Questionnaires in the behavioral and organizational sciences tend to be lengthy. However, literature suggests that survey length is a contributing factor to careless responding, with longer questionnaires yielding higher probability that participants start responding carelessly. Consequently, in long surveys a large number of participants may engage in careless responding, posing a major threat to internal validity. We propose a novel method for identifying the onset of careless responding (or an absence thereof) that searches for a changepoint in combined measurements of multiple dimensions in which carelessness may manifest, such as inconsistency and invariability. It is highly flexible, based on machine learning, and provides statistical guarantees for controlling the false positive rate. In simulation experiments, the proposed method achieves high accuracy in identifying carelessness onset and discriminates well between attentive and various types of careless responding, even when a large number of careless respondents are present. An empirical application highlights how identifying partial carelessness uncovers novel insights on careless responding behavior. Furthermore, we provide the freely available open source software package "carelessonset" to facilitate adoption by empirical researchers.

翻译：行为与组织科学领域的问卷往往篇幅冗长。然而，文献表明问卷长度是导致粗心应答的因素之一，较长的问卷会提高参与者开始粗心应答的概率。因此，在长问卷调查中，大量参与者可能进行粗心应答，这对内部效度构成重大威胁。我们提出了一种识别粗心应答起始点（或其缺失）的新方法，该方法通过搜索多个维度组合测量中的变点来检测粗心表现，例如不一致性和不变性。该方法基于机器学习，具有高度灵活性，并为控制误报率提供了统计保证。在模拟实验中，所提方法在识别粗心起始点方面达到了高精度，并能很好地区分专注应答与各类粗心应答，即使在存在大量粗心受访者的情况下也是如此。一项实证应用展示了识别部分粗心如何揭示关于粗心应答行为的新见解。此外，我们提供了免费开源软件包“carelessonset”，以方便实证研究者采用。