Basic Statistical Analysis
7th Edition
Published on 8. October 2002
Book
Hardback
672 pages
978-0-205-36066-6 (ISBN)
Description
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.
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.
More details
Edition
7th edition
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
Professional and scholarly
Dimensions
Width: 242 mm
Thickness: 29 mm
Weight
1173 gr
ISBN-13
978-0-205-36066-6 (9780205360666)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
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Richard C. Sprinthall
Basic Statistical Analysis
Book
10/2006
8th Edition
Pearson
€116.55
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Previous edition
Richard C. Sprinthall
Basic Statistical Analysis
Book
09/1999
6th Edition
Pearson
€86.84
Article exhausted; check for reprint
Content
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.
Cronbachs 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: Researchs 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.
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.
Cronbachs 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: Researchs 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.