Sampling methods

You can't survey every counselor in America, so you study a sample and hope it mirrors the population. The single most important fork on the NCE is this: did every member of the population have a known, nonzero chance of being chosen? If yes, it's probability sampling (the kind that supports generalization). If no, it's nonprobability sampling.

Probability methods all involve some form of random selection. Simple random sampling — every member has an equal chance (think names in a hat). Stratified sampling — divide the population into subgroups (strata) like age or gender, then randomly sample within each to guarantee representation. Cluster sampling — divide the population into naturally occurring groups (clusters) like schools or clinics, then randomly select whole clusters (efficient when the population is geographically spread out). Systematic sampling — pick every nth person from a list (e.g., every 10th name) after a random start.

Nonprobability methods sacrifice random selection for convenience or purpose. Convenience sampling — use whoever's easily available (the classic "psych majors for extra credit"). Purposive sampling — the researcher deliberately selects participants who fit specific criteria. Snowball sampling — existing participants refer others (great for hard-to-reach or hidden populations). Quota sampling — fill preset category counts (e.g., 50 men, 50 women) without random selection — it's the nonprobability cousin of stratified sampling.

Clinical Pearl: The dividing line is random selection: probability sampling (simple random, stratified, cluster, systematic) permits generalization to the population; nonprobability sampling (convenience, purposive, snowball, quota) does not. Don't confuse stratified (random within strata) with its look-alike quota (non-random category fill).