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ERIC Number: EJ1167891
Record Type: Journal
Publication Date: 2018
Pages: 26
Abstractor: As Provided
ISSN: ISSN-0022-0973
Class Identification Efficacy in Piecewise GMM with Unknown Turning Points
Ning, Ling; Luo, Wen
Journal of Experimental Education, v86 n2 p282-307 2018
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.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A