Cited by 488Statistical Methods and Bayesian InferencePsychometric Methodologies and TestingStatistical Distribution Estimation and Applications

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Evaluation of the Bayesian and Maximum Likelihood Approaches in Analyzing Structural Equation Models with Small Sample Sizes

Lee, Sik-Yum, Song, Xin-Yuan·2004·Multivariate Behavioral Research

DOI 10.1207/s15327906mbr3904_4

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Abstract

The main objective of this article is to investigate the empirical performances of the Bayesian approach in analyzing structural equation models with small sample sizes. The traditional maximum likelihood (ML) is also included for comparison. In the context of a confirmatory factor analysis model and a structural equation model, simulation studies are conducted with the different magnitudes of parameters and sample sizes n = da, where d = 2, 3, 4 and 5, and a is the number of unknown parameters. The performances are evaluated in terms of the goodness-of-fit statistics, and various measures on the accuracy of the estimates. The conclusion is: for data that are normally distributed, the Bayesian approach can be used with small sample sizes, whilst ML cannot.

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Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Structural Equations with Latent Variables
Bollen, K. A. (1989) · Wiley series in probability and statistics · doi:10.1002/9781118619179 · Cited by 17,944
Structural Equations with Latent Variables.
Clogg, C. C., & Bollen, K. A. (1991) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2072165 · Cited by 17,475
Inference from iterative simulation using multiple sequences
Gelman, A., & Rubin, D. B. (1992) · Statistical Science · doi:10.1214/ss/1177011136 · Cited by 16,627
EQS : structural equations program manual
Bentler, P. M. (1989) · Medical Entomology and Zoology · Cited by 9,669
Markov Chain Monte Carlo in Practice
Gilks, W. R., Richardson, S. T., & Spiegelhalter, D. J. (1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,927
Untitled work
(1993) · Medical Entomology and Zoology · Cited by 7,690
The Calculation of Posterior Distributions by Data Augmentation
Tanner, M. A., & Wong, W. H. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478458 · Cited by 3,929
Aspects of Multivariate Statistical Theory
Gleser, L. J., & Muirhead, R. J. (1984) · Technometrics · doi:10.2307/1268117 · Cited by 3,369
Bayesian Analysis of Binary and Polychotomous Response Data
Albert, J. H., & Chib, S. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476321 · Cited by 3,160

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Boomsma, A. (1982). The robustness of LISREL against small sample sizes in factor analysis model. In K. G. Joreskog & H. Wold (Eds.), Systems under indirect observation: Causality, structure, prediction (Part I, pp. 149-173). Amsterdam: North-Holland.
Gelman A., 1996, Statistica Sinica, 6, 733
10.1037//0033-2909.112.2.351 · DOI
Lindley D. V., 1972, Series B, 34, 1

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Authors: Lee, S.-Y., & Song, X.-Y.
Venue: Multivariate Behavioral Research
Publisher: Informa UK Limited
Published: 2004-10
Volume: 39
Issue: 4
Pages: 653-686
ISSN: 0027-3171 (ISSN-L)
1532-7906
DOI: 10.1207/s15327906mbr3904_4
OpenAlex: W1999291824
Cited by: 488
References: 46
Citations by year: 2026: 20 · 2025: 33 · 2024: 29 · 2023: 23 · 2022: 35 · 2021: 37 · 2020: 44 · 2019: 38 · 2018: 32 · 2017: 41 · 2016: 24 · 2015: 43 · 2014: 22 · 2013: 21 · 2012: 14
FWCI: 2.71
Type: journal article
Language: en
Open access: closed
Locations: Multivariate Behavioral Research (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
Keywords: Bayesian structural equation modeling, maximum likelihood estimation, small sample size, confirmatory factor analysis, goodness-of-fit statistics, estimation accuracy, simulation study, normal data

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Evaluation of the Bayesian and Maximum Likelihood Approaches in Analyzing Structural Equation Models with Small Sample Sizes

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