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Modeling intraindividual variability as a predictor with intensive longitudinal data.

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Researchers explored intraindividual variability (IIV) prediction of outcomes. Bayesian and time-parceling methods improve modeling of intraindividual variance (IVAR) and standard deviation (ISD) compared to traditional regression, especially with measurement errors.

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Area of Science:

  • Psychology
  • Quantitative Psychology
  • Behavioral Science

Background:

  • Intraindividual variability (IIV) is crucial for predicting behavioral and health outcomes.
  • Traditional methods using observed intraindividual variance (OIVAR) and standard deviation (OISD) suffer from low reliability, particularly with limited data.
  • Measurement errors complicate the accurate modeling of IIV.

Purpose of the Study:

  • To analytically examine the statistical properties of OIVAR and OISD.
  • To compare the performance of regular regression with alternative modeling approaches for IIV predictors.
  • To identify reliable methods for modeling intraindividual variance (IVAR) and standard deviation (ISD) in the presence of measurement error.

Main Methods:

  • Analytical examination of the mean and variance of OIVAR and OISD.
  • Comparison of regular regression with time-parceling, single indicator latent variable, and Bayesian variability modeling.
  • Simulation studies to validate analytical findings and assess method performance.

Main Results:

  • Regular regression yields more accurate coefficients for IVAR but worse for ISD with increasing occasions and measurement error.
  • Time-parceling with bootstrapping and Bayesian variability modeling outperform regression for modeling IVAR.
  • Only Bayesian variability modeling performs well for modeling ISD when measurement errors are present.

Conclusions:

  • The reliability of OIVAR and OISD is a significant concern in psychological research.
  • Proposed time-parceling and Bayesian variability modeling approaches offer superior alternatives to standard regression for IIV analysis.
  • Bayesian variability modeling is recommended for accurately assessing ISD in the presence of measurement error.