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Using dynamic factor analysis to provide insights into data reliability in experience sampling studies.

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Area of Science:

  • Psychological Science
  • Methodology
  • Affective Science

Background:

  • Experience sampling methods (ESMs) are increasingly used to study daily affective states.
  • Ensuring the reliability of test scores from ESM data presents methodological challenges.
  • Limited research addresses individual differences in ESM data reliability.

Purpose of the Study:

  • To demonstrate dynamic factor analysis (DFA) for quantifying reliability in ESM contexts.
  • To evaluate the impact of unreliable test scores on study findings.
  • To identify individual characteristics associated with unreliable ESM test scores.

Main Methods:

  • 127 participants completed baseline measures and a 7-day ESM phase measuring positive and negative state affect.
  • Dynamic factor analysis (DFA) was employed to quantify score reliability.
  • Analyses examined associations between reliability, personality traits, and model misspecification.

Main Results:

  • While sample-level affect measures showed adequate reliability, up to one-third of participants had unreliable scores.
  • Low reliability was not significantly linked to personality traits but sometimes indicated model misspecification.
  • Substantive results remained similar when excluding participants with unreliable scores.

Conclusions:

  • Typical analyses of ESM data appear robust to individual differences in data structure and quality.
  • DFA is a valuable tool for assessing reliability in ESM studies.
  • Further research can augment DFA to better understand unreliable cases in ESM data.