Research Paper DDI Working Paper Series Longitudinal Best Practice 4
Presenting longitudinal studies to end users effectively using DDI metadata
31 Mar 2011
Longitudinal studies are complex and present unique challenges in documenting and delivering data to end users on the Web. Data and metadata from longitudinal studies can be presented in a variety of ways, and there are currently no commonly accepted standards for providing information that users need. It is important to assist prospective users in exploiting the longitudinal data resource effectively.
Robust metadata are central to understanding complex longitudinal data systems, and DDI provides a standardized, machine-actionable metadata representation that can be leveraged to make longitudinal data easier to understand and use. Further, DDI metadata can serve as a foundation for implementers creating end-user-oriented interfaces (see the DDI Use Case Paper on ―Questasy: Documenting and Disseminating Longitudinal Data Online Using DDI 3‖ for an example).
Time is of course an important issue for any longitudinal study, but other dimensions like geography, cohorts, and multiple languages may also need to be addressed. Longitudinal data users need to be able to determine the degree of comparability across salient dimensions (see the related paper on harmonization and comparability), and DDI can provide a structured way of presenting information to enable researchers to gauge degree of comparability.
This paper is intended to provide implementers and those delivering longitudinal data with recommendations on how to use DDI most effectively to support the presentation of longitudinal studies, most commonly on the Web, and to describe best practice for structuring DDI instances. In addition,