Daniel McNeish

Assoc Professor
Faculty
TEMPE Campus
Mailcode
1104

Biography

Daniel McNeish is an Associate Professor in the Quantitative Area in the Department of Psychology. Prior to ASU, he was an Assistant Professor in the Department of Methodology and Statistics at Utrecht University in the Netherlands, and a Research Scientist at UNC-Chapel Hill. 

His research areas include multilevel models, longitudinal data analysis, analysis of small sample data, and structural equation models. His contributions to these areas have been acknowledged by the following, 

  • 2018 Anne Anastasi Dissertation Award from the American Psychological Association (APA)
  • 2018 Rising Star distinction by the Association for Psychological Science (APS)
  • 2019 Early Career Award in Statistics from the American Educational Research Association (AERA)
  • 2019 Anne Anastasi Early Career Award from the American Psychological Association (APA)
  • Elected Member of the Society of Multivariate Experimental Psychology (SMEP).

He also serves as a consulting editor for Psychological Methods and Behavior Research Methods and is an editorial board member of Organizational Research Methods and the Routledge Multivariate Applications Book Series

Education

  • PhD University of Maryland, Measurement & Statistics, 2015
  • MA University of Maryland, Measurement & Statistics, 2013
  • BA Wesleyan University (CT), Psychology, 2011

 

Google Scholar

Publications

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.

Courses

Fall 2020
Course NumberCourse Title
PSY 792Research
Spring 2020
Course NumberCourse Title
PSY 230Introduction to Statistics
PSY 792Research
Fall 2019
Course NumberCourse Title
PSY 537Longitudinal Growth Modeling
Spring 2019
Course NumberCourse Title
PSY 230Introduction to Statistics
Fall 2018
Course NumberCourse Title
PSY 539Multilevel Models Psych Resrch
Spring 2018
Course NumberCourse Title
GCU 593Applied Project
Fall 2017
Course NumberCourse Title
PSY 539Multilevel Models Psych Resrch