Stratified sampling is a tad more complicated than simple random sampling. It involves categorizing the members of your sample and picking a random sample from each category so that you are sure to have solid representation from each desired category in your survey.
Watch this short video clip from Simple Learning Pro to find out more about stratified sampling.
For Example…
Let’s say you’re doing a survey related to the relationship between the language spoken in the home and access to information on COVID-19 in Monterey County, CA. If you know that 40% of the population of Monterey County are Spanish speakers, but your simple random sample is only polling roughly 10% Spanish speakers, you would want to stratify your sample, because the survey answers from Spanish speakers are important to your research. Thus, you would break down your sample by language spoken at home (as listed by the census, perhaps), and draw names from there.
Here are some of the pros and cons of stratified sampling to consider.
| Pros | Cons |
|---|---|
| More precise | Require more administrative effort |
| Can use a smaller sample size | More complex to analyze |
| May guard against sampling error or sample selection bias | |
| Allow for better analysis of subgroups |
