Non-Response Bias Estimator
Estimate how much non-response may be skewing your survey results.
How the non-response bias estimate works
Non-response bias happens when the people who respond to a survey differ systematically from those who don't, skewing the results. Because non-respondents' true values are unknown, this calculator uses the standard wave-extrapolation method: treating late respondents (those who needed more reminders or effort to include) as a proxy for non-respondents.
Enter the mean of your outcome variable among all respondents, the mean among just the late-responding subgroup, how many people responded, and how many were invited. The calculator estimates what the full-sample mean might be, and how far your respondent-only mean may be from it.
This is a well-established estimation method (Armstrong & Overton, 1977), not a direct measurement of true non-response bias — treat the result as a diagnostic signal, and interpret a large estimated bias as a reason to investigate further, not a precise correction factor.
The formula
Estimated full mean = (Respondents × Respondent mean + Non-respondents × Late-respondent mean) ÷ Invited
Non-respondents are estimated as invited minus respondents. The late-respondent mean stands in for the unknown non-respondent mean. The bias estimate is the respondent-only mean minus this bias-adjusted estimate.
Worked example
800 of 1,000 invited responded (mean 60); late respondents averaged 45
Estimated full mean = (800×60 + 200×45) ÷ 1,000 = 57. Bias estimate = 60 − 57 = 3 points higher among respondents than the bias-adjusted estimate.
Frequently asked questions
Why use late respondents as a proxy for non-respondents?
Late respondents needed more effort or reminders to participate, similar to non-respondents. This "continuum of resistance" assumption is a well-documented, widely used approximation — not a guarantee that the two groups are identical.
What counts as a "late respondent"?
Typically the last wave of responses — for example, those who responded only after a second or third reminder, or in the final days of fieldwork.
What should I do if the estimated bias is large?
Treat it as a signal to investigate — consider weighting adjustments, additional outreach to under-represented groups, or reporting the limitation alongside your results rather than treating the respondent-only figure as unbiased.
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