:: Volume 1, Issue 1 (9-2004) ::
JSRI 2004, 1(1): 51-68 Back to browse issues page
Modeling Paired Ordinal Response Data
Anoshiravan Kazemnejad *1, Farid Zayeri
1- , kazemـan@modares.ac.ir
Abstract:   (2614 Views)

 About 25 years ago, McCullagh proposed a method for modeling univariate ordinal responses. After publishing this paper, other statisticians gradually extended his method, such that we are now able to use more complicated but efficient methods to analyze correlated multivariate ordinal data, and model the relationship between these responses and host of covariates. In this paper, we aim to present the recent progressions in modeling ordinal response data, especially in bivariate ordinal responses that arise from medical studies relating to paired organs such as ophthalmology, otology, nephrology etc. Additionally, we present a new model for analyzing correlated ordinal response data. This model is an appropriate alternative for bivariate cumulative probit regression model, when joint distribution of response data is not symmetric. Finally, as an applied example, we analyze the obtained data from an epidemiologic study relating to periodontal status among high school students in Tehran using this method and compare the results with the similar models.

Keywords: Correlated ordinal responses, bivariate latent distribution, generalized estimating equations, generalized linear models.
Full-Text [PDF 1176 kb]   (535 Downloads)    
Type of Study: Research | Subject: General
Received: 2016/02/9 | Accepted: 2016/02/9 | Published: 2016/02/9

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Volume 1, Issue 1 (9-2004) Back to browse issues page