Purposeful Sampling Sample Clauses

Purposeful Sampling. Purposeful sampling is an appropriate strategy to identify participants who can provide information-rich data relative to the questions under study (▇▇▇▇▇▇▇▇, 2007; ▇▇▇, 2016; ▇▇▇▇▇▇▇▇, ▇▇▇▇▇▇▇, Green, Wisdom, Duan, & ▇▇▇▇▇▇▇▇ (2015). Purposeful sampling is used in qualitative research to select individuals who can “purposefully inform an understanding of the research problem and central phenomenon in the study (p. 125). Homogenous and snowball sampling work well to identify study participants with the requisite experience in using AI in organizational change efforts (▇▇▇▇▇▇▇▇ et al., 2015; ▇▇▇▇▇▇▇▇, 2007; ▇▇▇▇▇▇▇ & ▇▇▇▇▇▇, 2002). In the case of this study, homogenous sampling looks for participants with similar experiences (▇▇▇▇▇▇, 2002), and snowball sampling requests referrals from participants identified through homogenous sampling (▇▇▇▇▇▇▇ and ▇▇▇▇▇▇, 2002). Given this, I sought participants via the Taos Institute network of practitioners. The Taos Institute’s community of AI practitioners was an ideal source for homogenous and snowball sampling. Taos is recognized as the epicenter of AI in North America, especially given that its board comprises AI methodology and research founders. Taos is also the sponsor of AI conferences and events which attract researchers and practitioners from across the globe. This community of practitioners is particularly well suited for this study given their interest, willingness, and experience using positive change processes, such as AI (Wengraf, 2001).