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      Bridging the links between Big Five personality traits and problematic smartphone use: A network analysis

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          Abstract

          Background

          Existing research has demonstrated that problematic smartphone use (PSU) may reflect a composition of heterogeneous symptoms, with individual PSU symptoms uniquely related to predisposing variables. The Big Five personality traits represent one of the most frequently examined predisposing variables in relation to PSU. However, no studies to date have examined the trait-to-symptom association between the Big Five personality traits and PSU. Using a network analysis approach, we aimed to understand: 1) specific pathways linking each of the Big Five personality traits to PSU symptoms and 2) the bridging effects of each Big Five personality trait on the PSU symptom cluster.

          Methods

          A regularised graphical Gaussian model was estimated among 1,849 Chinese university students. PSU symptoms were assessed with items from the Problematic Smartphone Use Scale. Facets of the Big Five personality traits were assessed with the subscales of the Chinese Big Five Personality Inventory-15. An empirical index (i.e., bridge expected influence) was used to quantify bridge nodes.

          Results

          Results revealed specific and distinct pathways between the Big Five personality traits and PSU symptoms (e.g., Neuroticism-Escapism/Avoidance, Conscientiousness-Preoccupation and Extraversion-Escapism/Avoidance). Further, Neuroticism showed the highest positive bridge centrality among the Big Five personality traits, while Conscientiousness had the highest negative bridge centrality.

          Discussion and conclusions

          The current study provided direct empirical evidence concerning specific pathways between the Big Five personality traits and PSU symptoms and highlighted the influential role of Neuroticism and Conscientiousness as potential targets for early detection and treatment of PSU.

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          Most cited references57

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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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            qgraph: Network Visualizations of Relationships in Psychometric Data

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              Graph drawing by force-directed placement

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                Author and article information

                Contributors
                Journal
                2006
                Journal of Behavioral Addictions
                JBA
                Akadémiai Kiadó (Budapest )
                2062-5871
                2063-5303
                10 February 2023
                30 March 2023
                : 12
                : 1
                : 128-136
                Affiliations
                [1 ] BrainPark, Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University , Clayton, Australia
                [2 ] School of Military Medical Psychology, Fourth Military Medical University , Xi'an, China
                [3 ] Monash Business School, Monash University , Caulfield, Australia
                [4 ] School of Psychology, Army Medical University , Chongqing, China
                [5 ] Psychological Counselling Centre, Xijing University , Xi'an, China
                [6 ] Department of Psychology, Renmin University of China , Beijing, China
                Author notes
                [* ]Corresponding author. E-mail: rl_fmmu@ 123456163.com
                Author information
                https://orcid.org/0000-0003-0324-8151
                https://orcid.org/0000-0002-5770-3271
                https://orcid.org/0000-0003-2570-2044
                https://orcid.org/0000-0002-1653-5419
                https://orcid.org/0000-0002-4705-452X
                https://orcid.org/0000-0001-5232-6414
                Article
                10.1556/2006.2022.00093
                10260210
                36763335
                b6507238-cc28-4e9d-b53d-012ee4f54949
                © 2022 The Author(s)

                Open Access. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium for non-commercial purposes, provided the original author and source are credited, a link to the CC License is provided, and changes – if any – are indicated.

                History
                : 24 May 2022
                : 31 October 2022
                : 19 December 2022
                : 25 December 2022
                Page count
                Figures: 1, Tables: 1, Equations: 0, References: 57, Pages: 00
                Funding
                Funded by: Fourth Military Medical University
                Award ID: 2021JSTS30
                Funded by: Monash University
                Funded by: National Health and Medical Research Council
                Award ID: APP1117188
                Funded by: Australian Research Council (ARC)
                Funded by: Australian Defense Science and Technology (DST)
                Funded by: Department of Industry, Innovation and Science (DIIS)
                Funded by: David Winston Turner Endowment Fund

                Evolutionary Biology,Medicine,Psychology,Educational research & Statistics,Social & Behavioral Sciences
                problematic smartphone use,Big Five personality traits,network analysis

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