Depression and anxiety are highly prevalent psychological disorders; our understanding of these conditions remains limited. Efforts to explain anxiety and depression have been constrained in part by binary classification systems. Dimensional approaches to understanding psychopathology may be more effective. The present study used latent profile analysis (LPA) to assess whether unique subgroups exist within a tri-level model of anxiety and depression. Participants (N = 627) completed self-report questionnaires from which tri-level model factors were derived. LPA was conducted on those factors. A 4-profile model offered optimal fit to the data at baseline. This model was replicated at a second time point. Models derived included profiles labelled ‘Mixed Fears,’ ‘Anxious Arousal,’ ‘Low Mood/Anhedonia,’ and ‘Sub-Clinical.’ Profiles were validated at Time 1 using diagnostic status and clinical severity ratings associated with mood and anxiety presentations. Profiles demonstrated flexibility in accommodating breadth in clinical presentations and common comorbidities. Latent variable models may offer more ecologically valid approaches to understanding psychopathology.

抑郁和焦虑是非常普遍的心理障碍；我们对这些情况的了解仍然有限。解释焦虑和抑郁的努力部分受到二元分类系统的限制。理解精神病理学的维度方法可能更有效。本研究使用潜在概况分析 (LPA) 来评估焦虑和抑郁的三级模型中是否存在独特的亚组。参与者 (N = 627) 完成了自我报告问卷，从中得出了三级模型因素。LPA 是针对这些因素进行的。4 剖面模型提供了与基线数据的最佳拟合。该模型在第二个时间点被复制。派生的模型包括标记为“混合恐惧”、“焦虑唤醒”、“情绪低落/快感缺乏”和“亚临床”的概况。' 在时间 1 使用与情绪和焦虑表现相关的诊断状态和临床严重程度评级来验证配置文件。概况显示了适应临床表现和常见合并症广度的灵活性。潜在变量模型可以提供更生态有效的方法来理解精神病理学。