Sampling Design for the National Health Survey, Brazil 2013
Paulo Roberto, Borges de Souza-Júnior, Paulo Soares de Freitas, Giuseppe de Abreu Antonaci, Rio de Janeiro-RJ, Célia Landmann Szwarcwald
Abstract
Paulo Roberto, Borges de Souza-Júnior, Paulo Soares de Freitas, Giuseppe de Abreu Antonaci, Rio de Janeiro-RJ, Célia Landmann Szwarcwald
Abstract
This paper describes the sample design used in the Brazilian 2013 National Health Survey. The target population was comprised of people resident in permanent private households throughout the country. The survey was household-based with stratified sampling and three clustering stages: census tracts form the primary sampling units; households are the units of second stage; and adults (aged 18 years or older) define the third-stage units. The sample size considered the desired level of precision for the estimates of some indicators at different levels of disaggregation and different population groups. The final weighting was a product of inverse selection probabilities at each stage of the sampling plan, including non-response correction procedures and adjustment calibrations for the known population totals. Since this is a complex sample, appropriate procedures must be used during data processing.
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This paper describes the sample design used in the Brazilian 2013 National Health Survey. The target population was comprised of people resident in permanent private households throughout the country. The survey was household-based with stratified sampling and three clustering stages: census tracts form the primary sampling units; households are the units of second stage; and adults (aged 18 years or older) define the third-stage units. The sample size considered the desired level of precision for the estimates of some indicators at different levels of disaggregation and different population groups. The final weighting was a product of inverse selection probabilities at each stage of the sampling plan, including non-response correction procedures and adjustment calibrations for the known population totals. Since this is a complex sample, appropriate procedures must be used during data processing.
Key concepts: Sampling design, Sampling (signal processing), Sample (material), Stratified sampling, Weighting, Census, Survey sampling, Statistics