Planned missing-data designs in experience-sampling research: Monte Carlo simulations of efficient designs for assessing within-person constructs
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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.
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- Authors
- Silvia, P. J., Kwapil, T. R., Walsh, M. A., & Myin-Germeys, I.
- Affiliations
- University of North Carolina at Greensboro (US)
- Corresponding author
- Silvia, Paul J.
- Venue
- Behavior Research Methods
- Published
- 2013-05-25
- Volume
- 46
- Issue
- 1
- Pages
- 41-54
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- PMC
- PMC3781177
- OpenAlex
- W2011681262
- Cited by
- 97
- References
- 53
- Citations by year
- 2026: 10 · 2025: 5 · 2024: 16 · 2023: 9 · 2022: 11 · 2021: 9 · 2020: 11 · 2019: 8 · 2018: 6 · 2017: 4 · 2015: 3 · 2014: 2 · 2013: 2
- FWCI
- 2.05
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Behavior Research Methods (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- NC Digital Online Collection of Knowledge and Scholarship (The University of North Carolina at Greensboro) (submittedVersion)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- Research Publications (Maastricht University) (submittedVersion)
- Funders
- National Institute of Mental Health (R15MH079374)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Keywords
- planned missing data, response rate, design efficiency, item reduction, within-person variability, between-person variation
- MeSH terms
- Data Interpretation, Statistical, Humans, Monte Carlo Method, Research Design, Likelihood Functions, Sample Size, Behavioral Research
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