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A study on the performance of three methods of estimation in SEM under conditions of misspecification and small sample sizes
Journal of the Korean Data & Information Science Society 2017;28:1153-65
Published online September 30, 2017
© 2017 Korean Data & Information Science Society.

Dong Gi Seo1 · Sunho Jung2

1Department of Psychology, Hallym University
2School of Management, Kyung Hee University
Correspondence to: Sunho Jung
Associate professor, School of Management, Kyung Hee University, Seoul 02447, Korea. E-mail: sunho.jung@khu.ac.kr
Received June 9, 2017; Revised September 20, 2017; Accepted September 21, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Structural equation modeling (SEM) is a basic tool for testing theories in a variety of disciplines. A maximum likelihood (ML) method for parameter estimation is by far the most widely used in SEM. Alternatively, two-stage least squares (2SLS) estimator has been proposed as a more robust procedure to address model misspecification. A regularized extension of 2SLS, two-stage ridge least squares (2SRLS) has recently been introduced as an alternative to ML to effectively handle the small-sample-size issue. However, it is unclear whether and when misspecification and small sample sizes may pose problems in theory testing with 2SLS, 2SRLS, and ML. The purpose of this article is to evaluate the three estimation methods in terms of inferences errors as well as parameter recovery under two experimental conditions. We find that: 1) when the model is misspecified, 2SRLS tends to recover parameters better than the other two estimation methods; 2) Regardless of specification errors, 2SRLS produces small or relatively acceptable Type II error rates for the small sample sizes.
Keywords : Maximum likelihood estimation, misspecification, small sample sizes, structural equation models, two-stage least squares, two-stage ridge least squares