Managing Surveyor Performance
This guide covers how field managers, field supervisors, and team leaders can plan productivity targets, set monitoring schedules, manage sample assignments, and maintain data quality and team motivation throughout data collection.
- Monitoring targets complement automated quality checks. Structured in-person monitoring by supervisors adds a layer of real-time observation that captures interview dynamics, visual aid use, and surveyor-respondent interaction.
- Targets are minimums, not ceilings. Monitoring targets are distributed across roles and should be met consistently, with coverage spread evenly across surveyors and teams.
- Motivation and accountability go together. Effective performance management combines public recognition of strong performers with private, constructive feedback for those who struggle.
Why Monitor Surveyor Performance?
Surveyor productivity directly affects survey budgets, timelines, and data quality. A study that falls behind on completed interviews may exhaust its budget before reaching the target sample; one with undetected interviewer errors produces data that is difficult or impossible to correct after the fact.
High-frequency checks (HFCs) and audio audits are essential tools for detecting data quality issues at scale. Structured in-person monitoring by supervisors complements these tools by capturing dimensions that automated checks cannot: whether a surveyor used the correct visual aids, interacted appropriately with the respondent, or followed skip patterns correctly in real time.
Surveyor productivity refers to what interviewers achieve when working to gain respondent cooperation and complete interviews. Common measures include the number of completed surveys per day, the number of contact attempts per respondent, refusal rates, average interview length, and monitoring scores. Tracking these measures consistently makes it possible to identify underperformance early, before it affects the study timeline or budget.
Before Data Collection Begins
Set productivity targets
Before data collection begins, field managers should define and communicate a minimum daily productivity target for all interviewers. The target should be grounded in realistic assumptions about interview length, travel or call time, and expected non-response. Without an explicit minimum, teams have no clear standard against which to measure underperformance or forecast whether the study will complete on time.
The examples below illustrate how productivity targets can be structured for two common data collection modes. The numbers are illustrative; each study should calculate its own targets based on its specific context.
In an 8-hour workday, an interviewer conducting 40-minute phone interviews could theoretically complete around 10 interviews if all contacts were successful. In practice, many calls result in no answer, a refusal, or a callback, so a realistic daily minimum might be 6 to 8 completed interviews. Supervisors monitor quality through live listening: joining the call in real time and completing an evaluation form for a sample of each interviewer’s calls each week.
The table below shows how a productivity framework maps completed surveys to the remaining call attempts required to keep pace with the daily target. Note that “additional calls required” refers to short contact attempts of 1 to 2 minutes each, not full interviews: in phone surveys, interviewers must cycle through many unanswered calls and callbacks before reaching a respondent, so the remaining time is used for these attempts.
| Completed surveys | Hours used | Hours remaining | Additional calls required |
|---|---|---|---|
| 8 or more | 5.3 | 1.7 | 0 |
| 7 | 4.7 | 2.3 | 28 |
| 6 | 4.0 | 3.0 | 36 |
| 5 | 3.3 | 3.7 | 44 |
| 4 | 2.7 | 4.3 | 52 |
| 3 | 2.0 | 5.0 | 60 |
| 2 | 1.3 | 5.7 | 68 |
| 1 | 0.7 | 6.3 | 76 |
| 0 | 0.0 | 7.0 | 84 |
This framework was adapted from the Ghana Core RECOVR Study (Tsinigo 2020), where interviewers were assigned samples through a centralized dashboard and minimum call expectations were updated daily based on each interviewer’s progress.
For in-person surveys, travel time between households significantly reduces effective working time, particularly when the sample is geographically dispersed. A surveyor conducting 45-minute interviews in a dense urban area might complete 5 to 7 interviews per day; in a rural area with long travel distances between households, the same interviewer might complete only 3 to 4.
Field managers should calculate realistic daily minimums before data collection begins by piloting the survey route, estimating travel time per household, and accounting for refusals and callbacks. A useful benchmark is to track completed surveys per effective working hour rather than per day, which adjusts naturally for differences in geographic density across teams and sites.
