ERIC Number: EJ1322427
Record Type: Journal
Publication Date: 2020
Pages: 6
Abstractor: As Provided
ISBN: N/A
ISSN: EISSN-2079-3200
EISSN: N/A
The Effects of Using Partial or Uncorrected Correlation Matrices When Comparing Network and Latent Variable Models
Journal of Intelligence, v8 Article 7 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.
Descriptors: Correlation, Matrices, Adults, Intelligence Tests, Networks, Network Analysis, Models, Scores, Psychometrics, Cognitive Processes
MDPI AG. Klybeckstrasse 64, 4057 Basel, Switzerland. e-mail: indexing@mdpi.com; e-mail: jintelligence@mdpi.com; Web site: https://www.mdpi.com/journal/jintelligence
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: N/A
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Assessments and Surveys: Wechsler Adult Intelligence Scale
Grant or Contract Numbers: N/A