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Publisher:

Oxford University Press

Publication Date:

2014

Number of Pages:

455

Format:

Hardcover

Price:

75.00

ISBN:

9780199751761

Category:

Textbook

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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

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