I love the package. Thank you Yves and others for developing it.
I was wondering whether you have interest/plans to extend lavaaan to allow moderated nonlinear factor analysis? This would be particularly helpful for testing measurement invariance across many different factors simultaneously, including continuous factors. The regularization approach proposed by Bauer et al. (2020) seems to be a promising approach for testing for measurement invariance across multiple factors.
Bauer, D. J., Belzak, W. C. M., & Cole, V. T. (2020). Simplifying the assessment of measurement invariance over multiple background cariables: Using regularized moderated nonlinear factor analysis to detect differential item functioning. Structural Equation Modeling, 27(1), 43–55. https://doi.org/10.1080/10705511.2019.1642754
Bauer, D. J. (2017). A more general model for testing measurement invariance and differential item functioning. Psychological Methods, 22(3), 507–526. https://doi.org/10.1037/met0000077
I love the package. Thank you Yves and others for developing it.
I was wondering whether you have interest/plans to extend lavaaan to allow moderated nonlinear factor analysis? This would be particularly helpful for testing measurement invariance across many different factors simultaneously, including continuous factors. The regularization approach proposed by Bauer et al. (2020) seems to be a promising approach for testing for measurement invariance across multiple factors.
Bauer, D. J., Belzak, W. C. M., & Cole, V. T. (2020). Simplifying the assessment of measurement invariance over multiple background cariables: Using regularized moderated nonlinear factor analysis to detect differential item functioning. Structural Equation Modeling, 27(1), 43–55. https://doi.org/10.1080/10705511.2019.1642754
Bauer, D. J. (2017). A more general model for testing measurement invariance and differential item functioning. Psychological Methods, 22(3), 507–526. https://doi.org/10.1037/met0000077