A Latent Cluster-Mean Approach to the Contextual Effects Model With Missing Data

By Yongyun Shin, Stephen Raudenbush. 2010

86 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/76d94f2c7edbec845a0be212e9081e954e0d4ac7

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Citations in the corpus

Alternative Methods for Assessing Mediation in Multilevel Data: The Advantages of Multilevel SEM (2011, methodology): “…used to separate Level 1 effects into Within and Between components is to group mean center the Level 1 predictor and introduce the cluster mean as a Level 2 predictor (Hedeker & Gibbons, 2006; Kreft & de Leeuw, 1998; Raudenbush & Bryk, 2002; Shin & Raudenbush, 2010; Snijders & Bosker, 1999).”
Classroom Climate and Contextual Effects: Conceptual and Methodological Issues in the Evaluation of Group-Level Effects (2012, background): “However, the conceptual framework presented here is also widely applicable to organizational, family, sociology, and medical research (eg, Bliese, 2000 ; Bliese, Chan, & Ployhart, 2007; Croon & van Veldhoven, 2007; Iverson, 1991 ; Kozlowski & Klein, 2000 ; LaHuis & Ferguson, 2009 ; Shin & Rauden”
Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data* (2014, background): “This can be corrected for by shrinking them back toward the grand mean, in a similar way to the residuals, through Equation 13 (see Grilli and Rampichini 2011; Shin and Raudenbush 2010).”
Multiple imputation as a flexible tool for missing data handling in clinical research. (2017, background): “…with incomplete data is often limited because software packages may restrict missing data handling to outcome variables, and programs that can handle incomplete predictors typically limit this functionality to random intercept models (Asparouhov and Muthen, 2010; Shin & Raudenbush, 2007, 2010).”
The Role of Sample Cluster Means in Multilevel Models (2011, methodology): “In particular, the missing data method of Shin and Raudenbush (2010) can be used with linear random slope models with several covariates, while the structural equation approach (Lüdtke et al., 2008) can be applied to a wide range of linear and nonlinear models.”
Multiple Imputation of Missing Data for Multilevel Models (2018, methodology): “However, if missing values occur on X , it is currently not possible to include X in the analysis model in Mplus without dropping cases with missing X from the analysis (for a discussion, see also Shin & Raudenbush, 2010).”
The Big-Fish-Little-Pond Effect in Mathematics (2014, background): “However, Lüdtke et al. (2008, also see Shin & Raudenbush, 2010) identified a second source of bias in multilevel models—sampling error—that is particularly relevant for the analysis of contextual effects and is not addressed by conventional multilevel modeling techniques (e.g., Hox, 2002;…”
Handbook of International Large-Scale Assessment : Background, Technical Issues, and Methods of Data Analysis (2013, methodology): “Multiple imputation for continuous (normal) data has been developed by Shin and Raudenbush (2007, 2010) and is described in Chapter ??. Van Buuren (2011, 2012) presents a method based on fully conditionally specified models that can be used to impute missing values for both continuous and discrete…”
A Bayesian Approach for Estimating Multilevel Latent Contextual Models (2016, methodology): “…literature that the observed group mean might not provide a reliable measure of the unobserved true group mean (e.g., Asparouhov & Muthén, 2007; Croon & van Veldhoven, 2007; Grilli & Rampichini, 2011; Lüdtke et al., 2008; Pokropek, 2015; Preacher, Zyphur, & Zhang, 2010; Shin & Raudenbush, 2010).”