Structural equation models of latent interaction and quadratic effects.
By Herbert W. Marsh, Zhonglin Wen, K. Hau, Benjamin Nagengast. 2013
180 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/4ca1bff71ed192423be3fd875a64f5cfeb1b9baf
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Citations in the corpus
- A Nonlinear Structural Equation Mixture Modeling Approach for Nonnormally Distributed Latent Predictor Variables (2014, unknown): “…2001; Bollen, 1995; Jaccard & Wan, 1995; Jöreskog & Yang, 1996; Kelava & Brandt, 2009; Kenny & Judd, 1984; Little, Bovaird, & Widaman, 2006; Marsh et al., 2004, 2006; Ping, 1995), distribution analytic approaches (Klein & Moosbrugger, 2000; Klein & Muthen, 2007), Bayesian approaches (e.g.,…”
- Advanced Nonlinear Latent Variable Modeling: Distribution Analytic LMS and QML Estimators of Interaction and Quadratic Effects (2011, methodology): “…(Klein & Muthén, 2007), the Mplus implementation of LMS (Klein & Moosbrugger, 2000, cf. Muthén & Muthén, 1998–2007), and the extended unconstrained PI approach (Kelava, 2009; Kelava & Brandt, 2009; Moosbrugger et al., 2009; cf. Marsh et al., 2004, 2006), using default settings for estimation.”
- Accuracy of Parameter Estimates and Confidence Intervals in Moderated Mediation Models (2017, methodology): “Readers are referred to Petrescu (2013) for a review on using single indicators in SEM and Bandalos and Finney (2001) and Little, Rhemtulla, Gibson, and Schoemann (2013) for reviews on item parceling issues in SEM.”
- The Analysis of Mechanisms and Their Contingencies: PROCESS versus Structural Equation Modeling (2017, background): “The proper estimation of interactions between latent variables remains highly controversial, and there are many methods available that require various (and sometimes different) assumptions (see Marsh et al., 2013).”
- Integrating Moderation and Mediation (2016, methodology): “The unconstrained approach (Marsh et al., 2004, 2006) simplifies the approach by omitting the constraints after mean centering the indicator variables (i.e., allowing the factor loadings and error components of interaction terms to be freely estimated).”