|Sampling method||Definition||Strengths||Limitations||Community engagement and rigor|
|Purposive Sampling [54, 55]||Strategy allows for selection of a sampling frame that may be most affected by a specific issue.||• Aims to maintain rigor and identify a sampling frame based on specific study driven variables or characteristics.||
• Requires collaboration from others to identify participants matching characteristics sought.|
• Can take time due to specific variables or characteristics sought.
|Convenience Sampling [10, 56]||Strategy uses existing relationships to identify participants.||
• Benefits from existing relationships to identify participants.|
• Can focus on recruitment from specific locations, settings or activities.
• Efficient and inexpensive.
• May complete quickly.
• May result in homogeneous sampling frame.|
• Limited generalizability to broader population.
• Less rigorous if organizations or partners do not follow a process to identify participants.
|Snowball Sampling [10, 29, 57]||Based on a referral approach where a small number of individuals with specific characteristics recruit others with these characteristics from their networks or community.||
• Reach to participants with same characteristics.|
• Often used in community engagement research studies and mixed methods approaches.
• Based on networks and relationships which may lend credibility to research.
• Referral contact may not be effective in identifying diverse individuals.|
• Referral contact may only identify participants meeting specific characteristics.
• Participants may not share information freely for fear of privacy or confidentiality – especially in qualitative study.
|Respondent Driven Sampling ||Used to reach hidden or most-vulnerable populations basing participation and reach on trust of respondent recruiting frame.||
• Seeds recruit a fixed number of participants.|
• Systematic information collected to identify potential biases.
• Requires training and time to capture and identify respondent relationships.|
• Reach may not be diverse.
• Bias if great percent of participants share characteristics.