2018
DOI: 10.1002/sim.7627
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A 3‐level Bayesian mixed effects location scale model with an application to ecological momentary assessment data

Abstract: Ecological momentary assessment studies usually produce intensively measured longitudinal data with large numbers of observations per unit, and research interest is often centered around understanding the changes in variation of people's thoughts, emotions and behaviors. Hedeker et al developed a 2-level mixed effects location scale model that allows observed covariates as well as unobserved variables to influence both the mean and the within-subjects variance, for a 2-level data structure where observations a… Show more

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Cited by 34 publications
(15 citation statements)
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“…MELS models are a relatively recent development in the long history of different approaches to modeling heterogeneous variances (e.g., Aitkin, 1987;Bryk & Raudenbush, 1988;Culpepper, 2010;Goldstein, 2011;Harvey, 1976;Leonard, 1975;Lindley, 1971;Pinheiro & Bates, 2000;Raudenbush, 1988). Researchers have discussed the development of these models in statistical journals and texts (e.g., Goldstein, 2011;Hedeker, Mermelstein, & Demirtas, 2008;Lin, Mermelstein, & Hedeker, 2018a, 2018bWalters, Hoffman, & Templin, 2018) and applied these models (most often) in fields where the collection of intensive longitudinal data is more common (e.g., medicine; Pugach, Hedeker, Richmond, Sokolovsky, & Mermelstein, 2014). For example, Watts, Walters, Hoffman, and Templin (2016) examined whether time-invariant (Level 2 predictors, e.g., gender, age, Alzheimer's disease status) as well as time-varying predictors (Level 1 predictors, e.g., day monitor worn) were associated with individual differences in mean level (location side) as well as intraindividual variability (scale side) of physical activity.…”
mentioning
confidence: 99%
“…MELS models are a relatively recent development in the long history of different approaches to modeling heterogeneous variances (e.g., Aitkin, 1987;Bryk & Raudenbush, 1988;Culpepper, 2010;Goldstein, 2011;Harvey, 1976;Leonard, 1975;Lindley, 1971;Pinheiro & Bates, 2000;Raudenbush, 1988). Researchers have discussed the development of these models in statistical journals and texts (e.g., Goldstein, 2011;Hedeker, Mermelstein, & Demirtas, 2008;Lin, Mermelstein, & Hedeker, 2018a, 2018bWalters, Hoffman, & Templin, 2018) and applied these models (most often) in fields where the collection of intensive longitudinal data is more common (e.g., medicine; Pugach, Hedeker, Richmond, Sokolovsky, & Mermelstein, 2014). For example, Watts, Walters, Hoffman, and Templin (2016) examined whether time-invariant (Level 2 predictors, e.g., gender, age, Alzheimer's disease status) as well as time-varying predictors (Level 1 predictors, e.g., day monitor worn) were associated with individual differences in mean level (location side) as well as intraindividual variability (scale side) of physical activity.…”
mentioning
confidence: 99%
“…Because the data are collected during everyday life and in naturalistic environments, it becomes hard or even impossible to have complete control over the setting, and, therefore, it is not possible to rule out the role of confounding variables. Nevertheless, due to the implementation of novel statistical procedures, a balance between research necessities and clinical utility could be achieved [90]. If this were the case in the near future, EMAs and EMIs would undoubtedly transform the field of mental health, greatly contributing to the bridging of science and practice [91,92].…”
Section: Discussionmentioning
confidence: 99%
“…In the social and medical sciences, DHGLMs are also known as location-scale regression models (with 'location' indicating the mean, and 'scale' indicating the variance; e.g. Lin et al, 2018;Rast et al, 2012).…”
Section: |Individual Differences In Predictabilitymentioning
confidence: 99%