Literature DB >> 30950737

How Mixed-Effects Modeling Can Advance Our Understanding of Learning and Memory and Improve Clinical and Educational Practice.

Katherine R Gordon1.   

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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Mesh:

Year:  2019        PMID: 30950737      PMCID: PMC6802904          DOI: 10.1044/2018_JSLHR-L-ASTM-18-0240

Source DB:  PubMed          Journal:  J Speech Lang Hear Res        ISSN: 1092-4388            Impact factor:   2.297


  31 in total

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Review 2.  Analysis of longitudinal data: the integration of theoretical model, temporal design, and statistical model.

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Authors:  Lauren A Katz; Abby Maag; Karen A Fallon; Katie Blenkarn; Megan K Smith
Journal:  Lang Speech Hear Serv Sch       Date:  2009-09-15       Impact factor: 2.983

Review 4.  Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data.

Authors:  A Cnaan; N M Laird; P Slasor
Journal:  Stat Med       Date:  1997-10-30       Impact factor: 2.373

Review 5.  The effect of testing versus restudy on retention: a meta-analytic review of the testing effect.

Authors:  Christopher A Rowland
Journal:  Psychol Bull       Date:  2014-08-25       Impact factor: 17.737

Review 6.  Treating stimuli as a random factor in social psychology: a new and comprehensive solution to a pervasive but largely ignored problem.

Authors:  Charles M Judd; Jacob Westfall; David A Kenny
Journal:  J Pers Soc Psychol       Date:  2012-05-21

7.  Stability of Audiometric Thresholds for Children with Hearing Aids Applying the American Academy of Audiology Pediatric Amplification Guideline: Implications for Safety.

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8.  Interactive Book Reading to Accelerate Word Learning by Kindergarten Children With Specific Language Impairment: Identifying Adequate Progress and Successful Learning Patterns.

Authors:  Holly L Storkel; Rouzana Komesidou; Kandace K Fleming; Rebecca Swinburne Romine
Journal:  Lang Speech Hear Serv Sch       Date:  2017-04-20       Impact factor: 2.983

9.  Predicting vocabulary growth in children with and without specific language impairment: a longitudinal study from 2;6 to 21 years of age.

Authors:  Mabel L Rice; Lesa Hoffman
Journal:  J Speech Lang Hear Res       Date:  2015-04       Impact factor: 2.297

10.  Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape.

Authors:  Christophe Coupé
Journal:  Front Psychol       Date:  2018-04-16
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2.  Rapid but specific perceptual learning partially explains individual differences in the recognition of challenging speech.

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4.  Word Learning by Preschool-Age Children With Developmental Language Disorder: Impaired Encoding and Robust Consolidation During Slow Mapping.

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Journal:  J Speech Lang Hear Res       Date:  2021-10-11       Impact factor: 2.674

5.  Younger and older adults show non-linear, stimulus-dependent performance during early stages of auditory training for non-native English.

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6.  Noise, Age, and Gender Effects on Speech Intelligibility and Sentence Comprehension for 11- to 13-Year-Old Children in Real Classrooms.

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  6 in total

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