fit_modsem_da

Calculates chi-sq test and p-value, as well as RMSEA for
the LMS and QML models. Note that the Chi-Square based fit measures should be calculated
for the baseline model, i.e., the model without the interaction effect

Estimation of interaction (i.e., moderation) effects between latent variables
in structural equation models (SEM).
The supported methods are:
The constrained approach (Algina & Moulder, 2001).
The unconstrained approach (Marsh et al., 2004).
The residual centering approach (Little et al., 2006).
The double centering approach (Lin et al., 2010).
The latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000).
The quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007)
The constrained- unconstrained, residual- and double centering- approaches
are estimated via 'lavaan' (Rosseel, 2012), whilst the LMS- and QML- approaches
are estimated via 'modsem' it self. Alternatively model can be
estimated via 'Mplus' (Muthén & Muthén, 1998-2017).
References:
Algina, J., & Moulder, B. C. (2001).
<doi:10.1207/S15328007SEM0801_3>.
"A note on estimating the Jöreskog-Yang model for latent variable interaction using 'LISREL' 8.3."
Klein, A., & Moosbrugger, H. (2000).
<doi:10.1007/BF02296338>.
"Maximum likelihood estimation of latent interaction effects with the LMS method."
Klein, A. G., & Muthén, B. O. (2007).
<doi:10.1080/00273170701710205>.
"Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects."
Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010).
<doi:10.1080/10705511.2010.488999>.
"Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies."
Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006).
<doi:10.1207/s15328007sem1304_1>.
"On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables."
Marsh, H. W., Wen, Z., & Hau, K. T. (2004).
<doi:10.1037/1082-989X.9.3.275>.
"Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction."
Muthén, L.K. and Muthén, B.O. (1998-2017).
"'Mplus' User’s Guide. Eighth Edition."
<https://www.statmodel.com/>.
Rosseel Y (2012).
<doi:10.18637/jss.v048.i02>.
"'lavaan': An R Package for Structural Equation Modeling."

Kjell Solem Slupphaug

modsem

Latent Interaction (and Moderation) Analysis in Structural
Equation Models (SEM)

Mehmet Mehmetoglu

Matthias Mittner

fit_modsem_da function

<dl><dt>model</dt>
<dd>fitted model. Thereafter, you can use 'compare_fit()'
to assess the comparative fit of the models. If the interaction effect makes
the model better, and e.g., the RMSEA is good for the baseline model,
the interaction model likely has a good RMSEA as well.</dd>
<dt>chisq</dt>
<dd>should Chi-Square based fit-measures be calculated?</dd>
<dt>lav.fit</dt>
<dd>Should fit indices from the <code>lavaan</code> model used to optimize
the starting parameters be included (if available)? This is usually only approprioate
for linear models (i.e., no interaction effects), where the parameter estimates
for LMS and QML are equivalent to ML estimates from lavaan.</dd></dl>

Arguments

Fit measures for QML and LMS models — fit_modsem_da

<dl>

<dt>model</dt>
<dd>fitted model. Thereafter, you can use 'compare_fit()'
to assess the comparative fit of the models. If the interaction effect makes
the model better, and e.g., the RMSEA is good for the baseline model,
the interaction model likely has a good RMSEA as well.</dd>


<dt>chisq</dt>
<dd>should Chi-Square based fit-measures be calculated?</dd>


<dt>lav.fit</dt>
<dd>Should fit indices from the <code>lavaan</code> model used to optimize
the starting parameters be included (if available)? This is usually only approprioate
for linear models (i.e., no interaction effects), where the parameter estimates
for LMS and QML are equivalent to ML estimates from lavaan.</dd>

</dl>

fit_modsem_da: Fit measures for QML and LMS models

Description

Usage

Arguments