Psychiatry
Global

Psychiatry Experts

Dr. Justin Macdonald

Associate Professor
Psychology
New Mexico State University
Mexico

Biography

Fields of Expertise Auditory Perception, Computational and Mathematical Modeling, Statistics, Decision-Making Education 1997 – 2003, Purdue University, West Lafayette, Indiana 1992 – 1996, University of Washington, Seattle, Washington Degrees Ph.D., Quantitative-Mathematical Psychology, Purdue University, 5/2003 M.S., Quantitative-Mathematical Psychology, Purdue University, 12/1999 B. S., Psychology, University of Washington, 6/1996 Professional Experience Assistant/Associate Professor, Engineering Psychology, New Mexico State University, Las Cruces, New Mexico, 8/07 – present. Main research foci include auditory interface design and perceptual phenomena, decision-making under uncertainty, and descriptive and inferential statistical procedures. Research Psychologist, Army Research Laboratory, Aberdeen Proving Ground, Maryland, 1/04 – 5/07. Developed and conducted basic and applied research related to auditory perception and communication systems. Studied spatial audio, bone-conducted hearing, and sound localization.

Research Interest

Engineering Psychology

Publications

  • Jonason, P. K., & Marks, M. J. (2009). Common vs. uncommon sexual acts: Evidence for the sexual double standard. Sex Roles, 60, 357-365.

  • Balakrishnan, J. D., & MacDonald, J.A. (2001). Signal detection theory. In: International Encyclopedia of Ergonomics and Human Factors, W. Karwowski (Ed). London: Taylor & Francis. PDF.

  • Hunter, K. N., Rice, S., MacDonald, J. A., & Madrid, J. (2014). What are the best predictors of opinions of mental illness in the Indian population? International Journal of Mental Health, 43, 35-51. PDF.

  • Trafimow, D., & MacDonald, J. A. (2016). Performing inferential statistics prior to data collection. Educational and Psychological Measurement, 77, 204-219.

  • Hout, M. C., Robbins, A., Cunningham, C., MacDonald, J. A. (under review). Stressed but determined: Simulating the fidelity of data for large stimulus set sizes in multidimensional scaling. Submitted to Sage Open.

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