应用群优化算法来构建一个简短版本的德国酒精决策平衡量级
Anne Moehring1, Christian Meyer2,3, Ulrich John2,3
1Department of Prevention Research and Social Medicine, Institute of Community Medicine, University Medicine Greifswald, Greifswald, Germany. anne.moehring@med.uni-greifswald.de.
殖民地优化 (ACO) 算法创建了一个简短的,有效的10项德国酒精决定平衡量级 (ADBS). 这种短尺度改进了现有的版本,用于健康和预防研究.
科学领域:
- 心理测量 心理测量 心理测量
- 健康心理学 心理健康心理学
- 计算统计学 计算统计学
背景情况:
- 在健康和预防科学中,简短,心理测量的问卷是非常重要的,以尽量减少参与者的负担.
- 传统的项目选择方法通常依赖于单一的标准,限制了规模的发展.
- 像殖民地优化 (ACO) 一样,元启发学提供了优化规模构建的先进方法.
研究的目的:
- 展示ACO算法的应用,以开发德国酒精决定平衡量 (ADBS) 的简短,心理测量有效版本.
- 为了比较新开发的短ADBS与全尺度和现有的短版本的性能.
主要方法:
- 利用了三项德国研究中的1834名酒精消费者的自我报告数据.
- 采用群优化 (ACO) 算法,同时优化多个模型匹配指数和理论考虑.
- 从德国酒精决策平衡量级 (ADBS) 构建了一个10项的短尺度.
主要成果:
- 该 ACO 算法成功生成了一个 10 项短 ADBS 规模.
- 与26项完整ADBS和先前建立的10项版本相比,新的短尺度显示出更高的心理测量有效性和可靠性.
- 优化标准包括模型合适度指数和理论稳定性.
结论:
- 殖民地优化 (ACO) 算法是开发简短,可靠和有效的心理测量尺度的有效工具.
- 开发的10项简短ADBS为评估研究中的酒精决策平衡提供了有效的替代方案.
- 该研究提供了R语法,用于构建类似的短尺度.
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