Dissertation/Thesis Abstract

Using latent profile models and unstructured growth mixture models to assess the number of latent classes in growth mixture modeling
by Liu, Min, Ph.D., University of Maryland, College Park, 2011, 211; 3478878
Abstract (Summary)

Growth mixture modeling has gained much attention in applied and methodological social science research recently, but the selection of the number of latent classes for such models remains a challenging issue. This problem becomes more serious when one of the key assumptions of this model, proper model-specification is violated.

The current simulation study compared the performance of a linear growth mixture model in determining the correct number of latent classes against two less parametrically restricted options, a latent profile model and an unstructured growth mixture model. A variety of conditions were examined, both for properly and improperly specified models. Results indicate that prior to the application of linear growth mixture model, the unstructured growth mixture model is a promising way to identify the correct number of unobserved groups underlying the data by using most model fit indices across all the conditions investigated in this study.

Indexing (document details)
Advisor: Hancock, Gregory R.
Commitee: Harring, Jeffrey R., Jiao, Hong, Mislevy, Robert J., Smith, Paul J.
School: University of Maryland, College Park
Department: Measurement, Statistics and Evaluation
School Location: United States -- Maryland
Source: DAI-B 73/02, Dissertation Abstracts International
Subjects: Educational tests & measurements, Statistics, Quantitative psychology
Keywords: Class enumeration, Growth mixture models, Latent class analysis, Latent profile models, Model fit indices
Publication Number: 3478878
ISBN: 978-1-124-96805-6
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