Colloquium

MAPSS is proud to announce the inauguration of a new colloquium series in 2006.

The series has at least four purposes:

1. To bring world-class methodologists from around the world to Stanford to give presentations on methodologies of use to social scientists across departments at Stanford.
2. To allow Stanford faculty and students to learn more about the methodological expertise of our own faculty, who will make presentations in the series. 
3. To create a sense of community among methodologically inclined researchers at Stanford.
4. To provide a weekly yummy and free snack and an interesting hour of learning for all members of the Stanford social science community. 

 2006-2007 MAPSS colloquium series

Date	Loc	Speaker (click name for Bio)	Affiliation	Title (click for Abstract)
10 Oct	Education Room 313	Persi Diaconis	Stanford University Professor of Statistics and Mathematics	"Horse Shoes and Politics Do Mix"
07 Nov	Education Room 313	Dave MacKinnon	Arizona State University Professor of Psychology	Designs for Assessing Mediating Variables
14 Nov	Bldg. 200, Room 305	Helena Chmura Kraemer and Michaela Kiernan	Stanford University Professor of Biostatistics in Psychiatry (Kraemer) and Senior Research Scientist, Stanford Prevention Research Center (Kiernan), School of Medicine	Moderators and Mediators:  Comparing the Baron & Kenny and MacArthur Approaches (link to PowerPoint file, 185k)
03 Dec	Hewlett 201	Edward R. Tufte	Yale University Professor Emeritus of Political Science, Computer Science, and Statistics, Senior Critic, School of Art.	An Academic and Otherwise Life, an N = 1
05 Dec	Bldg. 200, Room 305	Douglas Rivers	Stanford University Professor of Political Science & Senior Fellow, the Hoover Institution	Sampling for Web Surveys
Winter Term
16 Jan	Bldg. 550, Room 550A	Ken Bollen	University of North Carolina Professor of Sociology and Statistics	Structural Equation Models (SEMs) for Pooled Time-Series and Cross Sectional (Panal) Data
06 Feb	Bldg. 550, Room 550A	Shanto Iyengar	Stanford University Professor of Communication and Political Science	Experimental Designs for Political Communication Research:  From Shopping Malls to the Internet
13 Feb	Bldg. 550, Room 550A	Don Green	Yale University Professor of Political Science	Gauging the Influence of Political Advertising on Television and Radio: Results from a Large Scale Randomized Experiment (link to associated paper)
20 Feb	Bldg. 550, Room 550A	Robert N. Parker	University of California, Riverside, Professor of Sociology, Co-Director of Presley Center for Crime and Justice Studies	GIS and Social Research: Data Dissemination, Database Construction, and Multivariate Analysis
06 Mar	Bldg. 550, Room 550A	Sean Reardon	Stanford University Professor of Sociology and Education	Propensity Score Matching for Causal Inference: Possibilities, Limitations, and an Example (link to presentation slides)
Spring Term
24 Apr	Bldg. 200, Room 203	Jay McClelland	Stanford University Professor of Psychology,  and Director, Center for Mind, Brain and Computation	Dynamical Models of Decision Making: Optimality, human performance, and principles of neural information processing (link to presentation slides)
08 May	Bldg. 200, Room 203	Jim Moody	Duke University Professor of Sociology	Diffusion over Dynamic Networks
15 May	Bldg. 200, Room 203	Linda Piekarski	Vice President for Database and Research, Survey Sampling International	Surveys in the 21st Century
22 May	Bldg. 200, Room 203	Christian Wheeler	Stanford University Graduate School of Business Professor of Marketing	Manipulation and Measurement of Construct Accessibility
29 May	Bldg. 200, Room 203	Paul Sniderman and Michael Tomz	Stanford University Professors of Political Science	The Microfoundations of Issue Voting
05 June	Bldg. 200, Room 203	Mollyann Brodie	Director, Public Media and Media Research, Kaiser Family Foundation	Using Public Opinion Research to inform Public Policy Debates in 'Real Time'

 

All members of the Stanford community are invited to attend and to RSVP in advance to reserve a place. 
To RSVP, please click here.

Lunch will be served at 11:45  for those who have RSVP'd; the talks start at noon.

For more information, please contact mapss-info@lists.stanford.edu.

