Woods Carol M
Washington University in St. Louis, St. Louis, MO 63130-4899, USA.
Psychol Methods. 2006 Sep;11(3):253-70. doi: 10.1037/1082-989X.11.3.253.
Popular methods for fitting unidimensional item response theory (IRT) models to data assume that the latent variable is normally distributed in the population of respondents, but this can be unreasonable for some variables. Ramsay-curve IRT (RC-IRT) was developed to detect and correct for this nonnormality. The primary aims of this article are to introduce RC-IRT less technically than it has been described elsewhere; to evaluate RC-IRT for ordinal data via simulation, including new approaches for model selection; and to illustrate RC-IRT with empirical examples. The empirical examples demonstrate the utility of RC-IRT for real data, and the simulation study indicates that when the latent distribution is skewed, RC-IRT results can be more accurate than those based on the normal model. Along with a plot of candidate curves, the Hannan-Quinn criterion is recommended for model selection.
将单维项目反应理论（IRT）模型拟合到数据的常用方法假定潜在变量在受访者总体中呈正态分布，但这对某些变量而言可能不合理。拉姆齐曲线IRT（RC-IRT）就是为检测和校正这种非正态性而开发的。本文的主要目的是以比其他地方更通俗易懂的方式介绍RC-IRT；通过模拟评估RC-IRT用于有序数据的情况，包括模型选择的新方法；并用实证例子说明RC-IRT。实证例子证明了RC-IRT对实际数据的实用性，模拟研究表明，当潜在分布呈偏态时，RC-IRT的结果可能比基于正态模型的结果更准确。除了绘制候选曲线外，还建议使用汉南-奎因准则进行模型选择。
