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Abstract:
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Ecological momentary assessment and other modern data collection technologies facilitate research on both within-person and between-person variability of people's health outcomes and behaviors. For such intensively measured longitudinal data, Hedeker et al extended the regular two-level mixed-effects model to a two-level mixed-effects location scale (MELS) model to accommodate covariates' influence as well as random subject effects on both mean and variability of the outcome. However, there lacks existing standardized effect size measures for the MELS model. To fill this gap, our study extends Rights and Sterba's framework of R-squared measures for multilevel models, which are based on model-implied variances, to MELS models. Our proposed framework applies to two different specifications of the random location effects, namely, through covariate-influenced random intercepts and through random intercepts combined with random slopes of observation-level covariates. We also provide an R function, R2MELS, that outputs summary tables and visualization for values of our R-squared measures. These R-squared measures can help researchers who are using MELS models interpret their findings.
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