Display options
Cite
Lubke GH, Campbell I, McArtor D, et al. Assessing Model Selection Uncertainty Using a Bootstrap Approach: An update. Struct Equ Modeling. 2016;24(2):230-245doi: 10.1080/10705511.2016.1252265.
Lubke, G. H., Campbell, I., McArtor, D., Miller, P., Luningham, J., & van den Berg, S. M. (2017). Assessing Model Selection Uncertainty Using a Bootstrap Approach: An update. Structural equation modeling : a multidisciplinary journal, 24(2), 230-245. https://doi.org/10.1080/10705511.2016.1252265
Lubke, Gitta H, et al. "Assessing Model Selection Uncertainty Using a Bootstrap Approach: An update." Structural equation modeling : a multidisciplinary journal vol. 24,2 (2017): 230-245. doi: https://doi.org/10.1080/10705511.2016.1252265
Lubke GH, Campbell I, McArtor D, Miller P, Luningham J, van den Berg SM. Assessing Model Selection Uncertainty Using a Bootstrap Approach: An update. Struct Equ Modeling. 2017;24(2):230-245. doi: 10.1080/10705511.2016.1252265. Epub 2016 Dec 05. PMID: 28652682; PMCID: PMC5482523.
Copy Download .nbib Format: NLM
Share it on

Struct Equ Modeling. 2017;24(2):230-245. doi: 10.1080/10705511.2016.1252265. Epub 2016 Dec 05.

Assessing Model Selection Uncertainty Using a Bootstrap Approach: An update.

Structural equation modeling : a multidisciplinary journal

Gitta H Lubke, Ian Campbell, Dan McArtor, Patrick Miller, Justin Luningham, Stéphanie M van den Berg

Affiliations

  1. Department of Psychology, University of Notre Dame.
  2. Faculty of Behavioral, Management and Social Sciences, University of Twente.

PMID: 28652682 PMCID: PMC5482523 DOI: 10.1080/10705511.2016.1252265

Abstract

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 indices such as AIC or 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 two empirical examples. The current study consists of a series of simulations to assess the utility of the proposed bootstrap approach in multi-group 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.

References

  1. J Math Psychol. 2000 Mar;44(1):92-107 - PubMed
  2. Multivariate Behav Res. 2008 Oct;43(4):592-620 - PubMed
  3. Psychol Bull. 1991 May;109(3):512-9 - PubMed
  4. Psychol Methods. 2012 Mar;17(1):1-14 - PubMed
  5. Psychol Methods. 2012 Jun;17(2):228-43 - PubMed
  6. Struct Equ Modeling. 2010 Oct 1;17(4):605-628 - PubMed
  7. J Am Stat Assoc. 2014 Jul 1;109(507):991-1007 - PubMed
  8. Public Underst Sci. 2017 Aug;26(6):650-670 - PubMed
  9. Multivariate Behav Res. 2007 Apr-Jun;42(2):261-81 - PubMed
  10. Multivariate Behav Res. 2013 Jan;48(1):28-56 - PubMed
  11. Multivariate Behav Res. 2006 Dec 1;41(4):499-532 - PubMed
  12. Struct Equ Modeling. 2016;23(4):479-490 - PubMed

Publication Types

Grant support