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Autor/inn/enWang, Lijuan; Zhang, Zhiyong; McArdle, John J.; Salthouse, Timothy A.
TitelInvestigating Ceiling Effects in Longitudinal Data Analysis
QuelleIn: Multivariate Behavioral Research, 43 (2008) 3, S.476-496 (21 Seiten)
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN0027-3171
SchlagwörterLongitudinal Studies; Data Analysis; Evaluation Methods; Monte Carlo Methods; Models; Statistical Analysis; Cognitive Ability; Aging (Individuals); Comparative Analysis
AbstractScore 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. (Contains 2 figures and 7 tables.) (As Provided).
AnmerkungenPsychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2017/4/10
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