The Effect of Model Misspecification on Growth Mixture Model Class Enumeration

Daniel McNeish () and Jeffrey R. Harring
Additional contact information
Daniel McNeish: University of Maryland
Jeffrey R. Harring: University of Maryland

Journal of Classification, 2017, vol. 34, issue 2, No 4, 223-248

Abstract: Abstract Multiple criteria have been proposed to aid in deciding how many latent classes to extract in growth mixture models; however, studies are just beginning to investigate the performance of these criteria under non-ideal conditions. We review these previous studies and conduct a simulation study to address the performance of fit criteria under two previously uninvestigated assumption violations: (1) linearity of covariates and (2) proper specification of the growth factor covariance matrix. Results show that, provided that estimation is carried out with a large number of random starts and final stage optimizations, BIC and the bootstrap likelihood ratio test perform exceedingly well at identifying whether the data are homogenous or whether latent classes may be present, even with misspecifications present. Results were far less favorable when software default estimation choices were selected. We discuss implications to empirical studies and speculate on the relation between estimation choices and fit criteria perform.

Keywords: Growth mixture model; Class enumeration; Bootstrapped likelihood ratio test; Latent class; Misspecification (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s00357-017-9233-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jclass:v:34:y:2017:i:2:d:10.1007_s00357-017-9233-y

Ordering information: This journal article can be ordered from
http://www.springer. ... hods/journal/357/PS2

DOI: 10.1007/s00357-017-9233-y

Access Statistics for this article

Journal of Classification is currently edited by Douglas Steinley

More articles in Journal of Classification from Springer, The Classification Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().