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Fundamental Statistics for the Behavioral Sciences With Infotrac,9780534399511
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Fundamental Statistics for the Behavioral Sciences With Infotrac

by
Edition:
5th
ISBN13:

9780534399511

ISBN10:
0534399517
Format:
Paperback
Pub. Date:
6/16/2003
Publisher(s):
Wadsworth Publishing

Questions About This Book?

What version or edition is this?
This is the 5th edition with a publication date of 6/16/2003.
What is included with this book?
  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any CDs, lab manuals, study guides, etc.

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Summary

1. INTRODUCTION. The Importance of Context: An Example. Basic Terminology. Selection Among Statistical Procedures. Using Computers. Summary. Exercises. 2. BASIC CONCEPTS. Scales of Measurement. Variables. Random Sampling. Notation. Summary. Exercises. 3. DISPLAYING DATA. Plotting Data. Stem-and-Leaf Displays. Histograms. Reading Graphs. Alternative Methods of Planning Data. Describing Distributions. Using Computer Programs to Display Data. Summary. Exercises. 4. MEASURES OF CENTRAL TENDENCY. The Mode. The Median. The Mean. Advantages and Disadvantages of the Mode, the Median and the Mean. Obtaining Measures of Central Tendency Using MINITAB. A Simple DemonstrationSeeing Statistics. Summary. Exercises. 5. MEASURES OF VARIABILITY. Range. Interquartile Range and Other Range Statistics. The Average Deviation. The Variance. The Standard Deviation. Computational Formulae for the Variance and the Standard Deviation. The Mean and the Variance as Estimators. Boxplots: Graphical Representations of Dispersion and Extreme Scores. Obtaining Measures of Dispersion Using JMP. A Final Worked Example. Seeing Statistics. Summary. Exercises. 6. THE NORMAL DISTRIBUTION. The Normal Distribution. The Standard Normal Distribution. Setting Probable Limits on an Observation. Measures Related to z. Summary. Exercises. 7. BASIC CONCEPTS OF PROBABILITY. Probability. Basic Terminology and Rules. Discrete versus Continuous Variables. Probability Distributions for Discrete Variables. Probability Distributions for Continuous Variables. Summary. Exercises. 8. SAMPLING DISTRIBUTIONS AND HYPOTHESIS TESTING. Two Simple Examples Involving Course Evaluations and Rude Motorists. Sampling Distributions. Hypothesis Testing. The Null Hypothesis. Test Statistics and Their Sampling Distributions. Using the Normal Distribution to Test Hypotheses. Type I and Type II Errors. One- and Two-Tailed Tests. Seeing Statistics. A Final Worked Example. Back to Course Evaluations and Rude Motorists. Summary. Exercises. 9. CORRELATION. Scatter Diagrams. The Relationship Between Speed and Accuracy. The Covariance. The Pearson ProductMoment Correlation Coefficient (r). Correlations with Ranked Data. Factors That Affect the Correlation. If Something Looks Too Good To Be True, Perhaps It Is. Testing the Significance of a Correlation Coefficient. Intercorrelation Matrices. Other Correlation Coefficients. Using MINITAB and SPSS to Obtain Correlation Coefficients. Seeing Statistics. A Final Worked Example. Summary. Exercises. 10. REGRESSION. The Relationship Between Stress and Health. The Basic Data. The Regression Line. The Accuracy of Prediction. The Influence of Extreme Values. Hypothesis Testing in Regression. Computer Solution Using SPSS. Seeing Statistics. A Final Worked Example. Summary. Exercises. 11. MULTIPLE REGRESSION. Overview. Course Evaluations Again. Residuals. The Visual Representation of Multiple Regression. Hypothesis Testing. Refining the Regression Equation. A Second Example: Height and Weight. A Third Example: Psychological Symptoms in Cancer Patients. Summary. Exercises. 12. HYPOTHESIS TESTS APPLIED TO MEANS: ONE SAMPLE. Sampling Distribution of the Mean. Testing Hypotheses About Means When Ă Is Known. Testing a Sample Mean When Ă Is Unknown (The One-Sample t Test). Factors That Affect the Magnitude of t and the Decision About H0. A Second Example: The Moon Illusion. How Large Is Our Effect? Confidence Limits on the Mean. Using JMP to Run One-Sample t Tests. A Final Worked Example. Seeing Statistics. Summary. Exercises. 13. HYPOTHESIS TESTS APPLIED TO MEANS: TWO RELATED SAMPLES. Related Samples. An Example: Student's t Applied to Difference Scores. A Second Example: The Moon Illusion Again. Advantages and Disadvantages of Using Related Samples. How Large an Effect Have We Found? Using SPSS for t Tests on Related Samples. Summary. Exercises. 14. HYPOTHESIS TESTS APPLIED TO MEANS: TWO INDEPENDENT SAMPLES. Distribution of Differences Between Means. Heterogeneity

