Magnus Brooke E, Liu Yang
Boston College, Chestnut Hill, MA, USA.
University of Maryland, College Park, USA.
Educ Psychol Meas. 2022 Oct;82(5):938-966. doi: 10.1177/00131644211061820. Epub 2021 Dec 26.
Questionnaires inquiring about psychopathology symptoms often produce data with excess zeros or the equivalent (e.g., none, never, and not at all). This type of zero inflation is especially common in nonclinical samples in which many people do not exhibit psychopathology, and if unaccounted for, can result in biased parameter estimates when fitting latent variable models. In the present research, we adopt a maximum likelihood approach in fitting multidimensional zero-inflated and hurdle graded response models to data from a psychological distress measure. These models include two latent variables: susceptibility, which relates to the probability of endorsing the symptom at all, and severity, which relates to the frequency of the symptom, given its presence. After estimating model parameters, we compute susceptibility and severity scale scores and include them as explanatory variables in modeling health-related criterion measures (e.g., suicide attempts, diagnosis of major depressive disorder). Results indicate that susceptibility and severity uniquely and differentially predict other health outcomes, which suggests that symptom presence and symptom severity are unique indicators of psychopathology and both may be clinically useful. Psychometric and clinical implications are discussed, including scale score reliability.
询问精神病理学症状的问卷常常会产生大量零值或等效值（例如，无、从不、一点也不）的数据。这种零膨胀类型在非临床样本中尤为常见，在这类样本中，许多人并未表现出精神病理学症状，并且如果不加以考虑，在拟合潜在变量模型时可能会导致参数估计出现偏差。在本研究中，我们采用最大似然法来拟合多维零膨胀和障碍分级反应模型，以分析一项心理困扰测量的数据。这些模型包括两个潜在变量：易感性，它与完全认可该症状的概率相关；严重程度，它与症状出现时的频率相关。在估计模型参数后，我们计算易感性和严重程度量表得分，并将它们作为解释变量纳入对健康相关标准测量（例如自杀未遂、重度抑郁症诊断）的建模中。结果表明，易感性和严重程度分别且独特地预测了其他健康结果，这表明症状的存在和症状的严重程度是精神病理学的独特指标，并且两者在临床上可能都有用。我们还讨论了心理测量和临床意义，包括量表得分的可靠性。
