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Random intercept models using maximum-likelihood estimation provide more efficient estimates of effects with unbalanced data than the traditional repeated measures approach.
“Random intercept models, which utilise maximum-likelihood estimation, provide more efficient estimates of effects with unbalanced data than the traditional repeated measures approach [67].”
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- Authors
- Myung, I. J.
- Affiliations
- The Ohio State University (US)
- Corresponding author
- Myung, In Jae
- Venue
- Journal of Mathematical Psychology
- Publisher
- Elsevier BV
- Published
- 2003-02
- Volume
- 47
- Issue
- 1
- Pages
- 90-100
- ISSN
- 0022-2496 (ISSN-L)
- OpenAlex
- W2054137409
- Cited by
- 1,802
- References
- 23
- Citations by year
- 2026: 66 · 2025: 122 · 2024: 159 · 2023: 122 · 2022: 131 · 2021: 149 · 2020: 102 · 2019: 84 · 2018: 104 · 2017: 112 · 2016: 127 · 2015: 94 · 2014: 89 · 2013: 77 · 2012: 72
- FWCI
- 8.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Mathematical Psychology (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Keywords
- maximum likelihood estimation, parameter estimation, mathematical modeling of cognition, nonlinear models, non-normal data, least squares estimation, statistical modeling
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