Sample size in factor analysis
Sample size in factor analysis
复制标题
DOI：
10.1037/1082-989x.4.1.84
复制
发表时间：
1999-03-01
影响因子：
7
通讯作者：
Hong, SH
中科院分区：
文献类型：
--
作者：
MacCallum, RC;Widaman, KF;Hong, SH
因子分析文献包括一系列关于最小样本量的建议，这些最小样本量是获得足够稳定且与总体因子密切对应的因子解所必需的。关于这个问题的一个基本误解是，最小样本量，或样本量与变量数量的最小比率，在研究中是不变的。事实上，必要的样本量取决于任何特定研究的几个方面，包括变量的共同性水平和因素的过度确定水平。作者提出了一个理论和数学框架，为理解和预测这些影响提供了基础。假设的效果是通过使用人工数据的抽样研究进行验证。结果表明缺乏有效性的经验法则，并提供了一个基础，建立指导方针的样本量因素分析。
The factor analysis literature includes a range of recommendations regarding the minimum sample size necessary to obtain factor solutions that are adequately stable and that correspond closely to population factors. A fundamental misconception about this issue is that the minimum sample size, or the minimum ratio of sample size to the number of variables, is invariant across studies. In fact, necessary sample size is dependent on several aspects of any given study, including the level of communality of the variables and the level of overdetermination of the factors. The authors present a theoretical and mathematical framework that provides a basis for understanding and predicting these effects. The hypothesized effects are verified by a sampling study using artificial data. Results demonstrate the lack of validity of common rules of thumb and provide a basis for establishing guidelines for sample size in factor analysis.