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首页> 美国卫生研究院文献>Computational and Mathematical Methods in Medicine >A Simulation Study to Assess the Effect of the Number of Response Categories on the Power of Ordinal Logistic Regression for Differential Item Functioning Analysis in Rating Scales
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A Simulation Study to Assess the Effect of the Number of Response Categories on the Power of Ordinal Logistic Regression for Differential Item Functioning Analysis in Rating Scales

机译：评估量表中差异项功能分析的响应类别数对有序Logistic回归功效的影响的模拟研究

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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.
机译：目的。本研究使用模拟数据来找出在心理计量学研究中的差异项功能（DIF）分析的序数逻辑回归（OLR）模型中，要获得足够功效的最佳反应类别数。方法。假设的十项生活质量量表具有三个，四个和五个响应类别。在能力分布（θ），样本大小，样本大小比率以及参考和焦点小组中均匀DIF大小的不同组合下，研究了用于检测均匀DIF的OLR模型的功效和I型错误率。结果。当θ在参考组和焦点组中的分布相同时，将响应类别的数量从3增加到5，导致检测均匀DIF的OLR模型的功效增加了大约8％。当参考组和焦点组的能力分布分别高度偏向左侧和右侧时，OLR的功效小于0.36。结论。这项研究最明确的结论是，使用OLR进行DIF分析的响应类别的最小数量为5。但是，响应类别数对检测DIF的影响比预期的要低。

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