Speaker Bios / Talk Abstracts (as available)

 

Persi Diaconis

"Horse Shoes and Politics Do Mix"
Abstract: Many data displays aimed at reducing dimensionality result in an "artifact" called a horseshoe. In joint work with Sharad Goel and Susan Holmes, we have found an explanation and an interpretation. This allows detection of gradients (seriation). The ideas are applied to the current voting data in the US House of Representatives.

Bio: Dr. Persi Diaconis is Mary V. Sunseri Professor of Statistics and Mathematics. He is known for tackling mathematical problems involving randomness and randomization, such as coin flipping and shuffling playing cards. In his early teens Dr. Diaconis dropped out of Julliard, where he studied violin, to become a professional magician, which he was for eight years. He returned to school to study probability and gained his PhD in Mathematical Statistics from Harvard in 1974. He has published widely in mathematics and statistics, and has served as statistical consultant to many companies and research labs. In 1982 he was awarded a five-year MacArthur Fellowship, or "Genius Grant," and in 1995 was made a member of the National Academy of Sciences.

Dave MacKinnon

"Designs for Assessing Mediating Variables"
Abstract: Among the single hottest social science methodology innovations of the last century is the testing of mediation.   Almost every social science hypothesis proposes a mechanism by which one variable affects another. A "mediating variable" is that mechanism.   For example, if having almost no money prevents people from buying food, and being hungry causes people to become depressed, then ability to buy food is a mediator by which income influences depression.   Mediation is at the core of many social science theories: attitudes cause intentions, which in turn cause behavior; poverty reduces local social ties which increases crime rates; parent characteristics determine a child's environment, which determines the child's achievement. The most popular approach to testing mediational hypotheses involves analyzing correlations among variables and entails making a series of usually untestable assumptions.   This talk will describe alternative ways to test mediation through experimentation: designs in which a researcher randomly assigns participants to levels of the independent and mediating variables, designs that selectively block or enhance a mediational relation, and sequences of studies that test whether the pattern of results matches the hypothesized mediation relation.

Bio: Dr. David MacKinnon is one of the nation's leading experts on testing mediation.   He is a quantitative psychologist interested in (1) developing new statistical methods in social science research, and (2) prevention research and health psychology. He graduated from Harvard University in 1979 and received the Ph.D. degree in Measurement and Psychometrics from UCLA in 1986.   He was a professor at the University of Southern California before joining the Arizona State University faculty, where he is Professor of Psychology and director of the Research in Prevention Laboratory at Arizona State University.    He teaches graduate and undergraduate research methods and statistics courses and has received continuous federal funding of his research since 1990.

Helena Chmura Kraemer and Michaela Kiernan

"Moderators and Mediators:  Comparing the Baron & Kenny and MacArthur Approaches"
Abstract: This is our second talk in the series on mediation, and this one expands the scope to address moderation as well, which is the process by which one variable changes the effect of a second variable on a third variable.   Establishing moderators and mediators to elucidate causal chains is essential for social science research, and the most popular set of statistical tests used to do this were outlined by Baron and Kenny in 1986.  Recently, a new set of statistical testing methods were proposed by a MacArthur network subgroup to extend the Baron and Kenny approach and resolve its ambiguities by: (1) imposing screening criteria that identify whether a variable is eligible for consideration as a moderator or mediator; (2) imposing analytic criteria that demonstrate whether an eligible variable functions as a moderator or mediator; (3) not drawing causal inferences from observational data; and (4) considering other relations among variables besides moderation and mediation.  To assist researchers considering moderator/mediator issues in their choice of which approach is better suited to their research needs, we will explain these various approaches. 
 

Bios: Dr. Helena Chmura Kraemer is Professor of Biostatistics in Psychiatry in the Department of Psychiatry and Behavioral Sciences of Stanford University. Dr. Kraemer's research focuses on experimental and observational research methods in the behavioral aspects of medicine.   Her work has mostly been in psychiatry and health psychology, but has also touched on other fields of medicine, including cardiology, epidemiology, pediatrics, and oncology.   Her most recent work has focused on effect sizes that convey clinical significance and on moderators and mediators of treatment in randomized clinical trials.

Edward R. Tufte

"An Academic and Otherwise Life, an N = 1 "
Abstract: The 'da Vinci of data,' (New York Times, 1998), Edward Tufte is the author of books on graphic presentation of data that have been an inspiration for designers, information architects, engineers, and scientists around the world. He is the world's leading authority on the design of visual data displays. Winner of the 2004 American Institute of Graphic Arts medal and dozens of other awards, Tufte began his career as a world-renowed political scientists and statistician and transformed himself mid-career into a leader of analytical design. During this colloquium for Stanford students and faculty, Tufte will describe the progress of his life and career through its many fascinating stages.

