To identify faking, bifactor models were applied to Big Five personality data in three studies of laboratory and applicant samples using within‐subjects designs. The models were applied to homogenous data sets from separate honest, instructed faking, applicant conditions, and to simulated applicant data sets containing random individual responses from honest and faking conditions. Factor scores from the general factor in a bifactor model were found to be most highly related to response condition in both types of data sets. Domain factor scores from the faking conditions were found less affected by faking in measurement of Big Five domains than summated scale scores across studies. We conclude that bifactor models are efficacious in assessing the Big Five domains while controlling for faking.

中文翻译：

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使用双因素模型来识别五大问卷中的伪造品

为了识别伪造品，在三项关于实验室和申请人样本的研究中，使用受试者内部设计将双因素模型应用于大五人格数据。将模型应用于来自单独的诚实，受指示的伪造，申请人条件的同质数据集，以及应用于包含来自诚实和伪造条件的随机个人响应的模拟申请人数据集。在两种类型的数据集中，发现双因素模型中来自一般因素的因素得分与响应条件高度相关。研究发现，伪造条件下的域因子得分受伪造大五域度量的影响要小于研究中总和的规模得分。我们得出的结论是，双因素模型在评估伪造的同时可以有效评估五大域。