The material in this user-friendly text is presented as simply as possible to ensure that students will gain a solid understanding of statistical procedures and analysis. The goal of this book is to demystify and present statistics in a clear, cohesive manner. The student is presented with rules of evidence and the logic behind those rules. The book is divided into three major units: Descriptive Statistics, Inferential Statistics, and Advanced Topics in Inferential Statistics.

Each chapter has “Summary,” “Key Terms,” and “Problems.”

** UNIT I: DESCRIPTIVE STATISTICS. **

** 1. Introduction to Statistics. **

Stumbling Blocks to Statistics.

A Brief Look at the History of Statistics.

Benefits of a Course in Statistics.

General Field of Statistics.

** 2. Graphs and Measures of Central Tendency. **

Graphs.

Measures of Central Tendency.

Appropriate Use of the Mean, the Median, and the Mode.

** 3. Variability. **

Measures of Variability.

Graphs and Variability.

Questionnaire Percentages.

** 4. The Normal Curve and z Scores. **

The Normal Curve.

z Scores.

Translating Raw Scores into z Scores.

z Score Translations in Practice.

Fun with Your Calculator.

** 5. z Scores Revisited: t Scores and Other Normal Curve Transformations. **

Other Applications of the z Score.

The Percentile Table.

t Scores.

Normal Curve Equivalents.

Stanines.

Grade-Equivalent Scores: A Note of Caution.

The Importance of the z Score.

** 6. Probability. **

The Definition of Probability.

Probability and Percentage Areas of the Normal Curve.

Combining Probabilities for Independent Events.

A Reminder about Logic.

** UNIT II: INFERENTIAL STATISTICS. **

** 7. Statistics and Parameters. **

Generalizing from the Few to the Many.

Key Concepts of the Inferential Statistics.

Techniques of Sampling.

Exit Polling.

Sampling Distributions.

Back to z.

Some Words of Encouragement.

** 8. Parameter Estimates and Hypothesis Testing. **

The Standard Deviation Revisited.

Estimating the Standard Error of the Mean.

Estimating the Popular Mean: Interval Estimates and Hypothesis Testing.

The t ratio.

The Type 1 Error.

Alpha Levels.

Effect Size.

Interval Estimates: No Hypothesis Needed.

** 9. The Fundamentals of Research Methodology. **

Research Strategies.

Independent and Dependent Variables.

The Cause-and-Effect Trap.

Theory of Measurement.

Research: Experimental versus Post-Facto.

The Experimental Method: The Case of Cause and Effect.

Creating Equivalent Groups: The True Experiment.

Designing the True Experiment.

The Hawthorne Effect.

Repeated Measure Designs with Separate Control Groups.

Requirements for the True Experiment.

Post-Facto Research.

Combination Research.

Research Errors.

Experimental Error: Failure to Use an Adequate Control Group.

Post-Facto Errors.

Meta-Analysis.

Methodology as a Basis for More Sophisticated Techniques.

** 10. The Hypothesis of Difference. **

Sampling Distribution of Differences.

Estimated Standard Error of Difference.

Two-Sample t Test for Independent Samples.

Significance.

Two-Tail t Table.

Alpha and Confidence Levels.

Confidence Interval for Differences Between Two Independent Samples.

The Minimum Difference.

Outliers.

One-Tail t Test.

Importance of Having at Least Two Samples.

Power.

Effect Size.

** 11. The Hypothesis of Association: Correlation. **

Cause and Effect.

The Pearson r.

Interclass versus Intraclass.

Correlation Matrix.

The Spearman r.

An Important Difference between Correlation Coefficient and the t Test.

** 12. Analysis of Variance. **

Advantages of ANOVA.

Analyzing the Variance.

Applications of ANOVA.

The Factorial ANOVA.

Eta squared and d.

Graphing the Interaction.

** 13. Nominal Data and the Chi Square. **

Chi Square and Independent Samples.

Locating the Difference.

Chi Square and Percentages.

Chi Square and z Scores.

Chi Square and Dependent Samples.

Requirements for Using the Chi Square.

** UNIT III: ADVANCED TOPICS IN INFERENTIAL STATISTICS. **

** 14. Regression Analysis. **

Regression of Y on X.

Standard Error of Estimate.

Confidence Interval Equation.

Multiple R (Linear Regression with More Than Two Variables).

Path Analysis, the Multiple R, and Causation.

Partial Correlation.

** 15. Repeated-Measures and Matched-Subjects Designs with Interval Data. **

Problem of Correlated of Dependent Samples.

Repeated Measures.

Paired t Ratio.

Confidence Interval for Paired Differences.

Within-Subjects F Ration.

Within-Subjects Effect Size.

Testing Correlated and Experimental Data.

** 16. Nonparametrics Revisited: The Ordinal Case. **

Mann-Whitney U Test for Two Ordinal Distributions with Independent Selection.

Kruskal-Wallis H Test for Three of More Ordinal Distributions with Independent Selection.

Wilcoxon T Test for Two Ordinal Distributions with Correlated Selection.

Friedman ANOVA by Ranks for Three or More Ordinal Distributions with Correlated Selection.

Advantages and Disadvantages of Nonparametric Tests.

** 17. Tests and Measurements. **

Norm and Criterion Referencing: Relative versus Absolute Performance Measures.

The Problem of Bias.

Test Reliability, Validity, and Measurement Theory.

Cronbach's Alpha.

Test Validity.

Item Analysis.

** 18. Computers and Statistical Analysis. **

Computer Literacy.

The Statistical Programs.

Logic Checkpoints.

** 19. Research Simulations: Choosing the Correct Statistical Test. **

Methodology: Research's Bottom Line.

Checklist Questions.

Critical Decision Points.

Research Simulations: From A to Z.

The Research Enterprise.

A Final Thought: The Burden of Proof.

** Special Unit: The Binomial Case. **

** Appendix A. **

** Appendix B. **

** Glossary. **

** References. **

** Answer to Odd-Numbered Items (and Within-Chapter Exercises). **

** Index. **