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.

In Press

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.

2017

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.

2016

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.