Blaze, Thomas J.
(2014)
ENUMERATING THE CORRECT NUMBER OF CLASSES IN A SEMIPARAMETRIC GROUP-BASED TRAJECTORY MODEL.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
The semiparametric group-based trajectory model (GBTM), a special case of the more general growth mixture model, has been and increasingly employed technique for modeling heterogeneous change over time. A benefit of the GBTM is the ability to uncover distinct classes in the population that are characterized by their developmental trajectories. The characteristics of the developmental trajectories are affected by the number of classes extracted during estimation which in turn can affect inference, future investigation, and treatment or intervention. Thus, it is important that the measure(s) being relied upon for class enumeration are as accurate as possible. Only a handful of over 20 measures for class enumeration have been assessed in the context of GBTM using Monte Carlo methods prompting the need for a more thorough investigation. The purpose of this study was to determine if there were differences in the studied enumeration measures (information criteria, likelihood ratio test derivatives, and entropy based statistics and classification indices) abilities to correctly identify a true number of latent classes and to determine the common extraction errors for select enumeration measures in the context of a GBTM. A Monte Carlo study was performed and data were generated for true 4-class censored normal and binary logit models. Manipulated factors were sample size, the number of repeated measures, class mixing proportions, percent missing, and separation among the classes. Data were analyzed using a classification and regression tree approach. The results demonstrated that there were differences in the enumeration measures abilities to correctly identify the true 4-class solution in both model types. Correct classification rates were highest when the separation among the classes was high, the class mixing proportions were either equal or moderately unequal, and the sample size was 800 or above. The information criteria had the most accurate classification rates while entropy statistics and classification indices had the least accurate classification rates. There were higher rates of under extraction errors overall but in certain conditions some of the enumeration measures showed a tendency to over extract classes. The Bayesian information criterion and the sample size adjusted Bayesian information criterion were the two measures recommended overall.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
22 May 2014 |
| Date Type: |
Publication |
| Defense Date: |
9 December 2013 |
| Approval Date: |
22 May 2014 |
| Submission Date: |
9 April 2014 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
206 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Education > Psychology in Education |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Information criteria
Trajectory Analysis
Class enumeration
Censored Normal Model
Logistic Regression |
| Date Deposited: |
22 May 2014 13:47 |
| Last Modified: |
19 Dec 2016 14:41 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/21158 |
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