PURPOSIVE SAMPLING: A REVIEW AND GUIDELINES FOR QUANTITATIVE RESEARCH - Memon, Mumtaz Ali; Thurasamy, Ramayah; Ting, Hiram; Cheah, Jun-Hwa (Journal of Applied Structural Equation Modelling, 2025)
Purposive sampling has been extensively utilized across research disciplines, particularly in business and management studies. While existing literature has primarily focused on its application in qualitative research, the rationale for using it in quantitative contexts remains underexplored, leaving researchers with limited practical guidance. Moreover, much of the available literature is found within academic texts, specifically books, which may not always provide sufficiently actionable guidance on successfully implementing and reporting purposive sampling in quantitative research. This editorial addresses these gaps by presenting structured guidelines specifically tailored for applying purposive sampling in quantitative research. It provides practical recommendations for systematically implementing and transparently reporting purposive sampling. The editorial discusses different types of purposive sampling, including criterion sampling, maximum variation sampling, and theoretical sampling, and illustrates their applications in quantitative contexts. It also discusses how purposive sampling can be combined with other techniques, such as snowball sampling and quota sampling, to enhance data quality and relevance. Detailed step-by-step guidelines are presented for employing purposive sampling and reporting its use in quantitative studies in a robust manner. Additionally, we put forward a salient checklist for authors and reviewers to ensure rigor, transparency, and consistency in reporting. This work contributes to the growing discourse about sampling by presenting one of the first comprehensive frameworks for employing purposive sampling in quantitative research. By guiding researchers to adopt purposive sampling with greater rigor and transparency, we hope it will be useful to establish the representativeness of data and provide more credible estimations to explain or predict the phenomena under investigations.