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Stats & Probability for Behavioral/Social Sciences Textbooks

Edition: 7TH 03

Copyright: 2003

Publisher: Allyn & Bacon, Inc.

Published: 2003

International: No

Copyright: 2003

Publisher: Allyn & Bacon, Inc.

Published: 2003

International: No

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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.

Features :

- Written in an easy-to-read, engaging literary style with many new and interesting examples to grab students' attention and hold their interest.
- Includes definitions of key concepts in the glossary to prevent student confusion over terminology.
- Provides a convenient summary at the end of each chapter to reinforce learning.
- More than 400 problems and test questions throughout the text allow students to apply what they have learned.
- Introduces students to the language of research methodology and the basics of research reasoning (Ch. 9) prior to commencing work on any inferential techniques, such as the independent t or Pearson r.
- Provides methods for finding percentiles in Chapter 5.

New To This Edition :

- Now covers the popular SPSS statistical program to increase statistical sophistication of today's students. This edition has been re-written to fit with SPSS, and an appendix has been included to provide students with a complete user's guide to this program.
- Extensive updating has led to new examples from the fields of psychology, law enforcement, health sciences, education, and sociology.
- All problems now have research basis and include a full write-up of the conclusions to help students.

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.

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. Analysis of Variance.

Advantages of ANOVA.

Analyzing the Variance.

Applications of ANOVA.

The Factorial ANOVA.

Eta squared and d.

Graphing the Interaction.

7. 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.

8. 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.

9. 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.

10. 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.

11. 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.

12. Computers and Statistical Analysis.

Computer Literacy.

The Statistical Programs.

Logic Checkpoints.

13. 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.

Summary

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.

Features :

- Written in an easy-to-read, engaging literary style with many new and interesting examples to grab students' attention and hold their interest.
- Includes definitions of key concepts in the glossary to prevent student confusion over terminology.
- Provides a convenient summary at the end of each chapter to reinforce learning.
- More than 400 problems and test questions throughout the text allow students to apply what they have learned.
- Introduces students to the language of research methodology and the basics of research reasoning (Ch. 9) prior to commencing work on any inferential techniques, such as the independent t or Pearson r.
- Provides methods for finding percentiles in Chapter 5.

New To This Edition :

- Now covers the popular SPSS statistical program to increase statistical sophistication of today's students. This edition has been re-written to fit with SPSS, and an appendix has been included to provide students with a complete user's guide to this program.
- Extensive updating has led to new examples from the fields of psychology, law enforcement, health sciences, education, and sociology.
- All problems now have research basis and include a full write-up of the conclusions to help students.

Table of Contents

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.

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. Analysis of Variance.

Advantages of ANOVA.

Analyzing the Variance.

Applications of ANOVA.

The Factorial ANOVA.

Eta squared and d.

Graphing the Interaction.

7. 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.

8. 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.

9. 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.

10. 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.

11. 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.

12. Computers and Statistical Analysis.

Computer Literacy.

The Statistical Programs.

Logic Checkpoints.

13. 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.

Publisher Info

Publisher: Allyn & Bacon, Inc.

Published: 2003

International: No

Published: 2003

International: No

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.

Features :

- Written in an easy-to-read, engaging literary style with many new and interesting examples to grab students' attention and hold their interest.
- Includes definitions of key concepts in the glossary to prevent student confusion over terminology.
- Provides a convenient summary at the end of each chapter to reinforce learning.
- More than 400 problems and test questions throughout the text allow students to apply what they have learned.
- Introduces students to the language of research methodology and the basics of research reasoning (Ch. 9) prior to commencing work on any inferential techniques, such as the independent t or Pearson r.
- Provides methods for finding percentiles in Chapter 5.

New To This Edition :

- Now covers the popular SPSS statistical program to increase statistical sophistication of today's students. This edition has been re-written to fit with SPSS, and an appendix has been included to provide students with a complete user's guide to this program.
- Extensive updating has led to new examples from the fields of psychology, law enforcement, health sciences, education, and sociology.
- All problems now have research basis and include a full write-up of the conclusions to help students.

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.

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. Analysis of Variance.

Advantages of ANOVA.

Analyzing the Variance.

Applications of ANOVA.

The Factorial ANOVA.

Eta squared and d.

Graphing the Interaction.

7. 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.

8. 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.

9. 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.

10. 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.

11. 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.

12. Computers and Statistical Analysis.

Computer Literacy.

The Statistical Programs.

Logic Checkpoints.

13. 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.