C. Mitchell (Chan) Dayton 1230D Benjamin Building University of Maryland College Park, MD 20742 cdayton@umd.edu (301) 405-3626 Voice (301) 314-9245 Fax

Chan Dayton is a Professor and Chair in the Department of Measurement & Statistics. For more than 20 years, he has pursued a research interest in latent class analysis which is a specialized field within the realm of discrete mixture models. In 1999, he published a Sage book dealing with latent class scaling models. Recently, he has focused on model comparison procedures with a special interest in approaches based on information theory and Bayes factors. In particular, he has been working on an innovative alternative to pairwise comparison procedures such as Tukey test. His research has appeared in journals such as The Journal of The American Statistical Association, Psychometrika, American Statistician, Multivariate Behavioral Research, Applied Psychological Measurement, Journal of Educational and Behavioral Statistics, British Journal of Mathematical and Statistical Psychology, Psychological Methods, and Journal of Educational Measurement.

Some recent publications are:

Dayton, C. M. Applications and extensions of the two-point mixture index of model fit. In Advances in Latent Variable Mixture Models, Gregory R. Hancock & Karen M. Samuelsen (Eds.), Information Age Publishing, 2007

Pan, X. & Dayton, C. M. Factors influencing the mixture index of model fit in contingency tables showing independence. Paper presented at AERA 2007 Annual Meeting, Chicago PDF version

Dayton, C. M. & Macready, G. B. Latent class analysis in psychometrics. In Rao, C. R. & Sinharay, S. (eds) Handbook of Statistics, 421-446, Elsevier, 2007

Dayton, C. M. Latent structure of attitudes toward abortion. In Real Data Analysis, S.S.Sawilowsky (ed), AERA SIG/ES, 293-298, 2006

Dayton, C. M. & Pan, X. PCIC: Best subsets using information criteria. Journal of Modern Applied Statistical Methods, 4, 621-626, Nov. 2005. Link to JMASM

Pan, X. & Dayton, C. M. Sample size selection for pair-wise comparisons using information criteria. Journal of Modern Applied Statistical Methods, 4, 601-608, Nov. 2005. Link to JMASM

Dayton, C. M. “Nuisance Variables” in Everitt, B. & Howell, D. (Eds.), Encyclopedia of Statistics in Behavioral Science, Wiley, 2005. PDF version

Dayton, C. M. Model comparisons using information measures. Journal of Modern Applied Statistical Methods, 2, 281-292,2003. PDF version

Dayton, C. M. Applications and computational strategies for the two-point mixture index of fit. British Journal of Mathematical & Statistical Psychology, 56,1-13, 2003.PDF version

Dayton, C. M. Information criteria for pairwise comparisons. Psychological Methods, 8, 61-71, 2003.PDF version

DeAyala, R. J. Kim, S-H., Stapleton, L. M. & Dayton, C. M. Differential item functioning: a mixture distribution conceptualization. International Journal of Testing, 2, 243-276, 2003.

Gagne, P. & Dayton, C. M. Best regression using information criteria. Journal of Modern Statistical Methods, 1, 479-488, 2002. PDF version

Patterson, B., Dayton, C. M. & Graubard, B. Latent class analysis of complex survey data: application to dietary data. Journal of the American Statistical Association, 97, 721-729, 2002.PDF version

Dayton, C. M. & Macready, G. B. "Use of Categorical and Continuous Covariates in Latent Class Analysis." in Advances in Latent Class Modeling, Allan McCutcheon and Jacques Hagenaars (Eds.), Cambridge University Press, 2002.

Dayton, C. M. SUBSET: Best subsets using information criteria. Journal of Statistical Software, Vol. 06, Issue 02, April, 2001.Link to JSS

Ely, E. A., Chadburn, A., Dayton, C. M., Cesarman, E. & Knowles, D.M. Telomerase activity in B-Cell Non-Hodgkin Lymphomas. Cancer, 89, 445-452, 2000.PDF Version

DeAyala, R. J. & Dayton, C. M. YeoLCA2: A program for performing latent class analysis. Applied Psychological Measurement, 24, 266, 2000.PDF version