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Daniel McNeish is an Assistant 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 where he continues to be a Faculty Affiliate in the Center for Developmental Science.
This list is limited to papers in Multivariate Behavioral Research, Psychological Methods, or Structural Equation Modeling since 2016. A complete list may be found on my 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.