Journal of School of Public Health and

Background and Aim: Children are one of the most vulnerable groups in the population. Child abuse is a complex phenomenon with multiple causes. The purpose of this study was to do a confirmatory factor analysis of child abuse potential inventory among Ahvazi children in Ahvaz, Iran.

Materials and Methods: This was a descriptive-analytical study, including all Ahvazi primary school pupils aged 8-13 years in the academic year 2015-2016.

The sample size for confirmatory factor analysis was determined based on the number of questions per parameter.The variance was extracted on the basis of mean scores and

composite reliability for structural equation modeling was determined based on the first-order and second-order confirmatory factor analysisusing the LISREL software.

Results: Confirmatory factor analysis revealed a short form of the child abuse's original 3-actor structure, including the psychological, physical and neglect scales. The outcomes indicated that the firstorder model was a better fit for the data than the second.

Conclusion: It can be concluded that the Ahvazi version of the child abuse potential inventory questionnaire is acceptable from a psychometric point of view. We think it is essential to take into consideration the diversity of perspectives between parents and children in future research in this area.

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.