Linear Mixed Models: A Practical Guide Using Statistical Software
Abstract
Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-nav
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Marginal models estimate regression coefficients in repeated measures studies with unbalanced data using maximum likelihood estimation, making use of incomplete data without biasing parameter estimates.
“Marginal models estimate the regression coefficients in repeated measures studies with unbalanced data using maximum likelihood estimation, making use of the incomplete data in a way that does not bias the parameter estimates [24].”
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- Authors
- West, B. T., Welch, K. B., & Galecki, A. T.
- ORCID
- West, Brady T.
- Venue
- Chapman & Hall/CRC eBooks
- Publisher
- Chapman and Hall/CRC
- Published
- 2006-11-22
- OpenAlex
- W4233276628
- Cited by
- 300
- References
- 0
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 16 · 2023: 22 · 2022: 17 · 2021: 8 · 2020: 17 · 2019: 14 · 2018: 11 · 2017: 14 · 2016: 16 · 2015: 21 · 2014: 31 · 2013: 25 · 2012: 35
- FWCI
- 1.41
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Chapman & Hall/CRC eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Agriculture, Soil, Plant Science
- Keywords
- linear mixed models, clustered data, longitudinal data, statistical software, model interpretation, data visualization, random effects
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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