Harrell-Williams Leigh, Wolfe Edward W
Edward W. Wolfe, Pearson, 3974 Roberts Ridge, NE, Iowa City, IA 52240, USA,
J Appl Meas. 2014;15(3):267-75.
Previous research has investigated the influence of sample size, model misspecification, test length, ability distribution offset, and generating model on the likelihood ratio difference test in applications of item response models. This study extended that research to the evaluation of dimensionality using the multidimensional random coefficients multinomial logit model (MRCMLM). Logistic regression analysis of simulated data reveal that sample size and test length have a large effect on the capacity of the LR difference test to correctly identify unidimensionality, with shorter tests and smaller sample sizes leading to smaller Type I error rates. Higher levels of simulated misfit resulted in fewer incorrect decisions than data with no or little misfit. However, Type I error rates indicate that the likelihood ratio difference test is not suitable under any of the simulated conditions for evaluating dimensionality in applications of the MRCMLM.
先前的研究调查了样本量、模型误设、测试长度、能力分布偏移和生成模型对项目反应模型应用中似然比差异检验的影响。本研究将该研究扩展到使用多维随机系数多项逻辑模型（MRCMLM）进行维度评估。对模拟数据的逻辑回归分析表明，样本量和测试长度对似然比差异检验正确识别单维度性的能力有很大影响，测试越短、样本量越小，第一类错误率就越小。与无拟合或拟合度很低的数据相比，较高水平的模拟失配导致的错误决策更少。然而，第一类错误率表明，在任何模拟条件下，似然比差异检验都不适用于在MRCMLM应用中评估维度。
