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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.
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 |
Source Type: | DISSERTATION |
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 |