具有多个一般因素的双因素结构中的维度评估:网络心理测量方法
Marcos Jiménez1, Francisco J Abad1, Eduardo Garcia-Garzon2
1Department of Social Psychology and Methodology, Universidad Autonoma de Madrid.
Psychological methods
|July 6, 2023
概括
本研究比较了智力和人格研究中复杂结构的因素保留方法. 探索图分析 (EGA) 和并行分析 (PA) 准确地确定了组和一般因素,为双因素结构提供了强大的解决方案.
科学领域:
- 心理测量 心理测量 心理测量
- 结构方程建模 结构方程建模
- 多变量统计学 多变量统计学
背景情况:
- 维度研究往往忽视了对具有多个一般因素的复杂结构的因子保留精度,这在智力,个性和精神病理学中很常见.
- 现有的识别群体和一般因素的方法在现实条件下可能无法发挥最佳效果.
研究的目的:
- 为了比较各种因素保留方法的性能,包括一种新的网络心理测量方法 (探索性图形分析与卢凡集群 - EGA_LV),用于识别组和一般因素.
- 评估这些方法在估计双因子结构中的因子数量的准确性,特别是那些具有多个一般因子的结构.
主要方法:
- 对比凯泽标准,经验凯泽标准,与主要组件 (PA_PCA) 或主要轴并行分析,以及与卢瓦恩集群 (EGA_LV) 进行探索图分析,用于组因素估计.
- 开发并评估了PA_PCA (PAP_CA-FS) 和EGA_LV (EGA_LV-FS) 的"二级"版本,使用因子得分进行一般因子估计.
- 检查了来自 EGA_LV.LV 的直接多层解决方案.
- 进行了一项广泛的模拟研究,操纵了9个变量,包括人口误差.
主要成果:
- EGA_LV和PA_PCA在识别正确数量的组因子方面表现出卓越的表现.
- EGA_LV对高交叉负载更敏感,而PA_PCA在弱组因子和小样本大小方面表现出色.
- PAP_CA-FS 和 EGA_LV-FS 在估计一般因素数量时实现了近乎完美的准确性.
- EGA_LV在直接估计一般因素时是不准确的.
- 基于EGA的方法在可能在实践中遇到的条件下被证明是可靠的.
结论:
- EGA_LV对于识别组因子非常有效,特别是当交叉负载存在时.
- EGA_LV-FS提供了对双因素结构中的一般因素的准确估计.
- 组合的EGA_LV群组因素和EGA_LV-FS一般因素为分析复杂的心理结构提供了一个强大的和可靠的方法.
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