How can you use stratified sampling to reduce sampling bias in your population?

3 How to conduct stratified sampling

Conducting stratified sampling requires several steps. Start by defining your population and sampling frame, which is a list of accessible and eligible units for your study. Identify the variable or variables that will be used to create strata, making sure they are relevant to the research question or hypothesis and have clear and mutually exclusive categories. Divide the population into strata based on the variable or variables, ensuring each unit belongs to one stratum and the strata cover the entire population. Decide on sample size for each stratum, using either proportional allocation (sample size is proportional to population size) or equal allocation (sample size is the same regardless of population size). Select a sample from each stratum using random methods such as simple random sampling, systematic sampling, or cluster sampling. Finally, combine samples from each stratum to form your final sample and conduct your analysis.

4 What are the advantages of stratified sampling?

Stratified sampling has several advantages over other sampling methods, such as reducing sampling error and variability, improving the precision and reliability of estimates, allowing for comparisons and contrasts of subgroups, and ensuring that all relevant subgroups are represented in the sample. This method leads to more accurate results with smaller sample sizes and higher levels of confidence and power. Additionally, it enhances the validity and generalizability of the results.

5 What are the disadvantages of stratified sampling?

Stratified sampling also has some drawbacks, such as requiring prior knowledge and information about your population and the variable or variables that you want to use to create your strata. Additionally, it can be costly and time-consuming if your population is large, diverse, or geographically dispersed. Moreover, stratified sampling can introduce bias or error if your sampling frame is incomplete or inaccurate, if your strata are not mutually exclusive or exhaustive, or if your sampling method is not random or consistent.

6 What are some case studies of stratified sampling?

Stratified sampling is a widely used technique in various fields and disciplines, such as social sciences, health sciences, education, marketing, and engineering. For instance, Bhandari et al. (2017) used stratified sampling to assess the prevalence and risk factors of chronic obstructive pulmonary disease (COPD) among adults in rural India. They divided their population into four strata based on age and selected a proportional sample from each stratum using simple random sampling. Similarly, Chen et al. (2019) divided their population into two strata based on school type and selected an equal sample from each stratum using cluster sampling. Additionally, Kozak et al. (2018) divided their population into four strata based on income level and selected a proportional sample from each stratum using systematic sampling. Through these studies it was found that stratified sampling can help to uncover the impact of certain interventions as well as the influence of certain variables on consumer preferences and attitudes.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?