James E. Corter

Professional Background
Vitae
Courses
Papers
Centers & Projects
In the News

Professional Background

Educational Background

B.A. in Psychology (with highest honors), 1977, University of North Carolina - Chapel Hill

Graduate study, L. L. Thurstone Psychometric Laboratory, 1977-1979, University of North Carolina

Ph.D. in Experimental Psychology, 1983, Stanford University

Scholarly Interests

Judgment, Choice, and Decision-Making
Human Categorization and Learning
Multidimensional Scaling and Clustering Methods
Mathematics Problem Solving
Visualization in Reasoning and Problem Solving
Evaluation of New Educational Technologies

Selected Publications

Voiklis, J. & Corter, J. E. (2012). Conventional wisdom: Negotiating conventions of reference enhances category learning. In press, Cognitive Science.

Corter, J.E. (2011). Does investment risk tolerance predict emotional and behavioural reactions to market turmoil? International Journal of Behavioural Accounting and Finance, 2(3/4), 225-237.

Corter, J. E., Esche, S. K., Chassapis, C., Ma, J., & Nickerson, J. V. (2011). Process and learning outcomes from remotely-operated, simulated, and hands-on student laboratories. Computers & Education, 57(3), 2054-2067.

Zahner, D., & Corter, J. E. (2010). The process of probability problem solving: Use of external visual representations. Mathematical Thinking and Learning, 12(2), 177-204.

Corter, J.E., Rho, Y., Zahner, D., Nickerson, J.V., & Tversky, B. (2009). Bugs and biases: Diagnosing misconceptions in the understanding of diagrams. In N. A. Taatgen & H. van Rijn (Eds.),Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 756-761). Austin, TX: Cognitive Science Society.

Nickerson, J.V., Zahner, D., Corter, J.E., Tversky, B., Yu, L., and Rho, Y.J. (2009). Matching mechanisms to situations through the wisdom of the crowd, ICIS 2009 Proceedings, Paper 41, http://aisel.aisnet.org/icis2009/41.

Nickerson, J.V. & Corter, J.E. (2009) Clarity from confusion: Using intended interactions to design information systems. In Proceedings of the Fifteenth Americas Conference on Information Systems.

Matsuka, T., and Corter, J.E. (2008). Process tracing of attention allocation during category learning. Quarterly Journal of Experimental Psychology, 61(7), 1067-1097.

Im, S., & Corter, J. E. (2011). Statistical consequences of attribute misspecification in the Rule Space method. Educational and Psychological Measurement, 71(4), 712-731.

Lee, J., & Corter, J. E. (2011). Diagnosis of subtraction bugs using Bayesian networks. Applied Psychological Measurement, 35(1), 27-47.

Corter, J. E., Nickerson, J. V., Esche, S. K., Chassapis, C., Im, S. & Ma, J. (2007). Constructing reality: A study of remote, hands-on and simulated laboratories. ACM Transactions on Computer-Human Interaction (TOCHI), 14(2), 7:1-27.

Corter, J. E., Matuska, T., & Markman, A. B. (2007). Attention allocation in learning an XOR classification task. Proceedings of the Second European Cognitive Science Conference, 935.

Corter, J. E., & Zahner, D. C. (2007). Use of external visual representations in probability problem solving. Statistics Education Research Journal, 6(1), 22-50, http://www.stat.aukland.ac.nz/serj.

Corter, J. E., & Chen, Y.-J. (2006). Do investment risk tolerance attitudes predict portfolio risk? Journal of Business and Psychology, 20-3, 369-381.

Chen, Y.-J., & Corter, J. E. (2005). When mixed options are preferred in multiple-trial decision making. Journal of Behavioral Decision Making, 18, 1-26.

Tatsuoka, K. K., Corter, J. E., & Tatsuoka, C. (2004).Patterns of diagnosed mathematical content and process skills in TIMSS-R across a sample of twenty countries. American Educational Research Journal, 41(4), 901-926.

Matsuka, T., Corter, J. E., & Markman, A. (2002). Allocation of attention in neural network models of categorization. In Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society.

Corter, J. E. (1998). An efficient metric combinatorial algorithm for fitting additive trees. Multivariate Behavioral Research, 33, 249-272.

Corter, J. E. (1996). Tree Models of Similarity and Association. (Sage University Papers series: Quantitative Applications in the Social Sciences, series no. 07-112). Thousand Oaks CA: Sage.

Carroll, J. D., & Corter, J. E. (1995). A graph-theoretic method for organizing overlapping clusters into trees, multiple trees, or extended trees. Journal of Classification, 12, 283-314.

