ID
D1
D2
D3
Validation Use Cases: Raw Data
Description
Example
Single Values
Redundant values are equal.
Same data collected more than once must be
equal
Value must be (equal to/less than/greater
Students must be greater than classes
than) another value
Value must be no more than (percentage) of
Repeaters must be no greater than 50% of
another value
total students
Equations
(These equations are not transformations per se, but are used for internal validations. Most of these
types of equations are sums, but may also include subtraction (eg. Number of MALES is equal to the
TOTAL population – FEMALES)
D4
Total (equal to/less than/greater than) to
Total students must be equal to or greater
result of equation
than the sum of female students
D5
Result of equation must be (equal to/less
Sum of all units across categories should be
than/greater than) the result of another
equal to the sum of totals of all categories
equation
D6
Result of equation must be no more than
Multigrade classes max 50% of the sum of
(percentage) of the result of another equation
multigrade classes by grade
Constants
D7
Value must be (equal to/less than/greater
When supplying a percentage, must be less
than) a constant
than 100.
D8
Sum must be (equal to/less than/greater than) Percentage male + percentage female must
a constant
equal 100
Time Series
D9
Value must be (equal to/less than/greater than) Total graduating students must be equal to or
a value collected previously
less than total students enrolled at the
beginning of the school year
D10
Absolute variation compared to last year is
Net enrollment rate, a percentage, may not
higher than a percentage (Ex. 5%)
be more than 5% greater than last year
D11
Relative variation compared to last year is
Gender parity index must not show greater
higher than a percentage (Ex. 20%)
than a 20% relative increase from the
previous year’s value
Conditional
D12
Conditionals: If (condition), one of the other
If “part time employees” > 0 then Headcount
rules applies
must be greater than FTE. (Conditional
application of validation D2)
“Value” refers to a single raw/reported datapoint that can be described using one or more dimensions
Validation Use Cases: Transformations/Indicators1
ID
Description
Example
T1
Result must be (greater than/less than) a
Resulting percentage should be between 0
threshold
and 100
T2
Result must be (greater than/less than) another Mark Net Enrollment Rate for ISCED1 as dirty
result
if it is greater than the Adjusted Net
Enrollment Rate for ISCED1
T3
If result passes threshold (see rule T1) mark
Mark net enrollment rate dirty if the
dependant transformations dirty.
associated capping factor is > 1.1
T4
Result cannot be nil
Mark school life expectancy dirty if nil
T5
Mark result dirty if related result from previous Mark Apparent Intake Rate for ISCED1, Male
year dirty
dirty if Survival rate for ISCED1, Male from
the previous year is dirty
T6
Status may be viral between related results
Net enrollment rate for a specific
year/country will get marked as dirty if gross
enrollment ratio is dirty. This rule allows for
cascading validation (marking one
transformation as dirty and having all related
transformations flagged as dirty).
1
UIS terminology: data that has been transformed via calculation results in an indicator
“Dirty” data – data that is reported but whose quality is dubious. This data should not be published but
may be exchanged for internal use.
“Result” refers to a single value resulting from a transformation that can be described using one or more
dimensions.
NOTE 1: Two types of errors should exist for failed validations:
W- Warning – will flag the value or result as problematic, but will not mark it as “dirty” so the
value/result may still be used in further transformations or publications.
E- Error – will flag a value/result as invalid. This value/result will be marked as “dirty” and should not be
used in any further calculations or publications.
NOTE 2: All the above operations can be performed on any dimension or combination of dimensions of
the cube.
Example: “Total graduating students must be equal to or less than total students enrolled at the
beginning of the school year”. The data value for graduating students must be compared to the prior
year’s enrolled students, so the “<=” operator must be able to compare on data points that differ on 2
dimensions: TYPE (graduating versus enrolled) and TIME (current year versus prior year).
NOTE 3: More complicated validations can be accommodated by having multiple checks on the same
datapoint/value.
For example: Value C is the count of values in the intersection between groups A and B. Therefore,
value C needs to be less than or equal to both A and B. In this case, we would have 2 validation rules
(D2) which states: “C must be less than or equal to A” and another rule which states “C must be less
than or equal to B”.
NOTE 4: It may be useful to be able to specify “AND” in validations, so that a specific value or result
would need to fail multiple validations before being flagged as problematic. An “OR” should not be
required as it would be the default case (error will be flagged as soon as it fails any validation).
NOTE 5: It would be useful to be able to use standard functions when processing values or results. For
example, if a value must be between the MIN and MAX values of a particular list. “Total students in a
specific classroom must be between the least and most populated classrooms for a specific school”.
A >= min(B) and A<=max(B)
where A is the number of students in a specific classroom, and B is the list of the populations of each
classroom in the school.
Several mathematical functions could be useful in this capacity (MIN, MAX, AVG, ABS). It may not be
practical to have these functions in the first version of the validation rules as they will likely add
significant complexity to the rules definition.
For any comments or questions, please contact Marc Bouffard at the UNESCO Institute for Statistics
(m_bouffard@unesco.org)