CoChat
Sign Up
Cited by 47

The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.

Experience sampling self-reports of social media use have comparable predictive validity to digital trace measures

Verbeij, Tim, Pouwels, J. Loes, Beyens, Ine, Valkenburg, Patti M.·2022·Scientific Reports
DOI 10.1038/s41598-022-11510-3PMC PMC9084269
Open this paper in CoChat — free account

Abstract

Research agrees that self-reported measures of time spent with social media (TSM) show poor convergent validity, because they correlate modestly with equivalent objective digital trace measures. This experience sampling study among 159 adolescents (12,617 self-reports) extends this work by examining the comparative predictive validity of self-reported and digital trace measures of TSM, that is, the extent to which self-reported and digital trace measures of TSM correspond in their effects on self-esteem, well-being, and friendship closeness. Using an N = 1 method of analysis, we investigated the correspondence on a between-person, within-person, and person-specific level. Although our results confirmed the poor convergent validity of self-reported TSM reported earlier, we found that self-reports of TSM had comparable predictive validity to digital trace measures on all three levels. Because comparative predictive validity of self-reported TSM is crucial for investigating social media effects, our results have important implications for future research using self-reported TSM.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-022-11510-3

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Distribution and overlap of the person-specific effects of time spent on social media (TSM) according to the self-reported and digital trace measure on self-esteem (top), well-being (middle), and friendship closeness (bottom). Note. Standardized effect sizes are displayed on the x-axis, the type of psychosocial functioning on the y-axis. The dots below the distribution plots represent the person-specific effects of each adolescent. The vertical solid and dashed black lines represent the overall within-person effects for the self-reported measure (solid) and the digital trace measure (dashed).
Figure 1 Distribution and overlap of the person-specific effects of time spent on social media (TSM) according to the self-reported and digital trace measure on self-esteem (top), well-being (middle), and friendship closeness (bottom). Note. Standardized effect sizes are displayed on the x-axis, the type of psychosocial functioning on the y-axis. The dots below the distribution plots represent the person-specific effects of each adolescent. The vertical solid and dashed black lines represent the overall within-person effects for the self-reported measure (solid) and the digital trace measure (dashed).CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 17 references

