Abstract
Traditional parallel analysis (T-PA) estimates the number of factors by sequentially comparing sample eigenvalues with eigenvalues for randomly generated data. Revised parallel analysis (R-PA) sequentially compares the kth eigenvalue for sample data to the kth eigenvalue for generated data sets, conditioned on k− 1 underlying factors. T-PA and R-PA are conceptualized as stepwise hypothesis-testing procedures and, thus, are alternatives to sequential likelihood ratio test (LRT) methods. We assessed the accuracy of T-PA, R-PA, and LRT methods using a Monte Carlo approach. Although no method was uniformly more accurate across all 180 conditions, the PA approaches outperformed LRT methods overall. Relative to T-PA, R-PA tended to perform better within the framework of hypothesis testing and to evidence greater accuracy in conditions with higher factor loadings.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 428-457 |
| Number of pages | 30 |
| Journal | Educational and Psychological Measurement |
| Volume | 75 |
| Issue number | 3 |
| DOIs | |
| State | Published - Jun 6 2015 |
Fingerprint
Keywords
- factor analysis
- parallel analysis
- revised parallel analysis
ASJC Scopus subject areas
- Developmental and Educational Psychology
- Education
- Applied Psychology
- Applied Mathematics
Cite this
Type I and Type II Error Rates and Overall Accuracy of the Revised Parallel Analysis Method for Determining the Number of Factors. / Green, Samuel B.; Thompson, Marilyn; Levy, Roy; Lo, Wen Juo.
In: Educational and Psychological Measurement, Vol. 75, No. 3, 06.06.2015, p. 428-457.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Type I and Type II Error Rates and Overall Accuracy of the Revised Parallel Analysis Method for Determining the Number of Factors
AU - Green, Samuel B.
AU - Thompson, Marilyn
AU - Levy, Roy
AU - Lo, Wen Juo
PY - 2015/6/6
Y1 - 2015/6/6
N2 - Traditional parallel analysis (T-PA) estimates the number of factors by sequentially comparing sample eigenvalues with eigenvalues for randomly generated data. Revised parallel analysis (R-PA) sequentially compares the kth eigenvalue for sample data to the kth eigenvalue for generated data sets, conditioned on k− 1 underlying factors. T-PA and R-PA are conceptualized as stepwise hypothesis-testing procedures and, thus, are alternatives to sequential likelihood ratio test (LRT) methods. We assessed the accuracy of T-PA, R-PA, and LRT methods using a Monte Carlo approach. Although no method was uniformly more accurate across all 180 conditions, the PA approaches outperformed LRT methods overall. Relative to T-PA, R-PA tended to perform better within the framework of hypothesis testing and to evidence greater accuracy in conditions with higher factor loadings.
AB - Traditional parallel analysis (T-PA) estimates the number of factors by sequentially comparing sample eigenvalues with eigenvalues for randomly generated data. Revised parallel analysis (R-PA) sequentially compares the kth eigenvalue for sample data to the kth eigenvalue for generated data sets, conditioned on k− 1 underlying factors. T-PA and R-PA are conceptualized as stepwise hypothesis-testing procedures and, thus, are alternatives to sequential likelihood ratio test (LRT) methods. We assessed the accuracy of T-PA, R-PA, and LRT methods using a Monte Carlo approach. Although no method was uniformly more accurate across all 180 conditions, the PA approaches outperformed LRT methods overall. Relative to T-PA, R-PA tended to perform better within the framework of hypothesis testing and to evidence greater accuracy in conditions with higher factor loadings.
KW - factor analysis
KW - parallel analysis
KW - revised parallel analysis
UR - http://www.scopus.com/inward/record.url?scp=84930526296&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84930526296&partnerID=8YFLogxK
U2 - 10.1177/0013164414546566
DO - 10.1177/0013164414546566
M3 - Article
AN - SCOPUS:84988305981
VL - 75
SP - 428
EP - 457
JO - Educational and Psychological Measurement
JF - Educational and Psychological Measurement
SN - 0013-1644
IS - 3
ER -