This review considers longitudinal evidence on data minimisation, with attention to data provenance, population coverage, stable definitions and appropriate limits on inference.
Evidence relevant to longitudinal evidence on data minimisation
Its relevance to data minimisation should be assessed against the affected jurisdiction, learner population and form of provision.
For data minimisation, the required public outcome should be stated in operational terms.
Application to longitudinal evidence on data minimisation
The criteria applied to data minimisation should be settled and recorded before the evidence is assessed. A change in policy, coverage or recording practice can create an apparent movement that is not a change in the underlying educational condition.
In examining longitudinal evidence on data minimisation, in this case, a sound interpretation should identify the unit of analysis, reference period, denominator, exclusions, missing values and any change in definition or collection practice.
Controls for longitudinal evidence on data minimisation
Risk assessment of data minimisation should give particular attention to incomplete coverage, small differences overstated, and proxy measures treated as direct outcomes. A provider should also consider data revisions not carried through to published conclusions and changes in definition presented as changes in performance. Stronger controls are required where learners may not detect an error or where later correction cannot restore the lost opportunity.
Relevant evidence for the comparison will normally include coverage and missingness analysis, triangulation with administrative and qualitative evidence, indicator definitions and metadata, population and sampling information, and revision and comparability records. Across the defined scope, currency, provenance and representativeness should be established before evidence is used for assurance. For data minimisation, conflicting records require reconciliation before a complete assurance conclusion is reached.
Review of longitudinal evidence on data minimisation
The method for the issue is to establish a baseline, annotate every material change in definition or collection, compare like periods and retain revised series. For data minimisation, where comparability is interrupted, begin a new series or present the break clearly rather than joining unlike observations.
Publication of findings on data minimisation should distinguish observed values, estimates and interpretation.
Implications for longitudinal evidence on data minimisation
The analysis of data minimisation should remain within the limits of the evidence.
For decisions concerning data minimisation, a traceable record enables responsibility to be established and errors to be corrected fairly. For the available evidence, the responsible body should be able to identify the evidence considered, the judgement made, the person or body authorised to make it and the action that followed. The record for data minimisation should prevent a later amendment from being treated as if it applied when an earlier decision was made.
Public reporting on data minimisation should distinguish established fact, analytical judgement and planned action. Across the defined scope, changes to definitions or evidence should be recorded separately from changes in educational performance.
The decision record for data minimisation should connect the stated objective to suitable evidence and the position of those affected. An evidential gap in relation to data minimisation should lead to a qualified conclusion and continued action, not administrative closure.