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Sem with small samples : two-step modeling and factor score regression versus Bayesian estimation with informative priors
Book Contribution - Chapter
Using a simulation study, we investigated U+2013 under varying sample sizes U+2013 the performance of two-step modeling, factor score regression, maximum likelihood estimation and Bayesian estimation with default and informative priors. We conclude that with small samples, all frequentist methods showed signs of breaking down (in terms of non-convergence, negative variances, extreme parameter estimates), as did the Bayesian condition with default priors (in terms of mode-switching behavior). When increasing the sample size is not an option, we recommend using Bayesian estimation with informative priors. However, results should be interpreted with caution, because of the large influence of the prior on the posterior with relatively small samples. When researchers prefer not to include prior information, two-step modeling or factor score regression are recommended, as those led to higher convergence rates without negative variances, more stable results across replications and less extreme parameter estimates than maximum likelihood estimation with small samples.
Book: Small sample size solutions : a guide for applied researchers and practitioners
Series: European Association of Methodology Series
Pages: 239 - 254