Copyright

Non-Probability Sampling Methods: Definition & Types

An error occurred trying to load this video.

Try refreshing the page, or contact customer support.

Coming up next: Issues in Probability & Non-Probability Sampling

You're on a roll. Keep up the good work!

Take Quiz Watch Next Lesson
 Replay
Your next lesson will play in 10 seconds
  • 0:05 Non-Probability Sampling
  • 1:08 Convenience
  • 2:44 Quota
  • 4:30 Judgmental
  • 6:05 Lesson Summary
Add to Add to Add to

Want to watch this again later?

Log in or sign up to add this lesson to a Custom Course.

Login or Sign up

Timeline
Autoplay
Autoplay

Recommended Lessons and Courses for You

Lesson Transcript
Instructor: Natalie Boyd

Natalie is a teacher and holds an MA in English Education and is in progress on her PhD in psychology.

There are many different ways to choose a sample for a research study. In this lesson, we'll look at three types of non-probability sampling: convenience, quota, and judgmental (or purposive sampling) and when to use each type.

Non-Probability Sampling

Kiera is a psychologist. She's interested in studying why people believe the way they do about the death penalty. She puts together a survey asking people for reasons to support their side of the capital punishment debate.

But who should Kiera give the survey to? She wants her research to say something about adults over age 18 in the United States, but it wouldn't be possible for her to give the survey to every American adult. That would take forever!

So, Kiera needs to develop a sample, or group of subjects. This is done through a process called sampling. The goal is to choose a sample that represents the whole population so that Kiera can make inferences about the population from her sample.

One major category of sampling techniques is called non-probability sampling. In non-probability sampling, subjects are chosen to be part of the sample in non-random ways. Let's look closer at three non-probability sampling methods - convenience, quota, and judgmental sampling.

Convenience

OK, so Kiera wants to give her survey to a sample of people in order to learn why Americans feel the way they do about capital punishment. She and her two research assistants go to a shopping mall on a Tuesday morning and stop people to ask their opinion on the death penalty and why they feel that way.

Kiera is using the convenience sampling method, which is just what it sounds like: a researcher selects the sample based on convenience. The subjects selected to be part of the study's sample are there and are available to be tested.

Convenience sampling has a major problem: the people who are readily available are not necessarily representative of the population at large. Think about Kiera's study; if she and her research assistants poll the people at a shopping mall on a Tuesday morning, their sample is limited to subjects who are at a shopping mall on a Tuesday morning. Anyone with a nine-to-five job (which includes most adults in America) will be at work, not at the mall, which means that they won't be part of Kiera's sample. That's a problem!

With the problem of non-representativeness, you might be wondering why researchers would ever use convenience sampling. The answer is in its name: convenience. Psychologists do this a lot. If they teach at a university, they are most likely doing research on university students.

The truth is, it's not always practical to use a method other than convenience sampling. Other sampling methods might yield a better sample, but they also cost more in time and money, so many researchers end up using convenience sampling.

Quota

For a moment, though, let's say that Kiera and her research assistants are able to go to a mall at a time when the entire population of American adults is represented. She still has to choose which people to survey. How should she do that?

One way to choose is to use the quota sampling method, which involves setting quotas based on demographic information but not randomly selecting subjects for each quota.

For example, let's say that Kiera knows that approximately 51% of U.S. adults are women. She might tell her research assistants to interview 51 women and 49 men, a quota that roughly corresponds to the demographics for the population. However, the 51 women and 49 men are not chosen randomly; her assistants can choose which women and men to give the survey to.

The good thing about quota sampling is that the demographics are approximately correct for the population, especially if you make quotas for several different demographic categories. For example, Kiera can set quotas not only for gender but for race, age, income level, employment status, political party affiliation, or a host of other categories. The more categories there are, the more likely she will have a sample that represents the population.

To unlock this lesson you must be a Study.com Member.
Create your account

Register for a free trial

Are you a student or a teacher?

Unlock Your Education

See for yourself why 30 million people use Study.com

Become a Study.com member and start learning now.
Become a Member  Back
What teachers are saying about Study.com
Free 5-day trial

Earning College Credit

Did you know… We have over 160 college courses that prepare you to earn credit by exam that is accepted by over 1,500 colleges and universities. You can test out of the first two years of college and save thousands off your degree. Anyone can earn credit-by-exam regardless of age or education level.

To learn more, visit our Earning Credit Page

Transferring credit to the school of your choice

Not sure what college you want to attend yet? Study.com has thousands of articles about every imaginable degree, area of study and career path that can help you find the school that's right for you.

Create an account to start this course today
Try it free for 5 days!
Create An Account
Support