Stratified random sampling intends to guarantee that the sample represents specific subgroups or strata. Accordingly, application of stratified sampling method involves dividing population into different subgroups (strata) and selecting subjects from each strata in a proportionate manner. The table below illustrates simplistic example where sample group of 10 respondents are selected by dividing population into male and female strata in order to achieve equal representation of both genders in the sample group.
Stratified random sampling is superior to simple random sampling because the process of stratifying reduces sampling error.
Application of stratified random sampling contains the following two stages.
1. Identification of relevant stratums and ensuring their actual representation in the population. Apart from gender as illustrated in example above, range of criteria that can be used to divide population into different strata include age, highest level of education, status, nationality, religion and others. Specific patterns of categorization into different stratums depends aims and objectives of the study.
2. Application of random sampling method to choose sufficient numbers of subjects from each stratum. It is critically important for samples from each stratum to be selected in a random manner so that the relevance of bias can be minimized.
Stratified sampling can be divided into the following two groups: proportionate and disproportionate. Application of proportionate stratified random sampling technique involves determining sample size in each stratum in a proportionate manner to the entire population. In disproportionate stratified random sampling, on the contrary, numbers of subjects recruited from each stratum does not have to be proportionate to the total size of population. Accordingly, application of proportionate stratified random sampling generates more accurate primary data compared to disproportionate sampling.
Thanks to the choice of stratified random sampling adequate representation of all subgroups can be ensured. Additionally, when there is homogeneity within strata and heterogeheity between strata, the estimates can be as precise (or even more precise) as with the use of simple random sampling.
However, application of stratified random sampling requires the knowledge of strata membership a priori. Also, choice of this sampling method adds complexity to the analysis plan.
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