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      Online Behavioral Addictions: Longitudinal Network Analysis and Invariance Across Men and Women

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          Abstract

          Online activity has become increasingly prevalent worldwide, raising concerns about potential online behavioral addictions (e.g., problematic social media use, disordered online gambling, internet gaming disorder, and problematic internet use in general). The aim of this study was to conduct a longitudinal network analysis of symptoms associated with online behavioral addictions to examine their interrelations and potential differences across one’s biologically assigned gender (i.e., male, female). An online community sample of 462 adult participants (28.5% women, 69.5% men) completed self-rating questionnaires across two time-points one year apart. Participants’ responses were assessed with Least Absolute Shrinkage and Selection Operator regularized partial correlations (EBICglasso) and invariance methods. Gender differences were observed, with online gaming symptoms showing higher centrality in men and disordered social media use in women. Additionally, disordered gaming and internet use symptoms were highly influential, followed by online gambling, and social media use. Longitudinal differences were observed across genders, suggesting their different vulnerability to problematic behaviors associated with online activities. Additionally, mood modification associated with disordered internet use and impairment due to disordered gaming were highly influential in longitudinal measures, increasing the likelihood of developing coexisting or persistent symptoms of internet use disorders over time. Conclusions and implications are addressed considering the emerging literature.

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          mice: Multivariate Imputation by Chained Equations inR

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            Estimating psychological networks and their accuracy: A tutorial paper

            The usage of psychological networks that conceptualize behavior as a complex interplay of psychological and other components has gained increasing popularity in various research fields. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online. Electronic supplementary material The online version of this article (doi:10.3758/s13428-017-0862-1) contains supplementary material, which is available to authorized users.
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              A ‘components’ model of addiction within a biopsychosocial framework

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                Author and article information

                Journal
                Technology, Mind, and Behavior
                American Psychological Association
                2689-0208
                April 12, 2023
                : 4
                : 1
                : np
                Affiliations
                [1]Institute for Health and Sport, Victoria University
                [2]Department of Psychology, University of Athens
                Author notes
                Special Collection Editors: Nick Bowman, Douglas A. Gentile, C. Shawn Green, and Tracy Markle.
                Action Editor: C. Shawn Green was the action editor for this article.
                Funding: Vasileios Stavropoulos received funding by The Victoria University, Early Career Researcher Fund ECR 2020, No. 68761601. The Australian Research Council, Discovery Early Career Researcher Award, 2021, No. DE210101107.
                Disclosures: Vasileios Stavropoulos is an editorial board reviewer. The rest of the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.
                Ethical Standards—Animal Rights: All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.
                Informed Consent: Informed consent was obtained from all individual participants included in the study.
                Confirmation Statement: Authors confirm that this article has not been either previously published or submitted simultaneously for publication elsewhere.
                Authors Contributions: Daniel Zarate and Genevieve Dorman contributed to the article’s conceptualization, data curation, formal analysis, methodology, project administration, and writing of the original draft. Vasileios Stavropoulos, Maria Prokofieva, and Romana Morda contributed to writing, reviewing, and editing the final draft.
                Data Availability: Data, analytic methods, syntax, and study materials are available by accessing the following link https://github.com/Daniel28052/online_behavioral_addictions ( Zarate, 2022).
                Open Science Disclosures:
                [*] Daniel Zarate, Institute for Health and Sport, Victoria University, P.O. Box 14428, Melbourne, VIC 8001, Australia daniel.zarate@live.vu.edu.au
                Author information
                https://orcid.org/0000-0002-1508-8637
                Article
                tmb 2023-62825-001
                10.1037/tmb0000105
                83722bf8-e157-489b-baeb-95b8734151ea
                © 2023 The Author(s)

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND). This license permits copying and redistributing the work in any medium or format for noncommercial use provided the original authors and source are credited and a link to the license is included in attribution. No derivative works are permitted under this license.

                History
                Categories
                Behavioral Addiction to Technology

                Education,Psychology,Vocational technology,Engineering,Clinical Psychology & Psychiatry
                online behavioral addictions,internet use disorders,measurement invariance,longitudinal network analysis

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