Volume 10, Issue 1 (Vol 10, No 1 2014)                   irje 2014, 10(1): 15-24 | Back to browse issues page

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Asadollahi Z, Jafari P, Rezaeian M. Application of the Ordinal Logistic Regression Models in Quality of life Studies. irje. 2014; 10 (1) :15-24
URL: http://irje.tums.ac.ir/article-1-5203-en.html
1- Department of Social Medicine, School of Medicine, Rafsanjan University of Medical Sciences, Rafsanjan, Iran , asadollahi.zahra@gmail.com
2- Department of Biostatistics, Shiraz University of Medical Sciences, Shiraz, Iran
3- Environmental Health Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
Abstract:   (6982 Views)


Background & Objectives: Due to the increasing tendency to measure the quality of life in recent years and the extensive quality of life questionnaires, it is important to determine the appropriate method of analyzing data derived from these studies. The aim of the present study was to introduce ordinal logistic regression models as an appropriate method for analyzing the data of quality of life.

Methods: The data was derived from a cross-sectional study on quality of life survey of 938 students. For data analysis, two binary logistic regression models and ordinal logistic regression models were used and the results of these models were compared.

Results: The results of goodness of fit showed that all three models were fitted well. Based on the ordinal logistic regression models, the three variables out of the explanatory variables were statistically associated with the response while based on the binary logistic regression model, after combining two categories of response variable, only two variables were significant. Therefore, combining the categories of the response variable should be avoided as much as possible because it may lead to data loss due to ignoring some of the response categories.

Conclusion: It is concluded that to analyze quality of life data, due to the nature of the response variable, ordinal logistic regression models are recommended considering the fewer parameter estimates and easier interpretation of the results

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Type of Study: Research | Subject: General
Received: 2014/09/16 | Accepted: 2014/09/16 | Published: 2014/09/16

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