Daniel McNeish

For a full updated list of publications, please see my my personal website, https://sites.google.com/site/danielmmcneish/acdemic-work

Selected Publications

McNeish, D., Dumas, D.G., & Grimm, K.J. (2020). Estimating new quantities from longitudinal test scores to improve forecasts of future performance. Multivariate Behavioral Research

McNeish, D. & Hamaker, E.L. (2020). A primer on two-level dynamic structural equation modeling for intensive longitudinal data. Psychological Methods

McNeish, D. & Kelley, K. (2019). Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations. Psychological Methods, 24, 20-35.

McNeish, D. (2018). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 23, 412-433.

McNeish, D., An, J., & Hancock, G.R. (2018). The thorny relation between measurement quality and fit index cut-offs in latent variable models. Journal of Personality Assessment, 100, 43-52.

McNeish, D. & Matta, T. (2018). Differentiating between mixed effects and latent curve approaches to growth modeling. Behavior Research Methods, 50, 1398-1414.

McNeish, D., Stapleton, L. M., & Silverman, R.D. (2017). On the unnecessary ubiquity of hierarchical linear modeling. Psychological Methods, 22, 114-140.

McNeish, D. (2017). Small sample methods for multilevel modeling: A colloquial elucidation of REML and the Kenward-Roger correction. Multivariate Behavioral Research, 52, 661-670.

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. (2017). Challenging conventional wisdom for multivariate statistical models with small samples. Review of Educational Research, 87, 1117-1151.

Dumas, D. & McNeish, D. (2017). Dynamic measurement modeling: Using nonlinear growth models to estimate student learning capacity. Educational Researcher, 46, 284-292.

McNeish, D., & Stapleton, L.M. (2016). The effect of small sample size on two level model estimates: A review and illustration. Educational Psychology Review, 28, 295-314.

McNeish, D., & Stapleton, L. M. (2016). Modeling clustered data with very few clusters. Multivariate Behavioral Research, 51, 495-518.

McNeish, D. (2016). On using Bayesian methods to address small sample problems. Structural Equation Modeling, 23, 750-773.

McNeish, D. (2015). Using Lasso for predictor selection and to assuage overfitting: A method long overlooked in behavioral sciences. Multivariate Behavioral Research, 50, 474-481.

McNeish, D. (2014). Modeling sparsely clustered data: Design-based, model-based, and single-level methods. Psychological Methods, 19, 552-563.