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ssss1 Basic probability and nonprobability sampling designs

Method Purposes Procedures Probability methods Simple random sampling Generate representative sample when adequate sampling frame is available List all members of population (sampling frame); select subset at random Systematic random sampling Generate representative sample; may not need to enumerate entire sampling frame Select random starting point in sampling frame; select every N^th case Stratified random sampling Ensure that key subpopulations are represented; maximize between-group variation to increase precision Divide population into subgroups; select random sample from each subgroup Cluster sampling Generate representative sample when no convenient sampling frame exists; sample dispersed populations efficiently Divide population into clusters (e.g., neighborhoods, clinics); select random sample of clusters; sample within clusters Nonprobability methods Purposive sampling Sample theoretically important dimensions of variation Identify important theoretical criteria; select cases to satisfy criteria; multiple criteria-based methods are available Quota sampling Generate sample with fixed proportions of key subpopulations Divide population into subgroups; purposively select cases to fill quotas Chain referral (snowball, respondent-driven sampling [RDS]) Construct sample of hardto- find or hard-to-study populations Snowball: ask seed informants to recommend others who might participate RDS: Use structured incentives to reduce bias in selection Convenience (haphazard) sampling Recruit participants when no other methods are feasible Select groups or individuals that happen to be available and willing to participate

One of the most common nonprobability sampling designs is quota sampling. Quota sampling involves identifying relevant subgroups in a population and sampling fixed proportions from each subgroup. Schoenberg et al. (2005) used quota sampling to explore differences and similarities in lay knowledge about diabetes between African Americans, Mexican Americans, Great Lakes Indians, and rural Whites. They set a quota of 20 participants from each group. This design balanced a desire for larger subsample sizes against practical constraints on the number of time-intensive, in-depth interviews researchers could complete. Within each group, Schoenberg et al. selected respondents whose age, ethnicity, and residential area increased the likelihood of experiencing diabetes. This strategy reflects the theoretical purpose of sampling cultural knowledge rather than estimating individual attributes.