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Reasoning with Data: An Introduction to Traditional and Bayesian Statistics Using R

by Jeffrey Stanton Guilford Publications
Pub Date:
05/2017
ISBN:
9781462530267
Format:
Pbk 325 pages
Price:
AU$85.00 NZ$86.09
Product Status: In Stock Now
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Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian approaches to inference. Statistical techniques covered side by side from both frequentist and Bayesian approaches include hypothesis testing, replication, analysis of variance, calculation of effect sizes, regression, time series analysis, and more. Students also get a complete introduction to the open-source R programming language and its key packages.


 


Throughout the text, simple commands in R demonstrate essential data analysis skills using real-data examples. The companion website provides annotated R code for the book's examples, in-class exercises, supplemental reading lists, and links to online videos, interactive materials, and other resources.


 


Pedagogical Features:




    • Playful, conversational style and gradual approach; suitable for students without strong math backgrounds.

    • End-of-chapter exercises based on real data supplied in the free R package.

    • Technical Explanation and Equation/Output boxes.




Appendices on how to install R and work with the sample datasets.

Introduction
Getting Started
1. Statistical Vocabulary
Descriptive Statistics
Measures of Central Tendency
Measures of Dispersion
Distributions and Their Shapes
Conclusion
Exercises
2. Reasoning with Probability
Outcome Tables
Contingency Tables
Conclusion
Exercises
3. Probabilities in the Long Run
Sampling
Repetitious Sampling with R
Using Sampling Distributions and Quantiles to Think about Probabilities
Conclusion
Exercises
4. Introducing the Logic of Inference Using Confidence Intervals
Exploring the Variability of Sample Means with Repetitious Sampling
Our First Inferential Test: The Confidence Interval
Conclusion
Exercises
5. Bayesian and Traditional Hypothesis Testing
The Null Hypothesis Significance Test
Replication and the NHST
Conclusion
Exercises
6. Comparing Groups and Analyzing Experiments
Frequentist Approach to ANOVA
Bayesian Approach to ANOVA
Finding an Effect
Conclusion
Exercises
7. Associations between Variables
Inferential Reasoning about Correlation
Null Hypothesis Testing on the Correlation
Bayesian Tests on the Correlation Coefficient
Categorical Associations
Exploring the Chi-Square Distribution with a Simulation
The Chi-Square Test with Real Data
Bayesian Approach to Chi-Square Test
Conclusion
Exercises
8. Linear Multiple Regression
Bayesian Approach to Linear Regression
A Linear Regression Model with Real Data
Conclusion
Exercises
9. Interactions in ANOVA and Regression
Interactions in ANOVA
Interactions in Multiple Regression
Bayesian Analysis of Regression Interactions
Conclusion
Exercises
10. Logistic Regression
A Logistic Regression Model with Real Data
Bayesian Estimation of Logistic Regression
Conclusion
Exercises
11. Analyzing Change over Time
Repeated Measures Analysis
Time-Series Analysis
Exploring a Time Series with Real Data
Finding Change Points in Time Series
Probabilities in Change-Point Analysis
Conclusion
Exercises
12. Dealing with Too Many Variables
Internal Consistency Reliability
Rotation
Conclusion
Exercises
13. All Together Now
The Big Picture
Appendix A. Getting Started with R
Running R and Typing Commands
Installing Packages
Quitting, Saving, and Restoring
Conclusion
Appendix B. Working with Data Sets in R
Data Frames in R
Reading Data Frames from External Files
Appendix C. Using dplyr with Data Frames
References
Index
 

"What do R and traditional and Bayesian statistics have in common? They allow us to answer questions that are important for science and practice. Stanton has produced a wonderful book that will be useful for students as well as established scholars."--Herman Aguinis, PhD, Avram Tucker Distinguished Scholar and Professor of Management, George Washington University School of Business
Jeffrey M. Stanton, PhD, is Associate Provost for Academic Affairs and Professor in the School of Information Studies at Syracuse University. Dr. Stanton's interests center on research methods, psychometrics, and statistics, with a particular focus on self-report techniques, such as surveys. He has conducted research on a variety of substantive topics in organizational psychology, including the interactions of people and technology in institutional contexts. He is the author of numerous scholarly articles and several books, including Information Nation: Education and Careers in the Emerging Information Professions and The Visible Employee: Using Workplace Monitoring and Surveillance to Protect Information Assets—Without Compromising Employee Privacy or Trust. Dr. Stanton’s background also includes more than a decade of experience in business, both in established firms and startup companies.