Title | Conducting High-Value Secondary Dataset Analysis: An Introductory Guide and Resources |
Publication Type | Journal Article |
Year of Publication | 2011 |
Authors | Ayanian, JZ, Covinsky, KE, Landon, BE, McCarthy, EP, Wee, CC, Steinman, MA |
Journal | Journal of General Internal Medicine |
Volume | 26 |
Issue | 8 |
Pagination | 920-929 |
ISSN Number | 0884-8734 |
Keywords | Datasets, Meta-analyses, Survey Methodology |
Abstract | Secondary analyses of large datasets provide a mechanism for researchers to address high impact questions that would otherwise be prohibitively expensive and time-consuming to study. This paper presents a guide to assist investigators interested in conducting secondary data analysis, including advice on the process of successful secondary data analysis as well as a brief summary of high-value datasets and online resources for researchers, including the SGIM dataset compendium (www.sgim.org/go/datasets). The same basic research principles that apply to primary data analysis apply to secondary data analysis, including the development of a clear and clinically relevant research question, study sample, appropriate measures, and a thoughtful analytic approach. A real-world case description illustrates key steps: (1) define your research topic and question; (2) select a dataset; (3) get to know your dataset; and (4) structure your analysis and presentation of findings in a way that is clinically meaningful. Secondary dataset analysis is a well-established methodology. Secondary analysis is particularly valuable for junior investigators, who have limited time and resources to demonstrate expertise and productivity. |
DOI | 10.1007/s11606-010-1621-5 |
Citation Key | 8744 |
PubMed ID | 21301985 |
PubMed Central ID | PMC3138974 |