Economics Department, Concordia University, Montreal, Canada.
623 Econometrics, Montreal, Canada.
Int J Methods Psychiatr Res. 2017 Dec;26(4). doi: 10.1002/mpr.1551. Epub 2016 Dec 28.
Kessler k psychological distress scores are analyzed using a count model and item response theory (IRT) models are applied to the items which produce the k score and generate an alternative distress score, θ . Other ways of utilizing the constituent items are also examined. The data used in the analysis comes from the 2014 National Survey of Drug Use and Health. Three important results emerge. First, θ and k are not highly correlated and their distributions are quite different. The k score gives a much more favourable picture of mental health than θ . Second, k does a much better job in explaining participation in treatment programs than θ suggesting a very limited role for IRT methods in the analysis of psychological distress data. As a diagnostic tool k is an effective and simple way of summarizing the item data. Third, for researchers interested in which individual characteristics determine psychological distress better results are obtained by analyzing the six constituent items which are used to generate the k score using ordered probability models rather than k itself.
凯斯勒心理困扰评分采用计数模型进行分析，项目反应理论（IRT）模型应用于产生 k 评分的项目，并生成替代困扰评分θ。还研究了利用组成项目的其他方法。分析中使用的数据来自 2014 年全国毒品使用和健康调查。出现了三个重要结果。首先，θ 和 k 相关性不高，分布差异很大。k 评分对心理健康的描述要比θ好得多。其次，k 在解释参与治疗计划方面比θ做得更好，这表明 IRT 方法在分析心理困扰数据方面的作用非常有限。作为一种诊断工具，k 是一种有效且简单的方法，可以总结项目数据。第三，对于关注哪些个体特征决定心理困扰的研究人员来说，通过分析用于生成 k 评分的六个组成项目，使用有序概率模型而不是 k 本身，可以获得更好的结果。
