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

Research

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Matthew S. Johnson

Professional Background

Educational Background

PhD, Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, 2001
Thesis Title: Parametric and Non-parametric Extensions to Unfolding Response Models,
Thesis Adviser: Brian W. Junker, PhD

MS, Department of Statistics, Carnegie Mellon University, 1997

BS, Indiana University, Bloomington, IN, 1996
Major: Mathematics
Minor: Economics

Scholarly Interests

Selected Publications

professional experiences

Sept, 2008–Present Associate Professor of Statistics and Education, Department of Human Development, Teachers College, Columbia University, New York, NY
Jan, 2008–Aug, 2008 Associate Professor, Department of Statistics and Computer Information Systems, City University of New York, Baruch College, New York, NY
Sept, 2002–Dec, 2007 Assistant Professor, Department of Statistics and Computer Information Systems, City University of New York, Baruch College, New York, NY
Aug, 2000–Aug, 2002 Associate Research Scientist, Center for Large-Scale Assessment, Educational Testing Service, Princeton, NJ



publications

2011-
 
DeCarlo, L. T., Kim, Y. K.,& Johnson, M. S. (2011). A hierarchical rater model for constructed responses, with a signal detection rater model. Journal of Educational Measurement, 48, 333-356.


2010:
 
Yaseen, Z., Katz, C., Johnson,M.S., Eisenberg, D., Cohen, L.J., and Galynker,I.I. (2010). Construct development: The Suicide Trigger Scale (STS-2), a measure of a hypothesized suicide trigger state. BMC Psychiatry, 10:110.

El Barmi, H., Johnson, M.S. and Mukerjee, H. (2010). Restricted estimation of cumulative incidence functions corresponding to one risk across stochastically ordered populations. Accepted for Publication in Journal of Multivariate Analysis.

Neath, R.C., and Johnson, M.S. (2010). Discrimination and Classification. International Encyclopedia of Education, Article 1312. Elsevier.


2009:
 
Yin, H.S., Johnson, M.S., Mendelsohn, A.L., Abrams, M.A., Sanders, L.M., and Dreyer, B.P. (2009). The Health Literacy of Parents in the United States:A Nationally Representative Study. Pediatrics, 124, Supplement 3, S289-S298.
 

2008:
 
Johnson, M.S. and Sinharay, S. (2008). Use of Item Models in a Large-scale Admissions Test: A Case Study. International Journal of Testing, 8:3, 209-236.


2007:
 
Johnson, M.S. (2007).Modeling dichotomous item responses with free-knot splines. Computational Statistics & Data Analysis, 51, 4178-4192.

Johnson, M.S. (2007). Marginal Maximum Likelihood Estimation of Item Response Models in R. Journal of Statistical Software, 20, Article 10.

Johnson, M.S., Sinharay, S., and Bradlow, E.T. (2007). Hierarchical item response theory models. Handbook of Statistics, vol. 26: Psychometrics. Edited by C.R. Rao and S. Sinharay. Elsevier.
 

2006:
 
Sinharay, S., Johnson, M.S., and Stern, H.S. (2006). Posterior predictive assessment of item response theory models. Applied Psychological Measurement 30, 298-321.

El Barmi, H. and Johnson, M.S. (2006).A Unified Approach to Testing For and Against a Set of Linear Inequality Constraints in the ProductMultinomial Setting. Journal of Multivariate Analysis, 97:8, pp 1894-1912.

Johnson, M.S. (2006). Nonparametric Estimation of Item and Respondent Locations from Unfolding-type Items. Psychometrika, 71:2, pp 257-279.

Johnson, M.E. and Johnson, M.S. (2006). Fitting Game Scores to a Strength Model, Hack #71. Baseball Hacks: Tips and Tools for Analyzing and Winning with Statistics, Edited by Joseph Adler, pp 400-409. O’Reilly.

Johnson, M.E. and Johnson, M.S. (2006). OBP, SLG, and Scoring Runs, Hack #67. Baseball Hacks: Tips and Tools for Analyzing and Winning with Statistics. Edited by Joseph Adler, pp 361-365. O’Reilly.
 

2005:
 
Johnson, M.S. and Sinharay, S. (2005). Calibration of polytomous item families using Bayesian hierarchical modeling. Applied Psychological Measurement, 29, pp. 369-400.

Johnson, M.S. and Junker, B.W. (2005). Attitude Scaling. in Encyclopedia of Behavioral Statistics. Edited by Brain Everitt and David C. Howell. Wiley.

Johnson, M.S. (2005). “Methodological issues in measuring food insecurity and hunger." Prepared for the National Academy of Sciences’ Committee on National Statistics’ Panel to Review the USDA’s Measurement of Food Insecurity and Hunger, March 2005. Available online.
 

2004:

Johnson, M.S. (2004). “Item response models and their use in measuring food insecurity and hunger.” Prepared for the National Academy of Sciences’Matthew S. Johnson 4 Committee on National Statistics’ Panel to Review the USDA’s Measurement of Food Insecurity and Hunger. Available online.
 

2003:

Sinharay, S., Johnson, M.S. and Williamson, D.M. (2003). Calibrating item families and summarizing the results using family expected response functions. Journal of Educational and Behavioral Statistics, 28, pp. 295-313.

Johnson,M.S. and Junker, B.W. (2003). Using data augmentation and Markov chain Monte Carlo for the estimation of unfolding response models, Journal of Educational and Behavioral Statistics, 28, pp 195-230.
 

2002:
 
Patz, R., Junker, B.W. Johnson, M.S. and Mariano, L.T. (2002). The Hierarchical Rater Model For Rated Test Items and Its Applications to Large-Scale Educational Test Data. Journal of Educational and Behavioral Statistics, 27, pp. 341-384.

Weiss, A.R., Lutkus, A.D., Hildebrant, B.S., and Johnson, M.S. (2002). The Nation’s Report Card: Geography 2001, NCES 2002-484, U.S. Department of Education. Office of Educational Research and Improvement. National Center for Education Statistics. Washington, DC.
 

1999-2001:

Braswell, J.S., Lutkus, A.D., Grigg, W.S., Santapau, S.L., Tay-Lim, B., and Johnson, M.S. (2001). The Nation’s Report Card: Mathematics 2000, NCES 2001-517, U.S. Department of Education. Office of Educational Research and Improvement. National Center for Education Statistics. Washington, DC.

Fienberg, S.E., Johnson, M.S., and Junker, B.W. (1999). Classical Multi-Level and Bayesian Approaches to Population Size Estimation Using Multiple Lists. Journal of the Royal Statistical Society, Series A, 162, pp. 383-405.

professional organization membership

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 6026: Statistical treatment of mass data

Prerequisite: HUDM 5123 or equivalent. Examines problems involved in preparing and analyzing large data sets. Includes a survey of data manipulation and statistical tools in SAS (Statistical Analysis System). Optional topics: introduction to numerical methods and survey of data mining tools.

HUDM 6122: Multivariate analysis I

Permission required. Prerequisite: HUDM 5122 or equivalent; HUDM 5123 is recommended. An introduction to multivariate statistical analysis, including matrix algebra, general linear hypothesis and application, profile analysis, principal components analysis, discriminant analysis, and classification methods.

Documents & Papers

Matthew S. Johnson appeared in the following articles:

TC's Matthew Johnson Talks to ABC About Preventing Test Cheating (9/21/2011)

When Less is More (3/22/2011)

Testing New Standards for Standardized Testing (12/8/2010)

New Faces at TC (10/10/2008)