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Leigh Wang on Database Design

Hi, I’m Lihshing Leigh Wang, and I’m an Associate Professor of Psychometrics and Quantitative Methodology at University of Cincinnati. Today I want to share with you a tip about database design.

Evaluation research that involves large-scale, multi-level, multi-year, and multi-cohort data presents special challenges to evaluators. Most training programs and publication venues focus on the research design, data collection, and data analysis phases, but largely leave the database design phase out of the research cycle. This knowledge gap presents special obstacles in today’s climate that encourages interdisciplinary collaboration and systems integration to inform scientific discovery and policy decision making. Database design and management is being recognized as one of the priority research areas in the near future by many funding agencies, such as the National Science and Technology Council, National Institute of Health, and Institute of Education Sciences.

Hot Tip: A powerful web-based platform that supports complex database design and multi-site collaboration is SAS Enterprise Guide (http://www.sas.com/technologies/bi/query_reporting/guide/). Its graphical user interface provides visualization tools that users can easily navigate from multiple remote sites. Its centralized data repository warehouse collects data from distributed locations and controls data security in a hierarchical command structure. Its integrated analytics system provides seamless information flow in a shared framework that maximizes data transportability and minimizes data processing errors.

In a recent state-wide endeavor to examine teacher preparation accountability, we explored the causal relationships among three clusters of variables: one exogenous cluster (teacher education), one direct endogenous cluster (teacher quality), and one indirect endogenous cluster (student learning). The two endogenous clusters were repeated over seven years and collected from six cohorts at more than seventy sites. We used SAS EG as the shared platform for collaboration. The biggest challenge we encountered was political rather than technical—issues such as ownership of data collected through local sites but centrally deposited at the data warehouse. Another challenge we faced was linking multiple relational databases with unique identifiers, which again was a design issue rather than technical issue. Without a web-based platform such as SAS EG, conducting evaluation research on such a complex scale would be unimaginable.

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