Investigating heterogeneity in IRTree models for multiple response processes with score-based partitioning.

IF 1.5 3区 心理学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Rudolf Debelak, Thorsten Meiser, Alicia Gernand
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引用次数: 0

Abstract

Item response tree (IRTree) models form a family of psychometric models that allow researchers to control for multiple response processes, such as different sorts of response styles, in the measurement of latent traits. While IRTree models can capture quantitative individual differences in both the latent traits of interest and the use of response categories, they maintain the basic assumption that the nature and weighting of latent response processes are homogeneous across the entire population of respondents. In the present research, we therefore propose a novel approach for detecting heterogeneity in the parameters of IRTree models across subgroups that engage in different response behavior. The approach uses score-based tests to reveal violations of parameter heterogeneity along extraneous person covariates, and it can be employed as a model-based partitioning algorithm to identify sources of differences in the strength of trait-based responding or other response processes. Simulation studies demonstrate generally accurate Type I error rates and sufficient power for metric, ordinal, and categorical person covariates and for different types of test statistics, with the potential to differentiate between different types of parameter heterogeneity. An empirical application illustrates the use of score-based partitioning in the analysis of latent response processes with real data.

利用基于分数的分区研究多重响应过程 IRTree 模型中的异质性。
项目反应树(IRTree)模型是心理测量模型的一个系列,它允许研究人员在测量潜在特质时控制多种反应过程,如不同种类的反应风格。虽然 IRTree 模型可以捕捉所关注的潜在特质和使用反应类别方面的量化个体差异,但它们的基本假设是,潜在反应过程的性质和权重在整个受访者群体中是同质的。因此,在本研究中,我们提出了一种新方法,用于检测 IRTree 模型参数在不同反应行为的子群体中的异质性。该方法使用基于分数的检验来揭示参数异质性与无关人员协变量之间的差异,并可用作基于模型的分区算法,以确定基于特质的反应或其他反应过程的强度差异来源。模拟研究表明,对于度量、顺序和分类的人的协变量以及不同类型的测试统计,I 类误差率和足够的功率基本准确,并有可能区分不同类型的参数异质性。一个经验应用说明了基于分数的分区方法在真实数据的潜在反应过程分析中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.00
自引率
3.80%
发文量
34
审稿时长
>12 weeks
期刊介绍: The British Journal of Mathematical and Statistical Psychology publishes articles relating to areas of psychology which have a greater mathematical or statistical aspect of their argument than is usually acceptable to other journals including: • mathematical psychology • statistics • psychometrics • decision making • psychophysics • classification • relevant areas of mathematics, computing and computer software These include articles that address substantitive psychological issues or that develop and extend techniques useful to psychologists. New models for psychological processes, new approaches to existing data, critiques of existing models and improved algorithms for estimating the parameters of a model are examples of articles which may be favoured.
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