Multilevel models for longitudinal data
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Summary Repeated measures and repeated events data have a hierarchical structure which can be analysed by using multilevel models. A growth curve model is an example of a multilevel random-coefficients model, whereas a discrete time event history model for recurrent events can be fitted as a multilevel logistic regression model. The paper describes extensions to the basic growth curve model to handle auto-correlated residuals, multiple-indicator latent variables and correlated growth processes, and event history models for correlated event processes. The multilevel approach to the analysis of repeated measures data is contrasted with structural equation modelling. The methods are illustrated in analyses of children’s growth, changes in social and political attitudes, and the interrelationship between partnership transitions and childbearing.
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Multilevel models can be used to overcome problems caused by irregularly sampled measures, or missing measures, by estimating the exposure at common time points for all participants.
“In the special case where an exposure is measured repeatedly over the life course, multilevel models could be used to overcome the problems caused by irregularly sampled measures, or missing measures, by estimating the exposure at common time points for all participants.22”
From A structured approach to hypotheses involving continuous exposures over the life course (2016) · cites this paper as [22]
Multilevel models have the further benefit of reducing measurement error in the exposure.
“This has the further benefit of reducing measurement error in the exposure.”
From A structured approach to hypotheses involving continuous exposures over the life course (2016) · cites this paper as [22]
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- Authors
- Steele, F.
- Affiliations
- University of Bristol (GB)
- Corresponding author
- Steele, Fiona
- Venue
- Journal of the Royal Statistical Society Series A (Statistics in Society)
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-10-29
- Volume
- 171
- Issue
- 1
- Pages
- 5-19
- ISSN
- 0964-1998 (ISSN-L)
- 1467-985X
- OpenAlex
- W2104023838
- Cited by
- 247
- References
- 35
- Citations by year
- 2026: 13 · 2025: 11 · 2024: 6 · 2023: 12 · 2022: 12 · 2021: 22 · 2020: 16 · 2019: 23 · 2018: 23 · 2017: 15 · 2016: 14 · 2015: 19 · 2014: 13 · 2013: 10 · 2012: 12
- FWCI
- 5.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the Royal Statistical Society Series A (Statistics in Society) (publishedVersion)
- London School of Economics and Political Science Theses Online (London School of Economics and Political Science) (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- Funders
- Economic and Social Research Council (ES/E00234X/1)
- 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
- multilevel models, longitudinal data, repeated measures, growth curve models, random coefficient models, event history models, recurrent events, multilevel logistic regression, autocorrelated residuals, structural equation modeling, child growth, attitude change, childbearing
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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