In Public Health, Social Science and
Demography, large-scale surveys often follow a hierarchical
structure of data as the surveys are based on multistage
stratified cluster sampling. The appropriate way to
analyzing such survey data is therefore based on nested
sources of variability which come from different levels of
hierarchy. Many researchers do not take into account these
nested sources of variability for such multistage stratified
cluster sampling dataset. Using the 2004 Bangladesh
Demographic and Health Survey contraceptive binary data this
work is designed to assist in 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 MLwiN, and interpretation
of the results. The used MLwiN and SPSS code have been given
in the appendix. The data analysis of this work also ensures
helping other analysts who work with other softwares and
programmings. The analysis should be useful to
professionals, MSc or PhD students working especially in the
fields of Social Sciences, Demography, Public Health and
Statistics.