The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Estimating and interpreting latent variable interactions: A tutorial for applying the latent moderated structural equations method
Open accessAbstract
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.
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Cites
Multiple regression: testing and interpreting interactions
(1992) · Choice Reviews Online · doi:10.5860/choice.29-3352 · Cited by 36,654
Alternative Ways of Assessing Model Fit
Browne, M. W., & Cudeck, R. (1992) · Sociological Methods & Research · doi:10.1177/0049124192021002005 · Cited by 27,200
Testing Structural Equation Models.
Mueller, C. W., Bollen, K. A., & Long, J. S. (1994) · Contemporary Sociology · doi:10.2307/2074955 · Cited by 14,120
Johnston, L. D., O'Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2009) · PsycEXTRA Dataset · doi:10.1037/e560352009-001 · Cited by 6,921
A scaled difference chi-square test statistic for moment structure analysis
Satorra, A., & Bentler, P. M. (2001) · Psychometrika · doi:10.1007/bf02296192 · Cited by 5,282
Moderation in Management Research: What, Why, When, and How
Dawson, J. F. (2014) · Journal of Business and Psychology · doi:10.1007/s10869-013-9308-7 · Cited by 3,021
Structural Equation Modeling: A Second Course
(2007) · Personnel Psychology · doi:10.1111/j.1744-6570.2007.00081_13.x · Cited by 2,825
Ensuring Positiveness of the Scaled Difference Chi-square Test Statistic
Satorra, A., & Bentler, P. M. (2010) · Psychometrika · doi:10.1007/s11336-009-9135-y · Cited by 2,695
Applications of Structural Equation Modeling in Psychological Research
MacCallum, R. C., & Austin, J. T. (2000) · Annual Review of Psychology · doi:10.1146/annurev.psych.51.1.201 · Cited by 2,623
Longitudinal Structural Equation Modeling
Little, T. D. (2013) · Cited by 2,604
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Shao, K., Pekrun, R., Marsh, H. W., & Loderer, K. (2020) · Learning and Instruction · doi:10.1016/j.learninstruc.2020.101356 · Cited by 250
Holzer, J., Lüftenegger, M., Korlat, S., Pelikan, E., Salmela-Aro, K., Spiel, C., & Schober, B. (2021) · AERA Open · doi:10.1177/23328584211003164 · Cited by 183
Controlling images: How awareness of group stereotypes affects Black women’s well-being.
Jerald, M. C., Cole, E. R., Ward, L. M., & Avery, L. R. (2017) · Journal of Counseling Psychology · doi:10.1037/cou0000233 · Cited by 153
Mabbe, E., Soenens, B., Vansteenkiste, M., & Van Leeuwen, K. (2016) · Journal of Personality · doi:10.1111/jopy.12166 · Cited by 113
McDaniel, B. T., & Radesky, J. S. (2020) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2019.0478 · Cited by 96
Metadata
- Authors
- Maslowsky, J., Jager, J., & Hemken, D.
- Venue
- International Journal of Behavioral Development
- Publisher
- SAGE Publications
- Published
- 2014-10-13
- Volume
- 39
- Issue
- 1
- Pages
- 87-96
- ISSN
- 0165-0254 (ISSN-L)
- 1464-0651
- OpenAlex
- W2161551996
- Cited by
- 659
- References
- 29
- FWCI
- 23.41
- Type
- journal article
- Language
- en
- Open access
- green
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