Matthew S. Johnson
MS, Department of Statistics, Carnegie Mellon University, 1997
BS, Indiana University, Bloomington, IN, 1996
|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|
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.
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.
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.
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.
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.
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.
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.
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
Download: 2011_Decarlo_Kim_Johnson_ A hierarchical rater model [PDF]