An Introduction to Statistics: An Active Learning Approach

The authors encourage an active approach to learning statistics through many in-depth activities that give students the opportunity to test or demonstrate their understanding of statistical concepts. The Second Edition is based on contemporary research on memory and the “testing effect” which requires students to answer questions to facilitate their long-term retention.

Table of Contents:
Preface About the Authors 1 Introduction to Statistics and Frequency Distributions How to Be Successful in This Course Math Skills Required in This Course Why Do You Have to Take Statistics? Statistics and the Helping Professions Hypothesis Testing, Effect Size, and Confidence Intervals Testing Causal Hypotheses Populations and Samples Independent and Dependent Variables Scales of Measurement Discrete Versus Continuous Variables Graphing Data Shapes of Distributions Frequency Distribution Tables SPSS Overview of the Activity Chapter 1 Practice Test 2 Central Tendency Central Tendency Computing the Mean Find the Median Find the Mode SPSS Overview of the Activity Chapter 2 Practice Test 3 Variability Population Variability Steps in Computing a Population’s Standard Deviation Sample Variability SPSS Overview of the Activity Chapter 3 Practice Test 4 z Scores z for a Single Score Using X to Find Important “Cut Lines” z Scores and the Standard Normal Curve Example 1: Positive z Score Example 2: Negative z Score Example 3: Proportion Between Two z Scores Overview of the Activity Chapter 4 Practice Test 5 The Distribution of Sample Means and z for a Sample Mean Sampling and Sampling Error Distribution of Sample Means z for a Sample Mean Example: Computing and Interpreting the z for a Sample Mean Exact Probabilities Versus Probability Estimates Overview of the Activities Chapter 5 Practice Test 6 Hypothesis Testing With z Scores Introduction to Hypothesis Testing Hypothesis Testing With z for a Sample Mean Example (One-Tailed) What Does It Mean to Describe Something as “Statistically Significant”? Errors in Hypothesis Testing Hypothesis Testing Rules What Is a p Value? Why Statisticians “Fail to Reject the Null” Rather Than “Accept the Null” Why Scientists Say “This Research Suggests” Rather Than “This Research Proves” Overview of the Activities Chapter 6 Practice Test 7 Single-Sample t Test Single-Sample t Test Conceptual Information One-Tailed Single-Sample t Test Example Two-Tailed Single-Sample t Test Example Other Alpha Levels SPSS Overview of the Activity Chapter 7 Practice Test 8 Estimation With Confidence Intervals Three Statistical Procedures With Three Distinct Purposes Logic of Confidence Intervals Computing a Confidence Interval for a Population Mean Computing Confidence Intervals for a Mean Difference Reporting Confidence Intervals in APA Style Confidence Intervals for Effect Sizes Interpretations of Confidence Intervals SPSS Overview of the Activity Chapter 8 Practice Test 9 Related Samples t Test Repeated/Related Samples t Test Logic of the Single-Sample and Repeated/Related Samples t Tests Related Samples t (Two-Tailed) Example Related Samples t (One-Tailed) Example Statistical Results, Experimental Design, and Scientific Conclusions SPSS Overview of the Activities Chapter 9 Practice Test 10 Independent Samples t Test Independent Samples t Conceptual Formula for the Independent Samples t Two-Tailed Independent t Test Example One-Tailed Independent t Test Example Other Alpha Levels SPSS Overview of the Activities Chapter 10 Practice Test 11 One-Way Independent Samples ANOVA Independent Samples ANOVA An Example ANOVA Problem An Additional Note on ANOVAs: Family-Wise Error and Alpha Inflation SPSS Overview of the Activities Chapter 11 Practice Test 12 Two-Factor ANOVA or Two-Way ANOVA Purpose of the Two-Way ANOVA Describing Factorial Designs Logic of the Two-Way ANOVA Example of a Two-Way ANOVA SPSS Overview of the Activities Chapter 12 Practice Test 13 Correlation and Regression When to Use Correlations and What They Can Tell You Review of z Scores The Logic of Correlation Direction and Strength of Correlation Coefficients Computational Formulas Spearman’s (rs) Correlations Using Scatterplots Prior to Correlation Coefficients Alternative Use for Correlation Correlation and Causation Hypothesis Testing With Correlation Two-Tailed Pearson’s Correlation Example One-Tailed Pearson’s Correlation Example What If You Need to Do a Spearman’s Correlation? Confidence Intervals SPSS Overview of the Activities Chapter 13 Practice Test 14 Goodness of Fit and Independence Chi-Square Statistics Overview of Chi-Square Logic of the Chi-Square Test Comparing the Goodness-of-Fit Chi-Square and the Chi-Square for Independence Goodness-of-Fit Chi-Square Example Chi-Square for Independence SPSS Overview of the Activities Chapter 14 Practice Test Appendices Appendix A Unit Normal Table (z Table) Appendix B One-Tailed Probabilities t Table Two-Tailed Probabilities t Table Appendix C F Table (a = .05) F Table (a = .01) Appendix D The Studentized Range Statistic (q) Table Appendix E One-Tailed Pearson’s Correlation Table Two-Tailed Pearson’s Correlation Table Appendix F Spearman’s Correlation Table Appendix G Fisher r to z Table Appendix H Critical Values for Chi-Square Appendix I Computing SSs for Factorial ANOVA Appendix J Choosing Correct Test Statistics 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.

Review :
“The text is the most thorough I have seen as far as going through examples. After this text, students are almost guaranteed to be able to complete these statistical measures on their own.” —Elizabeth Axel, Adelphi University “The most impressive feature of the text is the manner in which it invites the student to interact with and digest the material covered in each chapter.” —Lyon Rathbun, University of Texas, Brownsville