Plan non probability sampling with quotas, response assumptions, and operational targets. Compare methods, estimate contacts, and document every planning choice.
| Quota Group | Target % | Target N | Achieved N | Gap |
|---|---|---|---|---|
| Students | 30 | 120 | 114 | 6 |
| Working Adults | 45 | 180 | 168 | 12 |
| Retired Adults | 25 | 100 | 92 | 8 |
This sample table shows how quota targets, achieved counts, and remaining gaps can be reviewed before final fieldwork closes.
1. Reference planning benchmark
n = (Z² × p × (1 − p)) ÷ e²
2. Finite population correction
n_adj = n ÷ (1 + ((n − 1) ÷ N))
3. Non probability planning adjustment
adjusted target = benchmark × method factor × risk factor
4. Operational contact requirement
contacts = final responders ÷ (eligibility × response × completion × quality keep)
5. Quota allocation
group target = adjusted target × group share
Important note
These formulas support planning and operations. They do not transform non probability sampling into random sampling or guarantee unbiased population inference.
Non probability sampling selects participants without known random selection chances. Researchers often use it when speed, access, eligibility, or hidden populations matter more than population representativeness.
This calculator helps plan realistic fieldwork by converting a benchmark sample into an adjusted operational target. It also estimates raw contacts, quota counts, and expected yield across several practical methods.
Convenience sampling fits quick access contexts. Purposive sampling supports expert or criterion based recruitment. Quota sampling tracks subgroup balance. Snowball sampling helps with networked or hard to reach populations. Consecutive sampling works well in clinics and service points.
Because selection is not random, error margins should be treated as planning references instead of strict inferential guarantees. Strong screening, transparent assumptions, and careful reporting remain essential.
Use this page to document assumptions before launch, compare methods, and monitor whether field capacity can realistically meet the desired target.
It estimates planning targets for non probability sampling, including adjusted sample size, quotas, contact volume, yield, and method specific operational capacity.
Yes, as planning references. They help size fieldwork benchmarks, but they do not provide the same inferential meaning as random sampling.
Different methods carry different practical risks of imbalance and inefficiency. The factor adds a transparent planning cushion.
It is the share of completed cases expected to survive validation, duplicate checks, and data quality rules after collection.
Use quota inputs when you need subgroup balance, such as age, region, customer type, or another important composition rule.
It multiplies seeds, average recruits per seed, and referral waves to show a simple network recruitment projection for planning.
No. It supports planning, but researchers still need sound recruitment rules, screening logic, and transparent reporting.
Exports make it easier to share assumptions, archive planning decisions, and attach sampling plans to project documentation.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.