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. 2012 Sep;27(3):560-76.
doi: 10.1037/a0026669. Epub 2012 Jan 23.

A Monte Carlo simulation study of the reliability of intraindividual variability

Ryne Estabrook  1 , Kevin J Grimm, Ryan P Bowles
Affiliations

A Monte Carlo simulation study of the reliability of intraindividual variability

Ryne Estabrook et al. Psychol Aging. 2012 Sep.

Abstract

Recent research has seen intraindividual variability become a useful technique to incorporate trial-to-trial variability into many types of psychological studies. Intraindividual variability, as measured by individual standard deviations (ISDs), has shown unique prediction to several types of positive and negative outcomes (Ram, Rabbit, Stollery, & Nesselroade, 2005). One unanswered question regarding measuring intraindividual variability is its reliability and the conditions under which optimal reliability is achieved. Monte Carlo simulation studies were conducted to determine the reliability of the ISD as compared with the intraindividual mean. The results indicate that ISDs generally have poor reliability and are sensitive to insufficient measurement occasions, poor test reliability, and unfavorable amounts and distributions of variability in the population. Secondary analysis of psychological data shows that use of individual standard deviations in unfavorable conditions leads to a marked reduction in statistical power, although careful adherence to underlying statistical assumptions allows their use as a basic research tool.

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Figures

Figure 1
Figure 1
Median reliability of intraindividual means for μISD=[.2, .4, .6, 1.0, 2.0] * σMEAN. For all curves, maximum reliability occurs in the minimum intraindividual variability condition (for μISD=.2 * σMEAN) and is indicated by a solid line. Number of occasions from 0 to 100; reliability from 0 to 1.
Figure 2
Figure 2
Median reliability of intraindividual standard deviations for μISD=[.2, .4, .6, 1.0, 2.0] * σMEAN. For all curves, maximum reliability occurs in the maximum intraindividual variability condition (for μISD=2.0 * σMEAN), and is indicated by the highest number of line breaks. Number of occasions from 0 to 100; reliability from 0 to 1.
Figure 3
Figure 3
Reliability of ISDs and estimated power in the Eizenman, et al (1997) logistic regression presented as functions of sample size.
Figure 4
Figure 4
Estimated logistic regression power for the Eizenman, et al (1997) study as a function of the reliability of ISDs.
See this image and copyright information in PMC

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