Parallel Analysis Engine to Aid Determining Number of Factors to Retain (Patil, Singh, Mishra, and Donovan 2007)
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Based
on parameters provided by the researcher, this engine calculates eigenvalues from randomly generated correlation
matrices. These can be then compared with eigenvalues
extracted from the researcher's dataset. The number of factors to retain will
be the number of eigenvalues
(generated from the researcher’s dataset using Principal Components
Analysis) that are larger than the corresponding random eigenvalues
(Horn 1965). The engine utilizes a SAS-based code written by O'Connor (2000).
The default (and recommended) values for number of random
correlation matrices and percentile of eigenvalues
are 100 and 95 respectively (see Cota et al. 1993; Glorfeld 1995; Turner 1998; Velicer
et al. 2000). Based on the nature of their particular dataset, researchers, can override these default options. Higher
(lower) values of number of correlation matrices generated increase (decrease)
computation time but provide more (fewer) data points in the distribution of
different eigenvalues. The percentile determines the
desired eigenvalue from this distribution, which is
then used for comparison purposes. Lower values of the percentile tend to
lead to over extraction (extraction of more factors than necessary).
Please Enter
Your Specifications:
Select References
Horn, J. L. (1965), “A Rationale and Test For the Number of Factors in Factor Analysis,” Psychometrika, 30, 179-85.
O'Connor, Brian P. (2000), "SPSS and SAS Programs for Determining the Number of Components Using Parallel Analysis and Velicer's MAP Test," Behavior Research Methods, Instruments and Computers, 32 (3), 396-402.
We thank Professor Brian O'Connor for the permission to utilize his program and appreciate the assistance provided by Patricia Oslund in the development of this engine.