Dimensionality Assessment of Ordered Polytomous Items With Parallel Analysis
Dimensionality Assessment of Ordered Polytomous Items With Parallel Analysis
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DOI：
10.1037/a0023353
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发表时间：
2011-06-01
影响因子：
7
通讯作者：
Lorenzo-Seva, Urbano
中科院分区：
文献类型：
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作者：
Timmerman, Marieke E.;Lorenzo-Seva, Urbano
平行分析（PA）是一种经常推荐的方法，用于评估变量集的维度。PA在不同的变体中是已知的，这可以产生不同的维度指示。在这篇文章中，作者认为最合适的PA程序来评估有序多分类评分变量的共同因素数量。他们提出了最小秩因子分析（MRFA）作为提取方法，而不是目前应用的主成分分析（PCA）和主轴因子。一项模拟研究，主要和次要因素的数据的基础上，表明，所有程序始终指向的主要共同因素的数量。基于多元的PA略优于基于皮尔逊的PA，但收敛问题可能会阻碍其实证应用。在实证实践中，PA-MRFA与95%的阈值的基础上多区相关性，或在不收敛的情况下，皮尔逊相关性与平均阈值似乎是一个很好的选择，用于识别的共同因素的数量。PA-MRFA是一种基于公共因子的方法，在仿真实验中表现最好。基于具有95%阈值的PCA的PA是第二好的，因为该方法在模拟实验的经验相关条件下表现出良好的性能。
Parallel analysis (PA) is an often-recommended approach for assessment of the dimensionality of a variable set. PA is known in different variants, which may yield different dimensionality indications. In this article, the authors considered the most appropriate PA procedure to assess the number of common factors underlying ordered polytomously scored variables. They proposed minimum rank factor analysis (MRFA) as an extraction method, rather than the currently applied principal component analysis (PCA) and principal axes factoring. A simulation study, based on data with major and minor factors, showed that all procedures consistently point at the number of major common factors. A polychoric-based PA slightly outperformed a Pearson-based PA, but convergence problems may hamper its empirical application. In empirical practice, PA-MRFA with a 95% threshold based on polychoric correlations or, in case of nonconvergence, Pearson correlations with mean thresholds appear to be a good choice for identification of the number of common factors. PA-MRFA is a common-factor-based method and performed best in the simulation experiment. PA based on PCA with a 95% threshold is second best, as this method showed good performances in the empirically relevant conditions of the simulation experiment.