What is Nonresponse Bias?
Nonresponse bias occurs when people who don’t answer a survey differ in important ways from those who do, causing results to misrepresent the true views of the whole population.
Nonresponse bias is a survey error that arises when the characteristics or opinions of non-respondents differ systematically from respondents. In workplace research this can happen if, for example, unhappy employees avoid a survey because they distrust confidentiality, or remote workers are less likely to see an invitation. Nonresponse bias can skew measures like engagement, satisfaction or facility usage, producing results that are not representative of the target group even if the survey had many responses.
Usage example
A facilities team runs a desk-booking survey and sees high satisfaction with the new hot-desking setup. Later they learn that several teams who rarely come into the office didn’t respond. If those non-responding teams are less satisfied, the published results may overstate overall satisfaction due to nonresponse bias.
Practical application
Nonresponse bias matters because workplace decisions—office redesigns, hybrid policies, wellbeing investments—rely on accurate feedback. Biased results can lead to wrong priorities or missed problems. To reduce risk: design clear, short surveys; promote participation through managers and multiple channels (email, Slack, Teams); guarantee and communicate confidentiality; use reminders and targeted follow-ups for under-represented groups; and where possible apply statistical adjustments (weighting, sensitivity analysis) and compare respondent demographics with known population data. Recognising and addressing nonresponse bias improves the credibility of findings and the confidence of stakeholders when making workplace changes.
FAQ
Is a low response rate the same as nonresponse bias?
Not necessarily. A low response rate increases the risk of nonresponse bias, but bias only exists if non-respondents differ systematically from respondents on things you care about. Conversely, a high response rate reduces risk but does not guarantee the absence of bias.
How can we tell if nonresponse bias is affecting our results?
Compare respondent demographics (team, location, role, tenure) with known population shares; look for unusually high or low scores in small respondent groups; run follow-up surveys or targeted outreach to non-responding segments; and perform sensitivity checks to see how results change under plausible assumptions about non-respondents’ views.
Can weighting and imputation fully fix nonresponse bias?
Weighting and imputation can reduce bias when you have reliable population benchmarks (e.g., headcount by team or location) and when differences are correlated with those benchmarks. They cannot fully correct bias if nonresponse is linked to unmeasured factors (for example, employees’ hidden dissatisfaction). Use these techniques alongside efforts to increase response and targeted follow-ups.
What practical steps can workplace teams take to reduce nonresponse bias?
Communicate survey purpose and confidentiality clearly, keep surveys short and relevant, use multiple distribution channels and reminders, involve managers to encourage participation, offer anonymous response options, track response rates by subgroup, and run targeted reminders or brief follow-up pulses for under-represented groups.