Unconstrained Structural Equation Models of Latent Interactions: Contrasting Residual- and Mean-Centered Approaches

By Herbert W. Marsh, Zhonglin Wen, K. Hau, T. Little, James A. Bovaird, K. Widaman. 2007

124 citations recorded by the source corpus.

Subfields: Applied Psychology, Experimental and Cognitive Psychology, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/9b9dc92dffaa8df14b5ee14f3a9546c7270ab765

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In Applied Psychology

In Experimental and Cognitive Psychology

In Management Science and Operations Research

Citations in the corpus

Structural Equation Models of Latent Interactions: An Appropriate Standardized Solution and Its Scale-Free Properties (2010, methodology): “, x1x3, x2x4) be used as indicators of the latent product term Ÿ1Ÿ2 (refer to their general discussion for details of how to match when two latent variables Ÿ1 and Ÿ2 have different numbers of items; also see alternatives suggested by Little, Bovaird, & Widaman, 2006; Marsh et al., 2007).”
Structural Equation Models of Latent Interactions: Clarification of Orthogonalizing and Double-Mean-Centering Strategies (2010, methodology): “The purpose of this investigation is to compare a new (double-mean-centering) strategy to estimating latent interactions in structural equation models with the (single) mean-centering strategy (Marsh, Wen, & Hau, 2004, 2006) and the orthogonalizing strategy (Little, Bovaird, & Widaman, 2006; Marsh e”
Estimating Latent Variable Interactions with the Unconstrained Approach: A Comparison of Methods to Form Product Indicators for Large, Unequal Numbers of Items (2011, methodology): “In the unconstrained method, there is a high likelihood that the main effects variables will be correlated with the interaction product term, which could lead to inaccuracy of parameter estimates (Marsh et al., 2007).”
An Update and Extension to SEM Guidelines for Admnistrative and Social Science Research (2011, methodology): “Marsh et al. (2007) showed that researchers using this approach could dispense with special and difficult parameter constraints, allowing estimation of the continuous latent variable interaction model using any standard CBSEM package.”
How Emotional Labor and Ethical Leadership Affect Job Engagement for Chinese Public Servants (2014, methodology): “We turn now to the structural equation modeling and moderation analyses according to the following steps recommended by Marsh et al. (2007).”
Estimating and interpreting latent variable interactions: A tutorial for applying the latent moderated structural equations method (2014, methodology): “Several methods for estimating and interpreting interactions between latent variables are available (e.g. Kenny & Judd, 1984; Klein & Moosbrugger, 2000; Little, Bovaird, & Widaman, 2006; Marsh et al., 2007).”
Some Common Myths About Centering Predictor Variables in Moderated Multiple Regression and Polynomial Regression (2012, background): “Similarly, Marsh et al. (2007) and Moosbrugger et al. (2009) showed that multicollinearity can cause nonessential ill-conditioning in the input matrix (see previous), which can cause nonconvergence issues in MSEM.”
Attitudes Toward Online Social Connection and Self-Disclosure as Predictors of Facebook Communication and Relational Closeness (2011, methodology): “…residuals were then combined into three parcels such that each interaction-term parcel contains only one instance of each of the first-order parcels (see Marsh et al., 2007), resulting in indicators that are entirely orthogonal to the first-order indicators (e.g., Soliz & Harwood, 2006).”
Knowledge Internalization and Product Development in Family Firms: When Relational and Affective Factors Matter (2016, unknown): “To test the moderation effect of generation in control, we followed the “mean-centered unconstrained approach for estimating interaction effects” from Marsh, Wen, and Hau (2004) and Marsh et al. (2007).9 Moderation was also tested through an ordinary least squares (OLS) analysis in STATA 12.”