Matthew S. Johnson

Professional Background
Vitae
Courses
Papers
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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

professional organization membership

Editor, Journal of Educational and Behavioral Statistics. 2011-2013 volume years.

Editorial Board Member of Psychometrika, July 2010–Present.

Member of the Technical Advisory Committee for Standard Setting (TACSS) for the 2012 NAEP Writing Assessment.

Associate Editor, Psychometrika, June 2004–Present.

Associate Editor, Journal of Educational and Behavioral Statistics, January 2002–July 2010.

2009 Program Chair for the Statistics in Sport section of the American Statistical Association.

Invited participant of the Tenth Annual Japanese-American Kavli Frontiers of Science. Jointly sponsored by the Japan Society for the Promotion of Science and the U.S. National Academy of Sciences. Shonan Village, Kanagawa, Japan. December 1-3, 2007.

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.