On Using Bayesian Methods to Address Small Sample Problems

By Daniel M. McNeish. 2016

522 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a5bd573f2786589b85026c347b1cc7313164731c

How researchers see this paper

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

1. R. van de Schoot
2. Daniel M. McNeish
3. Milica Miočević
4. Abhi Bhattacharya
5. Valerie Good
6. H. Sardashti
7. J. Peloza
8. Francis L. Huang

Citations in the corpus

• Small Sample Size Solutions (2020, methodology): “…sample sizes indicated for ML. Bayesian estimation with weakly informative priors roughly corresponds to the REML column, and Bayesian estimation with strongly informative priors is typically appropriate with lower samples than suggested for REML with the Kenward–Roger correction (McNeish, 2016b).”
• Multilevel Mediation With Small Samples: A Cautionary Note on the Multilevel Structural Equation Modeling Framework (2017, methodology): “This strategy was employed in the only two Bayesian papers found in our review, although recent research has noted that highly diffuse priors are not a wise decision with few clusters (e.g., McNeish, 2016b).”
• Beyond Warm Glow: The Risk-Mitigating Effect of Corporate Social Responsibility (CSR) (2020, result): “Small sample inference proceeds in the same manner as if one had a large sample (McNeish 2016).”