ArticleJournal of Intelligence2020
The Effects of Using Partial or Uncorrected Correlation Matrices When Comparing Network and Latent Variable Models.
Article in Journal of Intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Integrating job design and cognitive-emotional processes using latent network analysis: associations with performance and emotional well-being in navy personnel.BMC psychology · 2025Article
- Psychometric network analysis of the Patient Health Questionnaire-4 (PHQ-4) in Paraguayan general population.Psicologia, reflexao e critica : revista semestral do Departamento de Psicologia da UFRGS · 2024Article
- Investigating the Structure of the French WISC-V (WISC-VJournal of Intelligence · 2023Article
- Network analyses of Oppositional Defiant Disorder (ODD) symptoms in children.BMC psychiatry · 2022Article
- Investigating the Structure of Intelligence Using Latent Variable and Psychometric Network Modeling: A Commentary and Reanalysis.Journal of Intelligence · 2021Article
- How to Compare Psychometric Factor and Network Models.Journal of Intelligence · 2020Article
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1 author.
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Abstract
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.
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