Reasoning with Data: An Introduction to Traditional and Bayesian Statistics Usin

Description: Reasoning with Data by Jeffrey M. Stanton 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 books examples, in-class exercises, teaching notes, and slide decks. 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. FORMAT Paperback LANGUAGE English CONDITION Brand New Author Biography Jeffrey M. Stanton, PhD, is Associate Provost for Academic Affairs and Professor in the School of Information Studies at Syracuse University. Dr. Stantons 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. Stantons background also includes more than a decade of experience in business, both in established firms and startup companies. Table of Contents 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 Review "Reasoning with Data takes a careful and principled approach to guiding readers gracefully from the traditional moorings of frequentist statistics into Bayesian analyses and the functionality and frontiers of the R platform. Stanton provides a range of clear explanations, examples, and practice exercises, fueled by his unbounded enthusiasm and rock-solid expertise. This book is an indispensable resource for undergraduate and graduate students across disciplines--as well as researchers--who want to extend their thinking and their research into where the future is headed."--Frederick L. Oswald, PhD, Department of Psychology, Rice University "Offering an up-to-date and refreshing approach, this is a highly useful guide to the statistics our students will be using today, including Bayesian reasoning. Rather than providing an array of equations to memorize, the emphasis is on building conceptual knowledge. The equations that are provided are essential for understanding how to reason with statistics. I plan to use this book as as the text for the first in the series of statistical courses required for our doctoral students in education. It also would be appropriate for advanced undergraduates or anyone who wants to begin to use R. The book has a good focus on Bayesian inference, which is not covered consistently in stats courses, but is critical for the kinds of complex data we use in education and psychology."--Carol McDonald Connor, PhD, Chancellors Professor, School of Education, University of California, Irvine "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 "This may be an uncommon thing to say about a book on statistics, but Reasoning with Data is enjoyable and entertaining--really! Stanton takes the reader on an experiential hands-on tour of random sampling, statistical inference, and drawing conclusions from numerical results. The concreteness of the presentation and examples will make it easy for the reader to intuitively grasp the fundamental concepts. The book is very timely because both Bayesian inference and R are becoming must-have tools for social and behavioral scientists. At the same time, Stanton provides a solid grounding in the historical approach of null hypothesis significance testing, including both its strengths and weaknesses. This text should have a very wide audience, and would be appropriate as an upper-level undergraduate or entry-level graduate statistics text in any of the social sciences."--Emily A. Butler, PhD, Family Studies and Human Development, University of Arizona -Written with students and scholars in mind, this text is informative, reader-friendly, and, yes, enjoyable….Stanton emphasizes concepts, not formulas, and promotes hands-on examples. His timely introduction and coverage of the open-source R programming language for statistical data analysis is another strength of this text….This volume will be an invaluable addition to both undergraduate and graduate collections. Highly recommended. Upper-division undergraduates through faculty and professionals.--Choice Reviews, 8/1/2018 Long Description 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 books examples, in-class exercises, supplemental reading lists, and links to online videos, interactive materials, and other resources. User-Friendly 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. Review Quote "Written with students and scholars in mind, this text is informative, reader-friendly, and, yes, enjoyable....Stanton emphasizes concepts, not formulas, and promotes hands-on examples. His timely introduction and coverage of the open-source R programming language for statistical data analysis is another strength of this text....This volume will be an invaluable addition to both undergraduate and graduate collections. Highly recommended. Upper-division undergraduates through faculty and professionals." Details ISBN1462530265 Year 2017 ISBN-10 1462530265 ISBN-13 9781462530267 Format Paperback Place of Publication New York Country of Publication United States Author Jeffrey M. Stanton Publisher Guilford Publications Media Book Pages 325 DEWEY 519.5028553 Publication Date 2017-06-16 Subtitle An Introduction to Traditional and Bayesian Statistics Using R Short Title Reasoning with Data Language English Imprint Guilford Press NZ Release Date 2017-06-16 US Release Date 2017-06-16 UK Release Date 2017-06-16 Alternative 9781462530274 Audience Undergraduate AU Release Date 2017-06-15 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:136026240;

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