Moehring Anne, Meyer Christian, John Ulrich, Rumpf Hans-Juergen, Bischof Gallus, Freyer-Adam Jennis, Baumann Sophie, Staudt Andreas
Department of Prevention Research and Social Medicine, Institute of Community Medicine, University Medicine Greifswald, Greifswald, Germany.
DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany.
Sci Rep. 2025 Jul 25;15(1):27122. doi: 10.1038/s41598-025-12087-3.
Self-report questionnaires must be psychometrically sound, but also brief and efficient to avoid participant nonresponse and fatigue, especially in the health and prevention sciences. Meta-heuristics such as the Ant Colony Optimization (ACO) algorithm overcome limitations of the traditional stepwise approach of selecting items based on few or a single statistical criterion. The aim of this paper was to demonstrate the use of the ACO algorithm by constructing a short version of the German Alcohol Decisional Balance Scale (ADBS). Self-report data from three studies (N = 1,834; 19% women; mean age = 31.4 years) was used that proactively recruited alcohol consumers from the general population and general hospitals in Germany. All participants rated the perceived importance of different pros and cons in their decision to drink alcohol (decisional balance) on a 5-point Likert scale. Optimizing different model fit indices and theoretical considerations simultaneously, the ACO algorithm produced a psychometrically valid and reliable 10-item short scale that was superior to the 26-item full ADBS scale and an already established 10-item short version of the ADBS with respect to the a priori defined optimization criteria. The paper provides a customizable R syntax for building reliable, valid, and theoretically well-founded short scales.
自我报告问卷不仅必须在心理测量学上是合理的，而且要简洁高效，以避免参与者无应答和疲劳，尤其是在健康与预防科学领域。诸如蚁群优化（ACO）算法之类的元启发式方法克服了基于少数或单一统计标准选择项目的传统逐步方法的局限性。本文的目的是通过构建德国酒精决策平衡量表（ADBS）的简短版本来演示ACO算法的使用。使用了来自三项研究的自我报告数据（N = 1834；19%为女性；平均年龄 = 31.4岁），这些数据是从德国普通人群和综合医院中主动招募饮酒者得来的。所有参与者在5点李克特量表上对饮酒决策中不同利弊的感知重要性（决策平衡）进行评分。通过同时优化不同的模型拟合指数和理论考量，ACO算法生成了一个在心理测量学上有效且可靠的10项简短量表，就先验定义的优化标准而言，该量表优于26项完整的ADBS量表以及已有的10项ADBS简短版本。本文提供了一个可定制的R语法，用于构建可靠、有效且理论基础良好的简短量表。
