Chan Dayton is a Professor Emeritus and past 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.

Professional Biography

Chan Dayton is a Professor Emeritus and past 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.

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Selected Publications

Dayton, C. M. Introduction to Latent Class Analysis. In Menard, S. (ed.) Handbook of Longitudinal Research: Design, Measurement & Analysis, Elsevier, 2008

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

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.

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.

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

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

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.

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

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.

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.

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.

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.

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

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Dayton, C. M. Introduction to Latent Class Analysis. In Menard, S. (ed.) Handbook of Longitudinal Research: Design, Measurement & Analysis, Elsevier, 2008 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 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. 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.  Dayton, C. M. “Nuisance Variables” in Everitt, B. & Howell, D. (Eds.), Encyclopedia of Statistics in Behavioral Science, Wiley, 2005. Dayton, C. M. Model comparisons using information measures. Journal of Modern Applied Statistical Methods, 2, 281-292,2003. 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. Dayton, C. M. Information criteria for pairwise comparisons. Psychological Methods, 8, 61-71, 2003. 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. 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. 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. 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. DeAyala, R. J. & Dayton, C. M. YeoLCA2: A program for performing latent class analysis. Applied Psychological Measurement, 24, 266, 2000.