Daniel John Bauer is an American statistician, professor, and director of the quantitative psychology program at the University of North Carolina, where he is also on the faculty at the Center for Developmental Science. He is known for rigorous methodological work on latent variable models and is a proponent of integrative data analysis, a meta-analytic technique that pools raw data across multiple independent studies.
Bauer serves on the editorial boards of Psychological Assessment, Psychological Methods, and the Journal of Educational and Behavioral Statistics. He is an editor of or reviewer for dozens of other journals. In 2008, he cofounded the Curran–Bauer Analytics consulting firm with Patrick Curran, a colleague in the Thurstone Lab, and has taught numerous doctoral-level workshops in quantitative methods to social scientists worldwide. He was recognized by UNC in 2016 "for exceptional teaching of post-baccalaureate students."
Bauer, D.J., Gottfredson, N.C., Dean, D., & Zucker, R.A.. Analyzing repeated measures data on individuals nested within groups: accounting for dynamic group effects. Psychological Methods, 18, 1-14..
Bauer, D.J., Howard, A.L., Baldasaro, R.E., Curran, P.J., Hussong, A.M., Chassin, L., & Zucker, R.A.. A trifactor model for integrating ratings across multiple informants. Psychological Methods, 18, 475-493..
Bauer, D.J., Baldasaro, R. & Gottfredson, N.C.. Diagnostic procedures for detecting nonlinear relationships between latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 19, 157-177.
Bauer, D.J.. Evaluating individual differences in psychological processes. Current Directions in Psychological Science, 20, 115-118.
Bauer, D.J. & Sterba, S.K.. Fitting multilevel models with ordinal outcomes: performance of alternative specifications and methods of estimation. Psychological Methods, 16, 373-390.
Bauer, D.J. & Reyes, H.L.M.. Modeling variability in individual development: differences of degree or kind?. Child Development Perspectives, 4, 114-122.
Bauer, D.J.. A note on comparing the estimates of models for cluster-correlated or longitudinal data with binary or ordinal outcomes. Psychometrika, 74, 97-105.
Bauer, D.J. & Cai, L.. Consequences of unmodeled nonlinear effects in multilevel models. Journal of Educational and Behavioral Statistics, 34, 97-114.
Bauer, D.J. & Hussong, A.M. Psychometric approaches for developing commensurate measures across independent studies: traditional and new models. Psychological Methods, 14, 101-125.
Bauer, D.J., Sterba, S.K. & Hallfors, D.D.. Evaluating group-based interventions when control participants are ungrouped. Multivariate Behavioral Research, 43, 210-236.
Bauer, D.J.. Observations on the use of growth mixture models in psychological research. Multivariate Behavioral Research, 42, 757-786.
Bauer, D.J., Preacher, K.J. & Gil, K.M.. Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: new procedures and recommendations. Psychological Methods, 11, 142-163.
Bauer, D.J.. The role of nonlinear factor-to-indicator relationships in tests of measurement equivalence. Psychological Methods, 10, 305-316.
Bauer, D.J.. A semiparametric approach to modeling nonlinear relations among latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 4, 513-535.
Bauer, D.J. & Curran, P.J.. Probing interactions in fixed and multilevel regression: inferential and graphical techniques. Multivariate Behavioral Research, 40, 373-400.
Bauer, D.J. & Curran, P.J.. The integration of continuous and discrete latent variable models: potential problems and promising opportunities. Psychological Methods, 9, 3-29.
Bauer, D.J.. Estimating multilevel linear models as structural equation models. Journal of Educational and Behavioral Statistics, 28, 135-167.
Bauer, D.J. & Curran, P.J.. Distributional assumptions of growth mixture models: Implications for over-extraction of latent trajectory classes. Psychological Methods, 8, 338-363..