Allahyari Elahe, Jafari Peyman, Bagheri Zahra
Department of Biostatistics, Faculty of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
Comput Math Methods Med. 2016;2016:5080826. doi: 10.1155/2016/5080826. Epub 2016 Jun 15.
Objective. The present study uses simulated data to find what the optimal number of response categories is to achieve adequate power in ordinal logistic regression (OLR) model for differential item functioning (DIF) analysis in psychometric research. Methods. A hypothetical ten-item quality of life scale with three, four, and five response categories was simulated. The power and type I error rates of OLR model for detecting uniform DIF were investigated under different combinations of ability distribution (θ), sample size, sample size ratio, and the magnitude of uniform DIF across reference and focal groups. Results. When θ was distributed identically in the reference and focal groups, increasing the number of response categories from 3 to 5 resulted in an increase of approximately 8% in power of OLR model for detecting uniform DIF. The power of OLR was less than 0.36 when ability distribution in the reference and focal groups was highly skewed to the left and right, respectively. Conclusions. The clearest conclusion from this research is that the minimum number of response categories for DIF analysis using OLR is five. However, the impact of the number of response categories in detecting DIF was lower than might be expected.
目的。本研究使用模拟数据来确定在心理测量研究中进行差异项目功能（DIF）分析的有序逻辑回归（OLR）模型中，达到足够检验效能所需的最佳反应类别数。方法。模拟了一个假设的包含十个项目的生活质量量表，分别具有三个、四个和五个反应类别。在能力分布（θ）、样本量、样本量比以及参考组和目标组之间均匀DIF的大小的不同组合下，研究了OLR模型检测均匀DIF的检验效能和I型错误率。结果。当参考组和目标组中的θ分布相同时，将反应类别数从3增加到5会使OLR模型检测均匀DIF的检验效能提高约8%。当参考组和目标组中的能力分布分别严重左偏和右偏时，OLR的检验效能小于0.36。结论。本研究最明确的结论是，使用OLR进行DIF分析的反应类别数最少为五个。然而，反应类别数在检测DIF中的影响低于预期。
