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      Associations between symptoms of problematic smartphone, Facebook, WhatsApp, and Instagram use: An item-level exploratory graph analysis perspective

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          Abstract

          Background and aims

          Studies have demonstrated associations between both problematic smartphone and social networks use with everyday life adversities. However, examination of associations between problematic smartphone use (PSU) and problematic use of specific social networking platforms, especially on item-level data, has received relatively little attention. Therefore, the aim of the current study was to explore how items of problematic smartphone, Facebook, WhatsApp, and Instagram use are associated.

          Methods

          949 German-speaking adults participated in a web survey study. The participants were queried about their socio-demographics as well as levels of problematic smartphone, Facebook, WhatsApp, and Instagram use. In addition to bivariate correlation analysis, exploratory graph analysis (EGA), a type of network analysis, was conducted.

          Results

          The results showed that while problematic Facebook and Instagram use seem to be distinct phenomena, problematic smartphone and WhatsApp use were heavily intertwined. Furthermore, the only cross-platform symptom observed was the extent of reported pain in wrists and neck due to digital technology use. The EGA network models showed very good stability in bootstrap analyses.

          Discussion and conclusions

          In general, the results of this study suggest that while Instagram and Facebook use may potentially constitute distinct problematic behaviors, problematic smartphone/WhatsApp use scales may be measuring highly similar or even the same construct.

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          Most cited references85

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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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                Author and article information

                Contributors
                Journal
                2006
                Journal of Behavioral Addictions
                J Behav Addict
                Akadémiai Kiadó (Budapest )
                2062-5871
                2063-5303
                13 August 2020
                Affiliations
                [1 ] deptDepartment of Molecular Psychology, Institute of Psychology and Education , Ulm University, Ulm, Germany
                [2 ] deptInstitute of Mathematics and Statistics , University of Tartu, Tartu, Estonia
                [3 ] deptDepartment of Psychology , University of Toledo, Toledo, OH, USA
                [4 ] deptDepartment of Psychiatry , University of Toledo, Toledo, OH, USA
                [5 ] deptDepartment of Psychology , University of North Carolina at Greensboro, Greensboro, NC, USA
                Author notes
                [* ]Corresponding author. dmroz@ 123456ut.ee
                Author information
                https://orcid.org/0000-0002-1612-2040
                Article
                10.1556/2006.2020.00036
                7d7c3b4f-9b73-4429-bc2e-97ec1f6032b3
                © 2020 The Author(s)

                Open Access statement. 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
                : 22 January 2020
                : 01 April 2020
                : 15 May 2020
                Page count
                Figures: 01, Tables: 04, References: 85, Pages: 12
                Categories
                Full-length Report

                Medicine,Psychology,Social & Behavioral Sciences,Clinical Psychology & Psychiatry
                WhatsApp,Instagram,smartphone use disorder,smartphone addiction,problematic smartphone use,Facebook

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