An Introduction to Statistics: An Active Learning Approach

This updated and reorganized Third Edition of this textbook takes a workbook-style approach that encourages an active approach to learning statistics. Carefully placed reading questions throughout each chapter allow students to apply their knowledge right away, while in-depth activities based on current behavioral science scenarios, each with problem sets and quiz questions, give students the opportunity to assess their understanding of concepts while reading detailed explanations of more complex statistical concepts. Additional practice problems further solidify student learning. Most activities are self-correcting, so if a concept is misunderstood, this misunderstanding is corrected early in the learning process. After working through each chapter, students are far more likely to understand the material than when they only read the material.

Table of Contents:
Preface Acknowledgments About the Authors Part 1: Descriptive Statistics and Sampling Error Chapter 1: Introduction to Statistics and Frequency Distributions How to Be Successful in This Course Math Skills Required in This Course Statistical Software Options Why Do You Have to Take Statistics? The Four Pillars of Scientific Reasoning Populations and Samples Independent and Dependent Variables Identify How a Variable Is Measured Graphing Data Shapes of Distributions Frequency Distribution Tables Chapter 2: Central Tendency and Variability Frequency Distribution Graphs and Tables Central Tendency: Choosing Mean, Median, or Mode Computing Measures of Central Tendency Variability: Range or Standard Deviation Steps in Computing a Population’s Standard Deviation Steps in Computing a Sample’s Standard Deviation Constructing a Scientific Conclusion Chapter 3: z scores Computing and Interpreting z for a Raw Score Finding Raw Score “Cut Lines” Finding the Probability of z Scores Using the Standard Normal Curve Positive z Score Example Negative z Score Example Proportion Between Two z Scores Example Chapter 4: Sampling Error and Confidence Intervals with z and t Distributions Sampling and Sampling Error The Central Limit Theorem and the Standard Error of the Mean (SEM) Applying the SEM to Find Statistical Evidence Part 2: Applying the Four Pillars of Scientific Reasoning to Mean Differences Chapter 5: Single sample t, effect sizes, and confidence intervals Four Pillars of Scientific Reasoning Apply the Four Pillars of Scientific Reasoning Construct a Well-Supported Scientific Conclusion Chapter 6: Related samples t, effect sizes, and confidence intervals Related Samples t Test Logic of the Single Sample and Related Samples t Tests Apply the Four Pillars of Scientific Reasoning Construct a Well-Supported Scientific Conclusion Chapter 7: Independent samples t, effect sizes, and confidence intervals When to Use the Three t Tests The t Test Logic and the Independent Samples t Formula Apply the Four Pillars of Scientific Reasoning Construct a Well-Supported Scientific Conclusion How to Interpret High p Values Chapter 8: One-way ANOVA, effect sizes, and confidence intervals Independent Samples One-Way ANOVA Logic of the ANOVA Apply Four Pillars of Scientific Reasoning Chapter 9: Two-way ANOVA, effect sizes, and confidence intervals Purpose of Two-Way ANOVA Logic of Two-Way ANOVA Apply Four Pillars of Scientific Reasoning Part 3: Applying the Four Pillars of Scientific Reasoning to Associations Chapter 10: Correlations, effect sizes, and confidence intervals When to Use Correlations The Logic of Correlation Interpreting Correlation Coefficients Spearman’s (rs) Correlation Correlation Does Not Equal Causation: True but Misleading Apply the Four Pillars of Scientific Reasoning Construct a Well-Supported Scientific Conclusion Chapter 11: Chi square and effect sizes When to Use X2 Statistics Logic of the X2 Test Apply the Pillars of Scientific Reasoning Construct a Well-Supported Scientific Conclusion Apply the Pillars of Scientific Reasoning: X2 for Independence Appendices References Index

About the Author :
Kieth A. Carlson received his PhD in Experimental Psychology with an emphasis in Cognitive Psychology from the University of Nebraska in 1996. He is currently Professor of Psychology at Valparaiso University. He has published research on visual attention, memory, student cynicism toward college, and active learning. He enjoys teaching a wide range of courses including statistics, research methods, sensation and perception, cognitive psychology, learning psychology, the philosophy of science, and the history of psychology. Dr. Carlson was twice honored with the Teaching Excellence Award from the United States Air Force Academy. Jennifer R. Winquist is currently Professor of Psychology at Valparaiso University. Dr. Winquist received her PhD in Social Psychology from the University of Illinois at Chicago and her bachelor’s degree in Psychology from Purdue University. She has published research on self-focused attention, group decision making, distributive justice, and the scholarship of teaching and learning. Dr. Winquist regularly teaches courses in introductory and advanced statistics and research methods.