Daniel McNeish
Cited by
Cited by
Thanks coefficient alpha, we’ll take it from here.
D McNeish
Psychological Methods 23 (3), 412-433, 2018
The Effect of Small Sample Size on Two-Level Model Estimates: A Review and Illustration
DM McNeish, LM Stapleton
Educational Psychology Review, 2016
On the unnecessary ubiquity of hierarchical linear modeling.
D McNeish, LM Stapleton, RD Silverman
Psychological Methods 22 (1), 114, 2017
On using Bayesian methods to address small sample problems
D McNeish
Structural Equation Modeling: A Multidisciplinary Journal 23 (5), 750-773, 2016
Modeling clustered data with very few clusters
D McNeish, LM Stapleton
Multivariate behavioral research 51 (4), 495-518, 2016
The thorny relation between measurement quality and fit index cutoffs in latent variable models
D McNeish, J An, GR Hancock
Journal of personality assessment 100 (1), 43-52, 2018
Using Lasso for Predictor Selection and to Assuage Overfitting: A Method Long Overlooked in Behavioral Sciences
DM McNeish
Multivariate Behavioral Research 50 (5), 474-481, 2015
Modeling sparsely clustered data: Design-based, model-based, and single-level methods.
DM McNeish
Psychological Methods 19 (4), 552-563, 2014
Peer and teacher supports in relation to motivation and effort: A multi-level study
KR Wentzel, K Muenks, D McNeish, S Russell
Contemporary Educational Psychology 49, 32-45, 2017
Small sample methods for multilevel modeling: A colloquial elucidation of REML and the Kenward-Roger correction
D McNeish
Multivariate Behavioral Research 52 (5), 661-670, 2017
Missing data methods for arbitrary missingness with small samples
D McNeish
Journal of Applied Statistics 44 (1), 24-39, 2017
Using Data-Dependent Priors to Mitigate Small Sample Bias in Latent Growth Models: A Discussion and Illustration Using Mplus
DM McNeish
Journal of Educational and Behavioral Statistics 41 (1), 27-56, 2016
Exploratory factor analysis with small samples and missing data
D McNeish
Journal of Personality Assessment 99 (6), 637-652, 2017
Multilevel and single-level models for measured and latent variables when data are clustered
LM Stapleton, DM McNeish, JS Yang
Educational Psychologist 51 (3-4), 317-330, 2016
Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations.
D McNeish, K Kelley
Psychological Methods 24 (1), 20, 2019
Clustered data with small sample sizes: Comparing the performance of model-based and design-based approaches
DM McNeish, JR Harring
Communications in Statistics-Simulation and Computation 46 (2), 855-869, 2017
Differentiating between mixed-effects and latent-curve approaches to growth modeling
D McNeish, T Matta
Behavior Research Methods 50 (4), 1398-1414, 2018
The Role of Measurement Quality on Practical Guidelines for Assessing Measurement and Structural Invariance
Y Kang, DM McNeish, GR Hancock
Educational and Psychological Measurement, 2015
Multilevel mediation with small samples: A cautionary note on the multilevel structural equation modeling framework
D McNeish
Structural Equation Modeling: A Multidisciplinary Journal 24 (4), 609-625, 2017
Challenging conventional wisdom for multivariate statistical models with small samples
D McNeish
Review of Educational Research 87 (6), 1117-1151, 2017
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