A Review of Software Packages for Structural Equation Modeling: A Comparative Study

Ahmed A. El-Sheikh¹, Mohamed R. Abonazel^1, and Noha Gamil¹

¹Department of Applied Statistics and Econometrics, Institute of Statistical Studies and Research, Cairo University, Egypt

Cite this paper:
Ahmed A. El-Sheikh, Mohamed R. Abonazel and Noha Gamil. A Review of Software Packages for Structural Equation Modeling: A Comparative Study. Applied Mathematics and Physics. 2017; 5(3):85-94. doi: 10.12691/amp-5-3-2

Abstract

Structural equation modeling (SEM) is a widely used statistical method in most of social science fields. Similar to other statistical methods, the choice of the appropriate estimation methods affects the results of the analysis, thus it was of importance to review some of SEM software packages and the availability of different estimation methods in these packages. Therefore, in this paper five SEM software packages (AMOS, LISREL, and three packages in R) dealing with SEM analysis were reviewed to guide the researcher about the usage of each package. Moreover, an empirical study was presented to assess the performance of different estimation methods under the existence of missing data. The results showed that full information maximum likelihood (FIML) was the best estimation method to deal with different missingness rates.

Keywords:
AMOS Confirmatory factor analysis Goodness-of-fit indexes LISREL Missing data analysis R software

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Abonazel, Mohamed. R. (2014). Statistical analysis using R, Annual Conference on Statistics, Computer Sciences and Operations Research, Vol. 49. Institute of Statistical Studies and research, Cairo University.
[2]	Abonazel, Mohamed. R. (2015). How to create a Monte Carlo simulation study using R: with Applications on econometric models. Working paper, No. 68708. University Library of Munich, Germany. Online at https://mpra.ub.uni-muenchen.de/68708
[3]	Allison, P. D. (2003). Missing data techniques for structural equation modeling. Journal of abnormal psychology, 112(4), 545.
[4]	Arbuckle, J. L. (2010). IBM SPSS Amos 19 user’s guide. Crawfordville, FL: Amos Development Corporation, 635.
[5]	Bentler, P. M., and Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological bulletin, 88(3), 588.
[6]	Byrne, B. M. (2001). Structural equation modeling with AMOS, EQS, and LISREL: Comparative approaches to testing for the factorial validity of a measuring instrument. International journal of testing, 1(1), 55-86.
[7]	Byrne, B. M. (2013). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Routledge.
[8]	Cangur, S., Ercan, I. (2015). Comparison of model fit indices used in structural equation modeling under multivariate normality. Journal of Modern Applied Statistical Methods, 14(1), 152-167.
[9]	Crawley, M. J. (2012). The R Book, 2nd Ed. John Wiley and Sons Ltd.
[10]	El-Sheikh, Ahmed A., Abonazel, Mohamed R., Gamil, Noha (2017). A review of estimation methods for structural equation modeling. Working paper. Institute of Statistical Studies and Research. Cairo University, Egypt.
[11]	Enders, C. K., and Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 8(3), 430-457.
[12]	Fox, J. (2006). Teacher's corner: structural equation modeling with the sem package in R. Structural equation modeling, 13(3), 465-486.
[13]	Gamil, Noha (2017). Estimation methods of structural equation Models: A comparative study. MSc thesis. Institute of Statistical Studies and Research. Cairo University.
[14]	Henze, N., and Zirkler, B. (1990). A class of invariant consistent tests for multivariate normality. Communications in Statistics-Theory and Methods, 19(10), 3595-3617.
[15]	Hooper, D., Coughlan, J., and Mullen, M. R. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
[16]	Hox, J. J., Maas, C. J., and Brinkhuis, M. J. (2010). The effect of estimation method and sample size in multilevel structural equation modeling. StatisticaNeerlandica, 64(2), 157-170.
[17]	Hoyle, R. H. (2012). Handbook of structural equation modeling. Guilford Press.
[18]	Hu, L. T., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.
[19]	Iacobucci, D. (2010). Structural equations modeling: Fit indices, sample size, and advanced topics. Journal of Consumer Psychology, 20(1), 90-98.
[20]	Jöreskog, K. G., and Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Scientific Software International.
[21]	Kline, Rex B. (2011). Principles and practice of structural equation modeling. (3rded). New York: The Guilford Press.
[22]	Korkmaz, S., Goksuluk, D., and Zararsiz, G. (2014). MVN: an R package for assessing multivariate normality. The R Journal, 6(2), 151-162.
[23]	MacCallum, R. C., Browne, M. W., and Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological methods, 1(2), 130.
[24]	Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 519-530.
[25]	Newsom, J. T. (2015). Some clarifications and recommendations on fit indices. USP, 655.
[26]	Olsson, U. H., Foss, T., Troye, S. V., and Howell, R. D. (2000). The performance of ML, GLS, and WLS estimation in structural equation modeling under conditions of misspecification and nonnormality. Structural equation modeling, 7(4), 557-595.
[27]	Pornprasertmanit, S., Miller, P., Schoemann, A., Quick, C., Jorgensen, T., and Pornprasertmanit, M. S. (2016). Package ‘simsem’.
[28]	Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1-36.
[29]	Rosseel, Y., Oberski, D., Byrnes, J., Vanbrabant, L., Savalei, V., Merkle, E., and Rosseel, M. Y. (2015). Package ‘lavaan’.
[30]	Royston, J. P. (1983). Some techniques for assessing multivarate normality based on the Shapiro-Wilk W. Applied Statistics, 121-133.