Douglas Rivers

"Sampling for Web Surveys"
Abstract: With steeply increasing costs of conducting face-to-face and telephone interviews for surveys of representative samples of people and declines in response rates in such surveys, the appearance of the Internet as a way to conduct surveys has tremendous appeal.   When a researcher has a list of the entire population of interest and email addresses for them all, a survey of a representative sample is easy to conduct defensibly.   But researchers are often interested in doing Internet surveys of a representative sample of the general population, for which no complete list exists.   Challenges entailed include the fact that some members of the population do not have Internet access at all, even people who do have internet access are not listed in any way that would allow drawing a probability sample, Internet surveys sometimes elicit very low response rates, and respondents sometimes choose not to answer many questions.   This talk will review methods for overcoming these challenges and will make comparisons to random digit dial telephone surveys.

Bio: Dr. Douglas Rivers is one of the world's leading experts on survey research and a successful Silicon Valley entrepreneur. Before joining the Stanford faculty, he taught at Harvard University, Caltech, and UCLA.   At Stanford, he is Professor of Political Science and Senior Fellow at the Hoover Institution. Rivers has founded three successful technology companies, Preview Systems, Knowledge Networks, and Polimetrix. He has published numerous academic papers in such outlets as the American Political Science Review, American Journal of Political Science, Econometrics Journal, and the Journal of Econometrics, and is a consultant to CBS News. He holds a B.A. from Columbia University and a Ph.D. from Harvard University.

Ken Bollen

"Structural Equation Models (SEMs) for Pooled Time-Series and Cross Sectional (Panel) Data"
Abstract: Econometrics and statistics have developed a variety of models and estimators that take advantage of the special characteristics of pooled time-series and cross-sectional data.  Also known as “fixed” or “random” effect estimators for panel or longitudinal data, there are numerous applications of these techniques in the social science literature.   The purpose of this paper is to illustrate the integration of these techniques into a structural equation model (SEM) framework that permits easy estimation of standard and nonstandard variants of the fixed and random effects model.  I discuss likelihood ratio tests of fixed vs. random effects model and equal vs. unequal coefficients of the time-varying variables, including time-invariant variables in fixed effect models, treating missing data, and other extensions.   By integrating these pooled time-series and cross-sectional techniques into SEMs, we can take advantage of the estimation, testing, and fit assessment capabilities that are readily available for SEMs.

Bio: Kenneth A. Bollen is Director of the Odum Institute for Research in Social Science and the H.R. Immerwahr Distinguished Professor of Sociology at the University of North Carolina at Chapel Hill. He is the Year 2000 recipient of the Lazarsfeld Award for Methodological Contributions in Sociology. The ISI named him among the World’s Most Cited Authors in the Social Sciences. He is coauthor of Latent Curve Models: A Structural Equations Approach (with P. Curran, 2006, Wiley) and author of Structural Equation Models with Latent Variables (1989, Wiley) and of over 100 papers. Bollen’s primary areas of statistical research are structural equation models and latent curve models.

Shanto Iyengar

"Experimental Designs for Political Communication Research:  From Shopping Malls to the Internet"
Abstract: I argue that field methods and the technological advances associated with the Internet have gone a long way towards neutralizing the traditional weaknesses of experimentation.  First, experiments administered online can prove just as realistic and generalizable as conventional experiments.  Second, the greater reach of online experiments permits a more heterogeneous subject pool.  Moreover, sampling bias can be eliminated entirely through the use of standard probability sampling techniques to the recruitment of experimental participants.  Third, experimental design can be adapted to permit voluntary exposure to the stimulus, thus reflecting the inherent selectivity in the composition of real-world media audiences. Thus, experiments now represent a dominant methodology for mass communication research.

Bio: Shanto Iyengar is the Harry and Norman Chandler Professor of Communication at Stanford University.  He also has a joint faculty position in Political Science and is the director of the Political Communication Lab, which is a research group that utilizes the Internet to study politics and media. His research has been published in several journals including American Political Science Review, Communication Research, Journal of Personality and Social Psychology, and Public Opinion Quarterly.  Iyengar's books include Going Negative: How Political Advertisements Shrink and Polarize the Electorate (co-authored with Steven Ansolabehere), Do the Media Govern? (co-edited with Richard Reeves), and Is Anyone Responsible: How Television Frames Political Issues.

