Santos Nadine Correia, Costa Patrício Soares, Amorim Liliana, Moreira Pedro Silva, Cunha Pedro, Cotter Jorge, Sousa Nuno
Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal; ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.
Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal; ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal; Centro Hospitalar do Alto Ave-EPE, Guimarães, Portugal.
PLoS One. 2015 Apr 16;10(4):e0124229. doi: 10.1371/journal.pone.0124229. eCollection 2015.
Here we focus on factor analysis from a best practices point of view, by investigating the factor structure of neuropsychological tests and using the results obtained to illustrate on choosing a reasonable solution. The sample (n=1051 individuals) was randomly divided into two groups: one for exploratory factor analysis (EFA) and principal component analysis (PCA), to investigate the number of factors underlying the neurocognitive variables; the second to test the "best fit" model via confirmatory factor analysis (CFA). For the exploratory step, three extraction (maximum likelihood, principal axis factoring and principal components) and two rotation (orthogonal and oblique) methods were used. The analysis methodology allowed exploring how different cognitive/psychological tests correlated/discriminated between dimensions, indicating that to capture latent structures in similar sample sizes and measures, with approximately normal data distribution, reflective models with oblimin rotation might prove the most adequate.
在此，我们从最佳实践的角度关注因素分析，通过研究神经心理学测试的因素结构，并利用所得结果来说明如何选择合理的解决方案。样本（n = 1051 人）被随机分为两组：一组用于探索性因素分析（EFA）和主成分分析（PCA），以研究神经认知变量背后的因素数量；另一组用于通过验证性因素分析（CFA）来检验“最佳拟合”模型。对于探索性步骤，使用了三种提取方法（最大似然法、主轴因子分解法和主成分分析法）和两种旋转方法（正交旋转和斜交旋转）。该分析方法能够探究不同认知/心理测试在维度之间的相关性/区分度，表明在样本量和测量方法相似、数据分布近似正态的情况下，采用斜交旋转的反映性模型可能是最恰当的。
