Centering predictor variables in cross-sectional multilevel models: A new look at an old issue.
Abstract
Appropriately centering Level 1 predictors is vital to the interpretation of intercept and slope parameters in multilevel models (MLMs). The issue of centering has been discussed in the literature, but it is still widely misunderstood. The purpose of this article is to provide a detailed overview of grand mean centering and group mean centering in the context of 2-level MLMs. The authors begin with a basic overview of centering and explore the differences between grand and group mean centering in the context of some prototypical research questions. Empirical analyses of artificial data sets are used to illustrate key points throughout. The article provides a number of practical recommendations designed to facilitate centering decisions in MLM applications.
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- Authors
- Enders, C. K., & Tofighi, D.
- Affiliations
- Arizona State University (US)
- Venue
- Psychological Methods
- Published
- 2007
- Volume
- 12
- Issue
- 2
- Pages
- 121-138
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W2028871521
- Cited by
- 4,462
- References
- 0
- Citations by year
- 2026: 338 · 2025: 332 · 2024: 334 · 2023: 348 · 2022: 370 · 2021: 360 · 2020: 307 · 2019: 286 · 2018: 292 · 2017: 246 · 2016: 257 · 2015: 229 · 2014: 193 · 2013: 161 · 2012: 159
- FWCI
- 43.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Methods (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Keywords
- centering, two-level model
- MeSH terms
- Cross-Sectional Studies, Data Collection, Humans, Longitudinal Studies, Psychology, Experimental, Models, Statistical, Cluster Analysis, Linear Models, Least-Squares Analysis
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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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