The data entry process is the transfer of the data from the questionnaires to the
database. Ideally, the database will accurately reflect all of the data captured in the
questionnaires. Any difference between the data recorded in the questionnaires and
data in the database is considered data entry error. To minimize data entry error,
conduct a thorough training of the data entry team, supervise the data entry process
and conduct spot checks, and, lastly, clean the data once entered.
Train the data entry team, enterers and supervisors on the survey objectives, the
layout of the questionnaire, and on the database and the protocol for data entry. The
data enterers should be comfortable with the questionnaire layout and any skip rules
included, and they should be aware of any potential errors in data collection.
If possible, include the data entry team in the training given to the data collectors on
the survey tools. If this is not possible, conduct a separate training for the data entry
team to ensure they are familiar with the tools used and the objectives of the survey.
Assemble the team
Determine the number of data enterers needed based on the volume of data and the
timeline for completing data entry. Select data enterers who have a background in
data entry and are comfortable using the data entry program you have selected.
Identify a supervisor, perhaps one of the data enterers with additional experience, to
oversee the data entry process. The supervisor will enter data like the rest of the
team but also is responsible for checking the work of others and for backing up the
data each day.
Train the data enterers on the structure of the database and the protocol for data
entry. Go through the entire database during this portion of the training.
Give the data enterers an opportunity to enter at least two test questionnaires
(possibly those completed during the field test) during the data entry training and to
raise any questions based on these trials.
The data entry protocol includes the procedure for spot-checking (see below) and
quality control measures. Thoroughly document these procedures to support quality
control and audits (should they occur). Train the data enterers to recheck their data
frequently.
Supervision and spot checks are important steps in the data entry process for
reducing error. The supervisor should spot check approximately 1 in every 10
questionnaires entered. He or she should randomly select the questionnaires tospot
check and closely compare the data in each questionnaire with that entered in the
database. The supervisor should discuss any problem encountered with all data
enterers, in case multiple enterers are making similar mistakes.
o The data enterers should raise any questions with the supervisor so they can be
addressed immediately. The supervisor should coordinate with the project
manager or M&E advisor to address systematic problems in data collection or
data entry. If data collection is still occurring, the project manager or M&E
advisor should discuss the systematic or common data collection errors with the
teams in the field.
o The data enterers should initial each questionnaire after it has been entered (or
initial their specific section once it has been entered).
Ask that data enterers save the data after completing each questionnaire (or section).
The supervisor should back up the data at the end of each day with external memory
and record the identification numbers of the questionnaires that have been entered.
Create different file names for the database on each computer so they will not copy
over each other during the backup.
Data cleaning ensures that data is accurate before conducting an analysis. Unclean
data can ultimately distort your results. Data cleaning aims to identify mistakes
made during data collection or data entry. The mistakes made during data entry can
be corrected at this stage. Data cleaning involves running preliminary analyses and
cross-checking any unexpected results against the data in the questionnaires. Annex
A includes key steps in data cleaning.
Either the data entry supervisor or the data analyst, depending on the level of
experience of the data entry supervisor, can conduct data cleaning. Data cleaning
requires a sharp eye and experience with common data entry errors, as well as a solid
understanding of the survey population and context.
Document the data cleaning procedure to inform external quality checks or audits.
Documenting the data cleaning method and schedule also will help to reduce
duplication of efforts by other staff involved in the process.
Record all recommendations for the next survey based on common problems with data
collection or data entry found during cleaning.
