Are smartphones really that bad? Improving the psychological measurement of technology-related behaviors
Open accessPublished version of a preprint
Abstract
Understanding how people use technology remains important, particularly when measuring the impact this might have on individuals and society. To date, research within psychological science often frames new technology as problematic with overwhelmingly negative consequences. However, this paper argues that the latest generation of psychometric tools, which aim to assess smartphone usage, are unable to capture technology related experiences or behaviors. As a result, many conclusions concerning the psychological impact of technology use remain unsound. Current assessments have also failed to keep pace with new methodological developments and these data-intensive approaches challenge the notion that smartphones and related technologies are inherently problematic. The field should now consider how it might re-position itself conceptually and methodologically given that many ‘addictive’ technologies have long since become intertwined with daily life.
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Reducing complex experiences to a single index of retrospective self-reported screen time does not correspond well with objective measures of time online; correlations between objective and retrospective screen time are estimated at about r = .20.
“Finally, reducing a complex and multi-dimensional set of experiences into a single index of retrospective self-reports of the amount of time that youth spend in front of screens does not correspond well with objective measures of time spent online (correlations between objectively measured and retrospectively reported screen time are estimated to be ~r = .20 (Ellis, 2019)).”
From Annual Research Review: Adolescent mental health in the digital age: facts, fears, and future directions. (2020) · cites this paper as Ellis, 2019
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Experimental — accuracy not yet measuredThere are persistent questions around the effectiveness of addiction-based measures of PSMU.
“There are also persistent questions around the effectiveness of addiction-based measures of PSMU.”
From A mixed-methods study of problematic social media use, attention dysregulation, and social media use motives (2023) · cites this paper as Ellis, 2019
In §2, the paper questions the validity of scales for problematic smartphone use, and §4.1 says existing smartphone ‘addiction’ scales do not correlate with expected checking behavior. These passages support persistent concerns about addiction-based measures, but they do not specifically evaluate measures of problematic social media use (PSMU). (LLM, conf=0.83)
Experimental — accuracy not yet measuredMuch research on PSMU/SMA is at the stage of initial screening research, being correlational, cross-sectional, self-report studies focusing on young adult university students.
“much of it is considered as being at the stage of initial screening research, being correlational, cross-sectional, self-report studies focusing on young adult university students (Carbonell & Panova, 2017; Ellis, 2019).”
From A mixed-methods study of problematic social media use, attention dysregulation, and social media use motives (2023) · cites this paper as Ellis, 2019
The Introduction (page unavailable) says psychological research on smartphone and related technology use has focused almost exclusively on correlational research based on people's reported experiences. Section 2, “Capturing Smartphone Behaviors from Self-Report” (page unavailable), likewise describes widespread reliance on self-report measures. The copy does not establish that this research is chiefly cross-sectional, at an initial-screening stage, or focused on young adult university students; nor does it make that full characterization specifically of PSMU/SMA research. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe construct of social media addiction lacks a firm grounding.
“This has led to a growing acknowledgement that the construct lacks a firm grounding”
From A mixed-methods study of problematic social media use, attention dysregulation, and social media use motives (2023) · cites this paper as Ellis, 2019
Section 4.1 (Conceptual; page not provided) says that framing problematic technology use as behavioral addiction is poorly defined and that a growing body of evidence favors moving away from that framework. Section 2 (Capturing Smartphone Behaviors from Self-Report; page not provided) says there is little evidence for constructs such as addiction. These passages support the concern about grounding, but they do not specifically assess the construct of social media addiction. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThese methods reduce recall bias inherent in retrospective self-reports and facilitate more accurate assessments of time allocation and mental health symptoms over the day.
“More specially, these methods have been shown to reduce the recall bias that is inherent in retrospective self-reports of experiences (which as detailed above is quite poor for estimates of time spent using technology; Ellis, 2019) and facilitate more accurate assessments of time allocation and mental health symptoms over the course of the day.”
From Annual Research Review: Adolescent mental health in the digital age: facts, fears, and future directions. (2020) · cites this paper as Ellis, 2019
The addiction framework has much potential to problematize normal behaviour without necessarily predicting problematic behaviours or outcomes.
“the addiction framework has much potential to problematize normal behaviour, without necessarily predicting problematic behaviours or outcomes (see Aarseth et al. 2016 for a discussion of this related to gaming, and Ellis 2019 in relation to smartphone use, and Carbonell & Panova for similar discussion of social media).”
From A mixed-methods study of problematic social media use, attention dysregulation, and social media use motives (2023) · cites this paper as Ellis 2019
Self-report measures can be particularly ill-fitting for gaining accurate data on people's technology-related use.
“evidence suggests that these can be particularly ill-fitting for the purposes of gaining accurate data on people’s technology-related use [27,37,38]”
From The Conceptual and Methodological Mayhem of “Screen Time” (2020) · cites this paper as [37]
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References 96 · Cited by 186
Research works printed but not linked: 3
- Baym, N. K., Zhang, Y. B., & Lin, M. C. (2004). Social interactions across media: Interpersonal communication on the internet, telephone and face-to-face. New Media & Society, 6(3), 299-318
- Belk, R. W. (2013). Extended self in a digital world. Journal of Consumer Research, 40(3), 477-500
- Cohen, A. A., & Lemish, D. (2003). Real time and recall measures of mobile phone use: Some methodological concerns and empirical applications. New Media & Society, 5(2), 167- 183
- Authors
- Ellis, D. A.
- Affiliations
- Lancaster University (GB)
- Corresponding author
- Ellis, David A.
- ORCID
- Ellis, David A.
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 97
- Pages
- 60-66
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W2922417943
- Semantic Scholar
- 05e5bd26957987a5b2fc83220036f12d3970633e
- Cited by
- 186
- References
- 96
- Citations by year
- 2026: 17 · 2025: 14 · 2024: 15 · 2023: 28 · 2022: 28 · 2021: 24 · 2020: 46 · 2019: 14
- FWCI
- 56.34
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Computers in Human Behavior (publishedVersion)
- Lancaster EPrints (Lancaster University) (acceptedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- Topics
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Social Sciences › Education › Child Development and Digital Technology
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- smartphone use, psychometric assessment, problematic technology use, measurement validity
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