Modeling Response Styles in Cross-Classified Data Using a Cross-Classified Multidimensional Nominal Response Model

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED
Sijia Huang, Seungwon Chung, Carl F. Falk
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Abstract

In this study, we introduced a cross-classified multidimensional nominal response model (CC-MNRM) to account for various response styles (RS) in the presence of cross-classified data. The proposed model allows slopes to vary across items and can explore impacts of observed covariates on latent constructs. We applied a recently developed variant of the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm to address the computational challenge of estimating the proposed model. To demonstrate our new approach, we analyzed empirical student evaluation of teaching (SET) data collected from a large public university with three models: a CC-MNRM with RS, a CC-MNRM with no RS, and a multilevel MNRM with RS. Results indicated that the three models led to different inferences regarding the observed covariates. Additionally, in the example, ignoring/incorporating RS led to changes in student substantive scores, while the instructor substantive scores were less impacted. Misspecifying the cross-classified data structure resulted in apparent changes on instructor scores. To further evaluate the proposed modeling approach, we conducted a preliminary simulation study and observed good parameter and score recovery. We concluded this study with discussions of limitations and future research directions.

使用交叉分类多维名义响应模型为交叉分类数据中的响应风格建模
在本研究中,我们引入了一个交叉分类多维名义反应模型(CC-MNRM),以考虑交叉分类数据中的各种反应风格(RS)。所提出的模型允许斜率在不同项目间变化,并能探索观察到的协变量对潜在构造的影响。我们采用了最近开发的 Metropolis-Hastings Robbins-Monro (MH-RM) 算法的变体,以解决估计所提模型的计算难题。为了展示我们的新方法,我们分析了从一所大型公立大学收集的学生教学评价(SET)实证数据,并使用了三种模型:带 RS 的 CC-MNRM 模型、不带 RS 的 CC-MNRM 模型和带 RS 的多层次 MNRM 模型。结果表明,这三种模型对观察到的协变量做出了不同的推断。此外,在示例中,忽略/纳入 RS 会导致学生的实质分数发生变化,而教师的实质分数受到的影响较小。对交叉分类数据结构的错误定义导致了教师评分的明显变化。为了进一步评估所提出的建模方法,我们进行了初步的模拟研究,观察到参数和分数恢复良好。最后,我们讨论了本研究的局限性和未来的研究方向。
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来源期刊
CiteScore
2.30
自引率
7.70%
发文量
46
期刊介绍: The Journal of Educational Measurement (JEM) publishes original measurement research, provides reviews of measurement publications, and reports on innovative measurement applications. The topics addressed will interest those concerned with the practice of measurement in field settings, as well as be of interest to measurement theorists. In addition to presenting new contributions to measurement theory and practice, JEM also serves as a vehicle for improving educational measurement applications in a variety of settings.
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