• Corpus ID: 44616760

A Comparison of Ten Methods for Determining the Number of Factors in Exploratory Factor Analysis

@inproceedings{Pearson2013ACO,
  title={A Comparison of Ten Methods for Determining the Number of Factors in Exploratory Factor Analysis},
  author={Robert Pearson and Daniel J. Mundfrom and Adam Piccone},
  year={2013}
}
Datalogix The effectiveness of 10 methods for estimating the number of factors were compared. These methods were the minimum average partial procedure, the likelihood ratio test, the Akaike information criteria (AIC), the Schwarz information criteria, the common factor and principal component versions of parallel analysis, the standard error scree test, and the eigenvalues greater than average criterion. Two simulation studies were conducted. In the first study, the true number of factors… 

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