Journal of Mathematics and Statistics

Multilevel Mixed Analysis and Mapping on Divorce Problem in UAE

Faisal G. Khamis

DOI : 10.3844/jmssp.2016.238.247

Journal of Mathematics and Statistics

Volume 12, Issue 4

Pages 238-247

Abstract

Despite the wealth of research investigating the divorce problem in the developed countries, few and inconsistent studies investigated this problem at emirate-level in developing countries, such as United Arab Emirates (UAE). The Questions are raised whether divorce is changed over time and whether clustering in divorce exists within emirates in UAE. The objectives are to investigate the change in divorce over time, examining the variation between- and within emirates, determining whether the clustering in divorce within-emirates exists and forecasting divorce in each emirate for the next years. The study design was cross-sectional time-series data. Multilevel mixed-effects linear regression was carried out. The data of 7 emirates over 19 years (1995-2013) were obtained from the National Bureau of Statistics. After calculating the Divorce Rate (DR), visual inspection for the DR was investigated using mapping. Three multilevel models for the DR were estimated and comparison between these models was explained in Intra-Class Correlation (ICC), the proportional change in the variance of the DR and the deviance. The p<0.01 of Wald-2 was found significant in all models. Given 95% confidence interval, the fixed- and random-effects in all models were found significant. The ICC results were found significant, more than 46%, in all models. In terms of the statistical and social epidemiological concepts of contextual phenomena confirm that the DR from the same emirate is more similar to each other than those from different emirates. Using the time as a predictor for the DR reduced slightly the within-emirates variance and inflated dramatically the between-emirates variance.

Copyright

© 2016 Faisal G. Khamis. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.