Disproportionate sampling is a procedure in which the number of elements included in the sample from each stratum is not proportional to their representation in the total population. Elements of the population do not have the same opportunity to be included in the sample. The same sampling fraction is not applied to each stratum.
Furthermore, strata have different sampling fractions, and as such, this sampling procedure is not an equiprobable selection. In order to estimate population parameters, population composition must be used to compensate for disproportionality in the sample. However, for some research projects, disproportional stratified sampling may be more appropriate than proportional sampling.
Disproportionate sampling can be divided into three subtypes based on the purposes of our assignment, which for example could be to facilitate analysis within strata or to focus on optimizing costs, precision, or optimizing both: precision and costs.
The objective of a study may require a researcher to carry out detailed analysis of the sample strata. If proportional stratification is used, the sample size of a stratum is very small; thus, it may be difficult to meet the objectives of the study.
Proportional allocation may not produce a sufficient number of cases for this type of detailed analysis. One option is to oversample small or infrequent strata. Such oversampling would create a disproportionate distribution of the sample strata when compared to the population. However, there may be a sufficient number of cases to carry out the strata analysis required by the study objectives.
Using the hypothetical example described in the table above, if one wanted to jordan phone number perform a detailed analysis of zone 2, one could oversample items from that zone; for example, instead of sampling only 12 items, sample 130 items.
I invite you to read: How to do a stratified sampling ?
In order to carry out a more meaningful analysis, analyze zone 2 in detail, the sample size for that district should be greater than 12 elements. The results of the distribution of the elements in the sample by zone may look like the distribution presented in the following table:
proportional allocation stratified sampling
Weaknesses and strengths of stratified sampling
Stratified sampling has many of the strengths and weaknesses associated with most probability sampling procedures when compared to nonprobability sampling procedures.
Compared to simple random sampling , the strengths of stratified sampling include:
Ability to estimate not only population parameters, but also to make inferences within each stratum and comparisons between strata. Sufficient data on subgroups of interest may not be captured by simple random sampling. Stratified samples produce smaller random sampling errors than would be obtained with a simple random sample of the same sample size. A stratified sample will result in a sample that is at least as precise as a simple random sample of the same sample size.
Stratified samples tend to be more representative of a population because they ensure that elements of each stratum in the population are represented in the sample. Sampling may be stratified to ensure that the sample is spread over geographic subareas and population subgroups.