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Introduction | |

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The importance of Context | |

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Basic Terminology | |

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Selection among Statistical Procedures | |

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Using Computers | |

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Summary | |

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Exercises | |

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Basic Concepts | |

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Scales of Measurement | |

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Variables | |

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Random Sampling | |

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Notation | |

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Summary | |

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Exercises | |

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Displaying Data | |

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Plotting Data | |

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Stem-and-Leaf Displays | |

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Histograms | |

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Reading Graphs | |

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Alternative Methods of Plotting Data | |

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Describing Distributions | |

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Using Computer Programs to Display Data | |

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Summary | |

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Exercises | |

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Measures of Central Tendency | |

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The Mode | |

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The Median | |

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The Mean | |

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

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Obtaining Measures of Central Tendency Using SPSS | |

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A Simple Demonstration-Seeing Statistics | |

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Summary | |

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Exercises | |

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Measures of Variability | |

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Range | |

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Interquartile Range and Other Range Statistics | |

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The Average Deviation | |

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The Variance | |

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The Standard Deviation | |

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Computational Formulae for the Variance and the Standard eviation | |

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The Mean and the Variance as Estimators | |

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Boxplots: Graphical Representations of Dispersion and Extreme Scores | |

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A Return to Trimming | |

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Obtaining Measures of Dispersion Using SPSS | |

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A Final Worked Example | |

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Seeing Statistics | |

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Summary | |

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Exercises | |

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The Normal Distribution | |

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The Normal Distribution | |

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The Standard Normal Distribution | |

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Setting Probable Limits on an Observations | |

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Measures Related to z | |

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Seeing Statistics | |

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Summary | |

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Exercises | |

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Basic Concepts of Probability | |

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Probability | |

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Basic Terminology and Rules | |

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The Application of Probability to Controversial Issues | |

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Writing Up the Results | |

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Discrete versus Continuous Variables | |

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Probability Distributions for Discrete Variables | |

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Probability Distributions for Continuous Variables | |

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Summary | |

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Exercises | |

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Sampling Distributions and Hypothesis Testing | |

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Two Simple Examples Involving Course Evaluations and Rude Motorists | |

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Sampling Distributions | |

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Hypothesis Testing | |

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The Null Hypothesis | |

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Test Statistics and Their Sampling Distributions | |

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Using the Normal Distribution to Test Hypotheses | |

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Type I and Type II Errors | |

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One- and Two-Tailed Tests | |

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Seeing Statistics | |

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A Final Worked Example | |

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Back to Course Evaluations and Rude Motorists | |

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Summary | |

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Exercises | |

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Correlation | |

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Scatter Diagrams | |

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The Relationship Between Pace of Life and Heart Disease | |

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The Covariance | |

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

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Correlations with Ranked Data | |

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Factors that Affect the Correlation | |

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Beware Extreme Observations | |

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Correlation and Causation | |

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

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Testing the Significance of a Correlation Coefficient | |

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Intercorrelation Matrices | |

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Other Correlation Coefficients | |

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Using SPSS to Obtain Correlation Coefficients | |

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Seeing Statistics | |

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A Final Worked Example | |

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Summary | |

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Exercises | |

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Regression | |

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The Relationship Between Stress and Health | |

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The Basic Data | |

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The Regression Line | |

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The Accuracy of Prediction | |

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The Influence of Extreme Values | |

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Hypothesis Testing in Regression | |

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Computer Solutions using SPSS | |

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Seeing Statistics | |

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Summary | |

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Exercises | |

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Multiple Regression | |

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Overview | |

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A Different Data Set | |

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Residuals | |

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The Visual Representation of Multiple Regression | |

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Hypothesis Testing | |

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Refining the Regression Equation | |

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A Second Example: Height and Weight | |

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A Third Example: Psychological Symptoms in Cancer Patients | |

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Summary | |

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Exercises | |

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Hypothesis Testing Applied to Means: One Sample | |

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Sampling Distribution of the Mean | |

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Testing Hypotheses about Means When ?? is Known | |

