Planned Missing-data Designs in Experience-sampling Research: Monte Carlo Simulations of Efficient Designs for Assessing Within-person Constructs

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Thomas R. Kwapil, Associate Professor (Creator)
Paul Silvia, Professor (Creator)
Institution
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/

Abstract: 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.

Additional Information

Publication
Behavior Research Methods, 46(1), 41-54
Language: English
Date: 2014
Keywords
Missing data, Experience sampling methods, Efficient designs, Maximum likelihood, Ecological momentary assessment

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