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Stratified Random Sampling

The third type of random sampling is called stratified random sampling. Here is what you do:

First, stratify your sampling frame (e.g., divide it into the males and the females if you are using gender as your stratification variable).

Second, take a random sample from each of these groups (i.e., take a random sample of males and a random sample of females).

Third, put these two sets of people together and you have your final sample.

(Note: you can use systematic sampling for the second step if you want to.)

There are actually two different types of stratified sampling.

The first and most common type of stratified sampling is called proportional stratified sampling.

In proportional stratified sampling you must make sure your subsamples (e.g., the samples of males and females) are proportional to their sizes in the population.

Proportional stratified sampling is an equal probability sampling method (i.e., it is EPSEM), which is good!

The second type of stratified sampling is called disproportional stratified sampling.

In disproportional stratified sampling, the subsamples are not proportional to their

sizes in the population.

Here is an example showing the difference between proportional and disproportional stratified sampling:

Assume that your population is 75% female and 25% male. Assume that you want a sample of size 100 and you want to stratify on the variable called gender.

For proportional stratified sampling, you would randomly select 75 females and 25 males from the gender populations.   

For disproportional stratified sampling, you might randomly select 50 females and 50 males from the gender populations.

Cluster Random Sampling

In this type of sampling you randomly select clusters rather than randomly select individual type units (such as individual people) in the first stage of sampling.

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