MAPSS is proud to announce the inauguration of a new colloquium series in 2007.
2007-2008 MAPSS colloquium series - Fall Schedule
To RSVP, please click here.
Lunch will be served at 11:45 for those who have RSVP'd; the talks start at noon.
Speaker Bios / Talk Abstracts (as available)
Bio: David Moore is currently a Senior Fellow with the Carsey Institute at the University of New Hampshire. He was a senior editor with the Gallup Poll for 13 years, and before that was Professor of Political Science at UNH, where he founded and directed the UNH Survey Center. [He is author of The Super Pollsters: How They Measure and Manipulate Public Opinion in America, and How to Steal An Election: The Inside Story of How George Bush’s Brother and FOX Network Miscalled the 2000 Election and Changed the Course of History, as well as scores of articles on public opinion and survey research methods.]
Abstract for the talk:Currently media polls “manufacture” public opinion on policy matters, by failing to take into account respondents’ knowledge or engagement in the issues. [Previous efforts by George Gallup and Daniel Yankelovich, among others, to make public opinion research more meaningful have not succeeded.] A single follow-up question to policy preference questions, which allows opinion to be classified as either “permissive” or “directive,” may provide a new method for a more accurate assessment of public opinion.
Bio: Ron Nakao has been the data specialist in Social Sciences Data and Software (SSDS) in the Stanford Libraries for the past 15 years. He is Stanford's representative and liaison with two major data archives, the Inter-university Consortium for Political and Social Research (ICPSR) and the Roper Center for Public Opinion Research. He is also the vice-chair of the Data Documentation Initiative (DDI) expert committee, an international effort to establish a metadata standard for social science data [http://www.ddialliance.org/]
Abstract for the Talk: Data is the life blood for research in the social sciences. However, getting the data you need can often be a problem. Data is often expensive and time consuming to collect. And given that only a small percentage of primary data is ever archived, a vast collection of data for secondary use is inaccessible to researchers. In this talk, I will discuss the role of libraries and data archives in addressing the issue of data archiving and access, and cover some initiatives and projects, such as DEWI (Data Extraction Web Interface), DDI (Data Documentation Initiative), and efforts to capture and make accessible at-risk data.
Bio: Brian Wandell is the first Isaac and Madeline Stein Family Professor at Stanford University. He graduated from the University of Michigan in 1973 with a B.S. in mathematics and psychology. In 1977, he earned a Ph.D. in social science from the University of California at Irvine. After a year as a postdoctoral fellow at the University of Pennsylvania, he joined the faculty of Stanford University in 1979. Professor Wandell was promoted to associate professor with tenure in 1984 and became a full professor in 1988.
Brian Wandell's research includes image system engineering and visual neuroscience. In cooperation with Professor Emeritus Joseph Goodman (a faculty member in Stanford's School of Engineering), Professor Wandell founded the university's Stanford Center for Image Systems Engineering Program. As part of this research, Wandell and his team study and build devices used for digital imaging, including image sensors, high dynamic range displays, and software simulations of the digital imaging pipeline.
Abstract for the Talk: Visual cortex has been an excellent model system for developing a quantitative understanding of brain function. We understand a great deal about the physical signals that initiate vision, and this knowledge has led to a relatively advanced understanding of the organization of major structures in visual cortex. This talk will explain several measurements and neuroimaging methods that are used to understand human visual cortex.
First, we have developed functional magnetic resonance imaging (fMRI) methods for measuring and quantifying the properties of maps in individual human and macaque brains.
Second, we have made functional measurements of cortical plasticity to examine the consequences of abnormal retinal development, retinal disorders, and acquired damage. These experiments were performed in both human and macaque.
Third, we are combining fMRI with diffusion tensor imaging (DTI), to understand visual development of the pathways needed for reading. Specifically, as children develop and learn to read certain visual recognition skills become highly automatized and the brain develops specialized visual circuitry to support skilled reading. We are measuring how certain parts of the essential visual circuits develop, and how the signals from these circuits are transmitted to other cortical systems.
Bio: Mark Appelbaum is a Professor of Psychology at UCSD and until July 1 of this year was Associate Vice Chancellor for Undergraduate Education at UCSD. Appelbaum specializes in Quantitative Psychology including applied statistics, experimental design, applied measurement and assessment. He has been Editor of the Psychological Bulletin and was Founding Editor of Psychological Methods. He was a member of the SAT Committee of the College Board. Prior to joining the faculty at UCSD he was a on the faculties of the University of North Carolina, Chapel Hill and Vanderbilt University.
Abstract for the Talk: It is not uncommon, especially in studies of special populations and pilot studies, for researchers to be faced with special analytic problems due to the small numbers of subjects they have amassed. Under these circumstances, when power is already problematic, it may be difficult to rely on the asymptotic assumptions of the most commonly used statistical techniques. In this talk we will consider the consequences of the violation of asymptotic assumptions and will explore statistical techniques such as randomization/permutation tests that may be employed in small sample situations.
