Quality control

Quality control
Benefit s of gender equality th rough infrastruct ure provision: an EU-wid e survey 25
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 collec ted)
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 dierent languages by the
contractor of the EU Member States, pre-tests
were carried out to ascer tain whether ques-
tions and answer scales were clear, complete
and correct.
During the implementation phase, data-collec-
tion specications were followed to ensure that
the data collected were sound, robust and of
the highest quality. Quality assurance on com-
parability required clear denition 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
List of sampling management sys tems used;
indication of risk due to possible dierences
in eldwork across Member States due to
use of dierent sampling management sys-
List of CATI systems used; indication of risk in
using dierent C ATI systems.
List of the interview sta composition (age,
gender, education) for each Member State;
indication of risk due to dierent sta com-
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 af ter 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-
The data-collection system was computerised.
Fieldwork and sample quot as were closely mon-
itored throughout, with regular data check s
during the eldwork.
Interviewers at tended an in- depth brieng.
Supervisors then conducted qualit y control dur-
ing the eldwork by listening to inter views and
providing the inter viewers with feedback. Thor-
ough data cleaning took place after the eld-
An important indicator of the survey quality is
the eec tive 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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