Modeling between and within-subject variances using mixed effects location scale models for intensive longitudinal data
Intensive longitudinal data are increasingly encountered in many research areas. For example, ecological momentary assessment and/or experience sampling methods are often used to study subjective experiences within changing environmental contexts. In these studies, up to 30 or 40 observations are usually obtained for each subject over a period of a week or so. Because there are so many measurements per subject, one can characterize a subject’s mean and variance and can specify models for both. In this presentation, we focus on an adolescent smoking study using ecological momentary assessment where interest is on characterizing changes in mood variation. We describe how covariates can influence the mood variances and also extend the statistical model by adding a subject-level random effect to the within-subject variance specification. This permits subjects to have influence on the mean, or location, and variability, or (square of the) scale, of their mood responses. Models for both continuous and ordinal outcomes are described and will be illustrated with examples. These mixed-effects location scale models have useful applications in many research areas where interest centers on the joint modeling of the mean and variance structure.
Donald Hedeker, PhD., is a Professor of Biostatistics in the Department of Health Studies at The University of Chicago. Previously, from 1993 to 2014, Don was a faculty member of the School of Public Health, University of Illinois at Chicago. He received his Ph.D. in Quantitative Psychology from The University of Chicago.
Don’s main expertise is in the development and use of advanced statistical methods for clustered and longitudinal data, with particular emphasis on mixed-effects models. He is the primary author of several freeware computer programs for mixed-effects analysis: MIXREG for normal-theory models, MIXOR for dichotomous and ordinal outcomes, MIXNO for nominal outcomes, and MIXPREG for counts. In 2008, these programs were restructured into the Supermix software program distributed by Scientific Software, Inc.
With Robert Gibbons, Don is the author of the text “Longitudinal Data Analysis,” published by Wiley in 2006. More recently, Don has developed methods for intensive longitudinal data, resulting in the freeware MIXREGLS program.
In 2000, Don was named a Fellow of the American Statistical Association, and he is an Associate Editor for Statistics in Medicine and Journal of Statistical Software.
For more information about Dr. Hedeker, please visit his website.