Using Bayesian SEM in Small Samples
Dan McNeish (UNC)
March 8 at 4 p.m.
Gentry 144
Is Bayes a Solution for Small Samples?
Abstract
In educational research, small sample data are extremely common, especially when data have a hierarchical structure. Recent meta-analyses have found that between 20% and 50% of studies are classified as having small samples. As barriers to software implementation continue to fall, Bayesian methods are becoming an increasingly popular method by which to accommodate small sample data and such a strategy is often suggested. Although true that Bayesian methods have advantages over frequentist methods with small sample data, these advantages are not acquired automatically. This talk discusses how typical applications of Bayesian methods in empirical studies are not sufficient to effectively capitalize on small sample advantages and can actually exacerbate small sample issues known to affect frequentist methods. The relevance of small sample methods for emerging methodological developments is discussed. Growth models and multilevel mediation are shown as examples.
For more information, contact: Betsy McCoach/MEA-Neag at betsy.mccoach@uconn.edu