Zeina Mneimneh

By | | No Comments

Dr. Zeina Mneimneh is Assistant Research Scientist in the University of Michigan Survey Research Center.

Her research focuses on the use of social media and neighborhood contextual information to study social and health science topics and involves a collaboration between Michigan and Georgetown University.

Trivellore E. Raghunathan

By | | No Comments

Dr.¬†Raghunathan’s primary research interest is in developing methods for dealing with missing data in sample surveys and in epidemiological studies. The methods are motivated from a Bayesian perspective but with desirable frequency or repeated sampling properties. The analysis of incomplete data from practical sample surveys poses additional problems due to extensive stratification, clustering of units and unequal probabilities of selection. The model-based approach provides a framework to incorporate all the relevant sampling design features in dealing with unit and item nonresponse in sample surveys. There are important computational challenges in implementing these methods in practical surveys. He has developed SAS based software, IVEware, for performing multiple imputation analysis and the analysis of complex survey data. Raghunathan’s other research interests include Bayesian methods, methods for small area estimation, combining information from multiple surveys, measurement error models, longitudinal data analysis, privacy, confidentiality and disclosure limitations and statistical methods for epidemiological studies. His applied interests include cardiovascular epidemiology, social epidemiology, health disparity, health care utilization, and social and economic sciences. Raghunathan is also involved in the Survey Methodology Program at the Institute for Social Research, a multidisciplinary team of sociologists, statisticians and psychologists, provides an opportunity to address methodological issues in: nonresponse, interviewer behavior and its impact on the results, response or measurement bias and errors, noncoverage, respondent cognition, privacy and confidentiality issues and data archiving. The Survey Methodology Program has a graduate program offering masters and doctoral degrees in survey methodology.

Jason Owen-Smith

By | | No Comments

Our data architecture combines naturally-occurring data from research grant inputs with scientific outputs including publications, citations, dissertations, and patents, as well as with biographic data on researchers scraped from the web and in databases. These data integrate with STAR METRICS administrative data on grant purchases and employment, which can in turn be linked to Longitudinal Employer-Household Dynamics (LEHD) Census data enabling individuals to be traced as they move across employers and start businesses. These data are then linked using cutting edge disambiguation/name-entity resolution, web scraping and entity extraction. This IRIS methodology is advancing the underlying computational sciences and creating more useful data for broader applications.

One year snapshot of the collaboration network of a single large research university campus. Nodes are individuals employed on sponsored project grants, ties represent copayment on the same grant account in the same year. Ties are valued to reflect the number of grants in common. Node size is proportional to a simple measure of betweenness centrality and node color represents the results of a simple (walktrip) community finding algorithm. The image was created in Gephi.

One year snapshot of the collaboration network of a single large research university campus. Nodes are individuals employed on sponsored project grants, ties represent copayment on the same grant account in the same year. Ties are valued to reflect the number of grants in common. Node size is proportional to a simple measure of betweenness centrality and node color represents the results of a simple (walktrip) community finding algorithm. The image was created in Gephi.