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引用次数: 0
摘要
平行分析法被认为是确定因子分析中因子个数的最准确方法之一。与传统的因子保留方法(如凯撒法则)相比,平行分析法的一大优势在于它能解决从特征矩阵(代表零因子模型的相关矩阵)中获得的特征值的抽样变异性问题。本研究认为,我们还应该解决从观测数据中获得的特征值的抽样变异性问题,从而使研究结果能够告知从业人员不同随机样本中因子数量的变异性。因此,本研究建议修改并行分析,以提供建议 k 个因子(k = 0、1、2、...)的随机样本比例,而不是单一的建议因子数。模拟结果支持使用所建议的策略,尤其是在样本量有限、抽样波动令人担忧的研究场景中。
Improving the Use of Parallel Analysis by Accounting for Sampling Variability of the Observed Correlation Matrix.
Parallel analysis has been considered one of the most accurate methods for determining the number of factors in factor analysis. One major advantage of parallel analysis over traditional factor retention methods (e.g., Kaiser's rule) is that it addresses the sampling variability of eigenvalues obtained from the identity matrix, representing the correlation matrix for a zero-factor model. This study argues that we should also address the sampling variability of eigenvalues obtained from the observed data, such that the results would inform practitioners of the variability of the number of factors across random samples. Thus, this study proposes to revise the parallel analysis to provide the proportion of random samples that suggest k factors (k = 0, 1, 2, . . .) rather than a single suggested number. Simulation results support the use of the proposed strategy, especially for research scenarios with limited sample sizes where sampling fluctuation is concerning.
期刊介绍:
Educational and Psychological Measurement (EPM) publishes referred scholarly work from all academic disciplines interested in the study of measurement theory, problems, and issues. Theoretical articles address new developments and techniques, and applied articles deal with innovation applications.