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      Behavioral Functions Associated With Wanting to Reduce Internet Use


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          Someone might want to spend less time on an activity when there are other things they would rather be doing, but the activity fulfills some necessary function that prevents them from changing their behavior (e.g., going to work when they would prefer to stay home). Alternatively, a person might want to reduce time spent on a highly preferred or habitual activity because they find themselves unable to manage their behavior, as in overeating or chronic procrastination. This registered report compared internet use by people who wanted to reduce the amount of time they spend on the internet with internet use by people who did not. Eight hundred nineteen respondents completed a self-report measure of problematic internet use, the Young Diagnostic Questionnaire (YDQ), and an indirect functional assessment of behavioral factors that motivate internet use, the Preliminary Internet Consequences Questionnaire (ICQ-P). People who reported wanting to reduce internet use were more likely to have YDQ scores ≥5 than people who did not. They also had higher total ICQ-P scores and endorsed ICQ-P items related to negative reinforcement (i.e., escape from or avoidance of demands, social interaction, and thoughts or feelings) more than people who did not want to reduce their internet use. There were no group differences in endorsement of any positive reinforcement factors. Taken together, group differences support the theory that excessive unwanted internet use is a problem behavior maintained by negative reinforcement.

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            G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
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              A ‘components’ model of addiction within a biopsychosocial framework


                Author and article information

                Technology, Mind, and Behavior
                American Psychological Association
                April 24, 2023
                : 4
                : 2
                [1]Department of Psychology, California State University, East Bay
                Author notes
                Special Collection Editors: Nick Bowman, Douglas A. Gentile, C. Shawn Green, and Tracy Markle.
                Action Editor: Nicholas David Bowman was the action editor for this article.
                Funding: Support for this research was provided by a 2021–2022 Faculty Support Grant from the California State University East Bay Division of Academic Affairs. The funding source was not involved in the study design or the collection, analysis, or interpretation of data.
                Disclosures: The authors have no conflicts of interest to disclose.
                Author Contributions: Elizabeth G. E. Kyonka formalized hypotheses, designed the study, wrote first drafts of Stage 1 and Stage 2 reports, and supervised the contributions of other authors. All authors compiled relevant source material, developed the proposal, and wrote portions of the introduction and results. Analysis and writing occurred on the ancestral and unceded land of the Muwekma Ohlone tribe and other familial descendants of the Verona Band of Alameda County.
                Open Science Disclosures:

                The data are available at https://osf.io/kwf4e/.

                The experimental materials are available at https://osf.io/kwf4e/.

                The preregistered design is available at https://doi.org/10.17605/OSF.IO/UE8KG.

                [*] Elizabeth G. E. Kyonka, Department of Psychology, California State University, East Bay, 25800 Carlos Bee Boulevard, Hayward, CA 94542, United States liz.kyonka@csueastbay.edu
                Author information
                © 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.

                Self URI (journal-page): https://tmb.apaopen.org/
                Behavioral Addiction to Technology

                Education,Psychology,Vocational technology,Engineering,Clinical Psychology & Psychiatry
                internet consequences questionnaire,negative reinforcement,avoidance,positive reinforcement,functional behavioral assessment


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