Table of Contents

1. INTRODUCTION
The Importance of Context: An Example
Basic Terminology
Selection Among Statistical Procedures
Using Computers
Summary
Exercises
2. BASIC CONCEPTS
Scales of Measurement
Variables
Random Sampling
Notation
Summary
Exercises
3. DISPLAYING DATA
Plotting Data
Stem-and-Leaf Displays
Histograms
Reading Graphs
Alternative Methods of Planning Data
Describing Distributions
Using Computer Programs to Display Data
Summary
Exercises
4. MEASURES OF CENTRAL TENDENCY
The Mode
The Median
The Mean
Advantages and Disadvantages of the Mode, the Median and the Mean
Obtaining Measures of Central Tendency Using MINITAB
A Simple Demonstration—Seeing Statistics
Summary
Exercises
5. MEASURES OF VARIABILITY
Range
Interquartile Range and Other Range Statistics
The Average Deviation
The Variance
The Standard Deviation
Computational Formulae for the Variance and the Standard Deviation
The Mean and the Variance as Estimators
Boxplots: Graphical Representations of Dispersion and Extreme Scores
Obtaining Measures of Dispersion Using JMP
A Final Worked Example
Seeing Statistics
Summary
Exercises
6. THE NORMAL DISTRIBUTION
The Normal Distribution
The Standard Normal Distribution
Setting Probable Limits on an Observation
Measures Related to z
Summary
Exercises
7. BASIC CONCEPTS OF PROBABILITY
Probability
Basic Terminology and Rules
Discrete versus Continuous Variables
Probability Distributions for Discrete Variables
Probability Distributions for Continuous Variables
Summary
Exercises
8. SAMPLING DISTRIBUTIONS AND HYPOTHESIS TESTING
Two Simple Examples Involving Course Evaluations and Rude Motorists
Sampling Distributions
Hypothesis Testing
The Null Hypothesis
Test Statistics and Their Sampling Distributions
Using the Normal Distribution to Test Hypotheses
Type I and Type II Errors
One- and Two-Tailed Tests
Seeing Statistics
A Final Worked Example
Back to Course Evaluations and Rude Motorists
Summary
Exercises
9. CORRELATION
Scatter Diagrams
The Relationship Between Speed and Accuracy
The Covariance
The Pearson Product–Moment Correlation Coefficient (r)
Correlations with Ranked Data
Factors That Affect the Correlation
If Something Looks Too Good To Be True, Perhaps It Is
Testing the Significance of a Correlation Coefficient
Intercorrelation Matrices
Other Correlation Coefficients
Using MINITAB and SPSS to Obtain Correlation Coefficients
Seeing Statistics
A Final Worked Example
Summary
Exercises
10. REGRESSION
The Relationship Between Stress and Health
The Basic Data
The Regression Line
The Accuracy of Prediction
The Influence of Extreme Values
Hypothesis Testing in Regression
Computer Solution Using SPSS
Seeing Statistics
A Final Worked Example
Summary
Exercises
11. MULTIPLE REGRESSION
Overview
Course Evaluations Again
Residuals
The Visual Representation of Multiple Regression
Hypothesis Testing
Refining the Regression Equation
A Second Example: Height and Weight
A Third Example: Psychological Symptoms in Cancer Patients
Summary
Exercises
12. HYPOTHESIS TESTS APPLIED TO MEANS: ONE SAMPLE
Sampling Distribution of the Mean
Testing Hypotheses About Means When σ Is Known
Testing a Sample Mean When σ Is Unknown (The One-Sample t Test)
