On Formalizing Theoretical Expectations: Bayesian Testing of Central Structures in Psychological Networks
| Autor: | Josue E. Rodriguez, Donald Ray Williams, Philippe Rast, Joris Mulder |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Bayesian probability bepress|Social and Behavioral Sciences|Psychology|Quantitative Psychology Machine learning computer.software_genre PsyArXiv|Social and Behavioral Sciences|Quantitative Methods|Psychometrics PsyArXiv|Social and Behavioral Sciences PsyArXiv|Social and Behavioral Sciences|Quantitative Methods|Quantitative Psychology bepress|Social and Behavioral Sciences PsyArXiv|Social and Behavioral Sciences|Quantitative Methods|Statistical Methods Artificial intelligence PsyArXiv|Social and Behavioral Sciences|Quantitative Methods business computer |
| Popis: | Network theory has emerged as a popular framework for conceptualizing psychological constructs and mental disorders. Initially, network analysis was motivated in part by the thought that it can be used for hypothesis generation. Although the customary approach for network modeling is inherently exploratory, we argue that there is untapped potential for confirmatory hypothesis testing. In this work, we bring to fruition the potential of Gaussian graphical models for generating testable hypotheses. This is accomplished by merging exploratory and confirmatory analyses into a cohesive framework built around Bayesian hypothesis testing of partial correlations. We first present a motivating example based on a customary, exploratory analysis, where it is made clear how information encoded by the conditional (in)dependence structure can be used to formulate hypotheses. Building upon this foundation, we then provide several empirical examples that unify exploratory and confirmatory testing in psychopathology symptom networks. In particular, we (1) estimate exploratory graphs; (2) derive hypotheses based on the most central structures; and (3) test those hypotheses in a confirmatory setting. Our confirmatory results uncovered an intricate web of relations, including an order to edge weights within comorbidity networks. This illuminates the rich and informative inferences that can be drawn with the proposed approach. We conclude with recommendations for applied researchers, in addition to discussing how our methodology answers recent calls to begin developing formal models related to the conditional (in)dependence structure of psychological networks. |
| Databáze: | OpenAIRE |
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