Psychological impact of COVID-19: a cross-lagged network analysis from the English Longitudinal Study of Aging COVID-19 database
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Date
2023
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Frontiers Media S.A.
Abstract
The deleterious consequences of collinearity in linear regression on the precision of estimators of regression coefficients and the interpretability of the fitted model are widely recognized. In this study, we compare several methodologies for assessing collinearity in linear models and explore the effect of outliers on collinearity. The robustness of collinearity measures (individual and overall) is validated through two detailed Monte Carlo simulation study which also considers the effect of outliers on collinearity indices. The methods are illustrated with two real-world agricultural and fish morphology l data sets to show potential applications. The results do not provide any evidence for an effect from outliers on collinearity identification using the collinearity indices (individual and overall). The FG and Fj collinearity indices more robust as both sample size and collinearity degree increase. The VIF (individual measure) had a better performance on the fitted model with a greater number of parameters. © 2024, Science Publications. All rights reserved.
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Indexado en Scopus.
El texto completo de este trabajo no está disponible en el Repositorio Institucional UNIFE. Deberá acceder por el DOI ó URL de la casa editorial externa.
Frontiers in Psychiatry ; Vol. 14 ; 2023 ; pp. 1-11
El texto completo de este trabajo no está disponible en el Repositorio Institucional UNIFE. Deberá acceder por el DOI ó URL de la casa editorial externa.
Frontiers in Psychiatry ; Vol. 14 ; 2023 ; pp. 1-11
Keywords
Covid-19 -- Aspectos psicológicos