序列数据中的维度评估:平行分析和探索图形分析之间的比较
Angelos Markos1, Nikolaos Tsigilis2
1Department of Primary Education, Democritus University of Thrace, Alexandroupolis, Greece.
Frontiers in psychology
|May 21, 2024
概括
这项研究比较了平行分析 (PA) 和探索图分析 (EGA) 的尺度维度. 在复杂的结构中,EGA表现出色,而在更简单的单因素尺度中,PA表现更好.
科学领域:
- 社会科学 社会科学 社会科学
- 心理测量 心理测量 心理测量
- 量化心理学 量化心理学
背景情况:
- 准确的尺度维度对于理解社会科学构造至关重要.
- 选择正确的维度评估方法会影响构造的有效性.
研究的目的:
- 严格比较并行分析 (PA) 和探索图分析 (EGA) 以评估尺度维度.
- 在各种条件下评估方法性能,包括顺序数据,样本大小和因子结构复杂性.
主要方法:
- 广泛的模拟研究评估PA和EGA.
- 各种条件包括样本大小,因子数和关联,负载大小,物品分布对称性/曲率等.
- 在假定的正常性和非正常性下评估绩效.
主要成果:
- 探索图形分析 (EGA) 在确定正确的因素数量方面通常优于并行分析 (PA),特别是在复杂的场景中.
- 对于较简单的单元结构,具有较强的负载和较低的因子间相关性,建议使用PA.
- 扭曲的项目分配对这两种方法都有重大影响,特别是在复杂的情景中.
结论:
- 在PA和EGA之间做出选择取决于数据的具体特征和底层的因素结构.
- 结果为研究人员在规模开发和验证方面提供指导,以确保准确的结构测量.
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