常规结果状态空间模型用于密集的纵向数据
Teague R Henry1, Lindley R Slipetz2, Ami Falk2
1Department of Psychology and School of Data Science, University of Virginia, Charlottesville, USA. ycp6wm@virginia.edu.
Psychometrika
|June 11, 2024
概括
新的状态空间模型准确地分析顺序密集的纵向 (IL) 数据,与线性近似不同. 这提高了通过日常日记和生态瞬间评估捕捉到的心理动态的理解.
科学领域:
- 心理学科学 心理学科学
- 量化心理学 量化心理学
- 心理测量 心理测量 心理测量
背景情况:
- 密集的纵向 (IL) 数据,收集频繁 (例如,日常日记,生态瞬间评估),对于理解心理动态至关重要.
- 状态空间建模是分析IL数据的强大框架,但传统上需要连续测量.
- 心理学研究通常涉及顺序数据 (例如,利克特尺度),这对现有的状态空间模型构成了挑战.
研究的目的:
- 为容纳顺序测量的状态空间模型开发一个一般的估计方法.
- 在状态空间分析中使用分级响应模型来具体处理利克特尺度数据.
- 为了比较新的顺序模型与传统的线性近似方法的性能.
主要方法:
- 开发了一种用于顺序数据的新型状态空间建模方法,结合了分级响应模型.
- 采用模拟研究来评估拟议模型的准确性和偏差.
- 将拟议模型的参数估计和状态动态与线性近似方法进行比较.
主要成果:
- 建议使用顺序测量的状态空间模型产生了对状态动态的公正估计.
- 传统的线性近似方法,将顺序数据视为连续的数据,产生了明显偏差的估计.
- 引入了大致置信区间的"切片标准误差",并指出它们往往比真正的标准误差更自由 (更小).
结论:
- 开发的状态空间模型为使用顺序测量的强度纵向数据提供了准确的估计.
- 在状态空间模型中将顺序数据视为连续的处理可能会导致心理研究中的大量偏差.
- 新的方法提高了复杂的心理过程的分析,使用易于获得的顺序数据格式.
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