RELATION OF SAMPLE-SIZE TO THE STABILITY OF COMPONENT PATTERNS
RELATION OF SAMPLE-SIZE TO THE STABILITY OF COMPONENT PATTERNS
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DOI：
10.1037/0033-2909.103.2.265
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发表时间：
1988-03-01
影响因子：
22.4
通讯作者：
VELICER, WF
中科院分区：
文献类型：
--
作者：
GUADAGNOLI, E;VELICER, WF
已经提出了多种规则来确定在执行因子或成分分析时产生稳定解决方案所需的样本量。最流行的规则建议将样本量确定为变量数量的函数。然而，这些规则缺乏经验支持和理论依据。我们使用蒙特卡罗程序来系统地改变样本量、变量数量、成分数量和成分饱和度（即观察到的变量和成分之间的相关性大小），以检查样本成分模式相对于总体模式变得稳定的条件。我们通过单一汇总统计量 g2 以及使用 kappa 统计量进行直接模式比较来比较模式。结果表明，与总体规则相反，样本大小作为变量数量的函数并不是决定稳定性的重要因素。成分饱和度和绝对样本量是最重要的因素。在较小程度上，每个组件的变量数量也很重要，每个组件的变量越多，结果就越稳定。
A variety of rules have been suggested for determining the sample size required to produce a stable solution when performing a factor or component analysis. The most popular rules suggest that sample size be determined as a function of the number of variables. These rules, however, lack both empirical support and a theoretical rationale. We used a Monte Carlo procedure to systematically vary sample size, number of variables, number of components, and component saturation (i.e., the magnitude of the correlation between the observed variables and the components) in order to examine the conditions under which a sample component pattern becomes stable relative to the population pattern. We compared patterns by means of a single summary statistic, g2, and by means of direct pattern comparisons using the kappa statistic. REsults indicated that, contrary to the population rules, samples size as a function of the number of variables was not an important factor in determining stability. Component saturation and absolute sample size were the most important factors. To a lesser degree, the number of variables per component was also important, with more variables per component producing more stable results.