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|
|Subjects:||Educational tests & measurements, Statistics, Quantitative psychology|
|Keywords:||Class enumeration, Growth mixture models, Latent class analysis, Latent profile models, Model fit indices|
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