Purposive sampling in research: Types, justifications, applications, and limitations

Purposive sampling, also termed purposeful or judgmental sampling, is one of the most widely used non-probability sampling approaches in qualitative, quantitative, and mixed-methods research. It involves the deliberate selection of participants based on characteristics, experiences, or knowledge relevant to the research question, prioritizing information-rich cases that provide depth of understanding rather than statistical generalizability. Despite its widespread use, purposive sampling remains frequently misunderstood, inconsistently reported, and often conflated with convenience sampling. This commentary provides a consolidated overview of eleven purposive sampling strategies: maximum variation, homogeneous, typical case, extreme or deviant case, criterion, snowball, theoretical, stratified purposive, quota, purposeful random, and matrix sampling. It advances three methodological positions: convenience sampling is conceptually distinct from purposive sampling and should not be classified as a subtype; theoretical sampling represents a distinct methodology within grounded theory and should not be treated as interchangeable with purposive sampling; and information richness provides a more epistemologically appropriate framework than data saturation for evaluating sample adequacy in most purposive sampling contexts. To support practical application, this commentary introduces a summary table of sampling types, definitions, applications, limitations, and examples (Table 1); a ten-item reporting checklist aligned with COREQ and SRQR (Table 2); and a decision flowchart for strategy selection (Figure 1). These tools aim to assist researchers, reviewers, and educators in selecting, justifying, and transparently reporting purposive sampling decisions.