The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure is Ignored: A Monte Carlo Study

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This article investigates whether the correct number of classes can still be retrieved when a higher level of nesting in multilevel growth mixture model (MGMM) is ignored.

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19 p.

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Chen, Qi; Luo, Wen; Palardy, Gregory J.; Glaman, Ryan & McEnturff, Amber March 30, 2017.

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This article is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Education to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 15 times , with 5 in the last month . More information about this article can be viewed below.

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This article investigates whether the correct number of classes can still be retrieved when a higher level of nesting in multilevel growth mixture model (MGMM) is ignored.

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19 p.

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  • SAGE Open, 2017. New York, NY: Springer

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  • Publication Title: SAGE Open
  • Volume: 7
  • Issue: 1
  • Pages: 1-19

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UNT Scholarly Works

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  • March 30, 2017

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  • June 20, 2017, 9:03 a.m.

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  • July 13, 2017, 2:06 p.m.

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Chen, Qi; Luo, Wen; Palardy, Gregory J.; Glaman, Ryan & McEnturff, Amber. The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure is Ignored: A Monte Carlo Study, article, March 30, 2017; New York, NY. (digital.library.unt.edu/ark:/67531/metadc980851/: accessed October 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Education.