Zero responses and their equivalents—for example, never, none, not at all—are commonly observed on measures of psychopathology inquiring about symptom frequencies, particularly when these measures are administered to community samples. Measurement researchers typically accommodate multivariate zero inflation by including a nonpathological class of respondents who endorse zero for all symptoms. While this latent class approach accounts for test-level zero inflation (i.e., a proportion of individuals who do not experience any of the symptoms), it may be overly restrictive on questionnaires comprising items of differing severity. For example, an item about suicidal ideation is likely to exhibit a much higher degree of zero inflation than an item about low energy. Existing models do not account for this variability. We propose a multidimensional zero-inflated graded response model (MZI-GRM) as a more flexible approach for modeling zero inflation on questionnaires. According to the model, two distinct but correlated latent variables underlie ordinal item responses; one represents susceptibility to the construct, whereas the other represents severity. As a motivating example, we show how the MZI-GRM can be fit to data from the PHQ-9, a common depression screener. Results suggest that the MZI-GRM is better able to capture zero inflation across items than existing alternative models. Further, we find support for a multidimensional model that allows distinct but correlated latent variables to underlie each response process. Some items better measure susceptibility to depression (symptom presence), whereas others better capture severity of depression (symptom frequency). Implications for scale development and scoring are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

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用于有序症状数据的多维零膨胀分级响应模型。

在询问症状频率的精神病理学测量中，通常会观察到零响应及其等效项（例如，从不、没有、根本没有），特别是当这些测量针对社区样本时。测量研究人员通常通过纳入支持所有症状为零的非病态受访者类别来适应多元零通货膨胀。虽然这种潜在类别方法考虑了测试水平零膨胀（即没有经历任何症状的个体的比例），但它可能对包含不同严重程度的项目的调查问卷过于严格。例如，关于自杀意念的项目可能比关于低能量的项目表现出更高程度的零通货膨胀。现有模型没有考虑这种变化。我们提出了一种多维零膨胀分级响应模型（MZI-GRM），作为对问卷进行零膨胀建模的更灵活的方法。根据该模型，有序项目响应背后有两个不同但相关的潜在变量；一个代表对该结构的敏感性，而另一个代表严重性。作为一个激励性的例子，我们展示了 MZI-GRM 如何适合来自 PHQ-9（一种常见的抑郁症筛查仪）的数据。结果表明，与现有替代模型相比，MZI-GRM 能够更好地捕获各个项目的零通胀。此外，我们发现了对多维模型的支持，该模型允许不同但相关的潜在变量作为每个响应过程的基础。有些项目可以更好地衡量抑郁症的易感性（症状存在），而其他项目可以更好地捕捉抑郁症的严重程度（症状频率）。讨论了量表开发和评分的影响。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）