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

Planned missing data designs allow researchers to increase the amount and quality of data collected in a single study. Unfortunately, the effect of planned missing data designs on power is not straightforward. Under certain conditions using a planned missing design will increase power, whereas in other situations using a planned missing design will decrease power. Thus, when designing a study utilizing planned missing data researchers need to perform a power analysis. In this article, we describe methods for power analysis and sample size determination for planned missing data designs using Monte Carlo simulations. We also describe a new, more efficient method of Monte Carlo power analysis, software that can be used in these approaches, and several examples of popular planned missing data designs.

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Planned missing data designs in psychological research.

Psychological Methods ◽

10.1037/1082-989x.11.4.323 ◽

2006 ◽

Vol 11 (4) ◽

pp. 323-343 ◽

Cited By ~ 238

Author(s):

John W. Graham ◽

Bonnie J. Taylor ◽

Allison E. Olchowski ◽

Patricio E. Cumsille

Keyword(s):

Missing Data ◽

Psychological Research ◽

Planned Missing Data ◽

Planned Missing

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Reflection on modern methods: planned missing data designs for epidemiological research

International Journal of Epidemiology ◽

10.1093/ije/dyaa042 ◽

2020 ◽

Vol 49 (5) ◽

pp. 1702-1711 ◽

Cited By ~ 1

Author(s):

Charlie Rioux ◽

Antoine Lewin ◽

Omolola A Odejimi ◽

Todd D Little

Keyword(s):

Missing Data ◽

Gold Standard ◽

Epidemiological Research ◽

Missing Data Treatments ◽

Planned Missing Data ◽

Missing Completely At Random ◽

Modern Methods ◽

Missing Items ◽

Data Design ◽

Planned Missing

Abstract Taking advantage of the ability of modern missing data treatments in epidemiological research (e.g. multiple imputation) to recover power while avoiding bias in the presence of data that is missing completely at random, planned missing data designs allow researchers to deliberately incorporate missing data into a research design. A planned missing data design may be done by randomly assigning participants to have missing items in a questionnaire (multiform design) or missing occasions of measurement in a longitudinal study (wave-missing design), or by administering an expensive gold-standard measure to a random subset of participants while the whole sample is administered a cheaper measure (two-method design). Although not common in epidemiology, these designs have been recommended for decades by methodologists for their benefits—notably that data collection costs are minimized and participant burden is reduced, which can increase validity. This paper describes the multiform, wave-missing and two-method designs, including their benefits, their impact on bias and power, and other factors that must be taken into consideration when implementing them in an epidemiological study design.

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