Careless and insufficient effort responding (C/IER) on self-report measures results in responses that do not reflect the trait to be measured, thereby posing a major threat to the quality of survey data. Reliable approaches for detecting C/IER aid in increasing the validity of inferences being made from survey data. First, once detected, C/IER can be taken into account in data analysis. Second, approaches for detecting C/IER support a better understanding of its occurrence, which facilitates designing surveys that curb the prevalence of C/IER. Previous approaches for detecting C/IER are limited in that they identify C/IER at the aggregate respondent or scale level, thereby hindering investigations of item characteristics evoking C/IER. We propose an explanatory mixture item response theory model that supports identifying and modelling C/IER at the respondent-by-item level, can detect a wide array of C/IER patterns, and facilitates a deeper understanding of item characteristics associated with its occurrence. As the approach only requires raw response data, it is applicable to data from paper-and-pencil and online surveys. The model shows good parameter recovery and can well handle the simultaneous occurrence of multiple types of C/IER patterns in simulated data. The approach is illustrated on a publicly available Big Five inventory data set, where we found later item positions to be associated with higher C/IER probabilities. We gathered initial supporting validity evidence for the proposed approach by investigating agreement with multiple commonly employed indicators of C/IER.

中文翻译：

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自我报告措施中反应粗心和努力不足的解释性混合 IRT 模型

对自我报告措施的粗心和不充分的努力响应 (C/IER) 会导致响应不能反映要测量的特征，从而对调查数据的质量构成重大威胁。检测 C/IER 的可靠方法有助于提高从调查数据中得出的推论的有效性。首先，一旦检测到，C/IER 可以在数据分析中被考虑在内。其次，检测 C/IER 的方法有助于更好地了解其发生，这有助于设计抑制 C/IER 流行的调查。以前检测 C/IER 的方法是有限的，因为它们在总体受访者或规模级别上识别 C/IER，从而阻碍了对诱发 C/IER 的项目特征的调查。我们提出了一个解释性混合项目响应理论模型，该模型支持在逐个项目级别识别和建模 C/IER，可以检测广泛的 C/IER 模式，并有助于更深入地了解与其发生相关的项目特征。由于该方法只需要原始响应数据，因此适用于纸笔和在线调查的数据。该模型表现出良好的参数恢复能力，能够很好地处理模拟数据中多种类型C/IER模式的同时出现。该方法在一个公开可用的 Big Five 库存数据集上进行了说明，我们发现后来的项目位置与更高的 C/IER 概率相关联。我们通过调查与多个常用 C/IER 指标的一致性，收集了建议方法的初步支持有效性证据。