Daniel McNeish

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

Selected Publications

In Psychological Methods

1. McNeish, D. & Wolf, M.G. (in press). Dynamic fit index cutoffs for confirmatory factor analysis models. Psychological Methods.
2. McNeish, D., Harring, J.R., & Bauer, D.J. (in press).  Nonconvergence, covariance constraints, and class enumeration in growth mixture models. Psychological Methods.
3. McNeish, D., Bauer, D.J., Dumas, D., Clements, D.H., Cohen, J.R., Lin, W., Sarama, J., & Sheridan, M.A. (in press). Modeling individual differences in the timing of change onset and offset. Psychological Methods.
4. McNeish, D. & Mackinnon, D.P. (in press). Intensive longitudinal mediation in Mplus. Psychological Methods.
5. Levy, R. & McNeish, D. (in press). Alternative perspectives on Bayesian inference and their implications for data analysis. Psychological Methods.
6. McNeish, D. & Hamaker, E.L. (2020). A primer on two-level dynamic structural equation modeling for intensive longitudinal data. Psychological Methods
7. 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.
8. McNeish, D. (2018). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 23, 412-433.
9. 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.
10. McNeish, D., Stapleton, L. M., & Silverman, R.D. (2017). On the unnecessary ubiquity of hierarchical linear modeling. Psychological Methods, 22, 114-140.
11. 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.
12. 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

1. McNeish, D. & Wolf, M.G. (in press). Dynamic fit cutoffs for one-factor models. Behavior Research Methods.
2. McNeish, D. (in press). Psychometric properties of sum scores and factor scores differ even when their correlation is 0.98: A response to Widaman and Revelle. Behavior Research Methods.
3. Savord, A., McNeish, D., Iida, M., Quiroz, S., & Ha, T. (in press). Fitting the longitudinal actor-partner interdependence model as a dynamic structural equation model. Structural Equation Modeling.
4. 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.
5. McNeish, D. (2022). Limitations of the sum-and-alpha approach to measurement in behavioral research. Policy Insights from the Brain and Behavioral Sciences, 9 (2), 196-203.
6. McNeish, D. & Harring, J.R. (2021). Improving convergence in growth mixture models without covariance structure constraints. Statistical Methods in Medical Research, 30, 994-1012.
7. McNeish, D., Mackinnon, D.P., Marsch, L.A., & Poldrack, R.A. (2021). Measurement in intensive longitudinal data. Structural Equation Modeling, 28, 807-822.
8. McNeish, D. (2021). Location-scale models for heterogeneous variances as multilevel SEMs. Organizational Research Methods, 24, 630-653. 
9. 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.
10. McNeish, D. & Wolf, M.G. (2020). Thinking twice about sum scores. Behavior Research Methods, 52, 2287-2305.
11. 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.
12. McNeish, D. & Matta, T. (2018). Differentiating between mixed effects and latent curve approaches to growth modeling. Behavior Research Methods, 50, 1398-1414.
13. 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.
14. 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.
15. McNeish, D., & Stapleton, L. M. (2016). Modeling clustered data with very few clusters. Multivariate Behavioral Research, 51, 495-518.
16. McNeish, D. (2016). On using Bayesian methods to address small sample problems. Structural Equation Modeling, 23, 750-773.