Timothy R. Johnson

Timothy R. Johnson is Professor and Department Chair in the Department of Mathematics and Statistical Science at the University of Idaho, College of Science. He holds a Ph.D. in Quantitative Psychology and an M.S. in Statistics from the University of Illinois at Urbana-Champaign, and earlier degrees in Psychology from Western Washington University.

His research focuses on advanced statistical methodologies, particularly in Bayesian inference, response style modeling, and handling coarsened or aggregated data. He develops simulation-based inferential methods and employs specialized Monte Carlo techniques to address intractable likelihoods in data affected by statistical disclosure control. His work addresses fundamental issues in how human response behaviors and data processing affect statistical validity.

Johnson teaches core statistics courses including Statistical Methods (Stat 251), Survey Sampling (Stat 422), and Applied Regression Modeling (Stat 436/516). His research has interdisciplinary reach, with collaborations in psychology, business, economics, and natural resources. Though specific publications and advisees are not listed, his methodological focus supports a wide range of empirical research across social and natural sciences.

He leads the department and contributes to graduate and undergraduate programs in statistics and data science. His work with the Statistical Consulting Center and involvement in collaborative research projects highlight his engagement with real-world data challenges. He maintains a curriculum vitae and Google Scholar profile for scholarly dissemination.

Education:

• 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

Scientific Awards:

• No awards listed in the provided text.

Advising and Grants:

Dr. Johnson mentors students through graduate programs in statistics and data science, though specific advisees are not named. He is involved in collaborative research projects across disciplines, indicating active grant and research funding engagement, particularly in applied statistical methodology. His work on response styles and data coarsening likely involves external funding, though specific grants are not detailed.

Labs and Teams:

He is affiliated with the Statistical Consulting Center and contributes to the research ecosystem within the Department of Mathematics and Statistical Science. His involvement in collaborative projects suggests integration with interdisciplinary research teams in psychology, ecology, and social sciences.