在零碎增长模型中研究潜伏状态特征理论框架
Ihnwhi Heo1, Ren Liu1, Haiyan Liu1
1Department of Psychological Sciences, University of California, Merced, CA, USA.
Applied psychological measurement
|July 18, 2025
概括
这项研究将零碎增长模型 (PGMs) 引入潜态轨道 (LST) 理论,增强纵向数据分析. 多指标PGM (MI-PGMs) 在存在情境影响的情况下,证明比单指标PGM (SI-PGMs) 更强大.
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
- 发展心理学 发展心理学
背景情况:
- 潜在状态-特征 (LST) 理论为分析纵向数据中的长期特征变化和短期状态变化提供了一个框架.
- 现有的LST应用主要集中在线性潜增长模型上,使得非线性模型的集成尚未探索.
- 零碎增长模型 (PGMs) 适用于捕捉心理学和教育研究中常见的非线性发展过程中的不同阶段.
研究的目的:
- 引入一种新的测量方法,将PGM整合到LST理论框架中.
- 在LST理论中,介绍和详细说明单指标PGM (SI-PGMs) 和多指标PGM (MI-PGMs) 的规格.
- 通过模拟来评估SI-PGM和MI-PGM在恢复增长参数和可靠性的表现.
主要方法:
- 在LST框架内开发单指标零碎增长模型 (SI-PGMs) 和多指标零碎增长模型 (MI-PGMs).
- 关键系数的定义:SI-PGM的可靠性;MI-PGM的一致性,特殊性和可靠性.
- 在不同的条件下进行模拟研究以评估参数恢复和可靠性估计的准确性.
主要成果:
- 在没有局势影响的情况下,SI-PGM和MI-PGM都在恢复增长参数和可靠性方面取得了成功.
- 与SI-PGM相比,MI-PGM在引入局势影响时准确捕捉增长参数和可靠性方面表现出更好的表现.
- 模拟结果证实了将PGM集成到LST框架中用于纵向数据分析的可行性.
结论:
- 将PGM集成到LST理论中,为分析非线性发育轨迹提供了有价值的工具.
- 与SI-PGM相比,MI-PGM提供了更高的准确性和稳定性,特别是在情况影响的情况下.
- 拟议的模型和附带的Mplus语法促进了在心理和教育研究中的更广泛应用,用于细微的纵向分析.
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