| Thanks coefficient alpha, we’ll take it from here. D McNeish Psychological Methods 23 (3), 412-433, 2018 | 1045 | 2018 |
| The Effect of Small Sample Size on Two-Level Model Estimates: A Review and Illustration DM McNeish, LM Stapleton Educational Psychology Review, 2016 | 492 | 2016 |
| On the unnecessary ubiquity of hierarchical linear modeling. D McNeish, LM Stapleton, RD Silverman Psychological methods 22 (1), 114, 2017 | 453 | 2017 |
| On using Bayesian methods to address small sample problems D McNeish Structural Equation Modeling: A Multidisciplinary Journal 23 (5), 750-773, 2016 | 304 | 2016 |
| Modeling clustered data with very few clusters D McNeish, LM Stapleton Multivariate behavioral research 51 (4), 495-518, 2016 | 269 | 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 | 202 | 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 | 200 | 2018 |
| 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 | 142 | 2017 |
| Thinking twice about sum scores D McNeish, MG Wolf Behavior research methods, 1-19, 2020 | 139 | 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 | 132 | 2020 |
| 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 | 129 | 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 | 115 | 2017 |
| Missing data methods for arbitrary missingness with small samples D McNeish Journal of Applied Statistics 44 (1), 24-39, 2017 | 114 | 2017 |
| Exploratory factor analysis with small samples and missing data D McNeish Journal of personality assessment 99 (6), 637-652, 2017 | 109 | 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 | 103 | 2020 |
| Modeling sparsely clustered data: Design-based, model-based, and single-level methods. DM McNeish Psychological Methods 19 (4), 552-563, 2014 | 96 | 2014 |
| 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 | 76 | 2016 |
| Differentiating between mixed-effects and latent-curve approaches to growth modeling D McNeish, T Matta Behavior research methods 50 (4), 1398-1414, 2018 | 74 | 2018 |
| 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 | 71 | 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 | 59 | 2016 |
Laura M StapletonUniversity of MarylandVerified email at umd.edu
