A Bayesian approach for exploring person?*?environment interaction within the environmental sensitivity meta-framework / Francesca LIONETTI in Journal of Child Psychology and Psychiatry, 65-11 (November 2024)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 65-11 (November 2024) . - p.1486-1500 Titre : A Bayesian approach for exploring person?*?environment interaction within the environmental sensitivity meta-framework Type de document : Texte imprimé et/ou numérique Auteurs : Francesca LIONETTI, Auteur ; Antonio CALCAGNÌ, Auteur ; Giulio D'URSO, Auteur ; Maria SPINELLI, Auteur ; Mirco FASOLO, Auteur ; Michael PLUESS, Auteur ; Massimiliano PASTORE, Auteur Article en page(s) : p.1486-1500 Langues : Anglais (eng) Mots-clés : Differential susceptibility  diathesis-stress  vantage sensitivity  Bayesian approach  MCMC estimates Index. décimale : PER Périodiques Résumé : Background For investigating the individual?environment interplay and individual differences in response to environmental exposures as captured by models of environmental sensitivity including Diathesis-stress, Differential Susceptibility, and Vantage Sensitivity, over the last few years, a series of statistical guidelines have been proposed. However, available solutions suffer of computational problems especially relevant when sample size is not sufficiently large, a common condition in observational and clinical studies. Method In the current contribution, we propose a Bayesian solution for estimating interaction parameters via Monte Carlo Markov Chains (MCMC), adapting Widaman et al. (Psychological Methods, 17, 2012, 615) Nonlinear Least Squares (NLS) approach. Results Findings from an applied exemplification and a simulation study showed that with relatively big samples both MCMC and NLS estimates converged on the same results. Conversely, MCMC clearly outperformed NLS, resolving estimation problems and providing more accurate estimates, particularly with small samples and greater residual variance. Conclusions As the body of research exploring the interplay between individual and environmental variables grows, enabling predictions regarding the form of interaction and the extent of effects, the Bayesian approach could emerge as a feasible and readily applicable solution to numerous computational challenges inherent in existing frequentist methods. This approach holds promise for enhancing the trustworthiness of research outcomes, thereby impacting clinical and applied understanding. En ligne : https://doi.org/10.1111/jcpp.14000 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=537 [article] A Bayesian approach for exploring person?*?environment interaction within the environmental sensitivity meta-framework [Texte imprimé et/ou numérique] / Francesca LIONETTI, Auteur ; Antonio CALCAGNÌ, Auteur ; Giulio D'URSO, Auteur ; Maria SPINELLI, Auteur ; Mirco FASOLO, Auteur ; Michael PLUESS, Auteur ; Massimiliano PASTORE, Auteur . - p.1486-1500. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 65-11 (November 2024) . - p.1486-1500 Mots-clés : Differential susceptibility  diathesis-stress  vantage sensitivity  Bayesian approach  MCMC estimates Index. décimale : PER Périodiques Résumé : Background For investigating the individual?environment interplay and individual differences in response to environmental exposures as captured by models of environmental sensitivity including Diathesis-stress, Differential Susceptibility, and Vantage Sensitivity, over the last few years, a series of statistical guidelines have been proposed. However, available solutions suffer of computational problems especially relevant when sample size is not sufficiently large, a common condition in observational and clinical studies. Method In the current contribution, we propose a Bayesian solution for estimating interaction parameters via Monte Carlo Markov Chains (MCMC), adapting Widaman et al. (Psychological Methods, 17, 2012, 615) Nonlinear Least Squares (NLS) approach. Results Findings from an applied exemplification and a simulation study showed that with relatively big samples both MCMC and NLS estimates converged on the same results. Conversely, MCMC clearly outperformed NLS, resolving estimation problems and providing more accurate estimates, particularly with small samples and greater residual variance. Conclusions As the body of research exploring the interplay between individual and environmental variables grows, enabling predictions regarding the form of interaction and the extent of effects, the Bayesian approach could emerge as a feasible and readily applicable solution to numerous computational challenges inherent in existing frequentist methods. This approach holds promise for enhancing the trustworthiness of research outcomes, thereby impacting clinical and applied understanding. En ligne : https://doi.org/10.1111/jcpp.14000 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=537

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