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A gentle introduction to mixture modeling using physical fitness performance data
Morin, Alexandre J. S. Wang, John C. K.
Morin, Alexandre J. S.
Wang, John C. K.
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Abstract
This chapter provides a non-technical introduction to mixture modeling for sport and exercise sciences researchers. Although this method has been around for quite some time, it is still underutilized in sport and exercise research. The data set used for this illustration consists of a sample of 10,000 students who annually completed physical fitness tests for 7 years in Singapore. First, we illustrate latent profile analyses (LPA). Next, we illustrate how to include covariates in LPA and how to test the invariance of LPA solutions across groups, as well as over time using latent transition analyses. Following that, we illustrate the estimation of mixture regression models to identify subgroups of participants differing from one another at the levels of the relations among constructs. Finally, a growth mixture modeling example is shown to identify subgroups of participants following distinct longitudinal trajectories.
Keywords
person-centered analyses, mixture modeling, latent profile analyses, latent transition, covariates, mixture regression, growth mixture.
Date
2016
Type
Book chapter
Journal
Book
An introduction to intermediate and advanced statistical analyses for sport and exercise scientists
Volume
Issue
Page Range
183-209
Article Number
ACU Department
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Relation URI
DOI
Event URL
Open Access Status
Published as green open access
License
File Access
Open
Controlled
Controlled
Notes
This record includes the peer reviewed version of the following supplement to chapter: Morin, A.J.S., & Wang, J.C.K. (2016). A gentle introduction to mixture modeling using physical fitness data. In N. Ntoumanis, & N. Myers (Eds.), An Introduction to Intermediate and Advanced Statistical Analyses for Sport and Exercise Scientists (pp. 183-210). London, UK: Wiley, which has been published in final form at [DOI unknown]. This chapter may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.