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Performance of Parallel Analysis in Retrieving Unidimensionality in the Presence of Binary Data

机译：存在二进制数据时并行分析在检索单维性方面的性能

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摘要

Parallel analysis has been shown to be suitable for dimensionality assessment in factor analysis of continuous variables. There have also been attempts to demonstrate that it may be used to uncover the factorial structure of binary variables conforming to the unidimensional normal ogive model. This article provides both theoretical and empirical evidence that this is not appropriate. Results of a simulation study indicate that sample size, item discrimination, and type of correlation coefficient (Pearson vs. tetrachoric correlation) considerably influence the performance of parallel analysis. Reliability of parallel analysis with binary variables is found to be notably poor for Pearson correlations and also limited for tetrachoric correlations.
机译：并行分析已显示适用于连续变量的因子分析中的维度评估。还尝试证明它可以用来揭示符合一维法线模型的二元变量的阶乘结构。本文提供了理论和经验证据，证明这是不合适的。模拟研究的结果表明，样本大小，项目区分度和相关系数的类型（皮尔森与四色相关）极大地影响了并行分析的性能。发现二元变量并行分析的可靠性对于Pearson相关性特别差，而对四色相关性也很有限。

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