Statistics, Informatics & Qualitative Methods (SIQM) Core

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PROJECT SUMMARY/ABSTRACT Research Core: The Statistics, Informatics, and Qualitative Methods Core The Statistics, Informatics, and Qualitative Methods (SIQM) Core will promote and support rural health care delivery science. Following an integrated mixed-methods research framework and partnering with the Community Engagement and Outreach (CEO) Core, we seek to optimize the rigor, context, and advancement of research across the Center for Rural Health Care Delivery Science. The SIQM Core’s overarching goal is to improve the rigor of the research on important rural health care topics by integrating quantitative and qualitative data analyses into a two-way system where one feeds into the other. The specific areas of focus include supporting the attainment and development of unique datasets, innovative statistical modeling, the construction of accurate statistical inferences, the expansion of informatics and analytics, and the acuity and impact of data visualization. The Core will support the Center’s researchers by developing a robust research data warehouse that responds to their research goals and dually aids in their development as researchers and ultimate graduation to independent funding. The Core will work with datasets that incorporate health insurance claims, disease registry, US Census, electronic health records, geospatial and other sources and forms of data. To complement this, de novo data will be obtained from community stakeholders using surveys, interviews, Community-Engagement Studios, and other forms of mixed-methods research. The data repository will grow with time in breadth and depth to support community needs and priorities and support researchers by expanding the scope of data, to which they will have access as they further their work and prepare their own independent proposals. In addition to integrating and managing data, the SIQM Core will support projects by developing and refining cutting-edge statistical methods, including hierarchical, longitudinal, and geospatial modeling; causal inference methods to account for confounding in observational data (difference-in-difference designs, propensity score methods); and social network analysis. The Core will create an environment across the Center for identifying, sharing, and applying innovative statistical, geoinformatics, data science, and qualitative methods to assist each Research Project Leader. Once established, the SIQM Core will coordinate data and methods, share novel methodological solutions, and generalize these methods for future use.