survey-methodology

You can calculate sample size and design effect for stratified sampling by applying deterministic Python tooling using numpy and scipy to estimate effective sample size and evaluate complex survey designs.

Raking is a survey weighting technique that adjusts sample distributions to match known population margins. This skill computes raked weights and audits non-response patterns to mitigate bias and produce credible survey estimates.

Yes, you can use pandas and statsmodels alongside structured methodology to evaluate questionnaire design, ensuring clear wording, appropriate question types, and balanced scale construction to reduce common measurement errors.

The best way to design a multi-stage cluster sampling strategy for a web survey is to follow a structured methodology covering SRS, stratified, and cluster designs while applying deterministic Python sampling logic.

Yes, this approach supports cross-cultural adaptation by providing a translation and adaptation protocol that includes cognitive interviewing and pilot validation to ensure survey instrument reliability across different populations.

You should not use simple random sampling when your target population requires stratified or multi-stage cluster sampling to ensure representative coverage, or when complex design effects and non-response patterns demand advanced weighting adjustments.