制造一些噪音:从不完美的因素模型生成数据
Justin D Kracht1, Niels G Waller1
1Department of Psychology, University of Minnesota, Minneapolis, MN, USA.
Multivariate behavioral research
|October 16, 2024
概括
一种新的多目标塔克,库普曼和林 (TKL) 方法可以更准确地生成模型错误数据. 这种改进的模拟工具可以帮助研究人员创建具有特定模型合适指数目标的错误扰乱相关性矩阵.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 量化心理学 量化心理学
背景情况:
- 协差结构模型在统计分析中至关重要.
- 模拟模型不合适对于评估模型性能至关重要.
- 像TKL,CB和WB这样的现有方法在复制多个适合指数方面存在局限性.
研究的目的:
- 引入一种新的多目标TKL方法,用于生成错误扰乱数据.
- 为了使特定的根平均平方误差近似 (RMSEA) 和比较适合指数 (CFI) 值的复制.
- 为研究人员提供一种工具,以精确控制模拟模型的不合适性.
主要方法:
- 开发了一个多个目标的塔克,库普曼和林 (TKL) 方法.
- 对于因子分析模型的模拟错误扰乱的相关性矩阵.
- 将多目标TKL方法与Cudeck和Browne (CB) 和Wu和Browne (WB) 方法进行了比较.
主要成果:
- 多重目标TKL方法产生了RMSEA和CFI值,比CB和WB方法更接近目标值.
- 新方法成功地将目标RMSEA和CFI值单独和同时复制.
- 模拟证明了多重目标TKL方法的卓越准确性.
结论:
- 多重目标TKL方法是生成错误扰乱相关性矩阵的一个有价值的工具.
- 这种方法为研究人员提供了对模型不适合模拟的精确控制.
- 虚拟图书馆可以访问本研究中描述的功能.
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