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

By S. Depaoli, James P. Clifton. 2015

157 citations recorded by the source corpus.

Subfields: Health, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2ceb97998dc5ca4e3275ce1bddc3ce4311cb9f6b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health

1. Steffen Zitzmann
2. Alexander Robitzsch
3. Herbert W. Marsh
4. O. Lüdtke
5. Milica Miočević
6. R. van de Schoot
7. Daniel M. McNeish

In Management Science and Operations Research

1. Herbert W. Marsh
2. O. Lüdtke
3. Daniel M. McNeish
4. Alexander Robitzsch
5. Steffen Zitzmann
6. Milica Miočević
7. R. van de Schoot
8. Alexandre J. S. Morin

In Statistics and Probability

1. Milica Miočević
2. Daniel M. McNeish
3. R. van de Schoot
4. O. Lüdtke
5. Herbert W. Marsh
6. Alexander Robitzsch
7. Steffen Zitzmann
8. Francis L. Huang

Citations in the corpus

• Small Sample Size Solutions (2020, result): “Consistent with existing simulation literature (e.g., Depaoli & Clifton, 2015; Hox & Maas, 2001; Van de Schoot et al., 2015) we found that ML led to severe convergence problems and a large amount of negative variance parameters when sample sizes are small.”
• Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist (2017, unknown): “Further, some models (e.g., mixture or multilevel models) require Bayesian methods to improve convergence issues (Depaoli & Clifton, 2015; Skrondal & Rabe-Hesketh, 2012), aid in model identification (Kim, Suh, Kim, Albanese, & Langer, 2013), and produce more accurate parameter estimates (Depaoli,…”
• On Using Bayesian Methods to Address Small Sample Problems (2016, background): “For instance, a researcher with small sample growth data might not locate Depaoli and Clifton (2015) or, if they do locate this study, they might judge it to be unrelated because the title (“A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes”)…”
• A Bayesian Approach for Estimating Multilevel Latent Contextual Models (2016, methodology): “…Gelman & Hill, 2007; Hamaker & Klugkist, 2011; Zhang, Hamagami, Wang, Nesselroade, & Grimm, 2009), SEM (e.g., Arminger & Muthén, 1998; Kaplan & Depaoli, 2012; Lee, 2007; B. O. Muthén & Asparouhov, 2012; Song & Lee, 2009), and multilevel SEM (e.g., Ansari & Jedidi, 2000; Depaoli & Clifton, 2015).”
• Multilevel Mediation With Small Samples: A Cautionary Note on the Multilevel Structural Equation Modeling Framework (2017, methodology): “…software (e.g., Mplus) does not trigger these small sample advantages and researchers must set informative priors to benefit from the advantages provided by Bayesian methods (Depaoli & Clifton, 2015; Depaoli & van de Schoot, 2015; van de Schoot et al., 2015; van de Schoot et al., 2014).”