Is a less wrong model always more useful? Methodological considerations for using ant colony optimization in measure development.

IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Yixiao Dong, Denis Dumas
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Abstract

With the advancement of artificial intelligence (AI), many AI-derived techniques have been adapted into psychological and behavioral science research, including measure development, which is a key task for psychometricians and methodologists. Ant colony optimization (ACO) is an AI-derived metaheuristic algorithm that has been integrated into the structural equation modeling framework to search for optimal (or near optimal) solutions. ACO-driven measurement modeling is an emerging method for constructing scales, but psychological researchers generally lack the necessary understanding of ACO-optimized models and outcome solutions. This article aims to investigate whether ACO solutions are indeed optimal and whether the optimized measurement models of ACO are always more psychologically useful compared to conventional ones built by human psychometricians. To work toward these goals, we highlight five essential methodological considerations for using ACO in measure development: (a) pursuing a local or global optimum, (b) avoiding a subjective optimum, (c) optimizing content validity, (d) bridging the gap between theory and model, and (e) recognizing limitations of unidirectionality. A joint data set containing item-level data from German (n = 297) and the United States (n = 334) samples was employed, and seven illustrative ACO analyses with various configurations were conducted to illustrate or facilitate the discussions of these considerations. We conclude that measurement solutions from the current ACO have not yet become optimal or close to optimal, and the optimized measurement models of ACO may be becoming more useful. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

错误少的模型总是更有用吗?在度量开发中使用蚁群优化的方法学考虑。
随着人工智能(AI)的发展,许多人工智能衍生技术已被应用于心理和行为科学研究,包括测量开发,这是心理测量学家和方法学家的关键任务。蚁群优化(蚁群优化)是一种人工智能衍生的元启发式算法,已集成到结构方程建模框架中,用于搜索最优(或近最优)解。aco驱动的测量建模是一种新兴的量表构建方法,但心理学研究者普遍缺乏对aco优化模型和结果解决方案的必要理解。本文旨在探讨蚁群的解决方案是否确实是最优的,以及优化后的蚁群测量模型是否总是比人类心理测量学家建立的传统测量模型更有心理用途。为了实现这些目标,我们强调了在测量开发中使用蚁群算法的五个基本方法考虑因素:(a)追求局部或全局最优,(b)避免主观最优,(c)优化内容效度,(d)弥合理论和模型之间的差距,以及(e)认识到单向性的局限性。采用了一个包含德国(n = 297)和美国(n = 334)样本的项目级数据的联合数据集,并进行了七种不同配置的说明性蚁群分析,以说明或促进这些考虑的讨论。我们认为,目前的蚁群算法的测量解尚未达到最优或接近最优,优化后的蚁群算法测量模型可能会变得更有用。(PsycInfo Database Record (c) 2025 APA,版权所有)。
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
Psychological methods
Psychological methods PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
13.10
自引率
7.10%
发文量
159
期刊介绍: Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.
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