The DOI matches its Crossref record; 45 of the 46 references it cites were found in a registry or an index.
Evaluation of the Bayesian and Maximum Likelihood Approaches in Analyzing Structural Equation Models with Small Sample Sizes
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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“sample sizes equal to four times the number of parameters are needed [44] to achieve accurate parameter estimates and reliable goodness-of-fit tests under Bayesian estimation.”
In the citing paper
“The Bayesian approach was found to outperform the maximum likelihood approach in terms of accuracy of parameter estimates in factor analysis with small sample sizes (N < 200) [44].”
Citations
Cites
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
(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
Research works printed but not linked: 4
- 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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Structural Equation Modeling in Marketing: Some Practical Reminders
Chin, W. W., Peterson, R. A., & Brown, S. P. (2008) · The Journal of Marketing Theory and Practice · doi:10.2753/mtp1069-6679160402 · Cited by 1,044
A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research
van de Schoot, R., Kaplan, D. E., Denissen, J. J. A., Asendorpf, J. B., Neyer, F. J., & van Aken, M. A. G. (2013) · Child Development · doi:10.1111/cdev.12169 · Cited by 859
Guidelines for a graph‐theoretic implementation of structural equation modeling
Grace, J. B., Schoolmaster, D. R., Guntenspergen, G. R., Little, A., MITCHELL, B. R., Miller, K. M., & Schweiger, E. W. (2012) · Ecosphere · doi:10.1890/es12-00048.1 · Cited by 610
On Using Bayesian Methods to Address Small Sample Problems
McNeish, D. (2016) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2016.1186549 · Cited by 612
Homburg, C., Muller, M. J., & Klarmann, M. (2011) · Journal of Marketing · doi:10.1509/jmkg.75.2.55 · Cited by 538
Metadata
- 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
- 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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