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The Mixed Effects Modeling Lab focuses on development, evaluation, and application of statistical methods to appropriately model data that come from a correlated data structure. Examples of such structures include students nested within schools, patients nested within hospitals, and repeated measures nested within people. In particular, the lab’s research focuses on modeling small sample data, missing data, Bayesian estimation, and extending mixed effects models to novel situations where they may possess untapped advantages.
Lab Director: Dan McNeish, Assistant Professor
Link to CV]
Denis Dumas, Denis Dumas is an Assistant Professor in the Department of Research Methods and Information Science at the University of Denver. Denis’s research focuses on learning trajectories and non-linear models. Denis and Dan together created a non-linear mixed effect model for educational assessment scores and have published multiple papers on its formulation and application.
Rens van de Schoot, Rens is an Associate Professor in the Department of Methodology & Statistics at Utrecht University in the Netherlands and extra-ordinary professor at North-West University in South Africa. Rens’s research focuses on small sample inference and Bayesian statistics. Rens and Dan regularly give workshops and short courses on statistical topics including small sample analysis, Bayesian statistics, and structural equation modeling. They have also co-organized two statistical conferences together.
This list is limited to papers in Multivariate Behavioral Research, Psychological Methods, or Structural Equation Modeling since 2016. A complete list may be found in Dan’s CV [link to CV].
McNeish, D. (in press). Small sample methods for multilevel modeling: A colloquial elucidation of REML and the Kenward-Roger correction. Multivariate Behavioral Research.
McNeish, D. & Hancock, G.R. (in press). The effect of measurement quality on targeted structural model fit indices: A comment on Lance, Beck, Fan, and Carter (2016). Psychological Methods.
McNeish, D. (in press). Thanks coefficient alpha, we’ll take it from here. Psychological Methods.
Harring, J.R., McNeish, D., & Hancock, G.R. (in press). Using phantom variables in structural equation modeling to assess model sensitivity to external misspecification. Psychological Methods.
McNeish, D. (2017). Multilevel mediation with few clusters: A cautionary note on the multilevel structural equation modeling framework. Structural Equation Modeling, 24, 609-625.
McNeish, D., & Wentzel, K.R. (2017). Accommodating small sample sizes in three level models when the third level is incidental. Multivariate Behavioral Research, 52, 200-215.
McNeish, D. & Dumas, D. (2017). Non-linear growth models as psychometric models: A second-order growth curve model for measuring potential. Multivariate Behavioral Research, 52, 61-85.
McNeish, D., Stapleton, L. M., & Silverman, R.D. (2017). On the unnecessary ubiquity of hierarchical linear modeling. Psychological Methods, 22, 114-140.
McNeish, D. (2016). Estimation methods for mixed logistic models with small sample sizes. Multivariate Behavioral Research, 51, 790-804.
McNeish, D. (2016). On using Bayesian methods to address small sample problems. Structural Equation Modeling, 23, 750-773.
McNeish, D.,& Stapleton, L. M.(2016). Modeling clustered data with very few clusters. Multivariate Behavioral Research, 51, 495-518.
Dan will be giving a post-conference workshop on multilevel models with small samples at the Small Sample Size Solution (S4) conference in Utrecht, the Netherlands.
Dan and Rens van de Schoot will be giving a pre-conference workshop on Bayesian analysis for small sample data at the European Conference for Developmental Psychology (ECDP) in Utrecht, the Netherlands.