Corter, J. E. (1995). Using clustering methods to explore the structure of diagnostic tests. In P. Nichols, S. Chipman & R. Brennan (Eds.), Cognitively Diagnostic Assessment. Hillsdale NJ: Lawrence Erlbaum Associates, 305-326.

Corter, J.E., & Gluck, M.A. (1992). Explaining basic categories: feature predictability and information. Psychological Bulletin, 111, 291-303.

Corter, J. E., Gluck, M.A ., & Bower, G.H. (1988). Basic levels in hierarchically structured categories. In Proceedings of the Tenth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Lawrence Erlbaum Associates.

Corter, J. E. (1987). Similarity, confusability, and the density hypothesis. Journal of Experimental Psychology: General, 116, 238-249.

Butler, K. A. & Corter, J. E. (1986). Use of psychometric methods in knowledge acquisition: A case study. In W.A. Gale (Ed.), Artificial Intelligence and Statistics. Reading MA: Addison-Wesley.

Corter, J. E., & Tversky, A. (1986).Extended similarity trees. Psychometrika, 51, 429-451.

publications

professional experiences

Professor of Statistics and Education, Teachers College, Columbia University, September 2007-.
Program Coordinator, Applied Statistics, May 2009-January 2010
Chair, Department of Human Development, Teachers College, Columbia University, July 2000-August 2007
Acting Chair, Department of Human Development, Summer 1999
Program Coordinator, Cognitive Studies in Education, May 1998-August 2000
Associate Professor of Statistics and Education, Teachers College, Columbia University, 1989-August 2007.
Assistant Professor of Statistics and Education, Teachers College, Columbia University, 1983-1989.
Resident Visitor, AT&T Bell Laboratories, 1983-1988.
Consultant, Xerox Palo Alto Research Center (PARC), 1981-1983.

Teaching Experience:

Probability and Statistical Inference                      Psychological Scaling
Applied Regression Analysis                                  Individual Decision Making
Experimental Design                                               Cognition and Computers
Multivariate Statistics                                                 Factor Analysis
Multidimensional Scaling and Clustering          Statistical Treatment of Mass Data

Professional Affiliations and Activities:

American Educational Research Association Cognitive Science Society

Psychometric Society Psychonomic Society

American Psychological Society Society for Mathematical Psychology

Judgment and Decision-Making Society Classification Society of North America

Ad-hoc reviewing for: The Behavioral and Brain Sciences, Behavioral Research Methods, Instruments, and Computers, British Journal of Mathematical and Statistical Psychology, Cognitive Science, Journal of Experimental Psychology: General, Journal of Experimental Psychology: Learning, Memory, and Cognition, Journal of Mathematical Psychology, Memory and Cognition, Multivariate Behavioral Research, Perception and Psychophysics, Psychological Bulletin, Psychological Review, Psychometrika, Review of Educational Research, and the National Science Foundation.

National Science Foundation (NSF) grant review panelist, Spring 2003. Directorate: Behavioral and Cognitive Sciences, Program in Perception, Action, and Cognition.

National Science Foundation (NSF) site review team, Pittsburgh Science of Learning Center. Spring 2007, Spring 2008, Spring 2009.

National Science Foundation (NSF) grant review panelist, September 2008. Machine Learning program, CAREER Awards.

current projects

My research program includes work in cognitive and educational psychology, decision-making, psychometrics, and applied statistics. In the area of cognitive/educational psychology, I study categorization, judgment , decision-making, and problem-solving. The problem-solving research has been focused mainly in the area of probability and mathematics problem-solving, and has involved both laboratory studies (but grounded in a real educational context) and secondary analyses of large national databases on mathematics achievement. In psychometrics, I am involved with work exploring new "cognitively diagnostic" testing methods. I also continue to work in the cross-disciplinary field of quantitative methods, mainly in developing new scaling/clustering methods to analyze proximity data.

Several recent / current projects have combined several of these interests. With Kikumi Tatsuoka, I conducted an NSF-funded project involving statistical and empirical studies of mathematics problem solving, aimed at better understanding student performance on the Third International Math and Science Study - Revised (TIMSS-R). This work explored applications of "cognitively diagnostic" psychometric methods to the study of mathematics problem solving. A recent NSF grant, in collaboration with a group of researchers from Stevens Institute of Technology, examined the effectiveness of remotely-operated student labs and computer simulations, relative to traditional hands-on labs, in engineering courses. Two more recent NSF-funded projects, in collaboration with Barbara Tversky and Jeffrey Nickerson, examine the role of diagrams in reasoning, design, and problem solving. Finally, another current NSF-supported project explores the use of game-based simulations in science and medical education. In collaboration with several current and former doctoral students, I have been studying how students acquire skill in probability problem solving and what role external visual inscriptions play in these skills; other collaborative work has focused on developing and applying new measurement models to better understand problem-solving.

