Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 0 |
Since 2016 (last 10 years) | 1 |
Since 2006 (last 20 years) | 2 |
Descriptor
Correlation | 2 |
Factor Analysis | 2 |
Monte Carlo Methods | 2 |
Accuracy | 1 |
Comparative Analysis | 1 |
Data Analysis | 1 |
Evaluation Methods | 1 |
Evaluation Research | 1 |
Factor Structure | 1 |
Hypothesis Testing | 1 |
Observation | 1 |
More ▼ |
Author
Green, Samuel B. | 2 |
Levy, Roy | 2 |
Thompson, Marilyn S. | 2 |
Lo, Wen-Juo | 1 |
Lu, Min | 1 |
Redell, Nickalus | 1 |
Publication Type
Journal Articles | 2 |
Reports - Research | 2 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Green, Samuel B.; Redell, Nickalus; Thompson, Marilyn S.; Levy, Roy – Educational and Psychological Measurement, 2016
Parallel analysis (PA) is a useful empirical tool for assessing the number of factors in exploratory factor analysis. On conceptual and empirical grounds, we argue for a revision to PA that makes it more consistent with hypothesis testing. Using Monte Carlo methods, we evaluated the relative accuracy of the revised PA (R-PA) and traditional PA…
Descriptors: Accuracy, Factor Analysis, Hypothesis Testing, Monte Carlo Methods
Green, Samuel B.; Levy, Roy; Thompson, Marilyn S.; Lu, Min; Lo, Wen-Juo – Educational and Psychological Measurement, 2012
A number of psychometricians have argued for the use of parallel analysis to determine the number of factors. However, parallel analysis must be viewed at best as a heuristic approach rather than a mathematically rigorous one. The authors suggest a revision to parallel analysis that could improve its accuracy. A Monte Carlo study is conducted to…
Descriptors: Monte Carlo Methods, Factor Structure, Data Analysis, Psychometrics