识别诸如公园使用等行为结果的多层次预测因素:比较条件和边际建模方法
Marilyn E Wende1, S Morgan Hughey2, Alexander C McLain3
1Department of Health Education & Behavior, College of Health & Human Performance, University of Florida, Gainesville, FL, United States of America.
PloS one
|April 16, 2024
概括
这项研究比较了公园使用预测模型,发现有条件模型更好地预测了公园参观量. 两种模型都确定了类似的关键预测因素,如种族,教育,公园质量和距离.
科学领域:
- 环境健康 环境健康
- 城市规划 城市规划
- 生物统计学 生物统计学
背景情况:
- 了解影响公园使用的因素对于促进体育活动和社区福祉至关重要.
- 现有的研究通常采用各种统计模型,需要对其预测能力进行比较.
研究的目的:
- 为了比较边际和条件建模方法来识别个人,公园和社区公园使用的预测因素.
- 评估这些建模技术的预测性能和通用性.
主要方法:
- 利用了来自美国四个城市128个街区组的ParkIndex研究的数据.
- 收集个人,公园和区组级别的数据,包括公园参观人数,社会人口统计,公园特征和社区特征.
- 采用了通用的线性混合模型和通用的估计方程,用于性能比较的十倍交叉验证.
主要成果:
- 条件和边际模型都确定了公园使用的共同预测因素:参与者种族,教育,公园距离,公园质量和65岁以上的人口.
- 条件模型独特地确定了公园规模作为一个重要的预测因素.
- 与边际模型相比,条件模型显示出更高的预测值,具有类似的概括性.
结论:
- 条件建模方法可以为分析健康行为数据提供增强的预测准确性,例如公园使用.
- 建议进行交叉验证,以评估边际和条件模型在不同环境中的性能.
- 未来的研究应该探索这两种建模策略的实用性,以全面了解与健康相关的行为.
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