Cited by 302Statistical Methods and Bayesian InferenceSpatial and Panel Data AnalysisAgriculture, Soil, Plant Science

Linear Mixed Models: A Practical Guide Using Statistical Software

West, Brady T., Welch, Kathleen B., Galecki, Andrzej T·2006·Chapman & Hall/CRC eBooks

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

Integrity signals

Registry

Crossref · 10.1201/9781420010435

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cited by

Sorted by citations

Authors
West, B. T., Welch, K. B., & Galecki, A. T.
Venue
Chapman & Hall/CRC eBooks
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 ›

Are you an author, or do you see a problem with this page? Request a review