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Statistical Methods in Speech, Language, and Cognition Research (WEB3640)

Course Description


This journal self-study course is composed of papers from a 2019 Research Forum, Advancing Statistical Methods in Speech, Language, and Hearing Sciences. These selected articles provide advanced-level discussion about clinically relevant statistical methodologies to give speech-language pathologists a stronger foundation from which to analyze and understand the statistical research they come across to decide when and how to apply it in practice.

Learning Outcomes
You will be able to:

  • Describe best practices in basic and more advanced inferential statistics that avoid errors and find true clinical significance
  • Summarize the difference between frequential and Bayesian analyses as well as potential applications of each
  • Describe how and why mixed-effects models are used when analyzing longitudinal data
  • Explain types of clinical questions that could benefit from machine learning approaches

Assessment Type

Self-assessment—Think about what you learned and report on the Completion Form how you will use your new knowledge.

Articles in This Course

  1. Essential Statistical Concepts for Research in Speech, Language, and Hearing Sciences, by Jacob J. Oleson, Grant D. Brown, & Ryan McCreery, published in Journal of Speech, Language, and Hearing Research
  2. The Evolution of Statistical Methods in Speech, Language, and Hearing Sciences, by Jacob J. Oleson, Grant D. Brown, & Ryan McCreery, published in Journal of Speech, Language, and Hearing Research
  3. How Mixed-Effects Modeling Can Advance Our Understanding of Learning and Memory and Improve Clinical and Educational Practice, by Katherine R. Gordon, published in Journal of Speech, Language, and Hearing Research
  4. Functional Logistic Mixed-Effects Models for Learning Curves From Longitudinal Binary Data, by Giorgio Paulon, Rachel Reetzke, Bharath Chandrasekaran, & Abhra Sarkar, published in Journal of Speech, Language, and Hearing Research
  5. The Heterogeneity of Word Learning Biases in Late-Talking Children, by Lynn K. Perry & Sarah C. Kucker, published in Journal of Speech, Language, and Hearing Research
  6. Machine Learning Approaches to Analyze Speech-Evoked Neurophysiological Responses, by Zilong Xie, Rachel Reetzke and Bharath Chandrasekaran, published in Journal of Speech, Language, and Hearing Research
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Subscribers Ratings
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CONTINUING EDUCATION
PDH: 9
ASHA CEU*: 0.9
COURSE DETAILS
Item #(s): WEB3640
Available Through: October 08, 2023