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Technical survey manager
Carrying out surveys requires people with training and experience in all aspects of surveys, including planning, sampling, supervision, data cleaning and analysis, and writing the final report. Ideally, this person or persons would be on site during the entire process, but getting technical advice long-distance may also be feasible.
Team leaders have the ultimate responsibility for the data collected by their team. Therefore, they must observe all aspects of data collection in the field, including the interview, anthropometric measurements, data recording, etc. They must also be detail-oriented to catch as many mistakes as possible.
Data collection always results in some mistakes, such as skipping questions during the interview, failing to accurately record responses and measurements, and uncritically accepting illogical responses from the respondent. Many such mistakes result from the interviewer's inability to critically assess the responses obtained during an interview at the time of the interview. Team leaders must, therefore, review in detail each data collection form before the team leaves the household from which those data were collected.
Data collection teams
How do you decide who is needed on a survey team?
- Determine the tasks to be carried out at each household, for example:
- Interviewing an adult
- Weighing and measuring young children
- Physical examination of young children
- Obtaining biologic specimens from young children
- Decide what type of person needed to do each task
Other considerations regarding composition of survey teams:
- How many can fit in a vehicle?
- How many can fit in a survey subject's house?
- How do team members communicate with subjects?
- Can team members tolerate the rigors of field work?
Data entry personnel
Data entry personnel should have experience working on computers, ideally doing data entry. However, such people are not often available. Most data entry programmes have settings which can prevent most errors in data entry. In addition, many mistakes in data entry can be detected by entering the data twice and comparing the two datasets, comparison of a sample of the records entered two times, or comparing the dataset to the paper data collection forms after completion of data entry.