The Effect of Common Variance and Structure Pattern on Random Data Eigenvalues: Implications for the Accuracy of Parallel Analysis
The Effect of Common Variance and Structure Pattern on Random Data Eigenvalues: Implications for the Accuracy of Parallel Analysis
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共同方差和结构模式对随机数据特征值的影响：对并行分析准确性的影响
DOI：
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
N. Turner
中科院分区：
文献类型：
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作者：
N. Turner
在因子分析中选择正确数量的因子是发展心理测量工具或发展理论的关键步骤。本研究评估了平行分析的准确性，这是一种将观察到的特征值与模拟数据的特征值进行比较的技术，其中没有实际因素存在。研究1研究了一个真实因子的存在对后续噪声特征值大小的影响。实际因素的大小和样本量是经过操纵的。研究2检验了结构系数的模式和变量的连续性对真实特征值和噪声特征值大小的影响。研究3将研究1和研究2的结果与实际心理测量数据进行比较。这些例子说明了当使用并行分析来确定实际因素的数量时，更紧密地对数据建模的重要性。
Selecting the correct number of factors to retain in a factor analysis is a crucial step in developing psychometric tools or developing theories. The present study assessed the accuracy of parallel analysis, a technique in which the observed eigenvalues are compared to eigenvalues from simulated data in which no real factors are present. Study 1 investigated the effect of the presence of one real factor on the size of subsequent noise eigenvalues. The size of real factors and the sample size were manipulated. Study 2 examined the effect that the pattern of structure coefficients and continuousness of the variables have on the size of real and noise eigenvalues. Study 3 compared the results of Studies 1 and 2 to actual psychometric data. These examples illustrate the importance of modeling the data more closely when parallel analysis is used to determine the number of real factors.