Factors That Affect the Magnitude of t and the Decision About H0. A Second Example: The Moon Illusion
How Large Is Our Effect? Confidence Limits on the Mean
Using JMP to Run One-Sample t Tests
A Final Worked Example
Seeing Statistics
Summary
Exercises
13. HYPOTHESIS TESTS APPLIED TO MEANS: TWO RELATED SAMPLES
Related Samples
An Example: Student's t Applied to Difference Scores
A Second Example: The Moon Illusion Again
Advantages and Disadvantages of Using Related Samples
How Large an Effect Have We Found? Using SPSS for t Tests on Related Samples
Summary
Exercises
14. HYPOTHESIS TESTS APPLIED TO MEANS: TWO INDEPENDENT SAMPLES
Distribution of Differences Between Means
Heterogeneity of Variance
Nonnormality of Distributions
A Second Example with Two Independent Samples
Effect Size Again
Confidence Limits on μ1–μ2. Use of Computer Programs for Analysis of Two Independent Sample Means
A Final Worked Example
Seeing Statistics
Summary
Exercises
15. POWER
The Basic Concept
Factors That Affect the Power of a Test
Effect Size
Power Calculations for the One-Sample t Test
Power Calculations for Differences Between Two Independent Means
Power Calculations for the t Test for Related Samples
Power Considerations in Terms of Sample Size
You Don't Have to Do It by Hand
Seeing Statistics
Summary
Exercises
16. ONE WAY ANALYSIS OF VARIANCE
The General Approach
The Logic of the Analysis of Variance
Calculations for the Analysis of Variance
Unequal Sample Sizes
Multiple Comparison Procedures
Violations of Assumptions
Magnitude of Effect
Use of JMP for a One-Way Analysis of Variance
A Final Worked Example
Seeing Statistics
Summary
Exercises
17. FACTORIAL ANALYSIS OF VARIANCE
Factorial Designs
The Extension of the Eysenck Study
Interactions
Simple Effects
Unequal Sample Sizes
Measures of Effect Size
A Second Example: Maternal Adaptation Revisited
Using SPSS for Factorial Analysis of Variance
Seeing Statistics
Summary
Exercises
18. REPEATED-MEASURES ANALYSIS OF VARIANCE
An Example: The Treatment of Migraine Headaches
Multiple Comparisons
Effect Size
Assumptions Involved in Repeated-Measures Designs
Advantages and Disadvantages of Repeated-Measures Designs
Using SPSS to Analyze Data in a Repeated-Measures Design
A Final Worked Example
Summary
Exercises
19. CHI-SQUARE
One Classification Variable: The Chi-Square Goodness-of-Fit Test
Two Classification Variables: Contingency Table Analysis
Correction for Continuity
Chi-Square for Larger Contingency Tables
The Problem of Small Expected Frequencies
The Use of Chi-Square as a Test on Proportions
Non-Independent Observations
MINITAB Analysis of Contingency Tables
A Final Worked Example
Effect Size
Seeing Statistics
Summary
Exercises
20. NONPARAMETRIC AND DISTRIBUTION¡VFREE STATISTICAL TESTS
The Mann–Whitney Test
Wilcoxon's Matched-Pairs Signed-Ranks Test
Kruskal–Wallis One-Way Analysis of Variance
Friedman's Rank Test for k Correlated Samples
Summary
Exercises
21. CHOOSING THE APPROPRIATE ANALYSIS
Exercises and Examples
Appendix A: Arithmetic Review
Appendix B: Symbols and Notation
Appendix C: Basic Statistical Formulae
Appendix D: Dataset
Appendix E: Statistical Tables
Glossary
References
Answers to Selected Exercises
Index


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