Survey Pilot Participants
Typically, conducting a survey pilot requires several stages of planning and discussions. Apart from the impact evaluation team, survey pilot participants include interviewers, respondents, and even local government agencies. For instance, during the actual field data collection, such as in a computer-assisted personal interview (CAPI), it is the interviewer who reads out the questions to the respondent. But in the piloting stages before the actual survey, the field coordinators (FCs) and the principal investigators (PIs) oversee the overall preparation and finalize the survey protocols.
Read First
- Each of the pilot participants has a specific role during each of the three stages.
- Respondents should be as close to the target population as possible, but none of the sample for the actual survey should be included in the pilot.
- Interviewers should be fluent in all local languages of the study area and the language of the research team.
- Field coordinators (FCs) play a central role in organizing, managing, and supervising the pilot.
- Principal investigator (PI) should be briefed regularly during the pilot if not participating in person.
- Other research team members such as the impact evaluation coordinator, research manager, programmers, and translators should also participate in pilot discussions.
Participant Roles
| Stage 1 - Pre-Pilot | Stage 2 - Content-focused Pilot | Stage 3 - Data-focused Pilot | ||
|---|---|---|---|---|
| Respondents | Respondents for the pilot should be as similar as possible to respondents for the actual survey in terms of age, gender, education, and socioeconomic (income) status.
Neighboring villages, schools, or firms are good options for selecting respondents for the pilot. Never use any of these respondents in the final sample. |
Test the protocols for sampling in this stage. Use these protocols to select respondents for the pilot.
This helps to find out if the sampling frame (list from which respondents are selected) needs to be revised. It also helps to reduce selection bias. |
Re-test your sampling protocols.
Include revisions based on the previous round of piloting. | |
| Enumerators (or interviewers) | Language | Fully fluent (can read, write and/or speak) in the local language(s) and the language of research team. | Fully fluent in the local language(s) and the language of research team. | Fully fluent in the local language(s) and the language of research team. |
| Required experience | Qualitative experience as an enumerator. For instance, experience in identifying issues in wording and structure of questions.
Sectoral knowledge. For instance, knowledge about agricultural patterns in a survey that aims to collect data on farm output. |
Quantitative experience as an enumerator. For instance, experience in entering data electronically.
|
Quantitative experience as an enumerator.
Experience working with phones or tablets. | |
| Background | The pool of interviewers should be diverse in terms of age, race, religion, gender etc. | Interviewers should be mindful of (and learn about) cultural considerations. For instance, if women can interview men, or if men can interview women in the study area. | The background of the team of interviewers should be similar to that of the final survey team, which is decided based on previous stages of piloting. | |
| Size of the team | Very small - not more than 2.
A member of the research team should always accompany the enumerators. |
Small - between 2 and 4.
Ideally, a member of the research team should monitor each enumerator. |
Large - between 4 and 8.
More enumerators help simplify the process of monitoring data quality and debugging (identifying errors) in the coded instrument. | |
| Survey firm | Not part of this stage.
This stage is typically conducted before survey firm is on board. |
Not part of this stage.
This stage is typically conducted before survey firm is on board. |
Part of this stage.
In fact, the survey firm should lead the pilot in this round. | |
| Members of the research team | The field coordinators (FCs). Ideally they should participate in each stage.
The principal investigators (PIs) should participate directly. They should also conduct daily discussions to debrief (exchange notes with) the enumerators. |
The field coordinators (FCs).
|
The field coordinators (FCs).
Members of the research team who will be field supervisors in the actual survey should act as interviewers in this stage. This gives them experience with the instrument, which helps them conduct enumerator training. | |
| Others | Local research assistants (RAs). Includes short-term consultants (STCs) hired through the World Bank or through a local principal investigator (PI).
Local government staff. University students. |
Local research assistants (RAs).
Local government staff. University students. |
Local research assistants (RAs).
Local government staff. University students. |
Guidelines
Finally, the pilot participants should keep certain best practices in mind while planning and conducting a survey pilot. These can improve the outcomes of the pilot, for instance, by reducing the down-time (gap) between surveys.
- Throughout the process of planning the pilot, discuss with other members of the research team, and take note of what needs to be part of the pilot.
- Take careful notes of the clarifications that come up during the review sessions. These will be an important part of the enumerator manual which supervisors use to train enumerators.
- Hire a local mobilizer to coordinate with respondents. Mobilizers explain the purpose behind conducting the survey to the respondents, and facilitate the process of obtaining consent. This is particularly helpful in urban areas, or in cases where respondents are busy.
Related Pages
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Additional Resources
- DIME Analytics (World Bank), Team roles
- DIME Analytics (World Bank), Guidelines on piloting surveys
- DIME Analytics (World Bank), Survey instrument design and pilot
- DIME Analytics (World Bank), Survey preparing
- DIME Analytics (World Bank), Monitoring survey content
- DIME Analytics (World Bank), Monitoring data quality