Beck Mark F, Albano Anthony D, Smith Wendy M
University of Nebraska-Lincoln, NE, USA.
Appl Psychol Meas. 2019 Jul;43(5):374-387. doi: 10.1177/0146621618798666. Epub 2018 Sep 14.
Self-report measures are vulnerable to response biases that can degrade the accuracy of conclusions drawn from results. In low-stakes measures, inattentive or careless responding can be especially problematic. A variety of a priori and post hoc methods exist for detecting these aberrant response patterns. Previous research indicates that nonparametric person-fit statistics tend to be the most accurate post hoc method for detecting inattentive responding on measures with dichotomous outcomes. This study investigated the accuracy and impact on model fit of parametric and nonparametric person-fit statistics in detecting inattentive responding with polytomous response scales. Receiver operating curve (ROC) analysis was used to determine the accuracy of each detection metric, and confirmatory factor analysis (CFA) fit indices were used to examine the impact of using person-fit statistics to identify inattentive respondents. ROC analysis showed the nonparametric statistic offered the most area under the curve when predicting a proxy for inattentive responding. The CFA fit indices showed the impact of using the person-fit statistics largely depends on the purpose (and cutoff) for using the person-fit statistics. Implications for using person-fit statistics to identify inattentive responders are discussed further.
自我报告测量容易受到反应偏差的影响，这些偏差会降低从结果得出的结论的准确性。在低风险测量中，注意力不集中或粗心的回答可能尤其成问题。存在多种先验和事后方法来检测这些异常反应模式。先前的研究表明，非参数个体拟合统计往往是检测二分结果测量中注意力不集中回答的最准确的事后方法。本研究调查了参数和非参数个体拟合统计在检测多分类反应量表中注意力不集中回答时的准确性及其对模型拟合的影响。使用接收者操作特征曲线（ROC）分析来确定每个检测指标的准确性，并使用验证性因素分析（CFA）拟合指数来检验使用个体拟合统计识别注意力不集中的受访者的影响。ROC分析表明，在预测注意力不集中回答的替代指标时，非参数统计提供了最大的曲线下面积。CFA拟合指数表明，使用个体拟合统计的影响很大程度上取决于使用个体拟合统计的目的（和临界值）。进一步讨论了使用个体拟合统计识别注意力不集中的受访者的意义。
