Linear Mixed-Effects Models for Dependent Data: Power and Accuracy in Parameter Estimation-Reference-Cited by-同舟云学术

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Linear Mixed-Effects Models for Dependent Data: Power and Accuracy in Parameter Estimation

Published:2024-05-23 Issue: Volume: Page:1-17
ISSN:0027-3171
Container-title:Multivariate Behavioral Research
language:en
Short-container-title:Multivariate Behavioral Research

Author:

Liu Yue¹^ORCID,Hau Kit-Tai²,Liu Hongyun³⁴^ORCID

Affiliation:

1. Institute of Brain and Psychological Sciences, Sichuan Normal University

2. Faculty of Education, The Chinese University of Hong Kong

3. School of Psychology, Beijing Normal University

4. Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00273171.2024.2350236

Reference59 articles.

1. A solution to dependency: using multilevel analysis to accommodate nested data

2. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Caski (Eds.), Proceedings of the Second International Symposium on Information Theory (pp. 267–281) Akademiai Kiado.

3. Inspecting Gradual and Abrupt Changes in Emotion Dynamics With the Time-Varying Change Point Autoregressive Model

4. Statistical power in two-level models: A tutorial based on Monte Carlo simulation.

5. Asparouhov, T., Muthén, B. O., & Morin, A. J. (2015). Bayesian structural equation modeling with cross-loadings and residual covariances: Comments on Stromeyer et al. Sage.

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