Introduction | |

The importance of Context | |

Basic Terminology | |

Selection among Statistical Procedures | |

Using Computers | |

Summary | |

Exercises | |

Basic Concepts | |

Scales of Measurement | |

Variables | |

Random Sampling | |

Notation | |

Summary | |

Exercises | |

Displaying Data | |

Plotting Data | |

Stem-and-Leaf Displays | |

Histograms | |

Reading Graphs | |

Alternative Methods of Plotting Data | |

Describing Distributions | |

Using Computer Programs to Display Data | |

Summary | |

Exercises | |

Measures of Central Tendency | |

The Mode | |

The Median | |

The Mean | |

Relative Advantages of the Mode, the Median, and the Mean | |

Obtaining Measures of Central Tendency Using SPSS | |

A Simple Demonstration-Seeing Statistics | |

Summary | |

Exercises | |

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 eviation | |

The Mean and the Variance as Estimators | |

Boxplots: Graphical Representations of Dispersion and Extreme Scores | |

A Return to Trimming | |

Obtaining Measures of Dispersion Using SPSS | |

A Final Worked Example | |

Seeing Statistics | |

Summary | |

Exercises | |

The Normal Distribution | |

The Normal Distribution | |

The Standard Normal Distribution | |

Setting Probable Limits on an Observations | |

Measures Related to z | |

Seeing Statistics | |

Summary | |

Exercises | |

Basic Concepts of Probability | |

Probability | |

Basic Terminology and Rules | |

The Application of Probability to Controversial Issues | |

Writing Up the Results | |

Discrete versus Continuous Variables | |

Probability Distributions for Discrete Variables | |

Probability Distributions for Continuous Variables | |

Summary | |

Exercises | |

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 | |

Correlation | |

Scatter Diagrams | |

The Relationship Between Pace of Life and Heart Disease | |

The Covariance | |

The Pearson Product-Moment Correlation Coefficient (r) | |

Correlations with Ranked Data | |

Factors that Affect the Correlation | |

Beware Extreme Observations | |

Correlation and Causation | |

If Something Looks Too Good to be True, Perhaps it Is | |

Testing the Significance of a Correlation Coefficient | |

Intercorrelation Matrices | |

Other Correlation Coefficients | |

Using SPSS to Obtain Correlation Coefficients | |

Seeing Statistics | |

A Final Worked Example | |

Summary | |

Exercises | |

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 Solutions using SPSS | |

Seeing Statistics | |

Summary | |

Exercises | |

Multiple Regression | |

Overview | |

A Different Data Set | |

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 | |

Hypothesis Testing Applied to Means: One Sample | |

Sampling Distribution of the Mean | |

Testing Hypotheses about Means When ?? is Known | |

Testing a Sample Mean When ?? is Unknown (The One-Sample t) | |

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 SPSS to Run One-Sample t tests | |

A Final Worked Example | |

Seeing Statistics | |

Summary | |

Exercises | |

Hypothesis Tests Applied to Means: Two Related Samples | |

Related Samples | |

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? | |

Confidence Limits on Changes | |

Using SPSS for t Tests on Related Samples | |

Writing Up the Results | |

Summary | |

Exercises | |

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 Sizes Again | |

Confidence Limits on ??1 ?V ??2 | |

Writing Up the Results | |

Use of Computer Programs for Analysis of Two Independent Sample Means | |

A Final Worked Example | |

Seeing Statistics | |

Summary | |

Exercises | |

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 | |

One-Way Analysis of Variance | |

The General Approach | |

The Logic of the Analysis of Variance | |

Calculations for the Analysis of Variances | |

Unequal Sample Sizes | |

Multiple Comparison Procedures | |

Violations of Assumptions | |

The Size of the Effects | |

Writing Up the Results | |

The Use of SPSS for a One-Way Analysis of Variance | |

A Final Worked Example | |

Seeing Statistics | |

Summary | |

Exercises | |

Factorial Analysis of Variance Factorial Designs | |

The Extension of the Eysenck Study | |

Interactions | |

Simple Effects | |

Measures of Association and Effect Size | |

Reporting the Results | |

Unequal Sample Sizes | |

A Second Example: Maternal Adaptation Revisited | |

Using SPSS for Factorial Analysis of Variance | |

Seeing Statistics | |

Summary | |

Exercises | |

Repeated-Measures Analysis of Variance | |

An Example: Depression as a Response to an Earthquake | |

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 | |

Writing Up the Results | |

A Final Worked Example | |

Summary | |

Exercises | |

Chi-Square | |

One Classification Variable: the Chi-Square Goodness of Fit Test | |

Two Classification Variables: Analysis of Contingency Tables | |

Possible Improvements on Standard Chi-Square | |

Chi-Square for Larger Contingency Tables | |

The Problem of Small Expected Frequencies | |

The Use of Chi-Square as a Test of Proportions | |

Nonindependent Observations | |

SPSS Analysis of Contingency Tables | |

Measures of Effect Size | |

A Final Worked Example | |

Writing Up the Results | |

Seeing Statistics | |

Summary | |

Exercises | |

Nonparametric and Distribution-Free 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 | |

Measures of Effect Size | |

Writing Up the Results | |

Summary | |

Exercises | |

Choosing the Appropriate Analysis | |

Exercises and Examples | |

Arithmetic Review | |

Symbols and Notation | |

Basic Statistical Formulae | |

Dataset | |

Statistical Tables | |

Glossary | |

References | |

Answers to Selected Exercises | |

Index | |

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