Non-Probability Sampling

Non-probability sampling should be used only when probability sampling is not an option. Samples obtained with Non-probability sampling are very likely to be biased. Non-probability sampling is a technique used to collect samples without the use of probability theory.

Non-Probability Sampling Remember that bias is introduced on the sampling method. A biased sample will remain biased regardless of its size.

Non-Probability Sampling There are some situations when you have no other choice but to use Non-Probability sampling (e.g. when you don’t have a sampling frame)

Non-Probability Sampling Convenience Sample -> researcher relies on available subjects Judgmental Sample -> researcher selects “representative” subjects Respondent Driven Sample (RDS) -> respondents select subjects Quota Sample -> respondents are selected based on some observable characteristics

Convenience sample The sample is drawn from that part of the population which is close to hand. The sample is readily available and convenient It is also known as: – Accidental sampling – Opportunity sampling

Convenience Sample Suppose I want a sample of CHHS students and I survey only my students in CHHS 385 That will be a convenient sample because I see them every week. This sample would be biased because those students are not representative of the entire population. CHHS 302 CHHS 211 CHHS 111 CHHS 302 CHHS 405 CHHS 385 CHHS 320 CHHS 400

Convenience Sample Other examples of convenience samples: A student wanted a sample of CSUMB students so she surveyed 100 students she found at Pete’s Coffee. The mayor of the city of Marina wanted to know if Marina residents are happy with the services the city offers so he surveyed 500 people in the Marina library. I wanted to survey 200 CSUMB students so I placed some posters all over the school asking for volunteers.

Judgmental sample The researcher chooses the sample based on who they think would be appropriate for the study. It is also known as Purposive Sampling

Judgmental sample It is usually used when there is a limited number of subjects that have special knowledge in the area being researched.

Respondent Driven Sample (RDS) In a RDS the researcher chooses a few subjects and then those subjects chose others and so on. It is also known as Snowball Sampling

Respondent Driven Sample (RDS) When the population is hidden RDS may be the only way of obtaining a sample. For example: Imagine you want a sample of CSUMB students who use cocaine. You are not going to find a list of all cocaine users at CSUMB but you may know one. So you give a survey to that student you know and ask her to pass some surveys along to other people they know that use cocaine.

Quota Sampling In quota sample the researcher chooses elements based on some observable characteristics of the population to make the sample representative in terms of those observable characteristics. Even though the sample will be representative in terms of the chosen observable characteristics the unobservable characteristics will likely make the sample biased Beware of quota samples because they may seem representative, but they are not representative in all characteristics.

Total population = 100 people = 65 (65 % of the population is orange) = 9 (9% of the population is green) = 22 (22% of the population is blue) = 4 (4% of the population is pink) Population of CHHS students Example of quota sample Suppose I want my sample of 10 CHHS students to be representative in terms of color. Then my quotas need to be in terms of color so my sample will need to look like the population in terms of color. Sample of = 10 people = 7 (70 % of the sample is orange) = 1 (10% of the sample is green) = 2 (20% of the sample is blue) = 1 (10% of the sample is pink)

Quota Sampling Beware of quota samples because they may seem representative, but they are not representative in all characteristics.

Summary Non- probability samples are not representative. They should only be used when probability samples are not feasible. There is a place for non-probability sampling in research. Always ask how the sample was collected before you read the results. If the sample was collected with non probability sampling then be ware of the results.