Overview

Scientific literacy is the foundation to comprehending research methodology -- in its absence, medical professionals will practice "unethically" by failing to offer premier service to their communities. This text will discuss the fundamentals of statistical methods, including statistical vocabulary, population parameters, sampling methods and descriptive methods like measures, correlation and regression. Also, the topic of probability is emphasized, seeing that in order for any clinician to generate a precise diagnosis, a firm grasp of the probablilistic approach is critical.

Audience

Primary Subject: Speech and Language Pathology / Research Methods
Audience Level: Professional

• Introduction: Philosophical Preliminaries for Clinical Statistics
  • 0.1. Overview: Why Is Statistics Important?
  • 0.2. What Is Statistical Reasoning?
  • 0.3. How to Become a Better Clinician
  • 0.4. What is 'Evidence-Based' Practice?
  • 0.5. Future Role of a Clinician
  • 0.6. Moving Forward to Evidence Based Statistics: What really prevents us?
  • 0.7. Scientific Literacy and Ethical Practice: Time for a Check-Up?
  • 0.8. Exploring an Alternative Statistical Method
• Chapter 1: What is Statistics?
  • 1.1. Population and Sample
  • 1.2. Fundamentals of Statistical Inference
  • 1.3. Statistical Notations
  • 1.4. Key Terms and Concepts
  • 1.5. Step-by-Step Guides for Review
  • 1.6 Clinical Case Studies
  • 1.7 Exercises
• Chapter 2: Collecting and Organizing Data
  • 2.1. Frequency Distribution
  • 2.2. Class Interval Distribution
  • 2.3. Graphs and Charts
  • 2.4. Key Terms and Concepts
  • 2.5. Step-by-Step Guides for Review
  • 2.6. Clinical Case Studies
  • 2.7. Exercises
• Chapter 3: Descriptive Methods
  • 3.1. Measures of Central Tendency
  • 3.2. Measures of Dispersion
  • 3.3. Key Terms and Concepts
  • 3.4. Step-by-Step Guides for Review
  • 3.5. Clinical Case Studies
  • 3.6. Exercises
• Chapter 4: Fundamentals of Correlation and Regression
  • 4.1. Pearson Product-Moment Correlation Coefficient R
  • 4.2. Spearman Rank Corellation Coefficient Rho
  • 4.3. Simple Linear Regression
  • 4.4. Multiple Linear Regression: Overview
  • 4.5. Logistic Regression: Overview
  • 4.6. Key Terms and Concepts
  • 4.7. Step-by-Step Guides for Review
  • 4.8. Clinical Case Studies
  • 4.9. Exercises
• Chapter 5: Probability: The Basis for Clinical Decision-Making
  • 5.1. The Importance of Probability in the Clinical Sciences
  • 5.2. Probability Theory and Statistical Inference
  • 5.3. Probability and Level of Confidence
  • 5.4. Logical and Mathematical Basis of Probability
  • 5.5. Different Kinds of Events
  • 5.6. Types and Rules of Probability: Discrete and Binomial
  • 5.7. Conditional Probability and the Bayes' Rule
  • 5.8. Deductive Inference and Inductive Inference
  • 5.9. The Bayesian View of Probability
  • 5.10. Determining the Accuracy of Diagnostic Testing
  • 5.11. Key Terms and Concepts
  • 5.12. Step-by-Step Guides for Review
  • 5.13. Clinical Case Studies
  • 5.14. Exercises
• Chapter 6: Probability Distribution for Continuous Random Variables
  • 6.1. Continuous Probability Distributions
  • 6.2. The Normal Distribution
  • 6.3. The Standard Normal Distribution
  • 6.4. Calculation of Probability in Normal Distribution
  • 6.5. Normal Approximation to the Binomial Distribution
  • 6.6. The Central Limit Theorem
  • 6.7. Key Terms and Concepts
  • 6.8. Step-by-Step Guides for Review
  • 6.9. Clinical Case Studies
  • 6.10. Exercises
• Chapter 7: Statistical Inference Concerning One Parameter
  • 7.1. Hypothesis Testing Concerning a Population Menu
    • Case 1: Large Sample
    • Case 2: Small Sample
  • 7.2. Classical Approach and P-value Approach
  • 7.3. Hypothesis Concerning a Population Proportion
  • 7.4. Parametric Assumptions
  • 7.5. Type-I Error, Type-II Error and Power of a Test
