Effects of the Quantity and Magnitude of Cross-Loading and Model Specification on MIRT Item Parameter Recovery

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Mostafa Hosseinzadeh, Ki Lynn Matlock Cole
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引用次数: 0

Abstract

In real-world situations, multidimensional data may appear on large-scale tests or psychological surveys. The purpose of this study was to investigate the effects of the quantity and magnitude of cross-loadings and model specification on item parameter recovery in multidimensional Item Response Theory (MIRT) models, especially when the model was misspecified as a simple structure, ignoring the quantity and magnitude of cross-loading. A simulation study that replicated this scenario was designed to manipulate the variables that could potentially influence the precision of item parameter estimation in the MIRT models. Item parameters were estimated using marginal maximum likelihood, utilizing the expectation-maximization algorithms. A compensatory two-parameter logistic-MIRT model with two dimensions and dichotomous item–responses was used to simulate and calibrate the data for each combination of conditions across 500 replications. The results of this study indicated that ignoring the quantity and magnitude of cross-loading and model specification resulted in inaccurate and biased item discrimination parameter estimates. As the quantity and magnitude of cross-loading increased, the root mean square of error and bias estimates of item discrimination worsened.
交叉加载的数量和幅度以及模型规格对 MIRT 项目参数恢复的影响
在现实世界中,大规模测验或心理调查中可能会出现多维数据。本研究旨在探讨交叉负荷的数量和大小以及模型规格对多维项目反应理论(MIRT)模型中项目参数恢复的影响,尤其是当模型被错误地规格为简单结构,忽略了交叉负荷的数量和大小时。我们设计了一项模拟研究来复制这种情况,以操纵可能影响多维项目反应理论模型中项目参数估计精度的变量。项目参数采用边际最大似然法,利用期望最大化算法进行估计。我们使用了一个具有两个维度和二分项目反应的补偿性双参数逻辑-MIRT 模型来模拟和校准 500 次重复中每种条件组合的数据。研究结果表明,忽略交叉负荷的数量和大小以及模型的规格会导致项目区分度参数估计的不准确和偏差。随着交叉负荷数量和幅度的增加,项目辨别力的误差均方根和偏差估计值也在增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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