How mixed-effects modeling can advance our understanding of learning and memory and improve clinical and educational practice

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)507-524
Number of pages18
JournalJournal of Speech, Language, and Hearing Research
Volume62
Issue number3
DOIs
StatePublished - Mar 2019

Fingerprint

educational practice
Learning
learning
Research Personnel
Hearing
educator
statistical method
Analysis of Variance
Language
Population Characteristics
Research
Population
language
science
analysis of variance
Regression Analysis
Practice (Psychology)
Modeling
Education
Learning and Memory

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Speech and Hearing

Cite this

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title = "How mixed-effects modeling can advance our understanding of learning and memory and improve clinical and educational practice",
abstract = "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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