隐性增长曲线的比较:一个参数常数测试
Jesús F Rosel1, Sara Puchol, Marcel Elipe1
1Faculty of Health Sciences, Universidad Jaume I.
Psychological methods
|November 3, 2025
概括
本研究为潜增长曲线 (LGC) 模型引入了一个参数常数测试 (PCT). 通过评估参数稳定性,PCT增强了模型选择,这对于准确的纵向数据分析和预测至关重要.
科学领域:
- 心理测量 心理测量 心理测量
- 纵向数据分析 纵向数据分析
- 结构方程建模 结构方程建模
背景情况:
- 潜增长曲线 (LGC) 模型是分析发育轨迹的标准.
- 目前的模型选择依赖于合适度指数,忽视了参数时间常数.
- 参数稳定性对于可靠的预测和纵向数据的解释至关重要.
研究的目的:
- 为LGC模型引入一种新的参数常数测试 (PCT).
- 解决在LGC分析中评估时间参数稳定性的差距.
- 提高纵向发育和学习轨迹模型的可靠性和可解释性.
主要方法:
- 为 LGC 模型开发并应用了参数常数测试 (PCT).
- 使用结构方程建模来实现LGC.
- 使用PCT对现实数据进行二次和负指数模型的比较.
主要成果:
- 与二次模型相比,负指数模型显示出更高的参数常数,即使数据点较少.
- PCT有效地识别了潜在的断点,并确定了可靠建模所需的最小测量波.
- 不适当的模型选择或不稳定可能导致误解,特别是在评估干预或推断数据时.
结论:
- 在选择LGC模型时,参数常数与统计匹配一样重要.
- PCT集成增强了模型的一致性,优化了资源分配,并防止了纵向研究中的错误结论.
- 负指数模型是纵向分析的可靠选择,因为它具有一致的参数稳定性.
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