Straightforward Statistics: Understanding the Tools of Research

by Glenn Geher, Sara Hall

Overview

Straightforward Statistics: Understanding the Tools of Research is a clear and direct introduction to statistics for the social, behavioral, and life sciences. Based on Glenn Geher's extensive experience teaching undergraduate statistics, this book provides a narrative presentation of the core principles that provide the foundation for modern-day statistics. With step-by-step guidance on the nuts and bolts of computing these statistics, the book includes detailed tutorials how to use state-of-the-art software, SPSS, to compute the basic statistics employed in modern academic and applied research. Across 13 succinct chapters, this text presents statistics using a conceptual approach along with information on the relevance of the different tools in different contexts and summaries of current research examples.

Students should find this book easy useful and engaging in its presentation while instructors should find it detailed, comprehensive, accessible, and helpful in complementing a basic course in statistics.

Product Details

ISBN-13:	9780190276959
Publisher:	Oxford University Press
Publication date:	10/02/2015
Edition description:	Reprint
Pages:	416
Product dimensions:	6.90(w) x 9.90(h) x 0.60(d)

About the Author

Glenn Geher is Professor and Chair of Psychology at the State University of New York at New Paltz, where he has taught Statistics and various other courses related to psychology and evolution since 2000. He also is the founding director of New Paltz's Evolutionary Studies Program, which has been awarded thousands of dollars from the National Science Foundation to help advance evolution's place in higher education. He has over 60 publications including several books and articles on various topics related to evolution and social psychology. His work has been covered in several media outlets including the New York Times, Chronicle of Higher Education, Redbook, and Cosmopolitan. He lives with his wife Kathy and two children, Megan and Andrew, in rural upstate New York.

Sara Hall has earned degrees in both Psychology and Criminology. She lives in Oregon with her husband, Benjamin, and their four children, Jackson, Stella, Susanna, and Sailor.

Table of Contents

Preface
Acknowledgements

1. Prelude: Why Do I Need to Learn Statistics?
-The Nature of Findings and Facts in the Behavioral Sciences
- Statistical Significance and Effect Size
- Descriptive and Inferential Statistics
- A Conceptual Approach to Teaching and Learning Statistics
- The Nature of this Book
- How to Approach this Class and What You Should Get Out of It
- Key Terms

2. Describing a Single Variable
- Variables, Values, and Scores
- Types of Variables
- Describing Scores for a Single Variable
- Indices of Central Tendency
- Indices of Variability (and the Sheer Beauty of Standard Deviation!)
- Rounding
- Describing Frequencies of Values for a Single Variable
- Representing Frequency Data Graphically
- Describing Data for a Categorical Variable
- A Real Research Example
- Summary
- Key Terms

3. Standardized Scores
- When a Z-Score Equals 0, the Raw Score It Corresponds to Must Equal the Mean
- Verbal Scores for the Madupistan Aptitude Measure
- Quantitative Scores for the Madupistan Aptitude Measure
- Every Raw Score for Any Variable Corresponds to a Particular Z-Score
- Computing Z-Scores for All Students for the Madupistan Verbal Test
- Computing Raw Scores from Z-Scores
- Comparing Your GPA of 3.10 from Solid State University with Pat's GPA of 1.95 from Advanced Technical University
- Each Z-Score for Any Variable Corresponds to a Particular Raw Score
- Converting Z-Scores to Raw Scores (The Dorm Resident Example)
- A Real Research Example
- Summary
- Key Terms

4. Correlation
- Correlations Are Summaries
- Representing a Correlation Graphically
- Representing a Correlation Mathematically
- Return to Madupistan
- Correlation Does Not Imply Causation
- A Real Research Example
- Summary
- Key Terms

5. Statistical Prediction and Regression
- Standardized Regression
- Predicting Scores on Y with Different Amounts of Information
- Beta Weight
- Unstandardized Regression Equation
- The Regression Line
- Quantitatively Estimating the Predictive Power of Your Regression Model
- Interpreting r2
- A Real Research Example
- Conclusion
- Key Terms

