在PROMIS数据中比较潜在变量和心理网络模型:输出指标和因素结构
1Department of Psychology, McGill University, 2001 McGill College Ave., 7th Floor, Montreal, QC, H3A 1G1, Canada.
概括
高斯图形模型 (GGM) 和探索图分析 (EGA) 可能为健康研究中的确认因子分析 (CFA) 提供补充的见解. 这些心理测量模型在现实世界患者数据中显示了与传统指标的细微关系.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 分析健康数据 分析健康数据
背景情况:
- 传统的潜在变量模型,如确认因素分析 (CFA),被广泛使用.
- 新兴的心理测量模型,如高斯图形模型 (GGM) 和探索图分析 (EGA),提供了替代方法.
- 之前的研究显示,在比较GGM和CFA时,结果各不相同,特别是关于中心性指数和因子负载,以及EGA恢复因子结构的能力.
研究的目的:
- 使用现实世界的精神和身体健康症状数据,比较高斯图形模型 (GGM) 和确认因素分析 (CFA).
- 扩展GGM中心性指数与CFA因子负载的先前比较.
- 评估探索性图形分析 (EGA) 在分析健康症状数据中的性能.
主要方法:
- 将GGM和CFA模型与患者报告结果测量信息系统 (PROMIS) 的第1波数据相匹配.
- 使用16个测试表格,旨在测量9个心理和身体健康领域.
- 在结构方程建模中常见的缺失数据处理中采用双阶段方法.
主要成果:
- 在GGM中心性指数和CFA因子负载之间观察到的对应性比之前的研究中报告的要弱,尽管模式类似.
- EGA经常推不同于已建立的PROMIS领域的因子结构.
- 尽管存在差异,但EGA对PROMIS领域的维度提供了实质性的见解.
结论:
- 在真实心理和身体健康数据的背景下,GGM和EGA可以提供补充传统CFA指标的信息.
- 这些发现表明,GGM和EGA是分析复杂健康数据的有价值工具.
- 该研究强调了将新型心理测量方法与既定方法相结合的潜力.
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