SAS PROC IRT和R Mirt软件包:多维IRT模型参数估计的比较

Psych Pub Date : 2023-05-15 DOI:10.3390/psych5020028
Ki Cole, Insu Paek
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引用次数: 1

摘要

本文研究了SAS PROC IRT和R. mirt两种知名IRT程序中具有二分和多分数据的多维IRT模型的估计方法的性能,并通过仿真研究比较了简单结构Rasch模型、复杂结构2PL模型和双因素分级响应模型的性能。在项目参数恢复的均方根误差和偏差标准下,PROC IRT和mirt在简单结构条件下表现出几乎相同的性能。当使用复杂结构时,mirt在截距参数的恢复方面表现较好,而斜率参数的恢复取决于程序和样本量:根据RMSE, PROC IRT在小样本(N=500)中表现较好,根据RMSE和偏差,mirt在大样本(N=1000和2500)中表现较好。当使用双因素结构时,在所有情况下都首选mirt;差异随着样本量的增加而减小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SAS PROC IRT and the R Mirt Package: A Comparison of Model Parameter Estimation for Multidimensional IRT Models
This study investigates the performance of estimation methods for multidimensional IRT models with dichotomous and polytomous data in two well-known IRT programs: SAS PROC IRT and the mirt package in R. A simulation study was used to compare performance on a simple structure Rasch model, complex structure 2PL model, and bifactor graded response model. Under RMSE and bias criteria regarding item parameter recovery, PROC IRT and mirt showed nearly identical performance in the simple structure condition. When a complex structure was used, mirt performed better in terms of the recovery of intercept parameters, while the recovery of slope parameters depended on the program and the sample sizes: PROC IRT tended to be better with small samples (N=500) according to RMSE, and mirt was better for larger samples (N=1000 and 2500) according to RMSE and bias for the slope parameter recovery. When a bifactor structure was used, mirt was preferred in all cases; differences lessened as sample size increased.
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