# Understanding Stratified Samples and How to Make Them

A stratified sample is one that ensures that subgroups (strata) of a given population are each adequately represented within the whole sample population of a research study. For example, one might divide a sample of adults into subgroups by age, like 18–29, 30–39, 40–49, 50–59, and 60 and above. To stratify this sample, the researcher would then randomly select proportional amounts of people from each age group. This is an effective sampling technique for studying how a trend or issue might differ across subgroups.

Importantly, strata used in this technique must not overlap, because if they did, some individuals would have a higher chance of being selected than others. This would create a skewed sample that would bias the research and render the results invalid.

Some of the most common strata used in stratified random sampling include age, gender, religion, race, educational attainment, socioeconomic status, and nationality.

## When to Use Stratified Sampling

There are many situations in which researchers would choose stratified random sampling over other types of sampling. First, it is used when the researcher wants to examine subgroups within a population. Researchers also use this technique when they want to observe relationships between two or more subgroups, or when they want to examine the rare extremes of a population. With this type of sampling, the researcher is guaranteed that subjects from each subgroup are included in the final sample, whereas simple random sampling does not ensure that subgroups are represented equally or proportionately within the sample.

## Proportionate Stratified Random Sample

In proportional stratified random sampling, the size of each stratum is proportionate to the population size of the strata when examined across the entire population. This means that each stratum has the same sampling fraction.

For example, let’s say you have four strata with population sizes of 200, 400, 600, and 800. If you choose a sampling fraction of ½, this means you must randomly sample 100, 200, 300, and 400 subjects from each stratum respectively. The same sampling fraction is used for each stratum regardless of the differences in population size of the strata.

## Disproportionate Stratified Random Sample

In disproportionate stratified random sampling, the different strata do not have the same sampling fractions as each other. For instance, if your four strata contain 200, 400, 600, and 800 people, you may choose to have different sampling fractions for each stratum. Perhaps the first stratum with 200 people has a sampling fraction of ½, resulting in 100 people selected for the sample, while the last stratum with 800 people has a sampling fraction of ¼, resulting in 200 people selected for the sample.

The precision of using disproportionate stratified random sampling is highly dependent on the sampling fractions chosen and used by the researcher. Here, the researcher must be very careful and know exactly what they are doing. Mistakes made in choosing and using sampling fractions could result in a stratum that is over-represented or under-represented, resulting in skewed results.

Using a stratified sample will always achieve greater precision than a simple random sample, provided that the strata have been chosen so that members of the same stratum are as similar as possible in terms of the characteristic of interest. The greater the differences between the strata, the greater the gain in precision.

Administratively, it is often more convenient to stratify a sample than to select a simple random sample. For instance, interviewers can be trained on how to best deal with one particular age or ethnic group, while others are trained on the best way to deal with a different age or ethnic group. This way the interviewers can concentrate on and refine a small set of skills and it is less timely and costly for the researcher.

A stratified sample can also be smaller in size than simple random samples, which can save a lot of time, money, and effort for the researchers. This is because this type of sampling technique has a high statistical precision compared to simple random sampling.

A final advantage is that a stratified sample guarantees better coverage of the population. The researcher has control over the subgroups that are included in the sample, whereas simple random sampling does not guarantee that any one type of person will be included in the final sample.