  • 7.6. Confidence Interval Concerning a Population Menu
  • 7.7. Confidence Interval Concerning a Population Proportion
  • 7.8. Power and Sample Size Determination
  • 7.9. Key Terms and Concepts
  • 7.10. Step-by-Step Guides for Review
  • 7.11. Clinical Case Studies
  • 7.12. Exercises
• Chapter 8: Statistical Inference Concerning Two Parameters
  • 8.1. Paired Sample
  • 8.2. Unpaired Sample: Large-Sample Case
  • 8.3. F Distribution: Homogeneity of Variances
  • 8.4. Unpaired Sample: Small-Sample
  • 8.5. Inference Concerning Two Proportions
  • 8.6. Confidence Interval Concerning Two Parameters
  • 8.7. Power and Sample Size Determination
  • 8.8. Key Terms and Concepts
  • 8.9. Step-by-Step Guides for Review
  • 8.10. Clinical Case Studies
  • 8.11. Exercises
• Chapter 9: Inference Concerning Correlation and Regression
  • 9.1. Inference Concerning Correlation
  • 9.2. The Linear Regression Model: Overview
  • 9.3. Inference Concerning Slope
  • 9.4. Prediction Intervals and Confidence Intervals
  • 9.5. Key Terms and Concepts
  • 9.6. Step-by-Step Guides for Review
  • 9.7. Clinical Case Studies
  • 9.8. Exercises
• Chapter 10: Analysis of Variance (ANOVA)
  • 10.1. Logic Behind ANOVA
  • 10.2. One-Way ANOVA
  • 10.3. Multivariate ANOVA (MANOVA)
  • 10.4. Two-Way ANOVA: Non-interaction Model
  • 10.5. Two-Way ANOVA: Interaction Model
  • 10.6. Three-Way ANOVA: Latin Square Design
  • 10.7. Randomized-Blocks ANOVA (RBANOVA)
  • 10.8. Post-Hoc ANOVA Tests
  • 10.9. Key Terms and Concepts
  • 10.10. Step-by-Step Guides for Review
  • 10.11. Clinical Case Studies
  • 10.12. Exercises
• Chapter 11: Analysis of Covariance (ANCOVA)
  • 11.1. Logic Behind ANCOVA
  • 11.2 One-Way ANCOVA
  • 11.3. Multivariate ANCOVA (MANCOVA)
  • 11.4. Two-Way ANCOVA: Non-interaction Model
  • 11.5. Two-Way ANCOVA: Interaction Model
  • 11.6. Post-Hoc ANCOVA Tests
  • 11.7. Key Terms and Concepts
  • 11.8. Step-by-Step Guides for Review
  • 11.9. Clinical Case Studies
  • 11.10. Exercises
• Chapter 12: Other Statistical Methods: Logic Behind the Methods
  • 12.1. Meta-Analysis: Effect Size
  • 12.2. Power Analysis
  • 12.3. Kappa Formula: The Calculation of Agreement
  • 12.4. Bayesian Statistical Analysis: P-value Fallacy
  • 12.5. Key Terms and Concepts
  • 12.6. Step by Step Guides for Review
  • 12.7. Clinical Case Studies
  • 12.8. Exercises
• Chapter 13: Categorical Analysis
  • 13.1. The Accuracy of a Diagnostic Test
  • 13.2. Sensitivity and Specificity
  • 13.3. Predictive Value Positive and Predictive Value Negative
  • 13.4. Likelihood Ratio: Bayes' Factor
  • 13.5. Chi-Square Test: Goodness-of-Fit Test
  • 13.6. Chi-Square Test of Independence
  • 13.7. Chi-Square Test of Homogeneity
  • 13.8. Power and Sample Size Determination
  • 13.9. Key Terms and Concepts
  • 13.10. Step-by-Step Guides for Review
  • 13.11. Clinical Case Studies
  • 13.12. Exercises
• Chapter 14: Nonparametric Tests
  • 14.1. The Wilcoxon Signed-Rank Test
  • 14.2. Mann-Whitney U Test
  • 14.3. The Kruskal-Wallis Test (KWANOVA)
  • 14.4. Other Nonparametric Tests
  • 14.5. Key Terms and Concepts
  • 14.6. Step-by-Step Guides for Review
  • 14.7. Clinical Case Studies
  • 14.8. Exercises
• Chapter 15: Introduction to Single Subject Design (SSD)
  • 15.1. What is SSD?
  • 15.2. Several Types of SSD: Strengths and Limitations
  • 15.3. Statistical Analysis for SSD
  • 15.4. Key Terms and Concepts
  • 15.5. Step-by-Step Guides for Review
  • 15.6. Clinical Case Studies
  • 15.7. Exercises
• Appendix I: Introduction to Statistical Computing Using SPSS/ Minitab
• Appendix II: Statistical Tables

About The Author

Eiki Satake, Ph.D.

Eiki Satake, Ph.D., Associate Professor of Mathematics/Statistics, Department of Communication Sciences and Disorders, Emerson College.

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