Investigating Ceiling Effects in Longitudinal Data Analysis

• Published in: Multivariate behavioral research Vol. 43; no. 3; pp. 476 - 496
• Main Authors: Wang, Lijuan, Zhang, Zhiyong, McArdle, John J., Salthouse, Timothy A.
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis Group 01.07.2008; Psychology Press; Taylor & Francis Ltd
• Subjects: Aging (Individuals); Cognitive Ability; Comparative Analysis; Data Analysis; Estimation bias; Evaluation Methods; Growth models; Longitudinal Studies; Monte Carlo Methods; Monte Carlo simulation; Parameter estimation; Statistical Analysis; Studies
• ISSN: 0027-3171; 1532-7906
• DOI: 10.1080/00273170802285941

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.