Speakers
Description
DDI-Lifecycle utilises the variable cascade to organise and describe data from conception to collection.
The organisation of conceptual variables and conceptual variable groups allows comparison of data at different time points, universes, representations and many other dimensions through concordance tables and is well suited to iterative data collected as panels, cohorts, repeated surveys and periodic administrative files.
Within a single study design, such comparisons are straight-forward to describe and comprehend. Whilst it is possible to extend the comparison between studies or from other sources such as administrative data, by creating groups of conceptual variables, conveying to the user (human or machine) the data collection dimensions will likely require the alignment of this metadata also.
This is potentially a significant challenge for multi-study metadata infrastructures or national statistical agencies which hold data from diverse sources.
The presentation will discuss the outputs from a workshop organised between CLOSER, INSEE and Constance that seeks to address these issues