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

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Factors that Affect the Magnitude of t and the Decision about H0 | |

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A Second Example: the Moon Illusion | |

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How Large is Our Effect? | |

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Confidence Limits on the Mean | |

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Using SPSS to Run One-Sample t tests | |

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A Final Worked Example | |

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Seeing Statistics | |

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Summary | |

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Exercises | |

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Hypothesis Tests Applied to Means: Two Related Samples | |

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Related Samples | |

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Student's t Applied to Difference Scores | |

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A Second Example: the Moon Illusion Again | |

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Advantages and Disadvantages of Using Related Samples | |

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How Large an Effect Have We Found? | |

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Confidence Limits on Changes | |

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Using SPSS for t Tests on Related Samples | |

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Writing Up the Results | |

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Summary | |

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Exercises | |

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Hypothesis Tests Applied to Means: Two Independent Samples | |

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Distribution of Differences Between Means | |

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Heterogeneity of Variance | |

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Nonnormality of Distributions | |

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A Second Example with Two Independent Samples | |

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Effect Sizes Again | |

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

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Writing Up the Results | |

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

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A Final Worked Example | |

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Seeing Statistics | |

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Summary | |

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Exercises | |

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Power | |

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The Basic Concept | |

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Factors that Affect the Power of a Test Effect Size | |

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Power Calculations for the One-Sample t Test Power Calculations for Differences Between Two Independent Means | |

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Power Calculations for the t Test for Related Samples | |

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Power Considerations in Terms of Sample Size | |

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You Don't Have to Do it by Hand | |

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Seeing Statistics | |

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Summary | |

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Exercises | |

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One-Way Analysis of Variance | |

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The General Approach | |

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The Logic of the Analysis of Variance | |

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Calculations for the Analysis of Variances | |

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Unequal Sample Sizes | |

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Multiple Comparison Procedures | |

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Violations of Assumptions | |

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The Size of the Effects | |

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Writing Up the Results | |

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

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A Final Worked Example | |

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Seeing Statistics | |

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Summary | |

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Exercises | |

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Factorial Analysis of Variance Factorial Designs | |

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The Extension of the Eysenck Study | |

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Interactions | |

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Simple Effects | |

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Measures of Association and Effect Size | |

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Reporting the Results | |

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Unequal Sample Sizes | |

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A Second Example: Maternal Adaptation Revisited | |

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Using SPSS for Factorial Analysis of Variance | |

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Seeing Statistics | |

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Summary | |

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Exercises | |

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Repeated-Measures Analysis of Variance | |

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

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Multiple Comparisons | |

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Effect Size | |

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Assumptions involved in Repeated-Measures Designs | |

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Advantages and Disadvantages of Repeated-Measures Designs | |

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Using SPSS to Analyze Data in a Repeated-Measures Design | |

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Writing Up the Results | |

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A Final Worked Example | |

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Summary | |

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Exercises | |

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Chi-Square | |

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

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Two Classification Variables: Analysis of Contingency Tables | |

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Possible Improvements on Standard Chi-Square | |

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Chi-Square for Larger Contingency Tables | |

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The Problem of Small Expected Frequencies | |

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

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Nonindependent Observations | |

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SPSS Analysis of Contingency Tables | |

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Measures of Effect Size | |

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A Final Worked Example | |

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Writing Up the Results | |

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Seeing Statistics | |

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Summary | |

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Exercises | |

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Nonparametric and Distribution-Free Statistical Tests | |

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The Mann-Whitney Test Wilcoxon's Matched-Pairs Signed-Ranks Test | |

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Kruskal-Wallis One-Way Analysis of Variance | |

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Friedman's Rank Test for k Correlated Samples | |

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Measures of Effect Size | |

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Writing Up the Results | |

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Summary | |

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Exercises | |

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Choosing the Appropriate Analysis | |

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Exercises and Examples | |

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Arithmetic Review | |

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Symbols and Notation | |

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Basic Statistical Formulae | |

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Dataset | |

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Statistical Tables | |

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Glossary | |

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References | |

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Answers to Selected Exercises | |

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Index | |