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In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation (stratum) independently. Stratification is the process of dividing members of the population into homogeneous subgroups before sampling. The strata should define a partition of the population.
Graphic breakdown of stratified random sampling. In statistics, stratified randomization is a method of sampling which first stratifies the whole study population into subgroups with same attributes or characteristics, known as strata, then followed by simple random sampling from the stratified groups, where each element within the same subgroup are selected unbiasedly during any stage of the ...
Multilevel regression with poststratification (MRP) is a statistical technique used for correcting model estimates for known differences between a sample population (the population of the data one has), and a target population (a population one wishes to estimate for).
In statistics, survey sampling describes the process of selecting a sample of elements ... Stratification is the process of dividing members of the population into ...
In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power. In complex studies, different sample sizes may be allocated, such as in stratified surveys or experimental designs with multiple treatment groups.
In survey research, ... In these cases, we might use statistical procedures such as post-stratification, raking, or inverse propensity score weighting (where the ...
Benchmarking is sometimes referred to as 'post-stratification' because of its similarities to stratified sampling.The difference between the two is that in stratified sampling, we decide in advance how many units will be sampled from each stratum (equivalent to benchmarking cells); in benchmarking, we select units from the broader population, and the number chosen from each cell is a matter of ...
Principal stratification is a statistical technique used in causal inference when adjusting results for post-treatment covariates. The idea is to identify underlying strata and then compute causal effects only within strata.