ERIC Number: EJ1213156
Record Type: Journal
Publication Date: 2019-Mar
Pages: 11
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1092-4388
EISSN: N/A
Available Date: N/A
Functional Logistic Mixed-Effects Models for Learning Curves from Longitudinal Binary Data
Paulon, Giorgio; Reetzke, Rachel; Chandrasekaran, Bharath; Sarkar, Abhra
Journal of Speech, Language, and Hearing Research, v62 n3 p543-553 Mar 2019
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.
Descriptors: Longitudinal Studies, Bayesian Statistics, Guidelines, Speech Communication, Simulation, Models, Language Acquisition, Hearing (Physiology), Scientific Research
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
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Institute on Deafness and Other Communication Disorders (NIDCD)
Authoring Institution: N/A
Grant or Contract Numbers: R01DC013315; R01DC015504
Author Affiliations: N/A