Don Green

"Gauging the Influence of Political Advertising on Television and Radio: Results from a Large Scale Randomized Experiment"
Abstract: We evaluate the effects of a $2 million dollar television and radio advertising campaign on voters' candidate preferences.  Television and radio ads on behalf of an incumbent gubernatorial candidate were rolled out on a random basis in twenty media markets and several dozen cable television markets over a three week period. Voter preferences were measured using daily tracking polls (N=27,283) that began prior to the start of the advertising campaign and finished after its conclusion.  Our results suggest that television advertising had a powerful but short-lived effect on vote preference.  Radio's effects are found to be weak and insignificant, but radio's effectiveness is not measured as precisely as television's effectiveness.  We find no evidence that ads are more effective in markets where the opponent fails to run competing ads.

Bio: Donald P. Green is A. Whitney Griswold Professor of Political Science at Yale University, where he has taught since 1989.  He also heads Yale's Institution for Social and Policy Studies, an interdisciplinary research center that emphasizes field experimentation.  His research interests span a wide array of topics: voting behavior, partisanship, campaign finance, research methodology, and hate crime.  His recent books include Partisan Hearts and Minds: Political Parties and the Social Identities of Voters (Yale University Press 2002) and Get Out the Vote!: How to Increase Voter Turnout (Brookings Institution Press 2004).

Robert N. Parker

"GIS and Social Research: Data Dissemination, Database Construction, and Multivariate Analysis"
Abstract: Geographic Information Systems or GIS has the potential to transform and enhance Social Science Research in a number of ways. First, GIS can substantially enhance data dissemination in the classroom, facilitate the presentation of scientific data to policy makers, government officials, and the general public, and can be used to enhance the presentation of data to scientific audiences. The capabilities of GIS software created to support the construction and manipulation of maps can also facilitate the construction of more complex and potentially scientifically more useful data bases by allowing researchers to combine geospatial information, geographic structures, and other properties of space with what are traditionally seen as non spatial data: behavior, attitudes, and characteristics of geopolitical units. By creating the capability to merge these two disparate types of data, GIS allows social science researchers to discover new relationships, examine more interesting hypotheses, and construct more effective explanatory models. Finally, geospatial statistical models can address these more complex relationships, hypotheses, and models; such models have been developed in spatial econometrics and are necessary to properly accommodate spatial data and structures.

Bio: Robert Nash Parker is Professor of Sociology and Co-Director of the Presley Center for Crime and Justice Studies at the University of California,Riverside. His research concerns the role of alcohol, drugs, and substance abuse in violence, the causes of homicide and youth homicide, and the design, implementation, and evaluation of interventions which prevent or reduce youth violence and substance use. His research has appeared in a wide variety of journals including the Journal of the American Medical Association, The Journal of Psychoactive Drugs, and New Directions in Evaluation, as well as the usual sociological and criminological research journals. He is the author of Alcohol and Homicide: A Deadly Combination of Two American Traditions, the Co-Editor of a volume entitled "Pitfalls and Pratfalls: Issues of null and negative findings in evaluating interventions," and the author of two forthcoming books, one on GIS methods in the Social Sciences and the other on the Alcohol and Violence Relationship.

Sean Reardon

"Propensity Score Matching for Causal Inference: Possibilities, Limitations, and an Example"

Abstract:
Propensity score matching has become increasingly popular in recent years as an analytic strategy for estimating the causal impact of a treatment in the absence of random assignment.  In this talk, I will describe the conceptual logic and assumptions underlying propensity score matching (and other matching strategies as well).  I will describe a body of research work that attempts to evaluate the bias in propensity score matching estimators.  As an example, I will describe propensity score estimates of the effect of Catholic schooling (versus public schooling) in elementary school achievement.

Bio: Sean Reardon is associate professor of education at Stanford University, specializing in the effects of educational policy on educational and social inequality, the causes, patterns, and consequences of residential and school segregation, and applied statistical methods for educational research. His primary research examines the relative contribution of family, school, and neighborhood environments to racial/ethnic and socioeconomic achievement disparities. In addition, his research develops applied quantitative methods for examining variation in treatment effects and for measuring aspects of school and neighborhood context. He teaches graduate courses in applied statistical methods, with a particular emphasis on the application of experimental and quasi-experimental methods to the investigation of issues of educational policy and practice. Sean received his doctorate in education in 1997 from Harvard University. He is currently a recipient of a William T. Grant Foundation Scholar Award to fund his work on the causal effect of neighborhood conditions on adolescent educational and social outcomes. In addition, he is also a Carnegie Scholar, which funds his work on the effects of programs for English language learners on the educational trajectories of Latino students. Sean is also an associate professor of sociology (by courtesy) at Stanford.

