ABSTRACT

An important step in scale development and assessment is to evaluate differential item functioning (DIF) across segments of the population. Recent approaches use lasso regularization to simultaneously detect DIF in all items and avoid incorrect anchor item assumptions that incur inflated error rates for classical DIF evaluation methods. Although promising, lasso methods cause underestimated standard errors and incorrect p-values. An alternative is Bayesian regularization that provides empirical standard errors. However, we point out that using empirical criteria such as credible intervals for selecting DIF parameters has limited validity. We argue that using a spike-and-slab prior with an inclusion probability criterion provides more theoretically coherent DIF selection and inference over Bayesian regularizing priors with empirical selection rules or frequentist lasso. We demonstrate this by simulation studies with Multi-group Item Response Theory and Moderated Nonlinear Factor Analysis models. Practical utility of the spike-and-slab prior selection criterion is discussed.

摘要

量表开发和评估的一个重要步骤是评估不同人群的差异项目功能（DIF）。最近的方法使用 lasso 正则化来同时检测所有项目中的 DIF，并避免不正确的锚项目假设导致经典 DIF 评估方法的错误率膨胀。虽然很有希望，但套索方法会导致低估的标准误差和不正确的p-价值观。另一种方法是提供经验标准误差的贝叶斯正则化。然而，我们指出，使用诸如可信区间等经验标准来选择 DIF 参数的有效性有限。我们认为，使用带有包含概率标准的尖峰和平板先验可以提供更理论上连贯的 DIF 选择和推断，而不是使用经验选择规则或频率论套索的贝叶斯正则化先验。我们通过多组项目响应理论和调节非线性因子分析模型的模拟研究证明了这一点。讨论了钉板先验选择标准的实用性。