从项目反应和反应时间的半参数因子分析中我们可以学到什么?2015年PISA数据说明。

IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Psychometrika Pub Date : 2024-06-01 Epub Date: 2023-11-16 DOI:10.1007/s11336-023-09936-3
Yang Liu, Weimeng Wang
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引用次数: 0

摘要

人们普遍认为,项目反应和反应时间(RT)的联合因素分析可能会产生更精确的能力分数,而不是传统的仅从反应中预测。为此,简单结构因素模型通常是首选的,因为它只需要为项目级RT指定一个额外的测量模型,而保留原始的项目反应理论(IRT)模型。项目级RT表示的附加速度因子与IRT模型中的能力因子相关,允许RT数据携带有关被调查者能力的附加信息。然而,参数化的简单结构因子模型往往具有限制性,对经验数据的拟合效果较差,从而导致对简单因子结构的适用性缺乏信心。在本文中,我们使用半参数简单结构模型分析了2015年国际学生评估计划的数学数据。我们得出的结论是,在测量模型中的进一步参数假设充分放松后,简单的因素结构获得了良好的拟合。此外,我们的半参数模型表明,潜在能力与速度/慢度之间的关联在总体中很强,但这种关联的形式是非线性的。由此可见,基于拟合模型的评分可以大大提高能力评分的精度。
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What Can We Learn from a Semiparametric Factor Analysis of Item Responses and Response Time? An Illustration with the PISA 2015 Data.

It is widely believed that a joint factor analysis of item responses and response time (RT) may yield more precise ability scores that are conventionally predicted from responses only. For this purpose, a simple-structure factor model is often preferred as it only requires specifying an additional measurement model for item-level RT while leaving the original item response theory (IRT) model for responses intact. The added speed factor indicated by item-level RT correlates with the ability factor in the IRT model, allowing RT data to carry additional information about respondents' ability. However, parametric simple-structure factor models are often restrictive and fit poorly to empirical data, which prompts under-confidence in the suitablity of a simple factor structure. In the present paper, we analyze the 2015 Programme for International Student Assessment mathematics data using a semiparametric simple-structure model. We conclude that a simple factor structure attains a decent fit after further parametric assumptions in the measurement model are sufficiently relaxed. Furthermore, our semiparametric model implies that the association between latent ability and speed/slowness is strong in the population, but the form of association is nonlinear. It follows that scoring based on the fitted model can substantially improve the precision of ability scores.

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来源期刊
Psychometrika
Psychometrika 数学-数学跨学科应用
CiteScore
4.40
自引率
10.00%
发文量
72
审稿时长
>12 weeks
期刊介绍: The journal Psychometrika is devoted to the advancement of theory and methodology for behavioral data in psychology, education and the social and behavioral sciences generally. Its coverage is offered in two sections: Theory and Methods (T& M), and Application Reviews and Case Studies (ARCS). T&M articles present original research and reviews on the development of quantitative models, statistical methods, and mathematical techniques for evaluating data from psychology, the social and behavioral sciences and related fields. Application Reviews can be integrative, drawing together disparate methodologies for applications, or comparative and evaluative, discussing advantages and disadvantages of one or more methodologies in applications. Case Studies highlight methodology that deepens understanding of substantive phenomena through more informative data analysis, or more elegant data description.
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