@article{(Open Science Index):https://publications.waset.org/pdf/9997382,
	  title     = {The Effect of Nonnormality on CB-SEM and  PLS-SEM Path Estimates },
	  author    = {Z. Jannoo and  B. W. Yap and  N. Auchoybur and  M. A. Lazim},
	  country	= {},
	  institution	= {},
	  abstract     = {The two common approaches to Structural Equation Modeling (SEM) are the Covariance-Based SEM (CB-SEM) and Partial Least Squares SEM (PLS-SEM). There is much debate on the performance of CB-SEM and PLS-SEM for small sample size and when distributions are nonnormal. This study evaluates the performance of CB-SEM and PLS-SEM under normality and nonnormality conditions via a simulation. Monte Carlo Simulation in R programming language was employed to generate data based on the theoretical model with one endogenous and four exogenous variables. Each latent variable has three indicators. For normal distributions, CB-SEM estimates were found to be inaccurate for small sample size while PLS-SEM could produce the path estimates. Meanwhile, for a larger sample size, CB-SEM estimates have lower variability compared to PLS-SEM. Under nonnormality, CB-SEM path estimates were inaccurate for small sample size. However, CB-SEM estimates are more accurate than those of PLS-SEM for sample size of 50 and above. The PLS-SEM estimates are not accurate unless sample size is very large.  
},
	    journal   = {International Journal of Mathematical and Computational Sciences},
	  volume    = {8},
	  number    = {2},
	  year      = {2014},
	  pages     = {285 - 291},
	  ee        = {https://publications.waset.org/pdf/9997382},
	  url   	= {https://publications.waset.org/vol/86},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 86, 2014},
	}