Grid-enabled measures: using Science 2.0 to standardize measures and share data.

Scientists are taking advantage of the Internet and collaborative web technology to accelerate discovery in a massively connected, participative environment--a phenomenon referred to by some as Science 2.0. As a new way of doing science, this phenomenon has the potential to push science forward in a more efficient manner than was previously possible. The Grid-Enabled Measures (GEM) database has been conceptualized as an instantiation of Science 2.0 principles by the National Cancer Institute (NCI) with two overarching goals: (1) promote the use of standardized measures, which are tied to theoretically based constructs; and (2) facilitate the ability to share harmonized data resulting from the use of standardized measures. The first is accomplished by creating an online venue where a virtual community of researchers can collaborate together and come to consensus on measures by rating, commenting on, and viewing meta-data about the measures and associated constructs. The second is accomplished by connecting the constructs and measures to an ontological framework with data standards and common data elements such as the NCI Enterprise Vocabulary System (EVS) and the cancer Data Standards Repository (caDSR). This paper will describe the web 2.0 principles on which the GEM database is based, describe its functionality, and discuss some of the important issues involved with creating the GEM database such as the role of mutually agreed-on ontologies (i.e., knowledge categories and the relationships among these categories--for data sharing).

Comparing Cognitive Interviewing and Psychometric Methods to Evaluate a Racial/Ethnic Discrimination Scale.

Proponents of survey evaluation have long advocated the integration of qualitative and quantitative methodologies, but this recommendation has rarely been practiced. We used both methods to evaluate the "Everyday Discrimination" scale (EDS), which measures frequency of various types of discrimination, in a multi-ethnic population. Cognitive testing included 30 participants of various race/ethnic backgrounds and identified items which were redundant, unclear, or inconsistent (e.g., cognitive challenges in quantifying acts of discrimination). Psychometric analysis included secondary data from two national studies, including 570 Asian Americans, 366 Latinos, and 2,884 African Americans, and identified redundant items, as well as those exhibiting differential item functioning (DIF) by race/ethnicity. Overall, qualitative and quantitative techniques complemented one another, as cognitive interviewing findings provided context and explanation for quantitative results. Researchers should consider further how to integrate these methods into instrument pretesting as a way to minimize response bias for ethnic and racial respondents in population-based surveys.