Planned missing-data designs in experience-sampling research: Monte Carlo simulations of efficient designs for assessing within-person constructs

Paul J. Silvia*, Thomas R. Kwapil, Molly A. Walsh, Inez Myin-Germeys

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

48 Citations (Web of Science)


Experience-sampling research involves trade-offs between the number of questions asked per signal, the number of signals per day, and the number of days. By combining planned missing-data designs and multilevel latent variable modeling, we show how to reduce the items per signal without reducing the number of items. After illustrating different designs using real data, we present two Monte Carlo studies that explored the performance of planned missing-data designs across different within-person and between-person sample sizes and across different patterns of response rates. The missing-data designs yielded unbiased parameter estimates but slightly higher standard errors. With realistic sample sizes, even designs with extensive missingness performed well, so these methods are promising additions to an experience-sampler's toolbox.
Original languageEnglish
Pages (from-to)41-54
JournalBehavior Research Methods
Issue number1
Publication statusPublished - Mar 2014


  • Missing data
  • Experience-sampling methods
  • Efficient designs
  • Maximum likelihood
  • Ecological momentary assessment

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