Bio: Simon Jackman is a Professor of Political Science at Stanford University. He also holds a courtesy appointment as an Associate Professor with the Department of Statistics. Jackman directs Political Science Computational Laboratory, along with the MAPSS program.
Jackman is widely regarded as one of the top methodologists in the field on Political Science. He has published extensively on American and Australian Government, Public Opinion, and Statistical Methods for Political Analysis. He also has a forthcoming book, Bayesian Analysis for the Social Sciences. Jackman has a Ph.D. in Political Science from University of Rochester, and B.A. (with Honors) in Government from University of Queensland, Australia.
Abstract for the Talk: We live in a time when there is more social science data available than ever before. And increasingly, researchers are departing from analysis of large public-use, "canonical" data collections in favor of original data collection. In this hands-on seminar, I provide a brief review of tools, hacks and kludges for pulling data off web sites, replacing laborious hand-coding and manual transcription of web site contents with smart use of freeware computer tools. In an age where the web means that data is "everywhere", researchers who can use these tools have a competitive advantage in terms of research breadth and productivity over those who can't use these tools. Examples include: (1) an ongoing, realtime project tracking election betting markets; and (2) downloading electoral data from the web sites of various government agencies.
Bio: Arie Kapteyn is a senior economist at the RAND Corporation and director of RAND Labor and Population. He is a fellow of the Econometric Society, past president of the European Society for Population
Economics, and corresponding member of the Netherlands Royal Academy of Arts and Sciences. Before joining RAND, Dr. Kapteyn held a chair in econometrics at Tilburg University, where he served the university in numerous capacities, including dean of the Faculty of Economics and Business Administration; founder and director of Center (a research institute and graduate school) and of CentERdata (a survey research institute); and director of CentER Applied Research (a contract research institute). He has held visiting positions at several universities, including Princeton University, California Institute of Technology, Australian National University, University of Canterbury, New Zealand, University of Bristol, and University of Southern California.
Dr. Kapteyn’s research expertise covers microeconomics, public finance, and econometrics. Much of his recent applied work is in the fi eld of aging, with papers on topics related to retirement, consumption and savings, pensions and Social Security, disability, and economic well-being of the elderly. At RAND, he leads several projects, including one to incorporate Internet interviewing into the HRS; a center on the analysis of health and economic determinants of retirement in the United States and Western Europe; and a center on the analysis of economic decisionmaking related to retirement and saving and investing for retirement. He is also a lead researcher in a consortium designing and implementing the Survey of Health, Ageing, and Retirement in Europe (SHARE), which will provide unique opportunities for international comparative research.
He received his Ph.D. in economics from Leiden University.
Abstract for the Talk: Individuals are influenced by the types of people with whom they associate and who form their social networks. These social interactions may affect individual and social norms. We develop a direct test of this using Dutch survey data on how respondents evaluate work disability of hypothetical people with some work related health problem (vignettes). We analyze how the thresholds respondents use to decide what constitutes a (mild or more serious) work disability depend on the number of people receiving disability insurance benefits (DI) in their reference group. To account for endogeneity of DI receipt in a respondents reference group, we jointly model the respondents own self-reported work disability, the evaluation thresholds, and DI receipt in the reference group. We find that reference group e¤ects are significant, and contribute substantially to an explanation of why self-reported work disability in the Netherlands is much higher than in, e.g., the US. This implies an important role for social interactions and norms on the perception of work limitations.
Jonathan Wand is an Assistant Professor in the Department of Political Science at Stanford University and a Robert Wood Johnson Health Policy Scholar at the University of Michigan. His applied and computational statistical research interests include models of dynamic and strategic individual choice behavior, non-parametric and semi-parametric scaling methods, and shape constrained inference for testing formal models.Substantively, he works on elections, campaign finance, public opinion and health care policy. Jonathan is the recipient of both the Harold Gosnell Award from the APSA and the Robert H. Durr Award from the MPSA for his research on political methodology.
Abstract for the talk:
Attitudes and attributes of individuals are often measured by means of survey questions with ordered response categories, and these measures are commonly employed to make interpersonal comparisons. These types of comparisons, however, rely on the assumption that individuals agree on the meaning of the scale categories. The interpersonal incomparability of responses due to differences in standards is a central challenge in the study of surveys and public opinion. My talk will focus on the use of anchoring objects, such as anchoring vignettes, to improve our ability to draw reliable comparisons across individual's. Relevant applications range from measuring patient pain to racial discrimination, and from customer satisfaction to the influence of political corruption.
I investigate how to compare survey responses across individuals by asking all individuals to evaluate a common set of anchoring vignettes, or other common survey items. I offer an axiomatic derivation of building scales that illuminates previously unrecognized assumptions implicit in an earlier non-parametric methods using anchoring vignettes, and also leads to a new non-parametric scaling method. I also propose a new semi-parametric method for accomodating measurement error that overcomes earlier uses of strong assumptions concerning within-group homogeneity of the use of scales and the underlying attributes that are compared across groups.