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Measuring affect dynamics: An empirical framework
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 providin...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918585/ https://www.ncbi.nlm.nih.gov/pubmed/35381955 http://dx.doi.org/10.3758/s13428-022-01829-0 |
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author | Pirla, Sergio Taquet, Maxime Quoidbach, Jordi |
author_facet | Pirla, Sergio Taquet, Maxime Quoidbach, Jordi |
author_sort | Pirla, Sergio |
collection | PubMed |
description | 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? SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13428-022-01829-0. |
format | Online Article Text |
id | pubmed-9918585 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-99185852023-02-12 Measuring affect dynamics: An empirical framework Pirla, Sergio Taquet, Maxime Quoidbach, Jordi Behav Res Methods Article 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? SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13428-022-01829-0. Springer US 2022-04-05 2023 /pmc/articles/PMC9918585/ /pubmed/35381955 http://dx.doi.org/10.3758/s13428-022-01829-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Pirla, Sergio Taquet, Maxime Quoidbach, Jordi Measuring affect dynamics: An empirical framework |
title | Measuring affect dynamics: An empirical framework |
title_full | Measuring affect dynamics: An empirical framework |
title_fullStr | Measuring affect dynamics: An empirical framework |
title_full_unstemmed | Measuring affect dynamics: An empirical framework |
title_short | Measuring affect dynamics: An empirical framework |
title_sort | measuring affect dynamics: an empirical framework |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918585/ https://www.ncbi.nlm.nih.gov/pubmed/35381955 http://dx.doi.org/10.3758/s13428-022-01829-0 |
work_keys_str_mv | AT pirlasergio measuringaffectdynamicsanempiricalframework AT taquetmaxime measuringaffectdynamicsanempiricalframework AT quoidbachjordi measuringaffectdynamicsanempiricalframework |