Nielsen Marie Germund, Ørnbøl Eva, Vestergaard Mogens, Bech Per, Christensen Kaj Sparle
Research Unit for General Practice, Department of Public Health, Aarhus University, Denmark.
Research Clinic for Functional Disorders and Psychosomatics, Aarhus University Hospital, Denmark.
J Psychosom Res. 2017 Jun;97:70-81. doi: 10.1016/j.jpsychores.2017.04.001. Epub 2017 Apr 8.
We aimed to assess the measurement properties of the ten-item Major Depression Inventory when used on clinical suspicion in general practice by performing a Rasch analysis.
General practitioners asked consecutive persons to respond to the web-based Major Depression Inventory on clinical suspicion of depression. We included 22 practices and 245 persons. Rasch analysis was performed using RUMM2030 software. The Rasch model fit suggests that all items contribute to a single underlying trait (defined as internal construct validity). Mokken analysis was used to test dimensionality and scalability.
Our Rasch analysis showed misfit concerning the sleep and appetite items (items 9 and 10). The response categories were disordered for eight items. After modifying the original six-point to a four-point scoring system for all items, we achieved ordered response categories for all ten items. The person separation reliability was acceptable (0.82) for the initial model. Dimensionality testing did not support combining the ten items to create a total score. The scale appeared to be well targeted to this clinical sample. No significant differential item functioning was observed for gender, age, work status and education. The Rasch and Mokken analyses revealed two dimensions, but the Major Depression Inventory showed fit to one scale if items 9 and 10 were excluded.
Our study indicated scalability problems in the current version of the Major Depression Inventory. The conducted analysis revealed better statistical fit when items 9 and 10 were excluded.
我们旨在通过进行拉施分析，评估在全科医疗中基于临床怀疑使用十项重度抑郁量表时的测量属性。
全科医生让连续就诊的患者在临床怀疑有抑郁症时对基于网络的十项重度抑郁量表做出回应。我们纳入了22家医疗机构的245名患者。使用RUMM2030软件进行拉施分析。拉施模型拟合表明所有项目都对单一潜在特质有贡献（定义为内部结构效度）。使用莫肯分析来检验维度和可扩展性。
我们的拉施分析显示睡眠和食欲项目（第9项和第10项）存在拟合不佳的情况。八个项目的反应类别无序。在将所有项目的原始六点计分系统修改为四点计分系统后，我们使所有十个项目的反应类别有序。初始模型的人员分离信度可接受（0.82）。维度测试不支持将这十个项目合并以得出总分。该量表似乎针对此临床样本有良好的针对性。未观察到性别、年龄、工作状态和教育程度方面有显著的项目功能差异。拉施分析和莫肯分析揭示了两个维度，但如果排除第9项和第10项，十项重度抑郁量表符合一个量表。
我们的研究表明当前版本的十项重度抑郁量表存在可扩展性问题。进行的分析表明排除第9项和第10项时统计拟合更好。
