| James E. Corter Professor of Statistics and Education Department of Human Development 453 Grace Dodge Hall Teachers College, Columbia University 525 W. 120th St., New York, NY 10027 Affiliations: Center for Decision Sciences at Columbia Business School Applied Statistics Center at CU Dept. of Statistics E-mail: jec34@columbia.edu |
 |
PERSONAL STUFF
Family: pic1 pic2 pic3 Music: (w/ T Fleisher, A. Miranda) Blue in Green Nardis Bright Size Life |
EDUCATIONAL BACKGROUND
Ph.D. in Experimental Psychology, 1983, Stanford University
Graduate study, L. L. Thurstone Psychometric Laboratory, 1977-1979, University of North Carolina
B.A. in Psychology (highest honors) 1977, University of North Carolina
SCHOLARLY INTERESTS
Computational models of human learning and categorization.
Judgment and decision-making.
Cognitively diagnostic testing models/methods
Statistics expertise and probability problem-solving.
Clustering and scaling methods for multivariate data.
SELECTED PUBLICATIONS
Zahner, D. C., & 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.
Corter, J. E., Nickerson, J.V., Tversky, B., Zahner, D., & Rho, Y. (2008). Using diagrams to design information systems. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society, (pp. 2259–2264). Austin, TX: Cognitive Science Society.
Nickerson, J.V., Corter, J.E., Tversky, B., Zahner, D., & Rho, Y. (2008a). Diagrams as tools in the design of information systems. In J.S. Gero & A. Goel (Eds.), Design Computing and Cognition ’08. Dordrecht, Netherlands: Springer-Verlag.
Corter, J. E., & Matsuka, T. (2008). Observed attention allocation processes in category learning. Quarterly Journal of Experimental Psychology, 61(7), 1067-1097. (view citation and abstract)
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), article 7. (view citation and abstract)
Corter, J. E., & Zahner, D. C. (2007). Use of external visual representations in probability problem solving. Statistics Education Research Journal, 6(1), 22-50. (view pdf)
Corter, J. E., & Chen, Y.-J. (2006). Do investment risk tolerance attitudes predict portfolio risk? Journal of Psychology and Business, 20(3), 369-381. (view citation and abstract)
Chen, Y.-J., & Corter, J. E. (2006). When mixed options are preferred in multiple-trial decision making. Journal of Behavioral Decision Making, 19(1), 17-42. (view citation and abstract)
Corter, J. E. (2005). Additive trees. In B. Everitt & D. Howell (Eds.), Encyclopedia of Statistics in the Behavi ora l Sciences. London : Wiley.
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. (view citation and abstract)
Matsuka, T. & Corter, J.E. (2004). Stochastic learning algorithm for modeling human category learning. International Journal of Computational Intelligence, 1(1), 40-48.
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.
CLUSTERING SOFTWARE AVAILABILITY:
The ADDTREE/P (Corter, 1982)
and GTREE (Corter, 1998) programs for fitting additive trees and the EXTREE program for fitting
extended trees (Corter & Tversky, 1986) are available below.
Please note that the ADDTREE/P program (Sattath & Tversky, 1977; Corter,
1982) has been superceded by the GTREE program, which gives equivalent fits and
is order-n faster. The "generalized triples" algorithm used by GTREE
is described in Corter, J.E. (1998).
GENERAL REFERENCE:
Corter, J.E.
(1996). Tree Models of Similarity and Association.(Sage University Papers
series: Quantitative Applications in the Social Sciences, no. 112). Thousand
Oaks CA: Sage. (view table of contents)
SOFTWARE DISTRIBUTION SITES:
I. source files (PACAL) and
documentation for GTREE, ADDTREE/P, & EXTREE are available from the netlib resource now maintained by Sandia National Labs.
II. PASCAL source, documentation & DOS-executable versions available on this page:
1) The GTREE program for fitting ADDITIVE TREES. This program uses the "generalized triples" algorithm introduced by Corter (1998). It gives equivalent fits to the improved Sattath & Tversky ADDTREE algorithm implemented in the ADDTREE/P program (Corter, 1982), but does so order-N faster, which enables the analysis of larger data sets with more objects.
gtree.pas: PASCAL source code (text)
gtree.doc: documentation (text)
gtree.exe: DOS-executable
REFERENCE: Corter, J.E. (1998). An
efficient metric combinatorial algorithm for fitting additive trees.
Multivariate Behavioral Research, 33(2), 249-272. (download abstract from LEA)
2) The ADDTREE/P program for fitting ADDITIVE TREES. <NOTE -- THE GTREE PROGRAM IS NOW RECOMMENDED INSTEAD - SEE ABOVE >
This algorithm represents an improvement over the original Sattath & Tversky (1977) algorithm, resulting in slightly improved fits about 10% of the time.
addtree.pas: PASCAL source code (text)
addtree.doc: documentation (text)
addtree.exe: DOS-executable
REFERENCES:
Corter, J.E. (1982).
ADDTREE/P: A PASCAL program for fitting additive trees based on Sattath & Tversky's ADDTREE algorithm. Behavior Research Methods and Instrumentation, 14,
353-354.
Sattath, S., & Tversky, A. (1977). Additive similarity
trees. Psychometrika, 42, 319-345.
3) The EXTREE program for fitting EXTENDED TREES: Extended trees (Corter & Tversky, 1986) extend the hierarchical structure of a tree by adding marked segments to the tree branches representing cross-cutting features or "overlapping clusters".
extree.pas: PASCAL source code (text)
extree.doc: documentation (text)
extree.exe: DOS-executable
REFERENCE: Corter, J.E. & Tversky,
A. (1986). Extended similarity trees. Psychometrika, 51, 429-451. (download abstract from SpringerLink)