Statistical Methods and Reasoning for the Clinical Sciences: Evidence-Based Practice

Plural Publishing
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Statistical Methods and Reasoning for the Clinical Sciences: Evidence-Based Practice provides practitioners with the scientific literacy needed to understand statistical methods in order to increase the accuracy of their diagnoses.

With case studies included on a companion website, this text will help readers comprehend how the process of clinical research relates to the scientific method of problem solving. Readers will understand the importance of three key, interrelated tasks involved in a research study: description (why it was done), explanation (what was done and to whom), and contextualization (how the results relate to other bodies of knowledge).

This text also examines the following:

The two basic elements of statistical reasoning that constitute evidence-based practice: deductive inference (from effect to cause) and inductive inference (from cause to effect).Classical statistical methods-statistical terms/vocabulary, population parameters, and sampling methods-as well as descriptive statistical methods-measures, correlation, and regression.The fundamentals of statistical inference that include testing hypotheses using a z-test, t-test, ANOVA, and MANOVA.The concept of probability, through various concrete examples and a step-by-step approach, which is a fundamental part of the clinical decision-making process.Evidence-based probabilistic methods called Minimum Bayes Factor (MBF) for measuring the strength of clinical evidence more precisely and as an alternative to classical testing hypotheses methods.Rationales and procedures of other statistical methods frequently seen in clinical literature, like meta-analysis, nonparametric methods, categorical analyses, and single subject designs.

This text not only distinguishes between the concepts of "statistical significance" and "clinical significance," often not clearly addressed in other texts, but it also emphasizes the value of scientific literacy in evidence-based practice. 

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About the author

Eiki Satake, PhD, is an associate professor of mathematics and statistics at Emerson College in Boston, Massachusetts. He has written numerous scholastic articles and instructional textbooks on statistical methods and statistics education. He has also conducted several research seminars and short courses on evidence-based statistics at national and international academic conferences. His research interests include Bayesian statistical methods and probabilistic approaches to evidence-based practice.

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Additional Information

Publisher
Plural Publishing
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Published on
Aug 1, 2014
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Pages
592
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ISBN
9781597566575
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Language
English
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Genres
Medical / Audiology & Speech Pathology
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Content Protection
This content is DRM protected.
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Read Aloud
Available on Android devices
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Eligible for Family Library

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In the behavioral and clinical sciences, single-subject designs have increasingly become important tools for determining a treatment efficacy. Despite a large number of recommendations in recent years for more use of the designs, the majority of typical research methods textbooks still do not provide sufficient direction and information about single-subject designs. One of the main reasons is that data analysis of single-subject designs is still foreign to the vast majority of the investigators, practitioners, and students. 

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