Jonathan Templin has a joint appointment as an Associate Professor in the Research, Evaluation, Measurement, and Statistics (REMS) Program in the Department of Educational Psychology and as an Associate Scientist in the Achievement and Assessment Institute, both at University of Kansas. Dr. Templin received his Ph.D. in Quantitative Psychology at the University of Illinois at Urbana-Champaign, where he also received an M.S. in Statistics. He joined the faculty of Kansas in January of 2014, having previously served on the faculty at the University of Nebraska-Lincoln, the University of Georgia, and the University of Kansas. The main focus of Dr. Templin's research is in the field of diagnostic classification models—psychometric models that seek to provide multiple highly reliable scores from psychological tests. Dr. Templin’s research program has been funded by the National Science Foundation and has been published in journals such as Psychometrika, Psychological Methods, Applied Psychological Measurement, and the Journal of Educational Measurement. He currently serves as an Associate Editor for Psychometrika and Applied Psychological Measurement and as an editorial board member for the journal Educational Measurement: Issues and Practice. Dr. Templin is a coauthor of the book Diagnostic Measurement: Theory, Methods, and Applications, which won the 2012 American Educational Research Association Division D Award for Significant Contribution to Educational Measurement and Research Methodology. Dr. Templin teaches courses on advanced statistical and psychometric methods including multivariate statistics and structural equation modeling. For more information, please visit his website: http://jonathantemplin.com.
- Statistics, Measurement, Psychometrics, Statistical Computing
- Psychometrics, Diagnostic Modeling, Bayesian Statistics, Statistical Computing, Model Estimation
Templin, J. (2016). An overview of item response theory. In S. K Whitbourne (Ed.), The Encyclopedia of Adulthood and Aging. (pp. 699-704). West Sussex: Wiley.
Templin, J., Bradshaw, L. P, & Paek, P. (2016). A comprehensive framework for integrating innovated psychometric methodology into educational research. Journal of Research in Mathematics Education Monograph.
Li, F., Cohen, A., Bottge, B., & Templin, J. (2016). A latent transition analysis model for assessing change in cognitive skills. Educational and Psychological Measurement, 76, 181-204.
Izsak, A., & Templin, J. (2016). Coordinating descriptions of mathematical knowledge and psychometric models: Opportunities and challenges. Journal of Research in Mathematics Education Monograph.
Watts, A., Walters, R., Hoffman, L., & Templin, J. (2016). Intra-individual variability of physical activity in older adults with and without mild Alzheimer’s disease. PLoS ONE, 11(4), doi: 10.1371/journal.pone.0153898.
Izsak, A., Remillard, J., & Templin, J. (2016). Psychometrics assessment in mathematics education: Opportunities, challenges, and interdisciplinary collaborations (A. Izsak, J. Templin, & J. Remillard). The Journal of Research in Mathematics Education Monograph Series Reston, VA: The National Council of Teachers of Mathematics.
Templin, J. (2015). Diagnostic assessment: Methods for the reliable measurement of multidimensional abilities. In F. Drasgow (Ed.), Technology in Testing: Measurement Issues (pp. 285-304). New York: Taylor and Francis.
Brown, C., Templin, J., & Cohen, A. (2015). Comparing the two- and three-parameter logistic models via likelihood ratio tests: a commonly misunderstood problem.
. Applied Psychological Measurement, 39, 335-348.
Watkins, L., DiLillo, D., Hoffman, L., & Templin, J. (2015). Do negative emotion and self-control depletion contribute to intimate partner aggression? A lab-based study. Psychology of Violence, 5, 35-45.
Bradshaw, L., & Templin, J. (2014). Combining scaling and classification: A psychometric model for scaling ability and diagnosing misconceptions. Psychometrika, 79, 403-425.
Bradshaw, L., Izsák, A., Templin, J., & Jacobson, E. (2014). Diagnosing teachers’ understanding of rational number: Building a multidimensional test within the diagnostic classification framework. Educational Measurement: Issues and Practice, 33(1), 2-14.
Templin, J., & Bradshaw, L. (2014). Hierarchical diagnostic classification models: A family of models for estimating and testing attribute hierarchies. Psychometrika, 79, 317-339. DOI:10.1007/S11336-013-9362-0
Templin, J., & Bradshaw, L. P. (2014). The use and misuse of psychometric models. Psychometrika, 79(2), 347-354. DOI:10.1007/S11336-013-9364-Y
Templin, J., & Bradshaw, L. (2013). Measuring the reliability of diagnostic classification model examinee estimates. Journal of Classification , 30(2), 251-275. DOI:10.1007/S00357-013-9129-4
Templin, J., & Hoffman, L. (2013). Obtaining diagnostic classification model estimates using Mplus. Educational Measurement: Issues and Practice, 32(2), 37-50.
Hoffman, L., Templin, J., & Rice, M. (2012). Linking outcomes from Peabody Picture Vocabulary Test forms using item response models. Journal of Speech, Language, and Hearing Research, 55, 754-763.
Henson, R., Templin, J., & Willse, J. (2009). Defining a family of cognitive diagnosis models using log-linear models with latent variables. Psychometrika, 74(2), 191-210. DOI:10.1007/S11336-008-9089-5
Templin, Jonathan L., (Principal), MATH: EAGER: Developing a Learning Map for Introductory Statistics, National Science Foundation, $299,007, Submitted 05/01/2015 (11/01/2016 - 10/31/2018) . Federal. Status: Funded.
Templin, Jonathan, (Principal), Collaborative Research: Longitudinal Diagnostic Models, MMS; SES-1030337, National Science Foundation: Measurement, Methodology, and Statistics Program, $150,000, (01/01/2010 - 12/31/2013) . Federal. Status: Funded.
- Ph.D., Quantitative Psychology, University of Illinois at Urbana-Champaign, 2004
- M.S., Statistics, University of Illinois at Urbana-Champaign, 2002
- M.A., Quantitative Psychology, University of Illinois at Urbana-Champaign, 2004
- B.A., Psychology, California State University, Sacramento, 1998
Advanced quantitative methods and statistics, psychometrics, diagnostic modeling, Bayesian statistics, generalized linear mixed models, statistical computing.