Non-Graphical Solutions for Cattell’s Scree Test

IF 2 3区 心理学 Q2 PSYCHOLOGY, MATHEMATICAL
Gilles Raîche, Theodore A. Walls, D. Magis, Martin Riopel, J. Blais
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引用次数: 280

Abstract

Most of the strategies that have been proposed to determine the number of components that account for the most variation in a principal components analysis of a correlation matrix rely on the analysis of the eigenvalues and on numerical solutions. The Cattell's scree test is a graphical strategy with a nonnumerical solution to determine the number of components to retain. Like Kaiser's rule, this test is one of the most frequently used strategies for determining the number of components to retain. However, the graphical nature of the scree test does not definitively establish the number of components to retain. To circumvent this issue, some numerical solutions are proposed, one in the spirit of Cattell's work and dealing with the scree part of the eigenvalues plot, and one focusing on the elbow part of this plot. A simulation study compares the efficiency of these solutions to those of other previously proposed methods. Extensions to factor analysis are possible and may be particularly useful with many low-dimensional components. Several strategies have been proposed to determine the num- ber of components that account for the most variation in a principal components analysis of a correlation matrix. Most of these rely on the analysis of the eigenvalues of the corre- lation matrix and on numerical solutions. For example, Kaiser's eigenvalue greater than one rule (Guttman, 1954; Kaiser, 1960), parallel analysis (Buja & Eyuboglu, 1992; Horn, 1965; Hoyle & Duvall, 2004), or hypothesis signifi- cance tests, like Bartlett's test (1950), make use of numerical criteria for comparison or statistical significance criteria. Independently of these numerical solutions, Cattell (1966) proposed the scree test, a graphical strategy to determine the number of components to retain. Along with the Kaiser's rule, the scree test is probably the most used strategy and it is included in almost all statistical software dealing with principal components analysis. Unfortunately, it is generally recognized that the graphical nature of the Cattell's scree test does not enable clear decision-making about the number of components to retain. The previously proposed non-graphical solutions for
Cattell的屏幕测试的非图形解决方案
在相关矩阵的主成分分析中,大多数被提出的策略都依赖于特征值和数值解的分析,以确定占最大变化的成分的数量。卡特尔的屏幕测试是一种图形策略,具有非数值解决方案,以确定要保留的组件数量。就像Kaiser的规则一样,这个测试是决定要保留的组件数量的最常用策略之一。然而,屏幕测试的图形性质并不能确定要保留的组件数量。为了避免这个问题,提出了一些数值解,一个是在卡特尔的工作精神,处理特征值图的屏幕部分,另一个是关注这个图的肘部部分。仿真研究将这些解决方案的效率与其他先前提出的方法进行了比较。因子分析的扩展是可能的,并且可能对许多低维组件特别有用。已经提出了几种策略来确定在相关矩阵的主成分分析中占最大变化的成分数。这些方法大多依赖于相关矩阵特征值的分析和数值解。例如,Kaiser的特征值大于一个规则(Guttman, 1954;Kaiser, 1960),平行分析(Buja & Eyuboglu, 1992;角,1965;Hoyle & Duvall, 2004)或假设显著性检验,如Bartlett检验(1950),使用数值标准进行比较或统计显著性标准。独立于这些数值解决方案,卡特尔(1966)提出了屏幕测试,一种图形策略,以确定要保留的组件数量。与Kaiser规则一样,屏幕测试可能是最常用的策略,它包含在几乎所有处理主成分分析的统计软件中。不幸的是,人们普遍认为,卡特尔的屏幕测试的图形性质并不能明确地决定要保留的组件数量。的非图形化解决方案
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来源期刊
CiteScore
2.70
自引率
6.50%
发文量
16
审稿时长
36 weeks
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