Investigating Faking Using a Multilevel Logistic Regression Approach to Measuring Person Fit
介绍如何用多水平逻辑回归方法分析人格测验中的作假行为，通过项目难度和个体特质估计值预测作答概率，并用斜率变化识别作假。
This article describes how a multilevel logistic regression (MLR) approach to assessing person fit can be used to test hypotheses concerning faking on personality assessments. Item difficulty and person trait estimates obtained from a two-parameter logistic item response theory model are used to predict the probability of endorsing an item in a MLR equation. The regression slope for item difficulty reflects the extent to which the probability of endorsement decreases as item difficulty increases. Less negative slopes may indicate faking, and slope variance may be modeled with person-level variables using MLR. Two examples are presented. Example 1 models faking on a personality assessment with dichotomous items. Example 2 extends the approach to scales using polytomous items.