Killing Two Birds with One Stone: Accounting for Unfolding Item Response Process and Response Styles Using Unfolding Item Response Tree Models.

IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Multivariate Behavioral Research Pub Date : 2024-08-31 DOI:10.1080/00273171.2024.2394607
Zhaojun Li, Lingyue Li, Bo Zhang, Mengyang Cao, Louis Tay
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Abstract

Two research streams on responses to Likert-type items have been developing in parallel: (a) unfolding models and (b) individual response styles (RSs). To accurately understand Likert-type item responding, it is vital to parse unfolding responses from RSs. Therefore, we propose the Unfolding Item Response Tree (UIRTree) model. First, we conducted a Monte Carlo simulation study to examine the performance of the UIRTree model compared to three other models - Samejima's Graded Response Model, Generalized Graded Unfolding Model, and Dominance Item Response Tree model, for Likert-type responses. Results showed that when data followed an unfolding response process and contained RSs, AIC was able to select the UIRTree model, while BIC was biased toward the DIRTree model in many conditions. In addition, model parameters in the UIRTree model could be accurately recovered under realistic conditions, and mis-specifying item response process or wrongly ignoring RSs was detrimental to the estimation of key parameters. Then, we used datasets from empirical studies to show that the UIRTree model could fit personality datasets well and produced more reasonable parameter estimates compared to competing models. A strong presence of RS(s) was also revealed by the UIRTree model. Finally, we provided examples with R code for UIRTree model estimation to facilitate the modeling of responses to Likert-type items in future studies.

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一石二鸟:使用展开式项目反应树模型考虑展开式项目反应过程和反应风格。
关于李克特类型项目反应的两个研究流一直在并行发展:(a) 展开模型和 (b) 个人反应风格 (RS)。为了准确理解李克特类型项目的反应,从 RSs 中解析展开式反应至关重要。因此,我们提出了展开项目反应树(UIRTree)模型。首先,我们进行了蒙特卡罗模拟研究,考察了 UIRTree 模型与其他三种模型(Samejima 的分级反应模型、广义分级展开模型和优势项目反应树模型)相比在李克特型反应方面的性能。结果表明,当数据遵循展开式反应过程并包含 RS 时,AIC 能够选择 UIRTree 模型,而 BIC 在许多情况下偏向于 DIRTree 模型。此外,在现实条件下,UIRTree 模型中的模型参数可以准确恢复,而错误地指定项目反应过程或错误地忽略 RSs 则不利于关键参数的估计。然后,我们利用实证研究的数据集表明,UIRTree 模型能很好地拟合个性数据集,与其他竞争模型相比,它能产生更合理的参数估计。UIRTree 模型还揭示了 RS(s)的强烈存在。最后,我们提供了 UIRTree 模型估计的 R 代码示例,以方便在未来的研究中对李克特类型项目的反应进行建模。
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来源期刊
Multivariate Behavioral Research
Multivariate Behavioral Research 数学-数学跨学科应用
CiteScore
7.60
自引率
2.60%
发文量
49
审稿时长
>12 weeks
期刊介绍: Multivariate Behavioral Research (MBR) publishes a variety of substantive, methodological, and theoretical articles in all areas of the social and behavioral sciences. Most MBR articles fall into one of two categories. Substantive articles report on applications of sophisticated multivariate research methods to study topics of substantive interest in personality, health, intelligence, industrial/organizational, and other behavioral science areas. Methodological articles present and/or evaluate new developments in multivariate methods, or address methodological issues in current research. We also encourage submission of integrative articles related to pedagogy involving multivariate research methods, and to historical treatments of interest and relevance to multivariate research methods.
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