Display options
Cite
Maslowsky J, Jager J, Hemken D. Estimating and interpreting latent variable interactions: A tutorial for applying the latent moderated structural equations method. Int J Behav Dev. 2014;39(1):87-96doi: 10.1177/0165025414552301.
Maslowsky, J., Jager, J., & Hemken, D. (2015). Estimating and interpreting latent variable interactions: A tutorial for applying the latent moderated structural equations method. International journal of behavioral development, 39(1), 87-96. https://doi.org/10.1177/0165025414552301
Maslowsky, Julie, et al. "Estimating and interpreting latent variable interactions: A tutorial for applying the latent moderated structural equations method." International journal of behavioral development vol. 39,1 (2015): 87-96. doi: https://doi.org/10.1177/0165025414552301
Maslowsky J, Jager J, Hemken D. Estimating and interpreting latent variable interactions: A tutorial for applying the latent moderated structural equations method. Int J Behav Dev. 2015 Jan 01;39(1):87-96. doi: 10.1177/0165025414552301. Epub 2014 Oct 13. PMID: 26478643; PMCID: PMC4606468.
Copy Download .nbib Format: NLM
Share it on

Int J Behav Dev. 2015 Jan 01;39(1):87-96. doi: 10.1177/0165025414552301. Epub 2014 Oct 13.

Estimating and interpreting latent variable interactions: A tutorial for applying the latent moderated structural equations method.

International journal of behavioral development

Julie Maslowsky, Justin Jager, Douglas Hemken

Affiliations

  1. University of Texas at Austin, USA.
  2. Arizona State University, USA.
  3. University of Wisconsin, Madison, USA.

PMID: 26478643 PMCID: PMC4606468 DOI: 10.1177/0165025414552301

Abstract

Latent variables are common in psychological research. Research questions involving the interaction of two variables are likewise quite common. Methods for estimating and interpreting interactions between latent variables within a structural equation modeling framework have recently become available. The latent moderated structural equations (LMS) method is one that is built into Mplus software. The potential utility of this method is limited by the fact that the models do not produce traditional model fit indices, standardized coefficients, or effect sizes for the latent interaction, which renders model fitting and interpretation of the latent variable interaction difficult. This article compiles state-of-the-science techniques for assessing LMS model fit, obtaining standardized coefficients, and determining the size of the latent interaction effect in order to create a tutorial for new users of LMS models. The recommended sequence of model estimation and interpretation is demonstrated via a substantive example and a Monte Carlo simulation. Finally, extensions of this method are discussed, such as estimating quadratic effects of latent factors and interactions between latent slope and intercept factors, which hold significant potential for testing and advancing developmental theories.

Keywords: latent variables; methodology; structural equation modeling

References

  1. J Affect Disord. 1988 Nov-Dec;15(3):205-17 - PubMed
  2. Psychometrika. 2010 Jun;75(2):243-248 - PubMed
  3. Annu Rev Psychol. 2000;51:201-26 - PubMed
  4. Clin Child Fam Psychol Rev. 2006 Dec;9(3-4):201-20 - PubMed
  5. Multivariate Behav Res. 2012 Nov;47(6):840-876 - PubMed
  6. Psychol Methods. 2001 Sep;6(3):258-69 - PubMed
  7. Annu Rev Clin Psychol. 2005;1:31-65 - PubMed

Publication Types

Grant support