One tech report on TIMSS:

Corter, J. E., Tatsuoka, K., Guerrero, A., Dean, M. & Dogan, E. (2006). Technical report MES-06-01: Revised coding manual for identifying item involvement of content, context and process subskills for the TIMSS and TIMSS-R 8th grade mathematics tests.

In the area of category learning, a former student, Toshihiko Matsuka, and I are writing up research that collected empirical data investigating how attention is allocated across stimulus dimensions in the learning of complex categories, and examines how well prominent neural network models account for the data. Some of this work, with a modeling focus, has been conducted in collaboration with Art Markman of the University of Texas. With another former student, Yuh-Jia Chen, I have been investigating how people make repeated decisions, and the role of learning in shaping decision behavior in such contexts. In other decision-making research I have examined the relationship of attitudes towards risk and uncertainty with decision behavior. With a former student, Yi-Chun Chen, I have recently been studying how students make and use budgets to manage their expenditures while achieving goals. With Yun-Jin Rho and Huiyun Tseng (former students) and Prof. Matthew Johnson, I have been working on new cognitively diagnostic measurement models.

In the area of statistical methodology, I have been working on a long-term project on representing asymmetric proximity relationships using directed trees.

honors and awards

Distinguished Research Paper Award (August 2007), Japanese Association for Research in Testing (JART), for “International comparisons of mathematical thinking skills in the TIMSS-R” (Tatsuoka et al., 2006).

Appointed member of Graduate Faculty of College of Arts and Sciences, Columbia University, April 1994

Teachers College Research Professorship Award, 1992-1993

Secretary/Treasurer, Classification Society of North America, 1985 1987

National Science Foundation Pre-Doctoral Fellowship

John Motley Morehead Foundation Graduate Fellowship

Phi Beta Kappa

professional organization membership

Service to Field, Profession, and Society:

Statistics/psychometric consultant to AIR (Washington, DC) on NAEP initiative
Statistics/psychometric consultant to Ivy League Athletic Association

Service to Teachers College (partial list):

Psychology Ph.D. Research Methods Exam Committee (1993-present)
Chair, Human Development (Summer 2000-August 2007)
Coordinator, Cognitive Studies in Education program (1998-2000)
Medical Benefits Committee (2005-2006)
Intellectual Property Committee
Research Literacy Task Force
Area A Ph.D committee
Interviews for Human Resources Director
Middle States Reaccreditation Committee – Evaluation Standards Subcommittee
Faculty Executive Committee
Faculty Advisory Committee
Presidential Search Committee (AY 1994-1995)

HUDM 4122: Probability and statistical inference

Prerequisite: HUDM 4120 or undergraduate statistics course. Elementary probability theory; random variables and probability distributions; sampling distributions; estimation theory and hypothesis testing using binomial, normal, T, chi square, and F distributions.Lab fee $50.00

HUDM 5058: Choice and decision making

Prerequisite: HUDM 4122 or equivalent. Surveys quantitative models of individual decision making, from the introduction of the notion of "utility" by Daniel Bernoulli through current models such as Tversky and Kahnemans "Prospect Theory." The focus is on psychological or descriptive models of how people make decisions, although methods of rational decision analysis are briefly discussed.

HUDM 5122: Applied regression analysis

Prerequisite: HUDM 4122 or permission of instructor. Least squares estimation theory. Traditional simple and multiple regression models and polynomial regression models, with grouping variables including one-way ANOVA, two-way ANOVA, and analysis of covariance. Lab devoted to applications of SPSS regression program. Lab fee: $50.

HUDM 5123: Linear models and experimental design

Prerequisite: HUDM 5122. Analysis of variance models including within subject designs, mixed models, blocking, Latin Square, path analysis, and models with categorical dependent variables. Lab devoted to computer applications. Lab fee: $50.

HUDM 5124: Multidimensional scaling and clustering

Permission required. Prerequisites: HUDM 4122 and HUDM 5122 or equivalent. Methods of analyzing proximity data (similarities, correlations, etc.), including multidimensional scaling, which represents similarities among items by plotting the items into a geometric space, and cluster analysis for grouping items.