Multilevel Structural Equation Modeling

Rabe‐Hesketh, Sophia; Skrondal, Anders; Zheng, Xinqian

doi:10.1016/b978-044452044-9/50013-6

Cited by 66 publications

(50 citation statements)

References 25 publications

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“…A ML-SEM is considered doubly latent because it controls for error associated with manifest variables as well as error due to sampling clusters of individuals (Marsh et al, 2009). This type of model has been used previously to investigate the relation between school-level latent variables and student-level latent variables (Rabe-Hesketh et al, 2012). Although ML-SEM has been used to investigate multilevel mediation models (Preacher, Zyphur, & Zhang, 2010) and latent variables at both the within (i.e., student) and between (i.e., classroom) levels (Mehta & Neale, 2005), our study uses a simpler application of the ML-SEM framework.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, the latent variables in the model, which included the student-level school climate latent variables, necessitated the use of a structural equation model, while the nested nature of the data of students within classrooms required a multilevel approach (Raudenbush & Bryk, 2002). These two methods can be synthesized to create a multilevel structural equation modeling analysis design (Rabe-Hesketh, Skrondal, & Zheng, 2012). A ML-SEM is considered doubly latent because it controls for error associated with manifest variables as well as error due to sampling clusters of individuals (Marsh et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

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Examining classroom influences on student perceptions of school climate: The role of classroom management and exclusionary discipline strategies

Mitchell

Bradshaw

2013

Journal of School Psychology

145

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Examining classroom influences on student perceptions of school climate: The role of classroom management and exclusionary discipline strategies

Mitchell

Bradshaw

2013

Journal of School Psychology

145

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“…MCFA, which is a submodel of the multilevel SEM (MSEM) framework, was developed to address these issues (Ansari, Jedidi, & Dube, 2002;Ansari, Jedidi, & Jagpal, 2000;Chou, Bentler, & Pentz, 2000;Jedidi & Ansari, 2001;B. O. Muthén & Asparouhov, 2009;Rabe-Hesketh, Skrondal, & Pickles, 2004;Rabe-Hesketh, Skrondal, & Zheng, 2007;Raudenbush & Sampson, 1999;. MCFA can account for the covariation of observed variables in more than one level of the data hierarchy.…”

Section: Confirmatory Factor Analysis Modelmentioning

confidence: 99%

Ignoring Clustering in Confirmatory Factor Analysis: Some Consequences for Model Fit and Standardized Parameter Estimates

Pornprasertmanit

Lee

Preacher

2014

Multivariate Behavioral Research

101

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In many situations, researchers collect multilevel (clustered or nested) data yet analyze the data either ignoring the clustering (disaggregation) or averaging the micro-level units within each cluster and analyzing the aggregated data at the macro level (aggregation). In this study we investigate the effects of ignoring the nested nature of data in confirmatory factor analysis (CFA). The bias incurred by ignoring clustering is examined in terms of model fit and standardized parameter estimates, which are usually of interest to researchers who use CFA. We find that the disaggregation approach increases model misfit, especially when the intraclass correlation (ICC) is high, whereas the aggregation approach results in accurate detection of model misfit in the macro level. Standardized parameter estimates from the disaggregation and aggregation approaches are deviated toward the values of the macro- and micro-level standardized parameter estimates, respectively. The degree of deviation depends on ICC and cluster size, particularly for the aggregation method. The standard errors of standardized parameter estimates from the disaggregation approach depend on the macro-level item communalities. Those from the aggregation approach underestimate the standard errors in multilevel CFA (MCFA), especially when ICC is low. Thus, we conclude that MCFA or an alternative approach should be used if possible.

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“…For a more in-depth description of multilevel SEMs we refer the reader to Rabe-Hesketh, Skrondal, and Zheng (2012).…”

Section: Multilevel Semmentioning

confidence: 99%

A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes

Depaoli

Clifton

2015

Structural Equation Modeling: A Multidisciplinary Journal

179

181

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Multilevel Structural equation models are most often estimated from a frequentist framework via maximum likelihood. However, as shown in this article, frequentist results are not always accurate. Alternatively, one can apply a Bayesian approach using Markov chain Monte Carlo estimation methods. This simulation study compared estimation quality using Bayesian and frequentist approaches in the context of a multilevel latent covariate model. Continuous and dichotomous variables were examined because it is not yet known how different types of outcomes-most notably categorical-affect parameter recovery in this modeling context. Within the Bayesian estimation framework, the impact of diffuse, weakly informative, and informative prior distributions were compared. Findings indicated that Bayesian estimation may be used to overcome convergence problems and improve parameter estimate bias. Results highlight the differences in estimation quality between dichotomous and continuous variable models and the importance of prior distribution choice for cluster-level random effects.

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Multilevel Structural Equation Modeling

Cited by 66 publications

References 25 publications

Examining classroom influences on student perceptions of school climate: The role of classroom management and exclusionary discipline strategies

Examining classroom influences on student perceptions of school climate: The role of classroom management and exclusionary discipline strategies

Ignoring Clustering in Confirmatory Factor Analysis: Some Consequences for Model Fit and Standardized Parameter Estimates

A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes

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