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On the performance of Bayesian approaches in small samples : A comment on Smid, McNeish, Miocevic, and van de Schoot (2020). / Zitzmann, Steffen; Lüdtke, Oliver; Robitzsch, Alexander et al.

In: Structural Equation Modeling: A Multidisciplinary Journal, Vol. 28, No. 1, 02.01.2021, p. 40-50.

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Zitzmann, Steffen ; Lüdtke, Oliver ; Robitzsch, Alexander et al. / On the performance of Bayesian approaches in small samples : A comment on Smid, McNeish, Miocevic, and van de Schoot (2020). In: Structural Equation Modeling: A Multidisciplinary Journal. 2021 ; Vol. 28, No. 1. pp. 40-50.

BibTeX

@article{70dae1db0eda4501b6335b150c67ff37,
title = "On the performance of Bayesian approaches in small samples: A comment on Smid, McNeish, Miocevic, and van de Schoot (2020)",
abstract = "This journal recently published a systematic review of simulation studies on the performance of Bayesianapproaches for estimating latent variable models in small samples. The authors of this review high-lighted that Bayesian approaches can perform poorly (i.e., by exhibiting bias) when the prior distribu-tions are not thoughtfully constructed on the basis of previous knowledge. In this comment, wequestion whether the bias is the most important criterion when the sample size is small. We arguethat the variability is more important and should therefore not be ignored. Moreover, because one ofthe most important selling points of Bayesian approaches was not addressed in the article, we arguethat although somewhat biased, Bayesian approaches allow for more accurate estimates (i.e., a smallermean squared error) than Maximum Likelihood (ML) in small samples, and we show one such approachthat is more accurate than ML.",
keywords = "Bayesian estimation, Markov chain Monte Carlo, structural equation modeling, small sample",
author = "Steffen Zitzmann and Oliver L{\"u}dtke and Alexander Robitzsch and Martin Hecht",
year = "2021",
month = jan,
day = "2",
doi = "10.1080/10705511.2020.1752216",
language = "English",
volume = "28",
pages = "40--50",
journal = "Structural Equation Modeling: A Multidisciplinary Journal",
issn = "1070-5511",
publisher = "Psychology Press, Taylor & Francis Group",
number = "1",

}

RIS

TY - JOUR

T1 - On the performance of Bayesian approaches in small samples

T2 - A comment on Smid, McNeish, Miocevic, and van de Schoot (2020)

AU - Zitzmann, Steffen

AU - Lüdtke, Oliver

AU - Robitzsch, Alexander

AU - Hecht, Martin

PY - 2021/1/2

Y1 - 2021/1/2

N2 - This journal recently published a systematic review of simulation studies on the performance of Bayesianapproaches for estimating latent variable models in small samples. The authors of this review high-lighted that Bayesian approaches can perform poorly (i.e., by exhibiting bias) when the prior distribu-tions are not thoughtfully constructed on the basis of previous knowledge. In this comment, wequestion whether the bias is the most important criterion when the sample size is small. We arguethat the variability is more important and should therefore not be ignored. Moreover, because one ofthe most important selling points of Bayesian approaches was not addressed in the article, we arguethat although somewhat biased, Bayesian approaches allow for more accurate estimates (i.e., a smallermean squared error) than Maximum Likelihood (ML) in small samples, and we show one such approachthat is more accurate than ML.

AB - This journal recently published a systematic review of simulation studies on the performance of Bayesianapproaches for estimating latent variable models in small samples. The authors of this review high-lighted that Bayesian approaches can perform poorly (i.e., by exhibiting bias) when the prior distribu-tions are not thoughtfully constructed on the basis of previous knowledge. In this comment, wequestion whether the bias is the most important criterion when the sample size is small. We arguethat the variability is more important and should therefore not be ignored. Moreover, because one ofthe most important selling points of Bayesian approaches was not addressed in the article, we arguethat although somewhat biased, Bayesian approaches allow for more accurate estimates (i.e., a smallermean squared error) than Maximum Likelihood (ML) in small samples, and we show one such approachthat is more accurate than ML.

KW - Bayesian estimation

KW - Markov chain Monte Carlo

KW - structural equation modeling

KW - small sample

U2 - 10.1080/10705511.2020.1752216

DO - 10.1080/10705511.2020.1752216

M3 - Journal article

VL - 28

SP - 40

EP - 50

JO - Structural Equation Modeling: A Multidisciplinary Journal

JF - Structural Equation Modeling: A Multidisciplinary Journal

SN - 1070-5511

IS - 1

ER -

ID: 1388370