Jay McClelland

"Dynamical Models of Decision Making: Optimality, human performance, and principles of neural information processing'"

Abstract:
I will present a model of the dynamics of decision making. The model can be related to the theory of optimal decision making under conditions in which noisy information is sampled continuously from the environment, to details of human behavior seen in experiments requiring perceptual classification or preferential choice, and to principles of neural information processing. The process of model development and the application of the model to experimental findings will be discussed, and contrasts with other models attempting to address some of the same phenomena will be presented.

Bio: 
James L. (Jay) McClelland received his Ph.D. in Cognitive Psychology from the University of Pennsylvania in 1975. He served on the faculty of the University of California, San Diego, before moving to Carnegie Mellon in 1984, where he was a founding Co-Director of the Center for the Neural Basis of Cognition, a joint project of Carnegie Mellon and the University of Pittsburgh. In 2006 he moved to Stanford University, where he is now Professor of Psychology and the founding Director of the Center for Mind, Brain and Computation.
Over his career, McClelland has contributed to both the experimental and theoretical literatures, most notably in the application of connectionist/parallel distributed processing models to problems in perception, cognitive development, language learning, and the neurobiology of memory. He was a co-founder with David E. Rumelhart of the Parallel Distributed Processing research group, and together with Rumelhart he led the effort leading to the publication in 1986 of the two-volume book, Parallel Distributed Processing, in which the parallel distributed processing framework was laid out and applied to a wide range of topics in cognitive psychology and cognitive neuroscience.
McClelland and Rumelhart jointly received the 1996 Distinguished Scientific Contribution Award from the American Psychological Association and several other awards for their pioneering work in this area. McClelland is a member of the National Academy of Sciences, and he has received the APS William James Fellow Award for lifetime contributions to the basic science of psychology.
McClelland currently teaches cognitive neuroscience and conducts research on learning, memory, conceptual development, spoken language, decision making, and semantic cognition.

James Moody

"Diffusion over Dynamic Networks'"

Abstract:
We often care about networks for the "bits" -- information, money, disease, support -- that travel over the network.  In this presentation, I review the network foundations for diffusion and then extend classic treatments to dynamic networks.  The talk will cover a wide range of substantive topics in network methods ranging from the local distributions of contacts to the global patterns of connectivity evolving from such contacts.  

Bio: 
James Moody is associate professor of sociology at Duke University. He has published broadly on the dynamics of social networks, network methods, & theory.  His work covers a wide range of empirical settings from the dynamics of adolescent friendship structure to the diffusion of sexually transmitted diseases.  He is currently working on questions related to the evolution of science networks, the structure and dynamics of friendship networks, the foundations of belief systems and network diffusion. He can be reached at jmoody77@soc.duke.edu.

Mollyann Brodie

"Using Public Opinion Research to inform Public Policy Debates in 'Real Time'"

Abstract:
Researchers often plan and implement studies aimed at informing public policy debates.  However, ensuring that essential, in-depth research findings are available in a policy relevant time frame produces challenges to researchers in terms of the data collection, the analysis, and the communication and dissemination strategies employed.  This seminar will discuss how to effectively produce and communicate in-depth surveys of public opinion and experiences to a media and policy oriented audience in the course of ongoing highly visible policy debates.  We will use the recent enactment and implementation of Medicare Part D – the Medicare prescription drug benefit  - as a case study.  

Bio: 
Mollyann Brodie is Vice President, Director of Public Opinion and Media Research of the Henry J. Kaiser Family Foundation.  Currently, she directs the Foundations’ survey research group as well as a variety of public knowledge and survey related projects including ongoing survey partnerships with the Washington Post and USA Today.   Prior to joining the Foundation, Dr. Brodie was a Health Policy Fellow and the Assistant Director of the Program on Public Opinion and Health at the Harvard School of Public Health.  Dr. Brodie currently serves on the Board of Directors for the Roper Center for Public Opinion Research.  She also serves as chair of the Education Committee for the American Association of Public Opinion Research and previously served on its Executive Council in 2004-2005 and as president of its Pacific chapter in 2002.  She received her Ph.D. in Health Policy from Harvard University .

