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Structural Equation Modelling in the biomedical domain: Concepts and methods revisited with the help of R statistical package

Editor IJSMI

Abstract


Structural Equation Modelling (SEM) is widely used in the social sciences [1, 2] analysing complex relationships among multiple variables. It is defined by two models namely measurement model and construct model. Measurement model  defines the relationship between observed variables and constructs (or latent variables or factors or unobserved variables) which are derived from observed variables. Construct model defines the relationship between the construct or latent variables. SEM is able to model the error terms in the observed variables and error while defining the relationship in the model and it is a unique property of SEM. The objective of the SEM is to verify whether a model specified prior is a best fit to the given data or not. This paper provides an overview of Structural Equation Modelling, its application in biomedical domain and illustrated with the help of R Statistical Software.


Keywords


SEM;Latent;construct;measurement;model

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References


Ullman, J. B., & Bentler, P. M. (2003). Structural equation modelling. John Wiley & Sons, Inc..

Hox, J. J., & Bechger, T. M. (1998). An introduction to structural equation modelling.

Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation modeling. psychology press

Wu, A. D., & Zumbo, B. D. (2008). Understanding and using mediators and moderators. Social Indicators Research, 87(3), 367.

Weston, R., & Gore Jr, P. A. (2006). A brief guide to structural equation modeling. The counseling psychologist, 34(5), 719-751.

Zhang, Z. (2017). Structural equation modelling in the context of clinical research. Annals of translational medicine, 5(5).

Beran, T. N., & Violato, C. (2010). Structural equation modelling in medical research: a primer. BMC research notes, 3(1), 267.

Holmes, C. S., Chen, R., Streisand, R., Marschall, D. E., Souter, S., Swift, E. E., & Peterson, C. C. (2005). Predictors of youth diabetes care behaviors and metabolic control: a structural equation modeling approach. Journal of pediatric psychology, 31(8), 770-784

Bol, Y., Duits, A. A., Lousberg, R., Hupperts, R. M., Lacroix, M. H., Verhey, F. R., & Vlaeyen, J. W. (2010). Fatigue and physical disability in patients with multiple sclerosis: a structural equation modeling approach. Journal of behavioral medicine, 33(5), 355-363.

Höfer, S., Benzer, W., Alber, H., Ruttmann, E., Kopp, M., Schussler, G., & Doering, S. (2005). Determinants of health-related quality of life in coronary artery disease patients: a prospective study generating a structural equation model. Psychosomatics, 46(3), 212-223.

Lee, J. W., Lee, K. E., Park, D. J., Kim, S. H., Nah, S. S., Lee, J. H., ... & Lee, H. S. (2017). Determinants of quality of life in patients with fibromyalgia: A structural equation modeling approach. PloS one, 12(2), e0171186.

Roman-Urrestarazu, A., Ali, F. M. H., Reka, H., Renwick, M. J., Roman, G. D., & Mossialos, E. (2016). Structural equation model for estimating risk factors in type 2 diabetes mellitus in a Middle Eastern setting: evidence from the STEPS Qatar. BMJ Open Diabetes Research and Care, 4(1), e000231.

Bardenheier, B. H., Bullard, K. M., Caspersen, C. J., Cheng, Y. J., Gregg, E. W., & Geiss, L. S. (2013). A novel use of structural equation models to examine factors associated with prediabetes among adults aged 50 years and older: National Health and Nutrition Examination Survey 2001–2006. Diabetes care, 36(9), 2655-2662.

Amorim, L. D. A. F., Fiaccone, R. L., Santos, C. A. S., Santos, T. N. D., de Moraes, L. T. L., Oliveira, N. F., ... & Barreto, M. L. (2010). Structural equation modeling in epidemiology. Cadernos de Saúde Pública, 26(12), 2251-2262.

O'Rourke, N., Psych, R., & Hatcher, L. (2013). A step-by-step approach to using SAS for factor analysis and structural equation modeling. Sas Institute.

Byrne, B. M. (2016). Structural equation modelling with AMOS: Basic concepts, applications, and programming. Routledge.

Fox, J. (2006). Teacher's corner: structural equation modelling with the sem package in R. Structural equation modelling, 13(3), 465-486.




DOI: http://dx.doi.org/10.3000/ijsmi.v6i1.10

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