Class Identification Efficacy in Piecewise GMM with Unknown Turning Points Academic Article

abstract

• 2018 Taylor & Francis Group, LLC. Piecewise GMM with unknown turning points is a new procedure to investigate heterogeneous subpopulations' growth trajectories consisting of distinct developmental phases. Unlike the conventional PGMM, which relies on theory or experiment design to specify turning points a priori, the new procedure allows for an optimal location of turning points based on data. The advantage of the procedure has gained increasing attention in educational and behavioral research, but a major challenging issue, class enumeration performance of the model, has not yet been investigated. The current simulation study compared the performance of PGMMs with unknown turning points in identifying the correct number of latent classes under both Bayesian and ML/EM estimation methods.

authors

• Luo, Wen

published proceedings

• JOURNAL OF EXPERIMENTAL EDUCATION

author list (cited authors)

• Ning, L., & Luo, W.

citation count

• 0

complete list of authors

• Ning, Ling||Luo, Wen

publication date

• April 2018

publisher

• Taylor & Francis  Publisher

published in

• Journal of Experimental Education  Journal

keywords

• Bayesian Estimation
• Ml/em Estimation
• Model-selection Indexes
• Piecewise Growth Mixture Model
• Turning Point

Digital Object Identifier (DOI)

• 10.1080/00220973.2017.1301354

start page

• 282

end page

• 307

volume

• 86

issue

• 2

URL

• http://dx.doi.org/10.1080/00220973.2017.1301354