Stratified Random Samples: Definition, Characteristics & Examples

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  • 0:03 Stratified Random Sampling
  • 0:38 What is Stratified…
  • 2:16 Using Stratified…
  • 5:14 Lesson Summary
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Lesson Transcript
Instructor: Cathryn Jackson

Cat has taught a variety of subjects, including communications, mathematics, and technology. Cat has a master's degree in education and is currently working on her Ph.D.

Random sampling isn't always simple! There are many different types of sampling. In this lesson, you will learn how to use stratified random sampling and when it is most appropriate to use it.

Stratified Random Sampling

Jackie is the president of the party planning committee of her school. Right now, the party planning committee is planning a winter formal. She is researching the different DJs that are available to work at the winter formal. Each DJ has a different percentage of music that he or she is able to play.

Jackie needs to find the right DJ for the winter formal so that the students of her school are happy with the music choices. Jackie can't ask every member of the school his or her music preferences. How can she figure out which DJ to hire?

In this lesson, you will learn about stratified random sampling, what it is, and how to use it.

What Is Stratified Random Sampling?

Stratified random sampling is a random sampling method where you divide members of a population into 'strata,' or homogeneous subgroups.

Take a look at this chart:

Chart for example
chart with music genre data

This is the percentage of each music genre that each DJ will play. DJ Thunder Cat will only play 60% rock, 20% pop, 15% hip hop, and 5% country. DJ XtremeMix will only play 75% hip hop, 20% pop, 5% rock, and no country. DJ Midnight will only play 50% pop, 35% country, 10% hip hop, and 5% rock. Each of these genres is an example of a strata, or a homogeneous subgroup. The group or population is music, while the strata is each kind of music.

Stratified random sampling works the same way. Jackie doesn't have the ability to question every student in the school. However, she can use stratified random sampling to get an understanding of the music tastes of the students in the school. Jackie can divide the student body into different strata, or subgroups, and then ask each of these subgroups what types of music they prefer.

Stratified random sampling is different from other types of sampling because you are separating the population into groups first. You must be very familiar with the demographics of your population if you intend on using stratified random sampling. Let's discuss how to use stratified sampling and the ways you can use this sampling in an experiment.

Using Stratified Random Sampling

Jackie decides that a stratified random sample may be the best way to collect her information. When dividing the school into stratified random samples, she must keep two things in mind:

  1. Stratified random samples cannot have crossover.
  2. Stratified random samples must include all members of a population.

Stratified random samples cannot have crossover. In other words, each of the strata must be mutually exclusive. In Jackie's case, she must choose some type of group in which each student is a part of one group but not more than one. Jackie could use something like age or school classification, such as freshman, sophomore, junior, and senior. Jackie decides the simplest way to collect her data is to divide her subgroups by school classification, like this:

Subgroups by school classification
chart showing school classification

Stratified random samples must also include all members of a population. In Jackie's case, she has included all members of the population because all students must be under one of the four classifications.

Stratified random samples are best used when the researcher is familiar with the demographics of the population and the proportion of the demographics are important to the data being collected. With that said, it is important to have a small amount of strata when collecting data. No more than four to six strata is recommended, but you can theoretically have as many strata as you want. You want to keep the sample proportionate to the experiment.

Take a look at this chart:

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