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A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use

Abstract

There is widespread public and academic interest in understanding the uses and effects of digital media. Scholars primarily use self-report measures of the quantity or duration of media use as proxies for more objective measures, but the validity of these self-reports remains unclear. Advancements in data collection techniques have produced a collection of studies indexing both self-reported and log-based measures. To assess the alignment between these measures, we conducted a pre-registered meta-analysis of this research. Based on 106 effect sizes, we found that self-reported media use correlates only moderately with logged measurements, that self-reports were rarely an accurate reflection of logged media use and that measures of problematic media use show an even weaker association with usage logs. These findings raise concerns about the validity of findings relying solely on self-reported measures of media use.

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Fig. 1: PRISMA flow diagram for the study inclusion process.
Fig. 2: Forest plot of effect sizes for studies included in the meta-analysis for the association between self-reported and logged measures of digital media use.
Fig. 3: Contour-enhanced funnel plots.
Fig. 4: Forest plot of effect sizes for studies included in the meta-analysis for the association between self-reported and logged problematic media use.
Fig. 5: Forest plot of effect sizes for studies included in the meta-analysis for the ratio of means between self-reported and logged measures of digital media use.

Data availability

The raw and processed data are available on the Open Science Framework website (https://osf.io/dhx48/). These data include all extracted effect sizes, study descriptives and descriptive statistics. In cases where raw data were provided by study authors, as with all included studies, we only provide the necessary descriptive statistics and effective sizes used to compute the summary statistics in the meta-analyses, but do not share these original authors’ data. The data have been assigned a unique identifier: https://doi.org/10.17605/OSF.IO/JS6YE.

Code availability

The code (written in the R statistical language) used to analyse the relevant data is provided on the Open Science Framework website (https://osf.io/dhx48/). All materials needed to reproduce the analyses are available at this link.

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Acknowledgements

The authors thank the researchers who made their data available for inclusion in this review and especially those who completed extra analyses to provide relevant data and statistics. Additionally, the authors thank those who shared our public calls for relevant data and the valuable input provided by the reviewers of this paper. The authors received no specific funding for this work.

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D.A.P. and B.I.D. conceived the study. D.A.P., B.I.D., C.J.R.S., J.T.F. and H.M. collected the data. D.A.P. analysed the data with input from D.S.Q. D.A.P., B.I.D., C.J.R.S., J.T.F. and H.M. wrote the first draft of the paper. All authors discussed the results and contributed to revision of the final manuscript.

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Correspondence to Douglas A. Parry.

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Extended data

Extended Data Fig. 1 Digital media usage post hoc moderator and subgroup analyses.

Note: k: number of separate effect sizes included for the moderator level; r = Pearson correlation coefficient; F values correspond to the Approximate Hotelling-Zhang with small sample correction omnibus tests for moderators with more than two levels; 95% CI corresponds to the r values for individual moderator levels; p corresponds to the F value for moderators or the subgroup analysis for individual moderator levels. *This analysis did not include the adolescent population group as only two effect sizes were available. This analysis did not include the other category as only a single effect size was available.

Extended Data Fig. 2 Reporting accuracy post hoc moderator and subgroup analyses.

Note: k: number of separate effect sizes included for the moderator level; R = response ratio; Exp(𝛽) = exponential transformation of metaregression coefficient from a model in which a categorical moderator with two levels was entered as a predictor. F values correspond to the Approximate Hotelling-Zhang with small sample correction omnibus tests for moderators with more than two levels; 95% CI corresponds to the r values for individual moderator levels; p corresponds to the F value for moderators or the subgroup analysis for individual moderator levels. *This analysis did not include the adolescent population category, the general population category and the unknown population category as only two, one, and three effect sizes were available, respectively.

Extended Data Fig. 3 Descriptive statistics for additional post hoc moderator analyses.

Note: k: number of included effect sizes. *: One study used both a built-in tool and a third-party tool.

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Parry, D.A., Davidson, B.I., Sewall, C.J.R. et al. A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use. Nat Hum Behav (2021). https://doi.org/10.1038/s41562-021-01117-5

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