The DOI matches its Crossref record.
General class of covariance structures for two or more repeated factors in longitudinal data analysis
Abstract
The main difficulty in parametric analysis of longitudinal data lies in specifying covariance structure. Several covariance structures, which usually reflect one series of measurements collected over time, have been presented in the literature. However there is a lack of literature on covariance structures designed for repeated measures specified by more than one repeated factor. In this paper a new, general method of modelling covariance structure based on the Kronecker product of underlying factor specific covariance profiles is presented. The method has an attractive interpretation in terms of independent factor specific contribution to overall within subject covariance structure and can be easily adapted to standard software.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
(2004) · Springer Texts in Statistics · doi:10.1007/b98963 · Cited by 517
The analysis of multivariate longitudinal data: A review
Verbeke, G., Fieuws, S., Molenberghs, G., & Davidian, M. (2014) · Statistical Methods in Medical Research · doi:10.1177/0962280212445834 · Cited by 290
The effects of safety behaviors on health anxiety: An experimental investigation
Olatunji, B. O., Etzel, E. N., Tomarken, A. J., Ciesielski, B. G., & Deacon, B. (2011) · Behaviour Research and Therapy · doi:10.1016/j.brat.2011.07.008 · Cited by 192
Metadata
- Authors
- Galecki, A. T.
- Venue
- Communications in Statistics - Theory and Methods
- Publisher
- Informa UK Limited
- Published
- 1994-01
- Volume
- 23
- Issue
- 11
- Pages
- 3105-3119
- ISSN
- 0361-0926 (ISSN-L)
- 1532-415X
- OpenAlex
- W2010453346
- Cited by
- 206
- References
- 0
- FWCI
- 0.42
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: General class of covariance structures for two or more repeated factors in longitudinal data analysis. https://app.cochat.ai/papers/10.1080/03610929408831436
- The paper
- Galecki, A. T. (1994). General class of covariance structures for two or more repeated factors in longitudinal data analysis. Communications in Statistics - Theory and Methods. https://doi.org/10.1080/03610929408831436
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
