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http://dx.doi.org/10.7465/jkdi.2017.28.5.1153

A study on the performance of three methods of estimation in SEM under conditions of misspecification and small sample sizes  

Seo, Dong Gi (Department of Psychology, Hallym University)
Jung, Sunho (School of Management, Kyung Hee University)
Publication Information
Journal of the Korean Data and Information Science Society / v.28, no.5, 2017 , pp. 1153-1165 More about this Journal
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;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 Ansari, A., Kamel J. and Sharan J. (2000). A hierarchical Bayesian methodology for treating heterogeneity in structural equation models. Marketing Science, 19, 328-347.   DOI
2 Bagozzi, R. P. and Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40, 8-34.   DOI
3 Baron, R. M. and Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.   DOI
4 Bollen, K. A. (1989). Structural equations with latent variables, John Wiley & Sons, New York.
5 Bollen, K. A. (1996). An alternative two stage least squares (2SLS) estimator for latent variable equations. Psychometrika, 61, 109-121.   DOI
6 Bollen, K. A. and Bauer, D. J. (2004). Automating the selection of model-implied instrumental variables. Sociological Methods and Research, 32, 425-452.   DOI
7 Bollen, K. A., Kirby, J. B., Curran, P. J., Paxton, P. and Chen, F. (2007). Latent variable models under misspecification: Two-stage least squares (2SLS) and maximum likelihood (ML) estimators. Sociological Methods and Research, 36, 48-86.   DOI
8 Boomsma, A. and Hoogland, J. J. (2001). The robustness of LISREL modeling revisited. In Structural Equation Modeling: Present and Future (Cudeck, R., Du Toit, S. & Sorbom, D., eds), SSI Scientific Software, Chicago, 139-168.
9 Budaev, S. V. (2010). Using principal components and factor analysis in animal behaviour research: Caveats and guidelines. Ethology, 116, 472-480.   DOI
10 Chen, F., Bollen, K., Paxton, P., Curran, P. J. and Kirby, J. (2001). Improper solutions in structural equation models: Causes, consequences, and strategies. Sociological Methods and Research, 29, 468-508.   DOI
11 Choi, H. S., Kwon, Y. J. and Ha, J. C. (2013). Study of university students' perceptions on participation in elections via structural equation model-Focusing on K university students. Journal of the Korean Data & Information Science Society, 24, 379-390.   DOI
12 Cohen, J. (1988). Statistical power for the behavioral sciences, 2nd ed., Lawrence Erlbaum, New Jersey.
13 Efron, B. (1982). The jackknife, the bootstrap and other resampling Plans, SIAM, Philadelphia.
14 Fan, X. and Sivo, S. A. (2005). Sensitivity of fit indices to misspecified structural or measurement model components: Rationale of two-index strategy revisited. Structural Equation Modeling, 12, 343-367.   DOI
15 Fox, J. (2006). Structural equation modeling with the sem package in R . Structural Equation Modeling, 13, 465-486.   DOI
16 Grace, J. B., Anderson, T. M., Olff, H. and Scheiner, S. M. (2010). On the specification of structural equation models for ecological systems. Ecological Monographs, 80, 67-87.   DOI
17 Grewal, R., Cote, J. A. and Baumgartner, H. (2004). Multicollinearity and measurement error in structural equation models: Implications for theory testing. Marketing Science, 23, 519-529.   DOI
18 Hair, J. F., Ringle, C. M. and Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19, 139-151.   DOI
19 Hayduk, L., Cummings, G. G., Boadu, K., Pazderka-Robinson, H. and Boulianne, S. (2007). Testing! testing! one, two three-Testing the theory in structural equation models. Personality and Individual Differences, 42, 841-850.   DOI
20 Hastie, T., Tibshirani, R. and Friedman, J. (2001). The elements of statistical learning; Data mining, inference, and prediction, Sringer-Verlag, New York.
21 Henseler, J. (2012). Why generalized structured component analysis is not universally preferable to structural equation modeling. Journal of the Academy of Marketing Science, 40, 402-413.   DOI
22 Hoerl, A. E. and Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12, 55-67.   DOI
23 Hong, Y., Jang, G. and Choi, C. (2016). Life satisfaction and self-esteem of children from low-income class: Testing mediation model of depression. Journal of the Korean Data & Information Science Society, 27, 179-189.   DOI
24 James, L. R., Mulaik, S. A. and Brett, J. M. (2006). A tale of two methods. Organizational Research Methods, 9, 233-244.   DOI
25 Jarvis, C. B., MacKenzie, S. B. and Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30, 199-218.   DOI
26 Jung, S. (2013). Structural equation modeling with small sample sizes using two-stage least-squares estimation. Behavior Research Methods, 45, 75-81.   DOI
27 Kaplan, D. (2009). Structural equation modeling: Foundations and extensions, 2nd ed., Sage, California.
28 Lee, S. (2007). Problems caused by model equivalence in developing and testing models. Journal of Educational Evaluation, 20, 125-146.
29 MacKenzie, S., Podsakoff, P. and Jarvis, C. (2005). The problem of measurement model misspecification in behavioural and organizational research and some recommended solutions. Journal of Applied Psychology, 90, 710-730.   DOI
30 McArdle, J. J. and McDonald, R. P. (1984). Some algebraic properties of the reticular action model. British Journal of Mathematical and Statistical Psychology, 37, 234-251.   DOI
31 Min, D. K. and Choi, M. K. (2016). How depression affects girls who experienced violence in home or at school: Using mixed model. Journal of the Korean Data & Information Science Society, 27, 101-110.   DOI
32 Olsson, U. H., Foss, T., Troye, S. and Howell, R. (2000). The Performance of ML, GLS, and WLS estimation in structural equation modeling under conditions of misspecification and non-normality. Structural Equation Modeling, 7, 557-595.   DOI
33 Sideridis, G., Simos, P., Papanicolaou, A. and Fletcher, J. (2014). Using structural equation modeling to assess functional connectivity in the brain: Power and sample size considerations. Educational & Psychological Measurement, 74, 733-758.   DOI
34 Steenkamp, J. B. E. M. and Baumgartner, H. (2000). On the use of structural equation models for marketing modeling. International Journal of Research in Marketing, 17, 195-202.   DOI
35 Tenenhaus, M., Pages, J., Ambroisine, L. and Guinot, C. (2005). PLS methodology to study relationships between hedonic judgments and product characteristics. Food Quality and Preference, 16, 315-325.   DOI
36 Tenenhaus, M. (2008). Structural equation modeling for small samples, HEC school of management (GRECHEC).