AN IMPROVEMENT ON HORNS PARALLEL ANALYSIS METHODOLOGY FOR SELECTING THE CORRECT NUMBER OF FACTORS TO RETAIN
AN IMPROVEMENT ON HORNS PARALLEL ANALYSIS METHODOLOGY FOR SELECTING THE CORRECT NUMBER OF FACTORS TO RETAIN
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DOI：
10.1177/0013164495055003002
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发表时间：
1995-06-01
影响因子：
2.7
通讯作者：
GLORFELD, LW
中科院分区：
文献类型：
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作者：
GLORFELD, LW
在使用因子分析时可以做出的最重要的决定之一是要保留的因子数量。大量研究一致表明，Hem的平行分析是确定探索性因素分析中保留的因素数量的最接近准确的方法。尽管Hem的程序相对准确，但它仍然倾向于在表明保留的因素多于实际保证的一两个或保留的因素定义不清的方向上出错。介绍了霍恩并行分析的一种改进方法，该方法基于蒙特卡罗模拟由种群相关单位矩阵产生的特征值的零分布。这种修改允许识别任何期望的上1 - alpha百分位数，例如这组分布的第95百分位数。然后，可以使用1 - alpha百分位数来确定特征值是否大于随机预期的特征值，Horn基于从这组分布中得出的平均特征值的原始程序。改进后的程序减少了并行分析方法过度提取的倾向。通过实例验证了该方法的有效性，并说明了并行分析方法及其修改对生成特征值分布的数据的分布特性不敏感。
One of the most important decisions that can be made in the use of factor analysis is the number of factors to retain. Numerous studies have consistently shown that Hem's parallel analysis is the most nearly accurate methodology for determining the number of factors to retain in an exploratory factor analysis. Although Hem's procedure is relatively accurate, it still tends to error in the direction of indicating the retention of one or two more factors than is actually warranted or of retaining poorly defined factors. A modification of Horn's parallel analysis based on Monte Carlo simulation of the null distributions of the eigenvalues generated from a population correlation identity matrix is introduced. This modification allows identification of any desired upper 1 - alpha percentile, such as the 95th percentile of this set of distributions. The 1 - alpha percentile then can be used to determine whether an eigenvalue is larger than what could be expected by chance, Horn based his original procedure on the average eigenvalues derived from this set of distributions. The modified procedure reduces the tendency of the parallel analysis methodology to overextract. An example is provided that demonstrates this capability, A demonstration is also given that indicates that the parallel analysis procedure and its modification are insensitive to the distributional characteristics of the data used to generate the eigenvalue distributions.