2011 TC Research
Teachers College, Columbia University
Teachers College Columbia University

Research

Section Navigation

James E. Corter

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

1.       Nickerson, J. V., Corter, J. E., Tversky, B., Rho, Y.-J., Zahner, D., Yu, L. (2013). Cognitive tools shape thought: Diagrams in design. Cognitive Processing, 14(3), 255-272. doi 10.1007/s10339-013-0547-3.

Voiklis, J. & Corter, J. E. (2012).  Conventional wisdom: Negotiating conventions of reference enhances category learning. Cognitive Science, 36 (4), 607-634.

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 Measurement35(1), 27-47.

Corter, J.E. (2011). Does investment risk tolerance predict emotional and behavioural reactions to market turmoil?  International Journal of Behavioural Accounting and Finance2(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.

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.

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

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 countriesAmerican Educational Research Journal, 41(4), 901-926.

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. (1987). Similarity, confusability, and the density hypothesisJournal of Experimental Psychology: General, 116, 238-249.

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

 

publications

Papers (under review/ in preparation)
 
Tseng, H., Johnson, M. S., & Corter, J. E. (2011).  A linear compensatory counterpart to and generalization of the DINA Model.  Under revision.

Chen, Y.-J., & Corter, J. E.  (2010). Changes in risk preference over repeated decisions.  Under revision.

Matsuka, T., Corter, J. E., & Markman, A. (2010). Attention learning in adaptive network models of categorization.  Under revision.

Corter, J. E., & Monos, C. L. (2010).  Effects of category structure and task goals on reference behavior during category learning.  Under revision.

Rho, Y.-J., Corter, J. E., & Johnson, M. S. (2011).  Optimal strategy choice in fraction subtraction.  In preparation.
 

Publications/In Press/Accepted:

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.

Corter, J. E., Mason, D. L., Tversky, B., & Nickerson, J. V. (2011). Identifying causal pathways with and without diagrams. In C. Hoelscher, T. F. Shipley, and L. Carlson (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 2715-2720). Austin, TX: Cognitive Science Society.

Tversky, B., Corter, J. E., Yu, L., Mason, D. L., & Nickerson, J. V. (2011). Visualizing thought: Mapping category and continuum. In C. Hoelscher, T. F. Shipley, and L. Carlson (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 1577-1582). Austin, TX: Cognitive Science Society.

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.
 


Zahner, D., Nickerson, J. V., Tversky, B., Corter, J. E., and Ma. J. (2010). A Fix for Fixation? Re-representing and abstracting as creative processes in the design of information systems. In Maher, M., Kim, Y. S., and Bonnardel, N. (Eds.), Artificial Intelligence in Engineering Design, Analysis and Manufacturing, 24(2), 231-244.

Bobek, E. J., & Corter, J. E. (2010). Effects of problem difficulty and student expertise on the utility of provided diagrams in probability problem solving. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 2650-2655). Austin, TX: Cognitive Science Society.

Nickerson, J. V., Tversky, B., Corter, J. E., Yu, L., Rho, Y. J., & Mason, D. L. (2010). Thinking with networks.  In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 2662-2667). Austin, TX: Cognitive Science Society.

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 learningQuarterly Journal of Experimental Psychology, 61(7), 1067-1097.

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.

Nickerson, J. V., Corter, J. E., Tversky, B., Zahner, D., and Rho, Y. (2008b). The spatial nature of thought: Understanding information systems design through diagrams, in Boland, R., Limayem, M., and Pentland B., (Eds), Proceedings of the 29th International Conference on Information Systems, Paper 216. http://aisel.aisnet.org/icis2008/216

Tversky, B., Corter, J. E., Nickerson, J.V., Zahner, D., & Rho, Y. (2008).  Transforming descriptions and diagrams to sketches in information system design.  In G. Stapelton, J. Howse, & J. Lee (Eds.), Proceedings of the 5th International Conference on the Theory and Application of Diagrams 2008. Berlin:  Springer-Verlag.


 
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. 

Nickerson, J. V., Corter, J. E., Esche, S. K., & Chassapis, C. (2007).  A model for evaluating the effectiveness of remote engineering laboratories and simulations in education.  Computers and Education, 49(3), 7-8-725.

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.  (2006). When mixed options are preferred in multiple-trial decision making.  Journal of Behavioral Decision Making, 19, 1-26.

Tatsuoka, K., Guerrero, A., Corter, J.E., Tatsuoka, C., Yamada, T., Xin, T., Dogan, E., Dean, M., and Im, S. (2006). International comparisons of mathematical thinking skills in the TIMSS-R.  Japanese Journal for Research on Testing, 2(1), 3-39.

