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Michael Traugott

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Michael Traugott, PhD, is Professor Emeritus of Communication Studies, Professor Emeritus of Political Science, College of Literature, Science, and the Arts, Research Professor Emeritus, Center for Political Studies and Adjunct Research Professor, Center for Political Studies, Institute for Social Research.

Professor Traugott studies the mass media and their impact on American politics. This includes research on the use of the media by candidates in their campaigns and its impact on voters, as well as the ways that campaigns are covered and the impact of this coverage on candidates. He has a particular interest in the use of surveys and polls and the way news organizations employ them to cover campaigns and elections.

Matthew Shapiro

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Prof. Shapiro is the Lawrence R. Klein Collegiate Professor of Economics, College of Literature, Science, and the Arts and Research Professor, Survey Research Center, Institute for Social Research, at the University of Michigan, Ann Arbor.

Prof. Shapiro’s general area of research is macroeconomics. He has studied investment and capital utilization, business-cycle fluctuations, consumption and saving, financial markets, monetary policy, fiscal policy, and time-series econometrics. Among his current research interests are consumption, saving, retirement, and portfolio choices of households, the effects of tax policy on investment, using surveys in macroeconomics, and improving the quality of national economic statistics.

Zeina Mneimneh

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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.

Adriene Beltz

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The goal of my research is to leverage network analysis techniques to uncover how the brain mediates sex hormone influences on gendered behavior across the lifespan. Specifically, my data science research concerns the creation and application of person-specific connectivity analyses, such as unified structural equation models, to time series data; these are intensive longitudinal data, including functional neuroimages, daily diaries, and observations. I then use these data science methods to investigate the links between androgens (e.g., testosterone) and estradiol at key developmental periods, such as puberty, and behaviors that typically show sex differences, including aspects of cognition and psychopathology.

A network map showing the directed connections among 25 brain regions of interest in the resting state frontoparietal network for an individual; data were acquired via functional magnetic resonance imaging. Black lines depict connections common across individuals in the sample, gray lines depict connections specific to this individual, solid lines depict contemporaneous connections (occurring in the same volume), and dashed lines depict lagged connections (occurring between volumes).

A network map showing the directed connections among 25 brain regions of interest in the resting state frontoparietal network for an individual; data were acquired via functional magnetic resonance imaging. Black lines depict connections common across individuals in the sample, gray lines depict connections specific to this individual, solid lines depict contemporaneous connections (occurring in the same volume), and dashed lines depict lagged connections (occurring between volumes).

Kai S. Cortina

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Kai S. Cortina, PhD, is Professor of Psychology in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.

Prof. Cortina’s major research revolves around the understanding of children’s and adolescents’ pathways into adulthood and the role of the educational system in this process. The academic and psycho-social development is analyzed from a life-span perspective exclusively analyzing longitudinal data over longer periods of time (e.g., from middle school to young adulthood). The hierarchical structure of the school system (student/classroom/school/district/state/nations) requires the use of statistical tools that can handle these kind of nested data.

 

Matthew Kay

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Matthew Kay, PhD, is Assistant Professor of Information, School of Information and Assistant Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.

Prof. Kay’s research includes work on communicating uncertainty, usable statistics, and personal informatics. People are increasingly exposed to sensing and prediction in their daily lives (“how many steps did I take today?”, “how long until my bus shows up?”, “how much do I weigh?”). Uncertainty is both inherent to these systems and usually poorly communicated. To build understandable data presentations, we must study how people interpret their data and what goals they have for it, which informs the way that we should communicate results from our models, which in turn determines what models we must use in the first place. Prof. Kay tackles these problems using a multi-faceted approach, including qualitative and quantitative analysis of behavior, building and evaluating interactive systems, and designing and testing visualization techniques. His work draws on approaches from human-computer interaction, information visualization, and statistics to build information visualizations that people can more easily understand along with the models to back those visualizations.

 

Rie Suzuki

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Dr. Suzuki is a behavioral scientist and has major research interests in examining and intervening mediational social determinants factors of health behaviors and health outcomes across lifespan. She analyzes the National Health Interview Survey, Medical Expenditure Panel Survey, National Health and Nutrition Examination Survey as well as the Flint regional medical records to understand the factors associating with poor health outcomes among people with disabilities including children and aging.

Trivellore E. Raghunathan

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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.

Jessica K. Camp

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Jessica K. Camp, PhD, is Assistant Professor of social work in the Department of Health and Health Services at the University of Michigan, Dearborn.

Her research focuses on using large nationally representative data from the United States and internationally (SIPP, ACS, GSOEP) to explore trends in poverty and inequality. Specifically, I examine ways that marginalized and hyper-marginalized groups experience economic disparity and labor market exclusion. My most recent completed study showed how welfare reform can have a powerful impact on the well-being of working women, especially women with vulnerabilities. My area of expertise as a data analyst is in complex samples, regression, and longitudinal models. I am hoping my future work will inform ways that “Big Data” can be used in social work research.

Michael Elliott

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Michael Elliott is Professor of Biostatistics at the University of Michigan School of Public Health and Research Scientist at the Institute for Social Research. Dr. Elliott’s statistical research interests focus around the broad topic of “missing data,” including the design and analysis of sample surveys, casual and counterfactual inference, and latent variable models. He has worked closely with collaborators in injury research, pediatrics, women’s health, and the social determinants of physical and mental health. Dr. Elliott serves as an Associate Editor for the Journal of the American Statistical Association. He is currently serving as a co-investigator on the MIDAS-affiliated Reinventing Urban Transportation and Mobility project, working to develop methods to improve the representativeness of naturalistic driving data.