What is the difference between proportionate stratified random sampling and disproportionate stratified random sampling? Describe and distinguish each.

Question

What will be an ideal response?

Answer 1

By using proportionate stratified sampling, you would eliminate any possibility of error in the sample’s distribution of ethnicity. Each stratum would be represented exactly in proportion to its size in the population from which the sample was drawn.
In disproportionate stratified sampling, the proportion of each stratum that is included in the sample is intentionally varied from what it is in the population. In the case of the sample stratified by ethnicity, you might select equal numbers of cases from each racial or ethnic group: 125 African Americans (25% of the sample), 125 Hispanics (25%), 125 Asians (25%), and 125 Caucasians (25%). In this type of sample, the probability of selection of every case is known but unequal between strata. You know what the proportions are in the population, so you can easily adjust your combined sample accordingly. For instance, if you want to combine the ethnic groups and estimate the average income of the total population, you would have to weight each case in the sample. The weight is a number you multiply by the value of each case based on the stratum it is in. For example, you would multiply the incomes of all African Americans in the sample by 0.6 (75/125), the incomes of all Hispanics by 0.4 (50/125), and so on. Weighting in this way reduces the influence of the oversampled strata and increases the influence of the undersampled strata to just what they would have been if pure probability sampling had been used.