Functional Logistic Mixed-Effects Models for Learning Curves from Longitudinal Binary Data

Autor/inn/en	Paulon, Giorgio; Reetzke, Rachel; Chandrasekaran, Bharath; Sarkar, Abhra
Titel	Functional Logistic Mixed-Effects Models for Learning Curves from Longitudinal Binary Data
Quelle	In: Journal of Speech, Language, and Hearing Research, 62 (2019) 3, S.543-553 (11 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1092-4388
Schlagwörter	Longitudinal Studies; Bayesian Statistics; Guidelines; Speech Communication; Simulation; Models; Language Acquisition; Hearing (Physiology); Scientific Research + Suchen Sie Ihr Suchwort? Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Richtlinien; Simulation program; Simulationsprogramm; Analogiemodell; Sprachaneignung; Spracherwerb; Gehör; Hören
Abstract	Purpose: We present functional logistic mixed-effects models (FLMEMs) for estimating population and individual-level learning curves in longitudinal experiments. Method: Using functional analysis tools in a Bayesian hierarchical framework, the FLMEM captures nonlinear, smoothly varying learning curves, appropriately accommodating uncertainty in various aspects of the analysis while also borrowing information across different model layers. An R package implementing our method is available as part of the Supplemental Materials. Results: Application to speech learning data from Reetzke, Xie, Llanos, and Chandrasekaran (2018) and a simulation study demonstrate the utility of FLMEM and its many advantages over linear and logistic mixed-effects models. Conclusion: The FLMEM is highly flexible and efficient in improving upon the practical limitations of linear models and logistic linear mixed-effects models. We expect the FLMEM to be a useful addition to the speech, language, and hearing scientist's toolkit. (As Provided).
Anmerkungen	American Speech-Language-Hearing Association. 2200 Research Blvd #250, Rockville, MD 20850. Tel: 301-296-5700; Fax: 301-296-8580; e-mail: slhr@asha.org; Web site: http://jslhr.pubs.asha.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01