mean substitution, hot-decking, and multiple imputation. Bias, RMSE, means, and standard errors of the theta estimates for the focal group were adversely affected by the amount and magnitude of DIF items. RMSE and fidelity coefficients for both the reference and focal group were adversely impacted by the amount of missing data. While all methods of handling missing data performed fairly similarly, multiple imputation and hot-decking showed slightly better performance.
机译：本文使用部分信用模型和Andrich的评级量表模型模拟的数据，研究了差异项功能（DIF），缺失项值以及处理缺失项值的不同方法对theta估计的影响。在获得对受访者态度的估计时，通常使用两种Rasch家族模型。数据丢失的程度，DIF大小和DIF项目的百分比在MCAR数据条件下有所变化，其中焦点人群为总人口的10％。比较了处理缺失数据的四种方法：完整案例分析，均值替换，热变形和多重插补。 DIF项目的数量和大小对焦点小组的theta估计值的偏差，RMSE，均值和标准误产生不利影响。参比组和焦点组的RMSE和保真系数都受到丢失数据量的不利影响。尽管处理丢失数据的所有方法的执行情况都相当相似，但多次插补和热装版显示出略微更好的性能。

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