In educational and psychological measurement, a person‐fit statistic (PFS) is designed to identify aberrant response patterns. For parametric PFSs, valid inference depends on several assumptions, one of which is that the item response theory (IRT) model is correctly specified. Previous studies have used empirical data sets to explore the effects of model misspecification on PFSs. We further this line of research by using a simulation study, which allows us to explore issues that may be of interest to practitioners. Results show that, depending on the generating and analysis item models, Type I error rates at fixed values of the latent variable may be greatly inflated, even when the aggregate rates are relatively accurate. Results also show that misspecification is most likely to affect PFSs for examinees with extreme latent variable scores. Two empirical data analyses are used to illustrate the importance of model specification.

中文翻译：

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模型不合规格的个人拟合统计的表现

在教育和心理测量中，拟人统计（PFS）旨在识别异常的响应模式。对于参数PFS，有效推论取决于几个假设，其中之一是正确指定了项目响应理论（IRT）模型。先前的研究使用经验数据集来探索模型错误指定对PFS的影响。我们通过使用模拟研究来进一步开展这方面的研究，这使我们能够探索从业者可能感兴趣的问题。结果表明，根据生成和分析项目的模型，即使总汇率相对准确，潜在变量固定值下的I类错误率也会大大提高。结果还表明，对于具有极高潜在变量分数的考生，错误指定最有可能影响PFS。