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The Effects of Using Partial or Uncorrected Correlation Matrices When Comparing Network and Latent Variable Models

National Center for Adaptive Neurotechnologies, Albany, NY 12208, USA
Received: 16 December 2019 / Revised: 7 February 2020 / Accepted: 11 February 2020 / Published: 15 February 2020
Network models of the WAIS-IV based on regularized partial correlation matrices have been reported to outperform latent variable models based on uncorrected correlation matrices. The present study sought to compare network and latent variable models using both partial and uncorrected correlation matrices with both types of models. The results show that a network model provided better fit to matrices of partial correlations but latent variable models provided better fit to matrices of full correlations. This result is due to the fact that the use of partial correlations removes most of the covariance common to WAIS-IV tests. Modeling should be based on uncorrected correlations since these represent the majority of shared variance between WAIS-IV test scores. View Full-Text
Keywords: intelligence; process overlap theory; psychometric network analysis; latent variable modeling intelligence; process overlap theory; psychometric network analysis; latent variable modeling
MDPI and ACS Style

McFarland, D. The Effects of Using Partial or Uncorrected Correlation Matrices When Comparing Network and Latent Variable Models. J. Intell. 2020, 8, 7.

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