Survey Sampling Methods – Probability vs Non-probability
Probability and Non-probability Survey Sampling Methods – What the Difference?
As stated in this article on Wikipedia, survey sampling consists of two variations; probability and non-probability sampling.
Probability sampling methods means that everyone in the population has a chance of being sampled, and you can determine what the probability of people being sampled is.
Probability sampling includes: Simple Random Sampling, Systematic Sampling, Stratified Sampling, Probability Proportional to Size Sampling, and Cluster or Multistage Sampling. And have these elements in common
- Every one has a known (calculated) chance of being sampled
- There is random selection
Non-probability sampling means that you have excluded some of the population in your sample, and that exact number can not be calculated – meaning there are limits on how much you can determine about the population from the sample.
Simple Random Sampling
Random sampling, in its simplest and purest form, means that each member of the population has an equal (and known) chance at being selected. In a large population, this becomes prohibitive for cost and technical reasons, so the actual pool of respondents becomes biased.
This method is often preferable to simple random sampling, as you select members of the population systematically – that is, every Nth record. As long as there is no ordering of the list, the sampling method is just as good as random – only much simpler to manage.
This is a more commonly used techniques, and the population is divided into subsets with a common trait, or “strata”, and then random sampling is performed to reduce sampling bias. The key is to ensure that the sample size is large enough to represent the population.
Non-probability Sampling Methods
One of the most cost-effective sampling methods, researchers choose this method as they can recruit the sample from the population that is close at hand, or convenient to them. It is up to the researcher to ensure that a large enough sample is chosen that can closely represent the population being studied.
An extension of this is judgement sampling, where the research selects a representative sample based on their judgement.
Very similar to stratified sampling, where the researcher defines the segments or “stratums” and their representative proportion in the population – quota sampling differs in that respondents are typically filled by convenience or judgement sampling, vs random.
There is another method of acquiring respondents called snowball sampling, where initial subjects refer others to take the survey.