Table 1 Some examples of purposeful sampling methods [14]

Extreme or deviant case sampling

• Selecting illuminative cases that exemplify ‘extreme’ or ‘deviant’ contexts or examples, for instance:

  – where an innovation in a primary study was perceived notably as a success or failure

  – where findings of a primary study are very different from those of most studies identified for the synthesis

Maximum variation sampling

• Constructed by:

  – identifying key dimensions of variation, and then

  – finding cases that vary from each other as much as possible along these dimensions

• This sampling yields:

  – ‘high-quality, detailed descriptions of each case, which are useful for documenting uniqueness, and

  – important shared patterns that cut across cases and derive their significance from having emerged out of heterogeneity’ (Patton, 2002, p. 235)

Snowball or chain sampling

• Trying to locate a key work in the field through talking with experts or locating a key article that is often cited

• Then follow on with primary studies that have cited the key or landmark study

Theoretical or operational construct sampling

• Selecting cases that represent important theoretical or operational constructs about the phenomenon of interest

• Set out operational definitions of key theories or constructs related to the phenomenon of interest

• Develop boundaries for these by creating specific inclusion and exclusion criteria in relation to selecting primary studies for the synthesis

Criterion sampling

• Used by those trying to construct a comprehensive understanding

• Studies are sampled based on a predetermined criteria

• Specific inclusion and exclusion criteria are clearly stated

• Studies are then analysed as a whole

Stratified purposeful sampling

• Following on from criterion sampling where each of the criteria would become a sample

• Stratified samples are samples within samples where each stratum, or group, is fairly homogenous and are analysed within these groups

• Useful for examining variation in a key phenomena of interest

Purposeful random sampling

• Randomly select from the list of included studies for inclusion in the analysis

• For example, use a random internet based selector, choose every 3rd included study or pull study names from a hat

• Provides an unbiased way of selecting studies for inclusion but may not provide studies with rich data

Combination or mixed purposeful sampling

• Choosing a combination or mix of sampling strategies to best fit your purpose

  – For some syntheses, it may be useful to use a combination or mix of sampling strategies. For instance, by applying theoretical sampling in a first stage and deviant case sampling in a second stage. This should be guided by the review methods and purpose, and the time available