Application of a Mixed-Effects Location Scale Model for Analysis of Ecological Momentary Assessment (EMA) Data

• Published in: Biometrics Vol. 64; no. 2; pp. 627 - 634
• Main Authors: Hedeker, Donald, Mermelstein, Robin J., Demirtas, Hakan
• Format: Journal Article
• Language: English
• Published: Malden, USA Blackwell Publishing Inc 01.06.2008; Blackwell Publishing; Blackwell Publishing Ltd
• Subjects: Adolescents; Affect; Bias; Biometric Practice; Biometrics; Biometry - methods; Complex variation; Computer Simulation; Covariance; Data analysis; Data Interpretation, Statistical; Ecological modeling; Ecology; Environmental assessment; Heteroscedasticity; Log-linear variance; Longitudinal data; Models, Statistical; Multilevel; Research Design; Research methodology; Scale modeling; Standard deviation; Statistical discrepancies; Statistical variance; Variance modeling
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/j.1541-0420.2007.00924.x

For longitudinal data, mixed models include random subject effects to indicate how subjects influence their responses over repeated assessments. The error variance and the variance of the random effects are usually considered to be homogeneous. These variance terms characterize the within-subjects (i.e., error variance) and between-subjects (i.e., random-effects variance) variation in the data. In studies using ecological momentary assessment (EMA), up to 30 or 40 observations are often obtained for each subject, and interest frequently centers around changes in the variances, both within and between subjects. In this article, we focus on an adolescent smoking study using EMA where interest is on characterizing changes in mood variation. We describe how covariates can influence the mood variances, and also extend the standard mixed 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. Additionally, we allow the location and scale random effects to be correlated. 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.