QUALITY MEASUREMENT OF STATISTICS
by
Ioannis Nikolaidis
The planning of the statistical surveys in NSSG is based on the needs of
society, economic decision-making and scientific research. The data
needs of the customer, researcher or interest group are identified and
specified in the planning stage. The modes of operation, concepts,
classifications and standards set by the NSSG determine the operating
framework of the statistical surveys. The key areas in planning stage are
the definition of the content and the strategic decisions on data collection
methods. The resources, budget, personnel and equipment are also
established early in planning stage. At the same time the process and the
schedule for the statistical surveys are defined and it is ensured that the
data quality criteria are fulfilled. The quality measurement in NSSG
follows the structure of the seven main components of the Eurostat
quality concepts. These components are:
Relevance. Relevance is the degree to which statistics meet current and
potential users' needs. It refers to whether all statistics that are needed are
produced and the extent to which concepts used (definitions, classifications
etc.) reflect user needs. When reporting on relevance, the aim is to describe
the extent to which the statistics are useful to, and used by, the broadest
array of users. For this purpose, statisticians need to compile information,
firstly about their users (Who they are, How many they are, How important
is each one of them), secondly on their needs, and finally to assess how far
these needs are met.
Accuracy. Accuracy is defined as the closeness between the value finally
produced (after collection, editing, imputation, estimation, etc) and the
true, but unknown, population value. The difference between the two
values is the error.
Timeliness and punctuality. Punctuality refers to "the possible time lag
existing between the actual delivery date of data and the target date when
it should have been delivered, for instance, with reference to dates
announced in some official release calendar, laid down by Regulations or
previously agreed among partners. If both are the same, delivery is
punctual."
Timeliness refers to "the lapse of time between the delivery and the
reference dates. The latter being the date (or the period) to which data
mostly applies."
Accessibility and Clarity. Accessibility and clarity refer to the simplicity
and ease for users to access the statistics using simple and user-friendly
procedures, obtaining them in an expected form and within an acceptable
time period, with the appropriate user information and assistance: a global
context which finally enables them to make optimum use of the statistics.
Accessibility refers to the physical conditions in which users can access
statistics: distribution channels, ordering procedures, time required for
delivery, pricing policy, marketing conditions (copyright, etc.), availability
of micro or macro data, media (paper, CD-ROM, Internet...), etc. Clarity
refers to the statistics' information environment: appropriate metadata
provided with the statistics (textual information, explanations,
documentation, etc); graphs, maps, and other illustrations; availability of
information on the statistics' quality (possible limitation in use...);
assistance offered to users by the NSI.
Comparability. Comparability aims at measuring the impact of differences
in applied statistical concepts and definitions on the comparison of statistics
between geographical areas, non-geographical domains, or over time.
Coherence. Where similar statistics from various sources exist, they
should be identified and any differences should be quantified and
explained. A discrepancy between two sets of statistics produced by
different surveys may be due to differences in the data collection process
or differences in reporting units resulting in different estimates.
Coherence of statistics is their adequacy to be reliably combined in
different ways and for various uses. It is, however, generally easier to show
cases of incoherence than to prove coherence.
Completeness. Completeness is the extent to which statistics are available
- compared to what it should be available - for meeting the requirements
of the European Statistical System. There are clear relations between
completeness and relevance. Furthermore, the availability of statistics is,
in some cases, limited by accuracy and confidentiality reasons.