6. The Basic Elements of Hypothesis Testing
- The Basic Elements of Inferential Statistics
- The Normal Distribution
- A Real Research Example
- Summary
- Key Terms

7. Introduction to Hypothesis Testing
- The Basic Rationale of Hypothesis Testing
- Understanding the Broader Population of Interest
- Population versus Sample Parameters
- The Five Basic Steps of Hypothesis Testing
- A Real Research Example
- Summary
- Key Terms

8. Hypothesis Testing if N > 1
- The Distribution of Means
- Steps in Hypothesis Testing if N > 1
- Confidence Intervals
- Real Research Example
- Summary
- Key Terms

9. Statistical Power
- What Is Statistical Power?
- An Example of Statistical Power
- Factors that Affect Statistical Power
- A Real Research Example
- Summary
- Key Terms

10. t-tests (One-Sample and Within-Groups)
- One-Sample t-test
- Steps for Hypothesis Testing with a One-Sample t-test
- Here Are Some Simple Rules to Determine the Sign of tcrit with a One-Sample t-Test
- Computing Effect Size with a One-Sample t-Test
- How the t-Test Is Biased Against Small Samples
- The Within-Group t-Test
- Steps in Computing the Within-Group t-Test
- Computing Effect Size with a Within-Group t-test
- A Real Research Example
- Summary
- Key Terms

11. The Between-Groups t-test
- The Elements of the Between-Groups t-test
- Effect Size with the Between-Groups t-test
- Another Example
- Real Research Example
- Summary
- Key Terms

12. Analysis of Variance
- ANOVA as a Signal-Detection Statistic
- An Example of the One-Way ANOVA
- What Can and Cannot Be Inferred from ANOVA (The Importance of Follow-Up Tests)
- Estimating Effect Size with the One-Way ANOVA
- Real Research Example
- Summary
- Key Terms

13. Chi Square and Hypothesis-Testing with Categorical Variables
- Chi Square Test of Goodness of Fit
- Steps in Hypothesis Testing with Chi Square Goodness of Fit
- What Can and Cannot Be Inferred from a Significant Chi Square
- Chi Square Goodness of Fit Testing for Equality across Categories
- Chi Square Test of Independence
- Real Research Example
- Summary
- Key Terms

Appendix A: Cumulative Standardized Normal Distribution
Appendix B: t Distribution: Critical Values of t
Appendix C: F Distribution: Critical Values of F
Appendix D: Chi Square Distribution: Critical Values of χ² (Chi Squared) Distribution: Critical Values of χ²
Appendix E: Advanced Statistics to Be Aware of
- Advanced Forms of ANOVA
- Summary
- Key Terms
Appendix F: Using SPSS
- Activity 1: SPSS Data Entry Lab
- Activity 2: Working with SPSS Syntax Files
- Syntax Files, Recoding Variables, Compute Statements, Out Files, and the Computation of Variables in SPSS
- Recoding Variables
- Computing New Variables
- Output Files
- Example: How to Recode Items for the Jealousy Data and Compute Composite Variables
- Activity 3: Descriptive Statistics
- Frequencies, Descriptives, and Histograms
- Frequencies, Descriptives, and Histograms for Data Measured in Class
- The Continuous Variable
- The Categorical Variable
- Activity 4: Correlations
- Activity 5: Regression
- Activity 6: t-tests
- Independent Samples Test
- Activity 7: ANOVA with SPSS
- Post Hoc Tests
- Homogeneous Subsets
- Activity 8: Factorial ANOVA
- Recomputing Variables so as to Be Able to Conduct a One-Way ANOVA to Examine Specific Differences Between Means
- Activity 9: Chi Square
- Crosstabs

Glossary
References
Index

Editorial Reviews

"The book is very readable for students that may not be comfortable with a large number of formulas. Explanations are extensively textual in form and diagrams are used where needed. If you are teaching a service course for students in the social, behavioral or life sciences and don't want to use a standard basic statistics book, this is one that will work for you and your students." —Charles Ashbacher, Mathematical Association of America Reviews

From the Publisher