Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size
Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size
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DOI：
10.1080/02664763.2011.610445
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发表时间：
2012-01-01
影响因子：
1.5
通讯作者：
Dodou, D.
中科院分区：
文献类型：
--
作者：
de Winter, J. C. F.;Dodou, D.
主轴因子分析（PAF）和最大似然因子分析（MLFA）是探索性因子分析中最常用的两种估计方法。已知PAF能较好地恢复弱因子，极大似然估计是渐近有效的。然而，对于不同类型的因子模式和样本量，几乎没有证据表明哪种方法应该是首选的。模拟研究了理想简单结构和样本大小在25 ~ 5000之间的扭曲时，PAF和MLFA的因子恢复情况。结果表明，PAF适用于每因子指标较少的种群解和过度提取的种群解。MLFA优于PAF在不均匀负载的情况下，在因素和提取不足。进一步表明，PAF和MLFA并不总是随着样本量的增加而收敛。模拟结果得到了实证研究和经典等离子体模型Thurstone’s box问题的证实。所得结果对因子分析具有实用价值。
Principal axis factoring (PAF) and maximum likelihood factor analysis (MLFA) are two of the most popular estimation methods in exploratory factor analysis. It is known that PAF is better able to recover weak factors and that the maximum likelihood estimator is asymptotically efficient. However, there is almost no evidence regarding which method should be preferred for different types of factor patterns and sample sizes. Simulations were conducted to investigate factor recovery by PAF and MLFA for distortions of ideal simple structure and sample sizes between 25 and 5000. Results showed that PAF is preferred for population solutions with few indicators per factor and for overextraction. MLFA outperformed PAF in cases of unequal loadings within factors and for underextraction. It was further shown that PAF and MLFA do not always converge with increasing sample size. The simulation findings were confirmed by an empirical study as well as by a classic plasmode, Thurstone's box problem. The present results are of practical value for factor analysts.