Monitoring surveyor progress against this target daily, rather than weekly, allows field managers to identify and address underperformance before it compounds into a delay that threatens the study timeline.
Set monitoring targets by role
Monitoring targets define the minimum number of interview observations that each supervisory role must complete and submit during data collection. For in-person surveys, this means a supervisor accompanies or observes a surveyor in the field and completes an evaluation form assessing interview quality. For phone surveys, the equivalent is live listening: the supervisor listens to an interview in real time and completes the same evaluation form. These targets are not the ceiling: teams are encouraged to exceed them. Coverage should be distributed evenly across all surveyors and teams so that no individual is systematically over- or under-monitored.
The table below shows standard minimum monitoring targets by role. Targets are higher during the first two weeks of data collection, when surveyors are still learning the instrument and errors are most likely to occur.
| Role | Weeks 1 and 2 | Subsequent weeks |
|---|---|---|
| Field Manager | Observations for at least 20% of each enumerator’s weekly output, evenly distributed; observations for all Team Leaders and Field Supervisors | Observations for at least 10% of each enumerator’s weekly output, evenly distributed; observations for all Team Leaders and Field Supervisors |
| Field Supervisor | At least 10% observations per surveyor weekly; at least two observations per Team Leader per week | At least 7% observations per surveyor weekly; at least one observation per Team Leader per week |
| Team Leader | At least 15% observations per enumerator weekly | At least 10% observations per enumerator weekly |
These targets are minimum mandatory requirements. In situations where targets cannot be met due to insufficient supervisory staff, temporarily reassign staff from other projects to cover the first two weeks of data collection, when coverage is most critical.
Implement audio audits only after obtaining IRB approval. Confirm that the consent form covers audio recording of interviews before any monitoring of this type begins.
Set up a sample management system
A sample management system is a structured tool, digital or paper-based, used to assign respondents to interviewers, track contact attempts, and record interview outcomes. The field manager or research coordinator should set this up before data collection begins and maintain a central dashboard that includes at minimum: survey completion status by interviewer, call or visit records, non-response tracking, and overall survey statistics.
Key principles for managing the sample:
- Assign the responsibility for allocating and reassigning respondents to a single designated person, typically the programmer or research coordinator.
- Do not allow interviewers to exchange sample assignments among themselves without explicit supervisor approval. Unauthorized sample switching creates tracking errors and potential bias.
- Consider whether matching interviewers to respondents on a characteristic such as language or gender will improve cooperation rates. If respondent characteristics are unknown before data collection begins, establish a protocol for making such matching possible once contact is made.
During Data Collection
Handle refusals and non-response
Non-response reduces effective sample size and can introduce bias if refusals are correlated with the outcome of interest. Field managers should address refusals actively rather than treating them as a fixed feature of the study.
Practical approaches include developing and training interviewers on approved reluctance aversion scripts and consent statements tailored to common concerns in the study context, holding regular debriefings to identify emerging sources of reluctance and adjusting scripts accordingly, retraining interviewers whose refusal rates are consistently above the team average, and using non-response data to inform adjustment protocols in the analysis phase.
When offering participation incentives, adapt the type and amount to local customs and present them as a token of appreciation, not payment for responses. Document incentive use fully, including amount, type, timing, and any adjustments made during the study period.
Maintain motivation and accountability
Sustaining surveyor performance over a long data collection period requires consistent attention to both motivation and accountability. The two are most effective when applied together: publicly recognizing strong performers while addressing underperformance privately.
Approaches that have worked in IPA projects include random check-in calls with interviewers during the workday, congratulatory messages to top performers, performance-based bonuses, and debriefing sessions that give interviewers a structured opportunity to share qualitative observations from the field. Where performance falls persistently below expectations despite retraining, dismissal may be necessary; the expectation that underperformance has consequences is itself a motivating signal to the broader team.
The project management team should model the behavior expected of interviewers. Punctuality, preparation, and professional conduct from supervisors set the standard for the rest of the team.