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Statistical Methodology for the Analysis of Repeated Duration Data in Behavioral Studies

Frédérique Letué 1 Marie-José Martinez 1 Adeline Samson 1 Anne Vilain 2 Coriandre Vilain 2, 3
1 SVH - Statistique pour le Vivant et l’Homme
LJK - Laboratoire Jean Kuntzmann
3 GIPSA-Services [2007-2015] - GIPSA-Services
GIPSA-lab [2007-2015] - Grenoble Images Parole Signal Automatique
Abstract : Purpose: Repeated duration data are frequently used in behavioral studies. Classical linear or log-linear mixed models are often inadequate to analyze such data, because they usually consist of nonnegative and skew-distributed variables. Therefore, we recommend use of a statistical methodology specific to duration data. Method: We propose a methodology based on Cox mixed models and written under the R language. This semiparametric model is indeed flexible enough to fit duration data. To compare log-linear and Cox mixed models in terms of goodness-of-fit on real data sets, we also provide a procedure based on simulations and quantile–quantile plots. Results: We present two examples from a data set of speech and gesture interactions, which illustrate the limitations of linear and log-linear mixed models, as compared to Cox models. The linear models are not validated on our data, whereas Cox models are. Moreover, in the second example, the Cox model exhibits a significant effect that the linear model does not. Conclusions: We provide methods to select the best fitting models for repeated duration data and to compare statistical methodologies. In this study, we show that Cox models are best suited to the analysis of our data set.
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Contributor : Marie-José Martinez <>
Submitted on : Friday, March 16, 2018 - 10:29:20 AM
Last modification on : Friday, July 3, 2020 - 4:52:49 PM



Frédérique Letué, Marie-José Martinez, Adeline Samson, Anne Vilain, Coriandre Vilain. Statistical Methodology for the Analysis of Repeated Duration Data in Behavioral Studies. Journal of Speech, Language, and Hearing Research, American Speech-Language-Hearing Association, 2018, 61 (3), pp.561-582. ⟨10.1044/2017_JSLHR-S-17-0135⟩. ⟨hal-01735593⟩



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