Sampling is the process of selecting units (e.g., people, organizations, time periods) from a population of interest, studying these in greater detail and then drawing conclusions about the larger population to study them in greater detail.


Consider why you want to study your population of interest and what you want to do with the information that you have gathered, before you choose your option.

There are three clusters of sampling options: Probability; Purposive (or Purposeful); and Convenience. 


Probability sampling options use random or quasi-random options to select the sample, and then use statistical generalization to draw inferences about that population. To minimize bias, these options have specific rules on selection of the sampling frame, size of the sample, and managing variation within the sample. The options include:

  • Multi-stage: cluster sampling in which larger clusters are further subdivided into smaller, more targeted groupings for the purposes of surveying.
  • Sequential: selecting  every nth case from a list (e.g. every 10th client)
  • Simple random: drawing a sample from the population completely at random.
  • Stratified random: splitting the population into strata (sections or segments) in order to ensure distinct categories are adequately represented before selecting a random sample from each.

Purposive (or Purposeful)

Purposive sampling options study information-rich cases from a given population to make analytical inferences about the population. Units are selected based on one or more predetermined characteristics and the sample size can be as small as one (n=1). To minimize bias, this cluster of options encourages transparency in case selection, triangulation, and seeking out of disconfirming evidence. The options are:

  • Confirming and disconfirming: cases that match existing patterns (to explore them) and those that don’t match (to test them).
  • Criterion: cases that meet a particular condition
  • Critical case: a case of particular importance, or that can make a strong point 
  • Homogenous: cases that are very similar to each other.
  • Intensity: selecting cases which exhibit a particular phenomenon intensely.
  • Maximum variation: contains cases that are as different from each other as possible.
  • Outlier: analysing cases that are unusual or special in some way, such as outstanding successes or notable failures.
  • Snowball: asking initial informants to identify additional informants,  creating a snowball effect as the sample gets bigger and bigger 
  • Theory-based: selecting cases according to the extent to which they represent a particular theoretical construct.
  • Typical case: developing a profile of what is agreed as average, or normal.


Convenience sampling is a cluster of options that use samples which are readily available and which may not allow credible inference about the population. Convenience options are:

  • Convenience: based on the ease or "convenience" of gaining access to a sample. simply in which data is gathered from people who are readily available.
  • Volunteer: sampling by simply asking for volunteers





M. Q. Patton (2001 ) Qualitative Research and Evaluation Methods (3rd edition). Thousand Oaks, CA: Sage Publications.




Sberson's picture
Stephen Berson

I'm a bit unclear about sequential sampling. This method is grouped in the "probability" category in this page, but in the description of this method, it is stated that "sequential sampling is a non-probabilistic sampling technique."

Babajide Ogunsanya's picture
Babajide Ogunsanya

Good to join you all on this platform. l look forward to sharing experiences. I , however need further enlightenment on using only one unit as sample size. Would that guarantee precision and/or accuracy of estimation procedure therefrom?For me, from experience, the tradeoff might be too luxurious.