Conceptualization

Hierarchical database model

Demography

Multilevel model

Statistics

Stratified sampling

Accounting

Data mining

Computer science

Business

Regression analysis

Binary data

Variance (accounting)

Mathematics

Survey data collection

Multistage sampling

Econometrics

Population

Sociology

Cluster sampling

Artificial intelligence

Binary number

Logistic regression

Multinomial logistic regression

Survey sampling

Arithmetic

Journal of Data Science

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 MLwiN, comparison between different estimations, and investigation of the selected determinants of contraceptive use.

Multilevel Logistic Regression Analysis Applied to Binary Contraceptive Prevalence Data