Daniel McNeish is a 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 interests in applied statistics generally fall into three broad areas:
Models for clustered, longitudinal, and time-series data
Structural equation and measurement models
Methods for small sample data
These methodological interests often result in collaborations on empirical research addressing disparities in health and behavioral outcomes, particularly involving underrepresented groups or hard-to-reach populations where sample sizes tend to be more modest.
His contributions to these areas have been acknowledged by the following,
2024 Early Career Impact Award
Federation of Associations in Brain and Behavioral Sciences (FABBS)
2023 Distinguished Scientific Award for Early Career Contributions
American Psychological Association (APA)
2022, 2023, 2024 Highly Cited Researcher in Psychiatry/Psychology
Clarivate/Web of Science
2021 Tanaka Award
Society of Multivariate Experimental Psychology (SMEP)
2020 Early Career Research Award
Society of Multivariate Experimental Psychology (SMEP)
2019 Anne Anastasi Early Career Award
American Psychological Association (APA)
2019 Early Career Award in Statistics
American Educational Research Association (AERA)
2018 Rising Star early career award
Association for Psychological Science (APS)
2018 Anne Anastasi Dissertation Award
American Psychological Association (APA)
Elected Member of the Society of Multivariate Experimental Psychology (SMEP)
He currently serves as
Associate Editor, Multivariate Behavioral Research
Associate Editor, Behavior Research Methods
Consulting Editor, Psychological Methods
Editorial Board, Organizational Research Methods
Editorial Board, Routledge Multivariate Applications Book Series
Education
PhD University of Maryland, Measurement & Statistics, 2015
MA University of Maryland, Measurement & Statistics, 2013
McNeish, D. (in press). Dynamic fit index cutoffs for treating Likert items as continuous. Psychological Methods.
McNeish, D. (in press). A practical guide to selecting (and blending) approaches for clustered data: Clustered errors, multilevel models, and fixed effect models. Psychological Methods.
McNeish, D. & Mackinnon, D.P. (in press). Intensive longitudinal mediation in Mplus. Psychological Methods.
McNeish, D. & Wolf, M.G. (2023). Dynamic fit index cutoffs for confirmatory factor analysis models. Psychological Methods,28 (1), 61-88.
McNeish, D., Harring, J.R., & Bauer, D.J. (2023). Nonconvergence, covariance constraints, and class enumeration in growth mixture models. Psychological Methods, 28 (4), 962-992.
McNeish, D., Bauer, D.J., Dumas, D., Clements, D.H., Cohen, J.R., Lin, W., Sarama, J., & Sheridan, M.A. (2023). Modeling individual differences in the timing of change onset and offset. Psychological Methods, 28 (2), 401-421.
Levy, R. & McNeish, D. (2023). Alternative perspectives on Bayesian inference and their implications for data analysis. Psychological Methods, 28 (3), 719-739.
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. & Hancock, G.R. (2018). The effect of measurement quality on targeted structural model fit indices: A comment on Lance, Beck, Fan, and Carter (2016). Psychological Methods, 23, 184-190.
McNeish, D., Stapleton, L. M., & Silverman, R.D. (2017). On the unnecessary ubiquity of hierarchical linear modeling. Psychological Methods, 22, 114-140.
Harring, J.R., McNeish, D., & Hancock, G.R. (2017). Using phantom variables in structural equation modeling to assess model sensitivity to external misspecification. Psychological Methods, 22, 616-631.
McNeish, D. (2014). Modeling sparsely clustered data: Design-based, model-based, and single-level methods. Psychological Methods, 19, 552-563
In other statistics and methodology journals
McNeish, D. & Wolf, M.G. (2024). Direct discrepancy dynamic fit index cutoffs for arbitrary covariance structure models. Structural Equation Modeling, 31 (5), 835-862.
McNeish, D. (2024). Practical implications of sum scores being psychometrics’ greatest accomplishment. Psychometrika, 89 (4), 1148-1169.
McNeish, D. (2023). Dynamic fit index cutoffs for factor analysis with Likert-type, ordinal, or binary responses. American Psychologist, 79 (9), 1061-1075.
Savord, A.,McNeish, D., Iida, M., Quiroz, S., & Ha, T. (2023). Fitting the longitudinal actor-partner interdependence model as a dynamic structural equation model. Structural Equation Modeling, 30 (2), 296-314.
McNeish, D. & Bauer, D.J. (2022). Reducing incidence of nonpositive definite covariance matrices in mixed effect models. Multivariate Behavioral Research, 57 (2-3), 318-340.
McNeish, D. & Harring, J.R. (2021). Improving convergence in growth mixture models without covariance structure constraints. Statistical Methods in Medical Research, 30, 994-1012.
McNeish, D., Mackinnon, D.P., Marsch, L.A., & Poldrack, R.A. (2021). Measurement in intensive longitudinal data. Structural Equation Modeling, 28, 807-822.
McNeish, D.(2021). Location-scale models for heterogeneous variances as multilevel SEMs. Organizational Research Methods, 24, 630-653.
McNeish, D. & Dumas, D. (2021). A seasonal dynamic measurement model for summer learning loss. Journal of the Royal Statistical Society, Series A, 184, 616-642.
McNeish, D. & Wolf, M.G. (2020). Thinking twice about sum scores. Behavior Research Methods, 52, 2287-2305.
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. (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.