What are the differences between proportionate and disproportionate stratified sampling?
The Correct Answer and Explanation is:
Correct Answer:
The key difference between proportionate and disproportionate stratified sampling lies in how the sample sizes from each stratum (subgroup) are determined:
- Proportionate Stratified Sampling selects samples from each stratum in proportion to their size in the population.
- Disproportionate Stratified Sampling selects samples from each stratum without regard to their size in the population—sample sizes may be equal or adjusted based on other factors like variability or importance.
Explanation (300 words):
Stratified sampling is a technique used in survey research and statistics where a population is divided into distinct subgroups, or strata, based on shared characteristics (e.g., age, income level, education). After stratification, samples are drawn from each subgroup to ensure representation across key segments of the population.
In proportionate stratified sampling, the sample size from each stratum reflects its proportion in the overall population. For example, if a population consists of 60% Group A and 40% Group B, and a sample of 100 is needed, then 60 samples would come from Group A and 40 from Group B. This method helps maintain the population’s structure and is useful when generalizing results to the whole population.
In contrast, disproportionate stratified sampling involves selecting a different number of samples from each stratum, regardless of their proportion in the population. Using the same example, one might select 50 samples from Group A and 50 from Group B even though Group A is larger. This approach is often used when:
- Some strata are too small to be meaningfully analyzed if sampled proportionally.
- Greater precision is required from specific groups.
- Comparisons between groups are needed.
While disproportionate sampling can improve the quality of subgroup analysis, it often requires statistical adjustments (like weighting) during analysis to avoid bias in population-level estimates. Understanding these differences helps researchers choose the most appropriate sampling method based on their study’s goals.
