RESUMEN
Multistage sampling is a frequently-used method in random sampling survey in public health.Clustering or independence between observations often exists in the sampling,often called complex sample,generated by multistage sampling.Sampling error may be underestimated and the probability of type Ⅰ error may be increased if the multistage sample design was not taken into considerationin analysis.As variance (error) estimator in complex sample is often complicated,statistical software usually adopt ultimate cluster variance estimate (UCVE) to approximate the estimation,which simply assume that the sample comes from one-stage sampling.However,with increased sampling fraction of primary sampling unit,contribution from subsequent sampling stages is no more trivial,and the ultimate cluster variance estimate may,therefore,lead to invalid variance estimation.This paper summarize a method of variance estimation considering multistage sampling design.The performances are compared with UCVE and the method considering multistage sampling design by simulating random sampling under different sampling schemes using real world data.Simulation showed that as primary sampling unit (PSU) sampling fraction increased,UCVE tended to generate increasingly biased estimation,whereas accurate estimates were obtained by using the method considering multistage sampling design.
RESUMEN
Multistage sampling techniques are widely applied in the cross-sectional study of epidemiology, while methods based on independent assumption are still used to analyze such complex survey data. This paper aims to introduce the application of weighted estimation methods for the complex survey data. A brief overview of basic theory is described, and then a practical analysis is illustrated to apply to the weighted estimation algorithm in a stratified two-stage clustered sampling data. For multistage sampling survey data, weighted estimation method can be used to obtain unbiased point estimation and more reasonable variance estimation, and so make proper statistical inference by correcting the clustering, stratification and unequal probability effects.