The Comparison of WLS and DWLS Estimation Methods in SEM to Construct Health Behavior Model
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Keywords:
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Weighted Least Squares, Health Behavior, Diagonally Weighted Least Squares, Structural Equation Modeling
Abstract
It is unknown how reliable various point estimates, standard errors, and standard several test statistics are for standardized SEM parameters when categorical data used or misspecified models are present. This paper discusses the comparison between WLS and DWLS for examining hypothesized relations among ordinal variables. In SEM, the polychoric correlation is employed either in WLS or DWLS. This study constructs the Health behavior model as an endogenous latent variable in which exogenous latent variables are Perceived susceptibility and Health motivation. All indicators are in categorical types. Thus, data are not multivariate normal, or the model could be misspecified. This study compares the values of standard deviation and coefficient determination to determine a better model. The criteria for the goodness of fit for the overall model are based on RMSEA, CFI, and TLI values. This present study found that the WLS estimator method resulted in better values than DWLS’s.
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Bollen, K. A. and A. Maydeu-Olivares (2007). A Polychoric Instrumental Variable (PIV) Estimator for Structural Equa- tion Models with Categorical Variables. Psychometrika, 72(3); 309–326
Chen, F., P. J. Curran, K. A. Bollen, J. Kirby, and P. Paxton (2008). An Empirical Evaluation of the Use of Fixed Cut- off Points in RMSEA Test Statistic in Structural Equation Models. Sociological Methods & Research, 36(4); 462–494
DiStefano, C. and G. B. Morgan (2014). A Comparison of Diagonal Weighted Least Squares Robust Estimation Tech- niques for Ordinal Data. Structural Equation Modeling: A Multidisciplinary Journal, 21(3); 425–438
Eaton, L. A. and S. C. Kalichman (2020). Ocial and Behavioral Health Responses to COVID-19: Lessons Learned from Four Decades of An HIV Pandemic. Journal of Behavioral Medicine, 43(3); 341–345
Flora, D. B. and P. J. Curran (2004). An Empirical Evaluation of Alternative Methods of Estimation for Confirmatory Fac- tor Analysis With Ordinal Data. Psychological Methods, 9(4); 466
Hutchinson, S. R. and A. Olmos (1998). Behavior of De- scriptive Fit Indexes in Confirmatory Factor Analysis using Ordered Categorical Data. Structural Equation Modeling: A Multidisciplinary Journal, 5(4); 344–364
Isnayanti, A. (2019). Model Persamaan Struktural dengan Metode Diagonally Weighted Least Square (DWLS) Untuk Data Ordinal. Media Statistika, 12(100)
Karimy, M., H. Azarpira, and M. Araban (2017). Using Health Belief Model Constructs to Examine Differences in Adher- ence to Pap Test Recommendations among Iranian Women. Asian Pacific Journal of Cancer Prevention, 18(5); 1389
Khoso, P. A., V. W. Yew, and M. H. A. Mutalib (2016). Com- paring and Contrasting Health Behavior with Illness Behav- ior. e-Bangi, 11(2); 578–589
Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Publications
Li, C.H. (2016). Confirmatory Factor Analysis with Ordinal Data: Comparing Robust Maximum Likelihood and Diag- onally Weighted Least Squares. Behavior Research Methods, 48(3); 936–949
Mîndrilã, D. (2010). Maximum Likelihood (ML) and Diag- onally Weighted Least Squares (DWLS) Estimation Pro- cedures: A Comparison of Estimation Bias with Ordinal and Multivariate Non-Normal Data. International Journal of Digital Society, 1(1); 60–66
Muthén, L. K. and B. Muthén (2017). Mplus User’s Guide: Statistical Analysis with Latent Variables, User’s Guide.
Muthén & Muthén
Newsom, J. T. and N. A. Smith (2020). Performance of Latent Growth Curve Models with Binary Variables. Structural Equation Modeling: A Multidisciplinary Journal, 27(6); 888– 907
Olsson, U. H., T. Foss, S. V. Troye, and R. D. Howell (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
Rahmadita, A., F. Yanuar, and D. Devianto (2018). The Con-
struction of Patient Loyalty Model Using Bayesian Structural
Equation Modeling Approach. Cauchy, 5(2); 73–79
Sillice, M. A., A. L. Paiva, S. F. Babbin, H. A. McGee, J. S. Rossi, C. A. Redding, K. S. Meier, K. Oatley, and W. F. Velicer (2014). Testing Demographic Differences For Alco-
hol Use Initiation Among Adolescents For The Decisional Balance And Situational Temptation Prevention Inventories. Addictive Behaviors, 39(9); 1367–1371
Suh, Y. (2015). The Performance of Maximum Likelihood and Weighted Least Square Mean and Variance Adjusted Estimators in Testing Differential Item Functioning With Nonnormal Trait Distributions. Structural Equation Modeling: A Multidisciplinary Journal, 22(4); 568–580
Xia, Y. and Y. Yang (2019). RMSEA, CFI, and TLI in Struc- tural Equation Modeling with Ordered Categorical Data: The Story They Tell Depends on The Estimation Methods. Behavior Research Methods, 51(1); 409–428
Yanuar, F. (2015). The Use of Uniformative and Informative Prior Distribution in Bayesian SEM. Global Journal of Pure and Applied Mathematics, 11(5); 3259–3264
Yanuar, F. (2016). The Health Status Model in Urban and Rural Society in West Sumatera, Indonesia: An Approach of Structural Equation Modeling. Indian Journal of Science and Technology, 9(8); 1–8
Yanuar, F., K. Ibrahim, and A. A. Jemain (2010). On The Ap- plication of Structural Equation Modeling for The Construc- tion of A Health Index. Environmental Health and Preventive Medicine, 15(5); 285–291
Authors
Yanuar, F., Nisa Uttaqi, F. ., Zetra, A., Rahmi, I., & Devianto, D. (2022). The Comparison of WLS and DWLS Estimation Methods in SEM to Construct Health Behavior Model. Science and Technology Indonesia, 7(2), 164–169. https://doi.org/10.26554/sti.2022.7.2.164-169
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