Assessing the quality of risk factor survey data: lessons from the WHO MONICA Project

Background and purpose Survey data quality is a combination of the representativeness of the sample, the accuracy and precision of measurements, data processing and management with several subcomponents in each. The purpose of this paper is to show how, in the final risk factor surveys of the WHO MONICA Project, information on data quality were obtained, quantified, and used in the analysis. Methods and results In the WHO MONICA (Multinational MONItoring of trends and determinants in CArdiovascular disease) Project, the information about the data quality components was documented in retrospective quality assessment reports. On the basis of the documented information and the survey data, the quality of each data component was assessed and summarized using quality scores. The quality scores were used in sensitivity testing of the results both by excluding populations with low quality scores and by weighting the data by its quality scores. Conclusions Detailed documentation of all survey procedures with standardized protocols, training, and quality control are steps towards optimizing data quality. Quantifying data quality is a further step. Methods used in the WHO MONICA Project could be adopted to improve quality in other health surveys.

The graying of R&D workgroups: The effects of age diversity on developing publicly usable knowledge

Does the graying of scientific research teams matter? This study addresses how workgroup processes and external environmental factors contribute and inhibit the effect of age diversity in R&D project groups on the production of innovative publicly usable knowledge outcomes in the form of publication outputs. We examined the relationships between group age diversity (age cohort diversity, mean age, age dispersion), R&D workgroup member self-ratings of workgroup processes, their supervisor�s assessment of the external environmental factors the project groups faced, and their supervisor�s ratings of group performance, the number of scientific publicly available publications produced by the group and the use of multiple authorships on publications. Usable data was obtained from 32 R&D workgroups of a large Government Agricultural Research and Development Agency. Consistent with the literature, workgroup processes and external environmental factors were found to directly effect innovation outcomes. Contrary to expectation, but consistent with Social Identity theory, workgroup age diversity generally negatively impacted upon innovation outcomes. An exception was where multiple authorship on publications for project groups increased as the dispersion of age within groups increased. Importantly, workgroups that were both more age homogeneous and perceived to have optimally functioning work processes produced more R&D innovation outcomes than other groups. Generally, these differences appear to be related to the greater division of labor practices (and less multi-tasking) employed by the older and more homogeneous workgroups. Implications for R&D workgroup resource theory and practices are discussed.