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Multilevel logistic regression analysis applied to binary contraceptive prevalence data
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Khan, Md. Hasinur Rahaman and Shaw, J. Ewart H. (2011) Multilevel logistic regression analysis applied to binary contraceptive prevalence data. Journal of Data Science, Vol.9 (No.1). pp. 93-110. ISSN 1680-743X.
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Official URL: http://www.jds-online.com/volume-9-number-1-januar...
Abstract
In public health, demography and sociology, large-scale surveys often follow a hierarchical data structure as the surveys are based on multistage stratified cluster sampling. The appropriate approach to analyzing such survey data is therefore based on nested sources of variability which come from different levels of the hierarchy. When the variance of the residual errors is correlated between individual observations as a result of these nested structures, traditional logistic regression is inappropriate. We use the 2004 Bangladesh Demographic and Health Survey (BDHS) contraceptive binary data which is a multistage stratified cluster dataset. This dataset is used to exemplify all aspects of working with multilevel logistic regression models, including model conceptualization, model description, understanding of the structure of required multilevel data, estimation of the model via the statistical package \emph{MLwiN}, comparison between different estimations, and investigation of the selected determinants of contraceptive use.
Item Type: | Journal Article | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Journal or Publication Title: | Journal of Data Science | ||||
Publisher: | Columbia University | ||||
ISSN: | 1680-743X | ||||
Official Date: | January 2011 | ||||
Dates: |
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Volume: | Vol.9 | ||||
Number: | No.1 | ||||
Page Range: | pp. 93-110 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
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