Daniel McNeish
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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
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
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
Thinking twice about sum scores
D McNeish, MG Wolf
Behavior research methods, 1-19, 2020
A primer on two-level dynamic structural equation models for intensive longitudinal data in Mplus.
D McNeish, EL Hamaker
Psychological methods 25 (5), 610, 2020
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
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
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
Exploratory factor analysis with small samples and missing data
D McNeish
Journal of personality assessment 99 (6), 637-652, 2017
Bayesian versus frequentist estimation for structural equation models in small sample contexts: A systematic review
SC Smid, D McNeish, M Miočević, R van de Schoot
Structural Equation Modeling: A Multidisciplinary Journal 27 (1), 131-161, 2020
Missing data methods for arbitrary missingness with small samples
D McNeish
Journal of Applied Statistics 44 (1), 24-39, 2017
Modeling sparsely clustered data: Design-based, model-based, and single-level methods.
DM McNeish
Psychological Methods 19 (4), 552-563, 2014
Differentiating between mixed-effects and latent-curve approaches to growth modeling
D McNeish, T Matta
Behavior research methods 50 (4), 1398-1414, 2018
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
Dynamic fit index cutoffs for confirmatory factor analysis models.
D McNeish, MG Wolf
Psychological Methods, 2021
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
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Artiklar 1–20