在线隐性增长模型中检测斜率-截面关系中的过渡点
Dayeon Lee1, Gregory R Hancock1
1Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA.
Multivariate behavioral research
|November 21, 2025
概括
本研究引入了一种新的半参数方法,用于模拟潜增长模型中截面 (α) 和斜率 (β) 因素之间的非线性关系. 它有效地检测了α-β关系发生变化的过渡点,为增长过程提供了更深入的见解.
科学领域:
- 统计 统计 统计 统计
- 心理测量 心理测量 心理测量
- 发展心理学 发展心理学
背景情况:
- 潜增长模型 (LGMs) 通常假设截面 (α) 和斜率 (β) 因子之间的线性关系.
- 标准LGM中的协差参数本质上假定线性,这可能并不总是反映复杂的发育过程.
- α和β之间的非线性关系,可能有不同的段落和过渡点,是常见的,但往往没有解决.
研究的目的:
- 开发和验证一种半参数方法,用于在LGM中建模非线性α-β关系.
- 引入一种灵活的方法,能够检测α-β关系中的过渡点.
- 通过对细分的α-β关联进行核算,提供对个体增长轨迹的更细致的理解.
主要方法:
- 一个两阶段的半参数方法,结合贝叶斯的P-splines和细分回归.
- 贝叶斯式P-splines用于α-β关系的灵活非线性建模.
- 分段回归用于检测α-β关联中的过渡点.
主要成果:
- 拟议的方法有效地模拟非线性α-β关系,包括具有单个过渡点的关系.
- 模拟研究证明了该方法在估计参数和识别过渡点方面的准确性.
- 一个经验数据说明证实了该方法的实际实用性和可解释性.
结论:
- 半参数方法提供了一个强大的工具,用于分析复杂的,非线性α-β关系在潜增长建模.
- 这种方法通过识别生长过程中的关键过渡点来增强对发展过程的理解.
- 它为增长轨迹中的个体差异提供了更细微和更准确的表现.
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