Measuring affect dynamics: an empirical framework

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• dc.contributor.author Pirla López, Sergio Ventura
• dc.contributor.author Taquet, Maxime
• dc.contributor.author Quoidbach, Jordi
• dc.date.accessioned 2023-06-20T10:30:45Z
• dc.date.available 2023-06-20T10:30:45Z
• dc.date.issued 2023
• dc.description.abstract A fast-growing body of evidence from experience sampling studies suggests that affect dynamics are associated with well-being and health. But heterogeneity in experience sampling approaches impedes reproducibility and scientific progress. Leveraging a large dataset of 7016 individuals, each providing over 50 affect reports, we introduce an empirically derived framework to help researchers design well-powered and efficient experience sampling studies. Our research reveals three general principles. First, a sample of 200 participants and 20 observations per person yields sufficient power to detect medium-sized associations for most affect dynamic measures. Second, for trait- and time-independent variability measures of affect (e.g., SD), distant sampling study designs (i.e., a few daily measurements spread out over several weeks) lead to more accurate estimates than close sampling study designs (i.e., many daily measurements concentrated over a few days), although differences in accuracy across sampling methods were inconsistent and of little practical significance for temporally dependent affect dynamic measures (i.e., RMSSD, autocorrelation coefficient, TKEO, and PAC). Third, across all affect dynamics measures, sampling exclusively on specific days or time windows leads to little to no improvement over sampling at random times. Because the ideal sampling approach varies for each affect dynamics measure, we provide a companion R package, an online calculator (https://sergiopirla.shinyapps.io/powerADapp), and a series of benchmark effect sizes to help researchers address three fundamental hows of experience sampling: How many participants to recruit? How often to solicit them? And for how long?
• dc.format.mimetype application/pdf
• dc.identifier.citation Pirla S, Taquet M, Quoidbach J. Measuring affect dynamics: an empirical framework. Behav Res. 2023 Apr;55(1):285-300. DOI: 10.3758/s13428-022-01829-0
• dc.identifier.doi http://dx.doi.org/10.3758/s13428-022-01829-0
• dc.identifier.issn 1554-351X
• dc.identifier.uri http://hdl.handle.net/10230/57279
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.ispartof Behavior Research Methods. 2023 Apr;55(1):285-300
• dc.rights This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Affect dynamics
• dc.subject.keyword Experience sampling method
• dc.subject.keyword Ambulatory assessment
• dc.title Measuring affect dynamics: an empirical framework
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion