Social media and well-being: A methodological perspective

Douglas A. Parry; Jacob T. Fisher; Hannah Mieczkowski; Craig J.R. Sewall; Brittany I. Davidson

doi:10.1016/j.copsyc.2021.11.005

Social media and well-being: A methodological perspective

Douglas A. Parry, Jacob T. Fisher, Hannah Mieczkowski, Craig J.R. Sewall, Brittany I. Davidson

Communication Science

Research output: Contribution to Journal › Review article › Academic › peer-review

Abstract

Due to the methodological challenges inherent in studying social media use (SMU), as well as the methodological choices that have shaped research into the effects of SMU on well-being, clear conclusions regarding relationships between SMU and well-being remain elusive. We provide a review of five methodological developments poised to provide increased understanding in this domain: (a) increased use of longitudinal and experimental designs; (b) the adoption of behavioural (rather than self-report) measures of SMU; (c) focusing on more nuanced aspects of SMU; (d) embracing effect heterogeneity; and (e) the use of formal modelling and machine learning. We focus on how these advances stand to bring us closer to understanding relations between SMU and well-being, as well as the challenges associated with these developments.

Original language	English
Article number	101285
Pages (from-to)	1-6
Number of pages	6
Journal	Current Opinion in Psychology
Volume	45
Early online date	6 Dec 2021
DOIs	https://doi.org/10.1016/j.copsyc.2021.11.005
Publication status	Published - Jun 2022

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. C.J.R.S. receives research support from the National Institute of Mental Health ( T32 MH 018951 ).

Funders	Funder number
National Institute of Mental Health	T32MH018951

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title = "Social media and well-being: A methodological perspective",

abstract = "Due to the methodological challenges inherent in studying social media use (SMU), as well as the methodological choices that have shaped research into the effects of SMU on well-being, clear conclusions regarding relationships between SMU and well-being remain elusive. We provide a review of five methodological developments poised to provide increased understanding in this domain: (a) increased use of longitudinal and experimental designs; (b) the adoption of behavioural (rather than self-report) measures of SMU; (c) focusing on more nuanced aspects of SMU; (d) embracing effect heterogeneity; and (e) the use of formal modelling and machine learning. We focus on how these advances stand to bring us closer to understanding relations between SMU and well-being, as well as the challenges associated with these developments.",

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AU - Parry, Douglas A.

AU - Fisher, Jacob T.

AU - Mieczkowski, Hannah

AU - Sewall, Craig J.R.

AU - Davidson, Brittany I.

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N2 - Due to the methodological challenges inherent in studying social media use (SMU), as well as the methodological choices that have shaped research into the effects of SMU on well-being, clear conclusions regarding relationships between SMU and well-being remain elusive. We provide a review of five methodological developments poised to provide increased understanding in this domain: (a) increased use of longitudinal and experimental designs; (b) the adoption of behavioural (rather than self-report) measures of SMU; (c) focusing on more nuanced aspects of SMU; (d) embracing effect heterogeneity; and (e) the use of formal modelling and machine learning. We focus on how these advances stand to bring us closer to understanding relations between SMU and well-being, as well as the challenges associated with these developments.

AB - Due to the methodological challenges inherent in studying social media use (SMU), as well as the methodological choices that have shaped research into the effects of SMU on well-being, clear conclusions regarding relationships between SMU and well-being remain elusive. We provide a review of five methodological developments poised to provide increased understanding in this domain: (a) increased use of longitudinal and experimental designs; (b) the adoption of behavioural (rather than self-report) measures of SMU; (c) focusing on more nuanced aspects of SMU; (d) embracing effect heterogeneity; and (e) the use of formal modelling and machine learning. We focus on how these advances stand to bring us closer to understanding relations between SMU and well-being, as well as the challenges associated with these developments.

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Social media and well-being: A methodological perspective

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