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Straightforward Statistics: Understanding the Tools of Research

Glenn Geher and Sara Hall
Publisher: 
Oxford University Press
Publication Date: 
2014
Number of Pages: 
455
Format: 
Hardcover
Price: 
75.00
ISBN: 
9780199751761
Category: 
Textbook
[Reviewed by
Charles Ashbacher
, on
09/6/2014
]

At a fundamental level, basic statistics is taking raw data and computing valuable summaries such as mean and standard deviation, determining if there is a relationship between different data types or using data to determine if a hypothesis regarding a population is true. Different fields emphasize different areas of statistics and in this case the target is the student in the social, behavioral or life sciences.

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. Two sets of problems (called A and B) conclude each chapter with the solutions to all A following the problems. Solutions to all set B problems appear at the end of the book.

Approximately one-third of the book covers descriptive statistics, regression and correlation with the balance an explanation of hypothesis testing. The types of testing covered are one-sample and between-groups t-tests, ANOVA and chi-square tests. Appendix F is a 35 page description on how to use the SPSS statistics package. It is a compact description with no screen shots, which can sometimes be a problem with students that are uncertain about using computers to do what they perceive as math.

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 splits his time between consulting with industry in projects involving math and computers, teaching college classes and co-editing The Journal of Recreational Mathematics. In his spare time, he reads about these things and helps his daughter in her lawn care business.

Preface

1: Why Do I Need to Learn Statistics?
- Examples of statistics in the real world
- The nature of findings and facts in the behavioral sciences
- Descriptive and Inferential Statistics
- A conceptual approach to teaching and learning statistics
- What you should get out of this class

2. Describing a Single Variable
- The nature of variables: continuous vs. categorical
- Frequency distributions as descriptions of single variables
- Creating frequency distributions
- Representing frequency distributions graphically
- Interpreting frequency distributions
- Mean, median, and mode
- Why is the mean the most-utilized index of central tendency?
- The conceptual elements of standard deviation
- Computing standard deviation

3. Standardized Scores
- Why are standardized scores needed in the real world?
- Why are standardized scores needed in statistics?
- Computing Z scores
- Interpreting Z scores
- A real research example
- Summary

4. Correlation
- Real-world examples of correlations
- Representing correlations graphically (the scatterplot)
- Representing correlations quantitatively (Pearson's r: an index of correlation strength and direction)
- Computing r using Z-scores
- Interpreting r (what you can and cannot conclude knowing that a correlation between two variables exists)
- A real research example
- Summary

5. Statistical Prediction and Regression
- The basic rationale underlying regression
- Standardized model of bivariate regression
- Raw-score model of bivariate regression
- The regression line
- Estimating error of prediction
- Basic rationale underlying multiple regression
- A real research example
- Summary

6. The Basic Elements of Hypothesis Testing
- Probability
- The normal distribution
- Estimating likelihood of outcomes
- A real research example
- Summary

7. Introduction to Hypothesis Testing
- Basic rationale underlying hypothesis testing
- What is meant by statistical significance?
- The five steps of hypothesis testing:
- Stating the null and research hypotheses
- Delineating the nature of the comparison distribution
- Determining alpha (by defining a part of the comparison distribution is highly unlikely)
- Comparing a sample from the special population with the comparison distribution
- Commenting on the null hypothesis
- A real research example
- Summary

8. Hypothesis Testing if N > 1
- The basic steps of hypothesis testing always remain the same
- The comparison distribution needed for comparing a sample mean: The distribution of means
- Hypothesis testing using the distribution of means
- Confidence intervals
- A real research example
- Summary

9. Statistical Power
- Defining Power (p(rejecting the null hypothesis when the research hypothesis is true) and Beta (p(Type-II error))
- How N, population-level standard deviation, and effect size affect power
- Computing power
- How power affects real research
- A real research example
- Summary

10. t-tests (One-Sample and Within-Groups)
- How a t-test differs from a Z-test
- The nature of the t-distribution (and why it varies as it does)
- Computing a one-sample t-test
- Computing a repeated-measures t-test
- A real research example
- Summary

11. t-tests: Between-Groups
- The basic rationale of the between-groups t-test
- Computing the between-groups t-test
- Interpreting results
- A real research example
- Summary

12. Analysis of Variance
- Basic reasoning of F as a ratio between effect and error variance
- Concepts underlying a one-way ANOVA
- Computing a one-way ANOVA
- Factorial ANOVA
- What results from an ANOVA can and cannot tell you
- Post-hoc tests
- A real research example
- Summary

13. Chi-Square
- What happens when all our variables are categorical?
- Basic rationale underlying goodness of fit test
- Computing the chi-square goodness of fit
- Computing the chi-square test of independence
- Interpreting chi-square results
- A real research example
- Summary

Appendix A: Normal Curve (Z) Table

Appendix B: t Table

Appendix C: F Table

Appendix D: Chi Square Table

Appendix E: Advanced Statistics You May Run Into
- Factor Analysis
- Multiple regression
- Structural Equation Modeling
- Repeated-Measures ANOVA
- Mixed-Design ANOVA
- MANOVA


Appendix F: Using SPSS to Compute Basic Statistics
- Benefits of SPSS
- Different kinds of SPSS files
- Entering data with SPSS
- Computing frequency distributions with SPSS
- Describing variables with SPSS
- Using SPSS to examine correlations
- Using SPSS for a repeated-measures test
- Using SPSS for a between-groups test
- Using SPSS for a one-way ANOVA


Glossary

Answers to Set B Homework Problems

References
Index

Dummy View - NOT TO BE DELETED