Rejoinder to McNeish and Mislevy: What Does Psychological Measurement Require?

IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Klaas Sijtsma, Jules L Ellis, Denny Borsboom
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引用次数: 0

Abstract

In this rejoinder to McNeish (2024) and Mislevy (2024), who both responded to our focus article on the merits of the simple sum score (Sijtsma et al., 2024), we address several issues. Psychometrics education and in particular psychometricians' outreach may help researchers to use IRT models as a precursor for the responsible use of the latent variable score and the sum score. Different methods used for test and questionnaire construction often do not produce highly different results, and when they do, this may be due to an unarticulated attribute theory generating noisy data. The sum score and transformations thereof, such as normalized test scores and percentiles, may help test practitioners and their clients to better communicate results. Latent variables prove important in more advanced applications such as equating and adaptive testing where they serve as technical tools rather than communication devices. Decisions based on test results are often binary or use a rather coarse ordering of scale levels, hence, do not require a high level of granularity (but nevertheless need to be precise). A gap exists between psychology and psychometrics which is growing deeper and wider, and that needs to be bridged. Psychology and psychometrics must work together to attain this goal.

对 McNeish 和 Mislevy 的反驳:心理测量需要什么?
麦克尼什(McNeish,2024 年)和米斯莱维(Mislevy,2024 年)都对我们关于简单总分优点的重点文章(Sijtsma et al.心理测量学教育,特别是心理测量学家的宣传,可以帮助研究人员使用 IRT 模型,作为负责任地使用潜变量得分和总分的先导。不同的测验和问卷编制方法往往不会产生截然不同的结果,即使产生了截然不同的结果,也可能是由于未阐明的属性理论产生了嘈杂的数据。总分及其转换,如标准化测试分数和百分位数,可以帮助测试从业人员及其客户更好地交流测试结果。在更高级的应用中,如等差数列和适应性测试,潜变量被证明是重要的技术工具,而不是交流工具。根据测试结果做出的决定通常是二元的,或使用相当粗略的量表等级排序,因此不需要很高的粒度(但仍然需要精确)。心理学和心理测量学之间的差距越来越大,需要加以弥合。心理学和心理测量学必须共同努力实现这一目标。
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
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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