Journal
STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL
Volume 24, Issue 2, Pages 230-245Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/10705511.2016.1252265
Keywords
AIC; BIC; bootstrap model selection rate; model comparison
Funding
- National Institute on Drug Abuse (NIDA) [DA-018673]
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Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indexes such as Akaike's information criterion (AIC) or Bayesian information criterion (BIC), and inference is done based on the selected best-fitting model. This practice does not account for the possibility that due to sampling variability, a different model might be selected as the preferred model in a new sample from the same population. A previous study illustrated a bootstrap approach to gauge this model selection uncertainty using 2 empirical examples. This study consists of a series of simulations to assess the utility of the proposed bootstrap approach in multigroup and mixture model comparisons. These simulations show that bootstrap selection rates can provide additional information over and above simply relying on the size of AIC and BIC differences in a given sample.
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