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      Smartphones Hinder Prospective Memory in Users With High Levels of Smartphone Dependency


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          Prospective memory (PM) has rarely been investigated in the context of smartphones. We embedded a PM task in an online, smartphone-based survey that participants were asked to take on their smartphone, and we investigated PM performance in relation to self-report on strategies used, smartphone distractions, and smartphone dependency measures. Of the 478 participants who accepted the survey job through the Amazon Mechanical Turk platform, 295 total participants were included in the study as they met full inclusionary criteria, passed the PM attention check cue, and had complete data. Our sample was aged 21–71 years old ( M = 38.03; SD = 10.90) and was 62.5% male. Measures included a PM task that required participants to respond “N/A” to a question presented later in the survey and two questionnaires on smartphone dependency. The true purpose of the study was not disclosed in the PM portion of the survey to avoid ceiling effects, which are common in PM research. After completion of this portion and debriefing of participants, we asked what PM strategies they had used (e.g., attributing importance to the task and using reminders) and whether smartphone-based and self-initiated distractions had occurred during the survey. Overall, a third of the participants were successful on the smartphone PM task. We found that higher likelihood of PM success was predicted by (a) higher self-reported importance of the task; (b) lower use of external reminders; and (c) lower levels of smartphone dependency. Higher likelihood of PM success was also associated with fewer reported smartphone- and self-initiated distractions. This association was not influenced by levels of smartphone dependency. Findings suggest that smartphones are a hindrance to PM in those with significant smartphone dependency and in those who engage in smartphone-related distractions. Smartphones may induce competing social motives, possibly rendering some traditional PM strategies inefficient. Future research could decrease this social competition through mindfulness applications: In our sample, insight into use proved to be a potential protective factor against the negative effects that smartphone dependency and distractions have on PM performance.

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          Using Multivariate Statistics

          After the Introduction Chapter, the second Chapter gives a guide to the multivariate techniques that are covered in this book and palces them in context with the more familiare univeriate and bivariate statistics where possible. Included in this chapter is a flow chart that organizes statistical techniques on the basis of the major research questions asked. Chapter three provides a brief review of univariate and bivariate statistical techniques for those who are interested. Chapter four deals with the assumptions an limitations of mulitvariate statistical methods. Assessment and violation of assumptions are discussed, along with alternatives for dealing with violations when they occur. This chapter is also meant to be referred to often, and the reader ist guided back to it frequently in Chapters five through sixteen an eighteen (online). Chapters five through sixteen and eighteen (online) cover specific multivariate techniques. They include descriptive, conceptual sections as well as a guided tour through a real-world data set for which the analysis is apporopriate. The tour includes an example of a Results section describing the outcome of the statistical analysis apporopriate for submissions to a professional journal. Each technique chapter includes a comparision of cumputer programs. Chapter seventeen is an attempt to integrate univariate, bivariate, and multivariate statistics through the multivariate general linear model. The common elements underlying all the techniques are emphasized, rather than the differences among them. This Chapter is ment to pull together the material in the remainder of the book with a conceptual rather than pragmatic emphasis.
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            Modeling habitual and addictive smartphone behavior

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              The dark side of smartphone usage: Psychological traits, compulsive behavior and technostress


                Author and article information

                Technology, Mind, and Behavior
                American Psychological Association
                August 10, 2023
                : 4
                : 3
                [1]School of Graduate Psychology, Pacific University
                Author notes
                Action Editor: C. Shawn Green was the action editor for this article.
                Funding: This research received no external funding.
                Disclosures: The authors have no known conflicts of interest to disclose.
                Author contributions: Holly Elizabeth Phelps contributed in conceptualization, data curation, formal analysis, investigation, methodology, and writing–original draft. Claudia Jacova contributed in conceptualization, methodology, supervision, and writing–review and editing.
                Data availability: There have been no prior uses of these data. Data and study materials are publicly available to other researchers at https://osf.io/dqfxy/.
                Open Science Disclosures:

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

                [*] Claudia Jacova, School of Graduate Psychology, Pacific University, 222 Southeast 8th Avenue, Hillsboro, OR 97123, United States cjacova@pacificu.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/

                Education,Psychology,Vocational technology,Engineering,Clinical Psychology & Psychiatry
                nomophobia,prospective memory,smartphones,smartphone distractions,smartphone dependency


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