15 Jun 2024

Last update 12 / 09 / 2017

Responsibilities of Field Researcher in Household Energy Survey

 

The field work across all statistical activities involves interaction between interviewers and data providers who feature various educational, cultural and social levels. Therefore, the field team should focus on a number of key factors when visiting and interviewing a household as follows:

1.Choose the right time to visit a household.

2.The researcher starts with introducing himself to the household, briefing them on his task and showing his GaStat ID.

3.The researcher should not get carries away, get into personal matters, nor cite another household’s data.

4.Ask questions in an easy and tactful manner..

5.Putting questions in the actual wording of GaStat, with neither shortening nor modification..

6.Make sure to get the correct and precise answer from interviewee, with neither suggesting nor implying specific answers.

7.Take enough time to ask questions, without any rush to end the interview.

8.Comply with instructions contained in the training manual.

9.The researcher records the data according to the data collection method instructions, and ensures that all fields are completed before leaving.

10.Ensure that all answers are correct and logical before recording them in the device.

11.Stick to scheduled times for field and office work.

12.Keep data confidential, and never allow any access to others’ data.

13.At the end of the visit, the researcher should thank the household for their cooperation and responsiveness.

14.In case the household is temporarily absent, the researcher revisits it during the survey days at different times. If the household does not show up, the household survey status is other, specified as (temporarily absent).

15.Field and office audit is done on day-to-days basis.

16.Handing devices over to inspector after ensuring that all data have been recorded and finally audited.

17.Remember: The Royal Decree authorizing you to collect data holds you liable in the following cases:

  • Compromising data accuracy.
  • Intentional errors.
  • Negligence.
  • Non-compliance with data confidentiality.

 

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