Repeated measures regression mixture models

Kim, M. and Van Horn, M.L. and Jaki, T. and Vermunt, J. and Feaster, D. and Lichstein, K.L. and Taylor, D.J. and Riedel, B.W. and Bush, A.J. (2020) Repeated measures regression mixture models. Behavior Research Methods, 52 (2). pp. 591-606. ISSN 1554-351X

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Abstract

Regression mixture models are one increasingly utilized approach for developing theories about and exploring the heterogeneity of effects. In this study we aimed to extend the current use of regression mixtures to a repeated regression mixture method when repeated measures, such as diary-type and experience-sampling method, data are available. We hypothesized that additional information borrowed from the repeated measures would improve the model performance, in terms of class enumeration and accuracy of the parameter estimates. We specifically compared three types of model specifications in regression mixtures: (a) traditional single-outcome model; (b) repeated measures models with three, five, and seven measures; and (c) a single-outcome model with the average of seven repeated measures. The results showed that the repeated measures regression mixture models substantially outperformed the traditional and average single-outcome models in class enumeration, with less bias in the parameter estimates. For sample size, whereas prior recommendations have suggested that regression mixtures require samples of well over 1,000 participants, even for classes at a large distance from each other (classes with regression weights of.20 vs.70), the present repeated measures regression mixture models allow for samples as low as 200 participants with an increased number (i.e., seven) of repeated measures. We also demonstrate an application of the proposed repeated measures approach using data from the Sleep Research Project. Implications and limitations of the study are discussed.

Item Type:
Journal Article
Journal or Publication Title:
Behavior Research Methods
Additional Information:
The final publication is available at Springer via http://dx.doi.org/10.3758/s13428-019-01257-7
Subjects:
ID Code:
134721
Deposited By:
Deposited On:
22 Jun 2019 09:18
Refereed?:
Yes
Published?:
Published
Last Modified:
26 Sep 2020 06:03