PDF(Pubmed)
Abstract：
In research applications, mental health problems such as alcohol-related problems and depression are commonly assessed and evaluated using scale scores or latent trait scores derived from factor analysis or item response theory models. This tutorial paper demonstrates the use of cognitive diagnosis models (CDMs) as an alternative approach to characterizing mental health problems of young adults when item-level data are available. Existing measurement approaches focus on estimating the general severity of a given mental health problem at the scale level as a unidimensional construct without accounting for other symptoms of related mental health problems. The prevailing approaches may ignore clinically meaningful presentations of related symptoms at the item level. The current study illustrates CDMs using item-level data from college students (40 items from 719 respondents; 34.6% men, 83.9% White, and 16.3% first-year students). Specifically, we evaluated the constellation of four postulated domains (i.e., alcohol-related problems, anxiety, hostility, and depression) as a set of attribute profiles using CDMs. After accounting for the impact of each attribute (i.e., postulated domain) on the estimates of attribute profiles, the results demonstrated that when items or attributes have limited information, CDMs can utilize item-level information in the associated attributes to generate potentially meaningful estimates and profiles, compared to analyzing each attribute independently. We introduce a novel visual inspection aid, the lens plot, for quantifying this gain. CDMs may be a useful analytical tool to capture respondents\' risk and resilience for prevention research.
摘要：
在研究应用中,诸如酒精相关问题和抑郁之类的心理健康问题通常使用从因子分析或项目反应理论模型中得出的量表评分或潜在特征评分进行评估和评价.本教程论文演示了使用认知诊断模型（CDM）作为一种替代方法来表征项目级数据可用时的年轻人的心理健康问题。现有的测量方法侧重于在尺度水平上作为一维结构来估计给定心理健康问题的一般严重程度，而不考虑相关心理健康问题的其他症状。流行的方法可能会忽略项目级别相关症状的临床上有意义的表现。当前的研究使用来自大学生的项目级数据说明CDM（719名受访者中有40个项目；男性占34.6％，83.9%白色，和16.3%的一年级学生)。具体来说,我们评估了四个假定域的星座(即，与酒精有关的问题，焦虑,敌意,anddepression)asasetofattributeprofilesusingCDM.Afteraccountingfortheimpactofeachattribute(i.e.假定域)对属性配置文件的估计，结果表明，当项目或属性信息有限时，CDM可以利用相关属性中的项目级别信息来生成潜在有意义的估计和配置文件，与独立分析每个属性相比。我们介绍了一种新颖的视觉检查辅助工具，镜头图，用于量化这一增益。CDM可能是一种有用的分析工具，可以捕获受访者的风险和弹性，以进行预防研究。
