Timothy R. Johnson
Timothy R. Johnson
Brink Hall 417
Department of Mathematics and Statistical Science
University of Idaho
875 Perimeter Drive, MS 1103
Moscow, ID 83844-1103
- Ph.D., Quantitative Psychology, University of Illinois at Urbana-Champaign, 2001
- M.S., Statistics, University of Illinois at Urbana-Champaign, 1999
- M.S., Psychology, Western Washington University, 1994
- B.A., Psychology, Western Washington University, 1993
- Response Style
- Coarsened and Aggregated Data
- Monte Carlo Inferential Methods
- Categorical Data
Dr. Johnson is a Professor of Statistics in the Department of Statistical Science and an Affiliate Professor of Psychology in the Department of Psychology and Communication Studies. He earned a Ph.D. in Quantitative Psychology and M.S. in Statistics from the University of Illinois at Urbana-Champaign, and a M.S. and B.A. in Psychology from Western Washington University. His background and expertise is in statistics and quantitative psychology, with an emphasis on psychometric models and behavioral statistics. His research includes methodological research on the development and use of statistics in the behavioral sciences, as well as collaborative work with researchers in psychology, business and economics, and natural resources and ecology.
Response Style. Most statistical models for ratings elicited from human respondents implicitly assume that the use of the response scale is homogenous across respondents, despite evidence that there are considerable individual and cross-cultural differences (sometimes known as “response style”). For some time I have been working on item response models that either account individual or group differences in scale use. More recently I have been developing inferential methods that are robust with respect to such differences.
Coarsened and Aggregated Data. Procedures for data processing, dissemination, and statistical disclosure control sometimes results in particular missing data problems such as coarsening and aggregation. If not accounted for properly, this can compromise inference. But coarsening and aggregation can result in analytically and numerically intractable likelihood functions and posterior distributions. One of my areas of work is in the use of specialized Monte Carlo methods to circumvent these problems.
Collaborative Research. I am involved in a variety of collaborative projects with researchers in the social and natural sciences including psychology, business and economics, and natural resources and ecology.