2008/09/10 by Lijuan Wang, Zhiyong Zhang, John J. McArdle +1 · 338 citations
Decision Sciences · Economics, Econometrics and Finance · Engineering · Mathematics · #Ceiling (cloud) #Ceiling effect #Computer science #Data mining #Decision-Making and Behavioral Economics #Econometrics #Engineering #Growth curve (statistics) #Latent growth modeling #Longitudinal data #Mathematics #Monte Carlo method #Spatial and Panel Data Analysis #Statistical Methods and Bayesian Inference #Statistics #Structural engineering #Tobit model
paper · open access · doi:10.1080/00273170802285941
published in Multivariate Behavioral Research 43(3), 476-496 (Taylor & Francis)
openalex publication_date 2008/09/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.