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Person-Fit as an Index of Inattentive Responding: A Comparison of Methods Using Polytomous Survey Data

机译:人身合身作为无所不思的响应指标:使用多色调查数据的方法比较

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摘要

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 HT 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.
机译:自我报告措施易受响应偏见的影响,这可以降低从结果中得出的结论的准确性。在低赌注的措施中,绝不为止或粗心的响应可能是特别问题的。存在用于检测这些异常响应模式的优先先验和后HOC方法。以前的研究表明,非参数人拟合统计数据往往是最准确的后HOC方法,用于检测与二分法结果的措施无私地响应。本研究研究了参数和非参数的模型适应性的准确性和影响,在多个多重响应尺度检测不关注的响应中的统计学统计学的模型和影响。接收器操作曲线(ROC)分析用于确定每个检测度量的准确性,并使用确认因子分析(CFA)FIT指标来检查使用人身拟合统计数据来识别异常受访者的影响。 ROC分析显示,当预测无私响应的代理时,非参数HT统计信息在曲线下提供了最多的区域。 CFA FIT指标显示使用人员拟合统计数据的影响主要取决于使用人拟合统计的目的(和截止)。进一步讨论了使用人拟合统计数据来识别非专期响应者的影响。

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