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. 2014 Mar;46(1):41-54.
doi: 10.3758/s13428-013-0353-y.

Planned Missing-Data Designs in Experience-Sampling Research: Monte Carlo Simulations of Efficient Designs for Assessing Within-Person Constructs

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Free PMC article

Planned Missing-Data Designs in Experience-Sampling Research: Monte Carlo Simulations of Efficient Designs for Assessing Within-Person Constructs

Paul J Silvia et al. Behav Res Methods. .
Free PMC article

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.

Figures

Figure 1
Figure 1
A multilevel model with a latent outcome.
Figure 2
Figure 2
Effects of planned missing data design and sample sizes on termination failures.
Figure 3
Figure 3
Effects of planned missing data design and sample sizes on the Level 1 CFA loading parameter bias.
Figure 4
Figure 4
Effects of planned missing data design and sample sizes on the Level 1 CFA loading standard error.
Figure 5
Figure 5
Depiction of the low, high, and uniform response rates used in the simulations.
Figure 6
Figure 6
Effects of planned missing data design and response rates on the Level 1 CFA loading standard error.

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