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Examination of Mixed-Effects Models with Nonparametrically Generated Data

Abstract Previous research has shown functional mixed-effects models and traditional mixed-effects models perform similarly when recovering mean and individual trajectories (Fine, Suk, & Grimm, 2019). However, Fine et al. (2019) showed traditional mixed-effects models were able to more accurately recover the underlying mean curves compared to functional mixed-effects models. That project generated data following a parametric structure. This paper extended previous work and aimed to compare nonlinear mixed-effects models and functional mixed-effects models on their ability to recover underlying trajectories which were generated from an inherently nonparametric process. This paper introduces readers to nonlinear mixed-effects models and functional... (more)
Created Date 2019
Contributor Fine, Kimberly L (Author) / Grimm, Kevin J (Advisor) / Edward, Mike (Committee member) / O'Rourke, Holly (Committee member) / McNeish, Dan (Committee member) / Arizona State University (Publisher)
Subject Quantitative psychology / Functional mixed-effects model / Growth modeling / Longitudinal data / Nonlinear mixed-effects model
Type Doctoral Dissertation
Extent 92 pages
Language English
Note Doctoral Dissertation Psychology 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis