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Measuring affect dynamics: An empirical framework.

Sergio Pirla1, Maxime Taquet2,3, Jordi Quoidbach4

  • 1Department of Economics and Business, Universitat Pompeu Fabra, Barcelona, Spain. sergio.pirla@upf.edu.

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Summary
This summary is machine-generated.

Researchers can optimize experience sampling studies for affect dynamics with 200 participants and 20 observations per person. Study designs should strategically space out measurements for accurate affect variability estimates.

Keywords:
Affect dynamicsAmbulatory assessmentExperience sampling method

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Area of Science:

  • Psychological Science
  • Methodology
  • Computational Social Science

Background:

  • Experience sampling studies link affect dynamics to well-being and health.
  • Heterogeneity in study designs hinders reproducibility and scientific advancement.

Purpose of the Study:

  • To introduce an empirically derived framework for designing powerful and efficient experience sampling studies.
  • To provide guidance on participant numbers, sampling frequency, and study duration for affect dynamics research.

Main Methods:

  • Analysis of a large dataset (N=7016) with over 50 affect reports per individual.
  • Evaluation of different sampling strategies (e.g., close vs. distant, random vs. fixed times) for affect dynamic measures.

Main Results:

  • A sample size of 200 participants with 20 observations each offers sufficient power for most affect dynamic measures.
  • Distant sampling designs improve accuracy for trait-independent affect variability, but differences are minor for temporally dependent measures.
  • Random sampling times are as effective as fixed schedules for all affect dynamics measures.

Conclusions:

  • The study provides evidence-based principles for optimizing experience sampling study design.
  • A companion R package and online calculator are available to aid researchers in study planning.
  • Tailoring sampling approaches to specific affect dynamic measures is crucial for robust findings.