Corter, J. E. (2005).  Additive trees.  In B. Everitt & D. Howell (Eds.), Encyclopedia of Statistics in the Behavioral 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.

Matsuka, T. & Corter, J.E. (2004).  Stochastic learning algorithm for modeling human category learning.  International Journal of Computational Intelligence, 1(1), 40-48.

Corter, J. E., Nickerson, J. V., Esche, S. K., & Chassapis, C. (2004). Remote vs. hands-on labs: A comparative study.  In Proceedings of the 34th ASEE/IEEE Frontiers in Education Conference. Piscataway NJ: IEEE.

Matsuka, T., & Corter, J.E. (2004).  Modeling human category learning with stochastic optimization methods.  Proceedings of the Sixth International Conference on Cognitive Modeling (pp. 196-201). Mahwah NJ: Lawrence Erlbaum Associates.

Matsuka, T., Corter, J. E., & Hanson, S. J. (2004). Irresistibly attractive fruitless feature dimensions.  Proceedings of the Sixth International Conference on Cognitive Modeling (pp. 370-371). Mahwah NJ: Lawrence Erlbaum Associates.



Matsuka, T., & Corter, J. E. (2003).  Stochastic learning in neural network models of categorization.  In Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.  Hillsdale NJ:  Lawrence Erlbaum Associates.

Corter, J.E.  (2003). Centroid method.  In M. Lewis-Beck, A. E. Bryman, & T. F. Liao (Eds.), Encyclopedia of Social Science Research Methods.  Thousand Oaks CA: Sage.

Corter, J.E.  (2003). Tree diagram.  In M. Lewis-Beck, A. E. Bryman, & T. F. Liao (Eds.), Encyclopedia of Social Science Research Methods.  Thousand Oaks CA: Sage.

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

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

Corter, J. E. (1997).  [Review of Classification and Cognition].  Journal of Classification, 14(1), 171-173.

Corter, J. E. (1997).  GTREE: A PASCAL program to fit additive trees to proximity data.  Public domain software, published on Lucent Technologies Bell Labs NETLIB node (http://www.netlib.com/~mds).

Corter, J. E. (1997).  User's manual for the GTREE program to fit additive trees.  Documentation for software, published on Lucent Technologies Bell Labs NETLIB node (http://www.netlib.com/~mds).

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.

Stein, H., Corter, J. E., & Hull, J. (1996). Impact of therapist vacations on inpatients with borderline personality disorderPsychoanalytic Psychology, 13, 513-530.
 

 
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 informationPsychological Bulletin, 111(2), 291-303.

Corter, J. E. (1991).  Normative theories of categorization.  [Commentary on J.R. Anderson, Is human cognition adaptive?]  The Behavioral and Brain Sciences, 14, 491-492.

Corter, J. E. (1991).  [Review of  Data Theory and Dimensional Analysis].  Applied Psychological Measurement, 15, 423-424.
 
Corter, J. E., & Carroll, J. D. (1990). Potential uses of three-way multidimensional scaling and related techniques to integrate knowledge from multiple experts. Annals of Mathematics and Artificial Intelligence, 2(1-4), 77 92.
 


Corter, J. E. (1989).  Extended tree representation of relationships among languages.  In N.X. Luong (Ed.), Analyse Arbore des Donnes Textuelles [special issue].  Cahiers des Utilisateurs de Machines lectronique des Fins d'Information et de Documentation (CUMFID), 16[Juin], 139-155.

Corter, J. E. (1988).  Testing the density hypothesis:  Reply to Krumhansl.  Journal of Experimental Psychology: General, 117, 105 106.

Corter, J., 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. (1986).  Relevant features and statistical theories of generalization.  [Commentary on R.C. Schank, G.C. Collins & L.E. Hunter, Transcending inductive category formation in learning.]  The Behavioral and Brain Sciences, 9, 653-654.

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


Gluck, M. A., & Corter, J. E. (1985).  Information, uncertainty and the utility of categories.  In Proceedings of the Seventh Annual Conference of the Cognitive Science Society.  Hillsdale NJ: Lawrence Erlbaum.

Corter, J. E., & Gluck, M. A. (1985).  Machine generalization and human categorization:  An information-theoretic view.  In Proceedings of the Workshop on Uncertainty and Probability in Artificial Intelligence, Los Angeles: AAAI/RCA.

Corter, J. E. (1983).  Psychological similarity and the density hypothesis.  Unpublished Ph.D. dissertation, Stanford University.

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.

 

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:


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

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.

Documents & Papers

Centers and Projects

TIMSS-R Mathematics -- Diagnostic Assessment
Website: http://www.tc.edu/centers/timms-diag