Christian Wheeler 

"Manipulation and Measurement of Construct Accessibility"

Abstract: Accessible constructs (e.g., traits, stereotypes, goals, etc.) affect both judgments (e.g., impressions of others) and behaviors. In this talk, I will discuss laboratory methods used to investigate the influence of construct accessibility on judgment and behavior.  The talk will include examples of various means of manipulating and assessing construct accessibility without awareness on the part of the participant.  I will provide empirical examples of these techniques and discuss issues associated with their implementation. 

Bio: Christian Wheeler is an Associate Professor of Marketing at Stanford University where he teaches courses on Marketing Management, Attitudes and Persuasion, and Research Methodology. He received his BA from the University of Northern Iowa before moving to Ohio State, where he completed his MA and PhD. His research has been published in top marketing, organizational behavior, and psychology journals, including Journal of Consumer Research, Journal of Personality and Social Psychology, Organizational Behavior and Human Decision Processes, Personality and Social Psychology Review, and Psychological Bulletin. 

Paul Sniderman and Michael Tomz

"The Microfoundations of Issue Voting"

Abstract:
This paper, by Tomz and van Houweling, systematically examines how the issue positions of candidates affect voting behavior. Our analysis focuses on the three leading theories of issue voting: proximity, discounting, and directional theories. We frst explain why existing data and methods are insufficient to estimate the prevalence of these three voting rules. We then formally derive, for the first time, an exhaustive set of critical tests— situations in which the theories predict different vote choices. Finally, through survey experiments, we administer the tests to a nationally representative sample of adults. We find that around 54% of voters apply a pure proximity decision rule. Another significant proportion, 21%, discount the announced positions of candidates by taking into account the location of the status quo. Finally, about 10% of voters make choices consistent with a directional decision rule, and the remaining 15% display behavior that is not consistent with any of the three theories. These findings establish, and at the same time qualify, the foundational assumptions in models of democratic politics.

Bio: 
Michael Tomz is Assistant Professor of Political Science.  He holds an M.Phil. from the University of Oxford, where he was a Marshall Scholar, and a Ph.D. from Harvard University. Michael's interests include international relations, political economy, public opinion, and statistical methods.  His current research on the credibility of international commitments is supported by a five-year CAREER grant from the National Science Foundation.  Michael is also engaged in NSF-funded research with Paul Sniderman and Robert Van Houweling about spatial reasoning in politics.
Paul Sniderman is Fairleigh S. Dickinson Jr., Professor in Public Policy.  He pioneered the introduction of randomized experiments in public opinion surveys via computer-assisted interviewing.  He is P.I. of a NSF-funded research project on the behavioral foundations of rational choice in politics; his Co-PI’s are Michael Tomz and Rob van Houweling (of Berkeley).

Linda Piekarski

"Surveys in the 21st Century"

Abstract:
The quality of survey data has always been based on the quality of the sample design and sampling frame(s), the quality of the questionnaire, survey protocols and the response characteristics of the survey. Technology is rapidly changing the way in which surveys are conducted and will be conducted in the not too distant future. These changes are impacting all of the quality measurements mentioned above.
This presentation will address a few of the myriad of issues associated with building and maintaining different and overlapping frames, in particular telephone frames, address frames and internet panels. It will also discuss issues unique to each of these modes. Specifically, I will address the challenges and constraints presented to each mode by state and federal legislation, questionnaire design, declining response rates and differential nonresponse.

Bio: 
Linda Piekarski has been with SSI since 1980. Linda currently manages the Database and Telephone Production Groups. These departments are responsible for creating, cleaning, and maintaining SSI's numerous telephone and Internet databases and for producing all telephone samples. She holds a BA from Wells College and an MA from Yale University .
Linda also plays a key role in providing sample design support both internally and to SSI's clients. She has authored numerous papers on sampling topics presented at industry conferences (AAPOR, APDU, ASA, and CMOR) or published in industry journals. In recent years, Linda's investigations and presentations have focused on the challenges facing the research industry such as declining response rates, the increased use of cellular phones, and privacy concerns. She serves on the Technical Programs Advisory Board of Gibbs College in Norwalk , Connecticut .

Last updated 03-Dec-2007