Research works printed but not linked: 23

  1. Preedy, V. R. & Watson, R. R. in Handbook of Disease Burdens and Quality of Life Measures (eds Victor R. Preedy & Ronald R. Watson) pp. 4301–4301 (Springer New York, 2010). · DOI
  2. Ryff, C. D. Happiness is everything, or is it? Explorations on the meaning of psychological well-Being. J. Pers. Soc. Psychol. 57, 1069–1081. https://doi.org/10.1037/0022-3514.57.6.1069 (1989). · DOI · Paper page
  3. Orth, U. & Robins, R. W. The development of self-esteem. Curr. Dir. Psychol. Sci. 23, 381–387. https://doi.org/10.1177/0963721414547414 (2014). · DOI · Paper page
  4. Liu, D., Baumeister, R. F., Yang, C.-C. & Hu, B. Digital communication media use and psychological well-being: a meta-analysis. J. Comput. Mediat. Commun. 24, 259–274. https://doi.org/10.1093/ccc/zmz013 (2019). · DOI
  5. Paez, D. et al. A longitudinal study of the effects of internet use on subjective well-being. Media Psychol. 23, 676–710. https://doi.org/10.1080/15213269.2019.1624177 (2020). · DOI
  6. Aalbers, G., McNally, R. J., Heeren, A., de Wit, S. & Fried, E. I. Social media and depression symptoms: a network perspective. J. Exp. Psychol. 148, 1454–1462. https://doi.org/10.1037/xge0000528 (2019). · DOI
  7. Johannes, N., Nguyen, T.-V., Weinstein, N. & Przybylski, A. K. Objective, subjective, and accurate reporting of social media use: no evidence that daily social media use correlates with personality traits, motivational states, or well-being. Technol. Mind Behav. https://doi.org/10.1037/tmb0000035 (2021). · DOI
  8. Valkenburg, P. M., Beyens, I., Pouwels, J. L., van Driel, I. I. & Keijsers, L. Social media and adolescents’ self-esteem: heading for a person-specific media effects paradigm. J. Commun. 71, 56–78. https://doi.org/10.1093/joc/jqaa/039 (2021). · DOI
  9. Pouwels, J. L., Valkenburg, P. M., Beyens, I., van Driel, I. I. & Keijsers, L. Social media use and friendship closeness in adolescents’ daily lives: An experience sampling study. Dev. Psychol. 57, 309–323. https://doi.org/10.1037/dev0001148 (2021). · DOI
  10. Valkenburg, P. M., Pouwels, J. L., Beyens, I., van Driel, I. I. & Keijsers, L. Adolescents’ experiences on social media and their self-esteem: a person-specific susceptibility perspective. Technol. Mind Behav. https://doi.org/10.1037/tmb0000037 (2021). · DOI
  11. Harter, S. & Whitesell, N. R. Beyond the debate: Why some adolescents report stable self-worth over time and situation, whereas others report changes in self-worth. J. Personal. 71, 1027–1058. https://doi.org/10.1111/1467-6494.7106006 (2003). · DOI
  12. Eid, M. & Diener, E. Global judgments of subjective well-being: Situational variability and long-term stability. Soc. Indic. Res. 65, 245–277. https://doi.org/10.1023/B:SOCI.0000003801.89195.bc (2004). · DOI · Paper page
  13. Armsden, G. C. & Greenberg, M. T. The inventory of parent and peer attachment: individual differences and their relationship to psychological well-being in adolescence. J. Youth Adolesc. 16, 427–454. https://doi.org/10.1007/BF02202939 (1987). · DOI · Paper page
  14. Asparouhov, T., Hamaker, E. L. & Muthén, B. Dynamic structural equation models. Struct. Equ. Model. 25, 359–388. https://doi.org/10.1080/10705511.2017.1406803 (2018). · DOI
  15. Deng, T. et al. Measuring smartphone usage and task switching with log tracking and self-reports. Mob. Media Commun. 7, 3–23. https://doi.org/10.1177/2050157918761491 (2019). · DOI · Paper page
  16. Geyer, K., Ellis, D. A., Shaw, H. & Davidson, B. I. Open-source smartphone app and tools for measuring, quantifying, and visualizing technology use. Behav. Res. Methods 54, 1–12. https://doi.org/10.3758/s13428-021-01585-7 (2022). · DOI
  17. Griffioen, N., Rooij, M. V., Lichtwarck-Aschoff, A. & Granic, I. Toward improved methods in social media research. Technol. Mind Behav. https://doi.org/10.1037/tmb0000005 (2020). · DOI
  18. Robins, R. W., Hendin, H. M. & Trzesniewski, K. H. Measuring global self-esteem: construct validation of a single-item measure and the Rosenberg self-esteem scale. Pers. Soc. Psychol. Bull. 27, 151–161. https://doi.org/10.1177/0146167201272002 (2001). · DOI · Paper page
  19. Lee, S. J. Online communication and adolescent social ties: Who benefits more from internet use?. J. Comput.-Mediat. Commun. 14, 509–531. https://doi.org/10.1111/j.1083-6101.2009.01451.x (2009). · DOI · Paper page
  20. Furman, W. & Buhrmester, D. Children’s perceptions of the personal relationships in their social networks. Dev. Psychol. 21, 1016–1024. https://doi.org/10.1037/0012-1649.21.6.1016 (1985). · DOI
  21. McNeish, D. & Hamaker, E. L. A primer on two-level dynamic structural equation models for intensive longitudinal data in Mplus. Psychol Methods 25, 610–635. https://doi.org/10.1037/met0000250 (2020). · DOI
  22. Kline, R. B. Principles and practice of structural equation modeling (Guilford Press, 2011).
  23. Allen, M. et al. Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Res. https://doi.org/10.12688/wellcomeopenres.15191.2 (2021). · DOI

Cited by

Sorted by citations

See all 47

Metadata

Authors
Verbeij, T., Pouwels, J. L., Beyens, I., & Valkenburg, P. M.
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2022-05-09
Volume
12
Issue
1
ISSN
2045-2322
DOI
10.1038/s41598-022-11510-3
PMC
PMC9084269
Cited by
47
References
17
Language
en
Licence
Funders
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Spinoza Prize)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Experience sampling self-reports of social media use have comparable predictive validity to digital trace measures. https://app.cochat.ai/papers/10.1038/s41598-022-11510-3
The paper
Verbeij, T., Pouwels, J. L., Beyens, I., & Valkenburg, P. M. (2022). Experience sampling self-reports of social media use have comparable predictive validity to digital trace measures. Scientific Reports. https://doi.org/10.1038/s41598-022-11510-3