Quality control
Pages | 25-30 |
Quality control
Benefit s of gender equality th rough infrastruct ure provision: an EU-wid e survey25
6. Quality control
Data quality was ensured during the whole data
life cycle, across the processes of planning,
implementation and assessment.
During the planning stage, the methodology
(e.g. items of the questionnaire, number, loca-
tion and timing of the samples to be collected)
was key to ensuring data quality. The concepts
under study used measurements tested in ear-
lier studies. As the questionnaire needed to be
made available in 25 dierent languages by the
contractor of the EU Member States, pre-tests
were carried out to ascertain whether ques-
tions and answer scales were clear, complete
and correct.
During the implementation phase, data-collec-
tion specications were followed to ensure that
the data collected were sound, robust and of
the highest quality. Quality assurance on com-
parability required clear denition and state-
ment instructions to be followed.
• Coverage of sample, representing the target
population regionally and across types and
size and comparable across Member States.
• Non-response computed according to one
internationally accepted standard across all
participating Member States.
• Non-response bias estimated in a stand-
ard fashion across all participating Member
States.
• List of sampling management systems used;
indication of risk due to possible dierences
in eldwork across Member States due to
use of dierent sampling management sys-
tems.
• List of CATI systems used; indication of risk in
using dierent C ATI systems.
• List of the interview sta composition (age,
gender, education) for each Member State;
indication of risk due to dierent sta com-
position.
• Documentation of translation process and
outcomes; indication of risk involved due to
translation or adaptation.
Data checks were carried out on the pre-test
data then on the soft launch. Completed inter-
views were checked during and after eldwork.
Phone les of the population under study had
to meet established quality standards, includ-
ing checking for missing or erroneous data, l-
tering, interview duration, duplicates, straight
liners, cross-consistency, outliers and non-re-
sponses.
The data-collection system was computerised.
Fieldwork and sample quotas were closely mon-
itored throughout, with regular data checks
during the eldwork.
Interviewers at tended an in-depth brieng.
Supervisors then conducted qualit y control dur-
ing the eldwork by listening to interviews and
providing the interviewers with feedback. Thor-
ough data cleaning took place after the eld-
work.
An important indicator of the survey quality is
the eective RR: higher RRs ensure more accu-
racy in the survey. The RR was satisfactory in
most cases using the t wo estimation proce-
dures (see Section 5 for detail).
During the assessment stage, data were vali-
dated through an in-depth statistical analysis,
focusing on the following aspects.
• Error detection and debugging data le.
• Analysis of missing values.
• Data ltering.
• Analysis of reliability, validity and robustness.
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