Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 340

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Investigating Ceiling Effects in Longitudinal Data Analysis

Open access
Wang, Lijuan, Zhang, Zhiyong, McArdle, John J., Salthouse, Timothy A.·2008·Multivariate Behavioral Research

Abstract

Score limitation at the top of a scale is commonly termed "ceiling effect." Ceiling effects can lead to serious artifactual parameter estimates in most data analysis. This study examines the consequences of ceiling effects in longitudinal data analysis and investigates several methods of dealing with ceiling effects through Monte Carlo simulations and empirical data analyses. Data were simulated based on a latent growth curve model with T = 5 occasions. The proportion of the ceiling data [10%-40%] was manipulated by using different thresholds, and estimated parameters were examined for R = 500 replications. The results showed that ceiling effects led to incorrect model selection and biased parameter estimation (shape of the curve and magnitude of the changes) when regular growth curve models were applied. The Tobit growth curve model, instead, performed very well in dealing with ceiling effects in longitudinal data analysis. The Tobit growth curve model was then applied in an empirical cognitive aging study and the results were discussed.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/00273170802285941

Cites

5 of 5 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Cites

Show all 5 references

Cited by

See all 340

Metadata

Authors
Wang, L., Zhang, Z., McArdle, J. J., & Salthouse, T. A.
Venue
Multivariate Behavioral Research
Published
2008-09-10
Volume
43
Issue
3
Pages
476-496
ISSN
  • 0027-3171 (ISSN-L)
  • 1532-7906
OpenAlex
W2167506898
Cited by
340
References
5
FWCI
3.42
Type
journal article
Language
en
Open access
green

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: Investigating Ceiling Effects in Longitudinal Data Analysis. https://app.cochat.ai/papers/10.1080/00273170802285941
The paper
Wang, L., Zhang, Z., McArdle, J. J., & Salthouse, T. A. (2008). Investigating Ceiling Effects in Longitudinal Data Analysis. Multivariate Behavioral Research. https://doi.org/10.1080/00273170802285941

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.