Jin Kuan-Yu, Wang Wen-Chung
a Department of Psychology , The Education University of Hong Kong.
Multivariate Behav Res. 2017 May-Jun;52(3):391-402. doi: 10.1080/00273171.2017.1299615. Epub 2017 Mar 22.
Multifaceted data are very common in the human sciences. For example, test takers' responses to essay items are marked by raters. If multifaceted data are analyzed with standard facets models, it is assumed there is no interaction between facets. In reality, an interaction between facets can occur, referred to as differential facet functioning. A special case of differential facet functioning is the interaction between ratees and raters, referred to as differential rater functioning (DRF). In existing DRF studies, the group membership of ratees is known, such as gender or ethnicity. However, DRF may occur when the group membership is unknown (latent) and thus has to be estimated from data. To solve this problem, in this study, we developed a new mixture facets model to assess DRF when the group membership is latent and we provided two empirical examples to demonstrate its applications. A series of simulations were also conducted to evaluate the performance of the new model in the DRF assessment in the Bayesian framework. Results supported the use of the mixture facets model because all parameters were recovered fairly well, and the more data there were, the better the parameter recovery.
多层面数据在人文科学中非常常见。例如，考生对论述题的回答由评分者打分。如果用标准层面模型分析多层面数据，假定层面之间不存在交互作用。但在现实中，层面之间可能会出现交互作用，称为层面差异功能。层面差异功能的一种特殊情况是被评价者与评价者之间的交互作用，称为评价者差异功能（DRF）。在现有的DRF研究中，被评价者的群体归属是已知的，如性别或种族。然而，当群体归属未知（潜在）因而必须从数据中估计时，也可能会出现DRF。为解决这一问题，在本研究中，我们开发了一种新的混合层面模型，用于在群体归属为潜在时评估DRF，并提供了两个实证例子来展示其应用。还进行了一系列模拟，以评估新模型在贝叶斯框架下DRF评估中的性能。结果支持使用混合层面模型，因为所有参数都能得到较好的恢复，而且数据越多，参数恢复得越好。
