TY - JOUR
T1 - How mixed-effects modeling can advance our understanding of learning and memory and improve clinical and educational practice
AU - Gordon, Katherine R.
N1 - Funding Information:
The author’s data and findings presented in this publication were supported by funding by organizations within the National Institutes of Health including the National Institute on Deafness and Other Communication Disorders Grant F32DC013704-03 (principal investigator: Katherine R. Gordon) and the National Institute of General Medical Sciences Grant P20GM109023 (principal investigator: Lori J. Leibold). The author would like to thank the reviewers for the suggestion to make this article more accessible and directly relevant to clinicians and educators. The author would also like to thank Karla McGregor and Nancy Ohlmann for suggestions on how to tailor the article for the clinical audience and Jacob Oleson for answering questions about the nuanced differences between statistical analyses.
Publisher Copyright:
© 2019 American Speech-Language-Hearing Association.
PY - 2019/3
Y1 - 2019/3
N2 - Purpose: A key goal of researchers, clinicians, and educators within the fields of speech, language, and hearing sciences is to support the learning and memory of others. To do so, they consider factors relevant to the individual, the material to be learned, and the training strategy that can maximize learning and retention. Statistical methods typically used within these fields are inadequate for identifying the complex relationships between these factors and are ill equipped to account for variability across individuals when identifying these relationships. Specifically, traditional statistical methods are often inadequate for answering questions about special populations because samples drawn from these populations are usually small, highly variable, and skewed in distribution. Mixed-effects modeling provides advantages over traditional statistical techniques to answer complex questions while taking into account these common characteristics of special populations. Method and Results: Through 2 examples, I illustrate advantages of mixed-effects modeling in answering questions about learning and memory and in supporting better translation of research to practice. I also demonstrate key similarities and differences between analysis of variance, regression analyses, and mixed-effects modeling. Finally, I explain 3 additional advantages of using mixed-effects modeling to understand the processes of learning and memory: the means to account for missing data, assess the contribution of variations in delay intervals, and model nonlinear relationships between factors. Conclusions: Through mixed-effects modeling, researchers can disseminate accurate information about learning and memory to clinicians and educators. In turn, through enhanced statistical literacy, clinicians and educators can apply research findings to practice with confidence. Overall, mixed-effects modeling is a powerful tool to improve the outcomes of the individuals that researchers and practitioners serve within the fields of speech, language, and hearing sciences.
AB - Purpose: A key goal of researchers, clinicians, and educators within the fields of speech, language, and hearing sciences is to support the learning and memory of others. To do so, they consider factors relevant to the individual, the material to be learned, and the training strategy that can maximize learning and retention. Statistical methods typically used within these fields are inadequate for identifying the complex relationships between these factors and are ill equipped to account for variability across individuals when identifying these relationships. Specifically, traditional statistical methods are often inadequate for answering questions about special populations because samples drawn from these populations are usually small, highly variable, and skewed in distribution. Mixed-effects modeling provides advantages over traditional statistical techniques to answer complex questions while taking into account these common characteristics of special populations. Method and Results: Through 2 examples, I illustrate advantages of mixed-effects modeling in answering questions about learning and memory and in supporting better translation of research to practice. I also demonstrate key similarities and differences between analysis of variance, regression analyses, and mixed-effects modeling. Finally, I explain 3 additional advantages of using mixed-effects modeling to understand the processes of learning and memory: the means to account for missing data, assess the contribution of variations in delay intervals, and model nonlinear relationships between factors. Conclusions: Through mixed-effects modeling, researchers can disseminate accurate information about learning and memory to clinicians and educators. In turn, through enhanced statistical literacy, clinicians and educators can apply research findings to practice with confidence. Overall, mixed-effects modeling is a powerful tool to improve the outcomes of the individuals that researchers and practitioners serve within the fields of speech, language, and hearing sciences.
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U2 - 10.1044/2018_JSLHR-L-ASTM-18-0240
DO - 10.1044/2018_JSLHR-L-ASTM-18-0240
M3 - Article
C2 - 30950737
AN - SCOPUS:85064314998
SN - 1092-4388
VL - 62
SP - 507
EP - 524
JO - Journal of Speech, Language, and Hearing Research
JF - Journal of Speech, Language, and Hearing Research
IS - 3
ER -