Volume 16, Issue 1 (6-2018)                   sjsph 2018, 16(1): 50-62 | Back to browse issues page

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Barmar S, Alimohammadian M, Sadjadi S A, Poustchi H, Hosseini S M, Yasseri M. Generalized Structural Equation Modeling (GSEM) and its Application in Health Researches. sjsph. 2018; 16 (1) :50-62
URL: http://sjsph.tums.ac.ir/article-1-5624-en.html
1- MSc. Student, Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
2- MSc. Instructor, Digestive Diseases Research Center, Digestive Diseases Research Institute, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
3- MD. Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
4- Ph.D. Associate Professor, Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
5- Ph.D. Professor, Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran, Tehran, Iran
6- Ph.D. Assistant Professor, Department of Epidemiology and Biostatistics, School of Public Health, University of Medical Sciences, Tehran, Iran , m.yaseri@gmail.com
Abstract:   (150 Views)

Background and Aims: Generalized Structural Equation Modeling (GSEM) is a family of statistical techniques utilized in the analysis of multivariate, categorical and ordinal data in order to measure latent variables and their connection with each other. The aim of this study is to consider the structure of data, and introducing GSEM to medical science researchers and presenting a practical example of in medical science researches.

Materials and Methods: An introduction to Structural Equation Modeling (SEM), along with its advantages and disadvantages was presented, and also GSEM and its all kind of forms was specified. An example to study hypertension risk factors in patients suffering from diabetes was carried out, which was a demonstration of using GSEM method for binary response variables. The data includes a random sample of 2716 people from Golestan province cohort studies.

Results: Age, body mass index, abdominal obesity, residence place, socioeconomic status, salt intake had direct effect on hypertension. Race, education, vitamin D and physical activity had direct and reverse effect on hypertension (p.value<0.05).

Discussion: Unlike SEM, the limitative hypothesis that our data should have a normal distribution do not needed in this model, also GSEM is powerful tool in the analysis of categorized data. Nevertheless this method cannot perform goodness of fit test, and adjustment and modification method of the model directly, and that they are some limitation in using this method.

Full-Text [PDF 1126 kb]   (86 Downloads)    
Type of Study: Research | Subject: Public Health
Received: 2018/06/20 | Accepted: 2018/06/20 | Published: 2018/06/20

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