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Timothy R. Johnson

Timothy R. Johnson

Professor

Office

Brink Hall 417

Phone

(208) 885-2928

Mailing Address

Department of Statistical Science
University of Idaho
875 Perimeter Drive, MS 1104
Moscow, ID 83844-1104

  • 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

  • Psychometrics
  • 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.

Methodological Research

  • Johnson, T. R. (in press). A note on conditional and quasi-conditional maximum likelihood estimation for polytomous item response models: Robust inference in the presence of individual differences in response scale use. Applied Psychological Measurement.
  • Johnson, T. R. & Wiest, M. M. (2014). Generalized linear models with coarsened covariates: A practical Bayesian approach. Psychological Methods, 19, 281-299.
  • Johnson, T. R. (2013). Item response modeling with sum scores. Applied Psychological Measurement, 37, 638-652.
  • Johnson, T. R. & Bodner, T. E. (2013). Posterior predictive checks of tetrad subsets for covariance structures of measurement models. Psychological Methods, 18, 494-513.
  • Johnson, T. R. & Kuhn, K. M. (2013). Bayesian Thurstonian models for ranking data using JAGS. Behavior Research Methods, 45, 857-872.

Applied Research

  • Camp, M. J., Rachlow, J. L., Shipley, L. A., Johnson, T. R., & Bockting, K. D. (2014). Grazing in sagebrush rangelands in western North America: Implications for habitat quality for a sagebrush specialist, the pygmy rabbit. The Rangeland Journal, 36, 151-159.
  • DeMay, S., Becker, P., Edison, C., Rachlow, J., Johnson, T., & Waits, L. (2013). Evaluating DNA degradation rates in faecal pellets of the endangered pygmy rabbit. Molecular Ecology Resources, 13, 654-662.
  • Elias, B. A., Shipley, L. A., McCusker, S., Sayler, R. D., & Johnson, T. R. (2013). Effects of genetic management on reproduction, growth, and survival in captive endangered pygmy rabbits (Brachylagus idahoensis). Journal of Mammalogy, 94, 1282-1292.
  • Kuhn, K. M., Johnson, T. R., & Miller, D. (2013). Applicant desirability influences reactions to discovered resume embellishments. International Journal of Selection and Assessment, 21, 111-120.
  • Watson, P. & Johnson, T. (2012). Federal fishery policy and the geographic distribution of commercial U.S. West Coast fish landings: Insights from the 2003 federal groundfish permit buyback. Marine Resource Economics, 27, 289-301.

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.

Contact

Physical Address:
Brink Hall 415A

Mailing Address:
875 Perimeter Drive, MS 1104
Moscow, ID 83844-1104

Phone: (208) 885-2929

Fax: (208) 885-7959

Email: stat@uidaho.edu

Web: Department of Statistical Science