The link between data quality and research is inextricable; after all, scientific conclusions are based on data. However, key determinants of information quality in research have not been articulated. Likewise, there are no formal constructs relating aspects of research design to data quality. In the absence of such theories, investigators and research teams formulate independent mental models and rely on personal experience to design data collection and processing operations for their studies. We applied an iterative consensus process among four experts each with experience over the spectrum of prospective and retrospective research in both industry and government funded settings to identify key determinants of the accuracy of research data. From this work, we posit that the relative timing of three key data-related milestones 1) occurrence of the event of interest, 2) data collection about the event, and 3) data cleaning, impact information quality and research results, and therefore should be included in a broad spectrum of research design decisions that impact results. We offer a link between aspects of data collection and processing and data quality and apply the resulting framework to a case study to illustrate its use.