Gregory J. Privitera is a three-time national-award-winning author and a professor of psychology at St. Bonaventure University where he is a recipient of its highest teaching honor, The Award for Professional Excellence in Teaching, and its highest honor for scholarship, The Award for Professional Excellence in Research and Publication. Dr. Privitera received his PhD in behavioral neuroscience in the field of psychology at the State University of New York at Buffalo and continued with his postdoctoral research at Arizona State University. His texts span diverse topics in psychology and the behavioral sciences and include an introductory psychology text, three statistics texts, two research methods texts, and multiple other texts bridging knowledge creation across health, health care, and analytics. In addition, Dr. Privitera has authored more than three dozen peer-reviewed papers aimed at advancing our understanding of health, health literacy, and informing policy in health care. His research has earned recognition by the American Psychological Association and in media to include Oprah’s Magazine, Time Magazine and the Wall Street Journal. He mentors a variety of undergraduate research projects at St. Bonaventure University, where dozens of students, many of whom have gone on to earn graduate and doctoral degrees at various institutions, have coauthored and presented research work. In addition to his teaching, research, and advisement, Dr. Privitera is a veteran of the U.S. Marine Corps, is an identical twin, and is married with two daughters, Grace Ann and Charlotte Jane, and two sons, Aiden Andrew and Luca James.

The clarity with which the information is covered and the variety of ways that the information is presented are extremely impressive. For every concept, there is the text, figures, examples, and applications.

• PART I. INTRODUCTION AND DESCRIPTIVE STATISTICS1. Introduction to Statistics1.1 The Use of Statistics in Science1.2 Descriptive and Inferential Statistics1.3 Research Methods and Statistics1.4 Scales of Measurement1.5 Types of Variables for Which Data Are Measured1.6 SPSS in Focus: Entering and Defining Variables2. Summarizing Data: Frequency Distributions in Tables and Graphs2.1 Why Summarize Data?2.2 Simple Frequency Distributions for Grouped Data2.3 Other Ways of Summarizing Grouped Data in Frequency Distributions2.4 Identifying Percentile Points and Percentile Ranks2.5 SPSS in Focus: Frequency Distributions for Quantitative Data2.6 Frequency Distributions for Ungrouped Data2.7 SPSS in Focus: Frequency Distributions for Categorical Data2.8 Pictorial Frequency Distributions2.9 Graphing Distributions: Continuous Data2.10 Stem-and-Leaf Displays2.11 Graphing Distributions: Discrete and Categorical Data2.12 SPSS in Focus: Histograms, Bar Charts, Pie Charts, and Stem-and-Leaf Displays3. Summarizing Data: Central Tendency3.1 Introduction to Central Tendency3.2 Measures of Central Tendency: The Mean3.3 Measures of Central Tendency: The Weighted Mean3.4 Measures of Central Tendency: The Median and the Mode3.5 Characteristics of the Mean3.6 Choosing an Appropriate Measure of Central Tendency3.7 SPSS in Focus: Mean, Median, and Mode4. Summarizing Data: Variability4.1 Introduction to Variability4.2 The Range4.3 Quartiles and Interquartiles4.4 The Variance4.5 The Computational Formula for Variance4.6 Explaining Variance for Populations and Samples4.7 The Standard Deviation4.8 The Informativeness of Standard Deviation4.9 SPSS in Focus: Range, Quartiles, Variance, and Standard DeviationPART II. PROBABILITY AND THE FOUNDATIONS OF INFERENTIAL STATISTICS5. Probability5.1 Introduction to Probability5.2 Probability and Relative Frequency5.3 The Relationship Between Multiple Outcomes5.4 Conditional Probabilities and Bayes’s Theorem5.5 SPSS in Focus: Probability Tables5.6 Probability Distributions5.7 The Mean of a Probability Distribution and Expected Value5.8 The Variance and Standard Deviation of a Probability Distribution5.9 Expected Value and the Binomial Distribution6. Probability, Normal Distributions, and z Scores6.1 Characteristics of the Normal Distribution6.2 The Standard Normal Distribution and the z Transformation6.3 The Unit Normal Table: A Brief Introduction6.4 Locating Proportions6.5 Locating Scores6.6 SPSS in Focus: Converting Raw Scores to Standard z Scores6.7 The Normal Approximation to the Binomial Distribution7. Probability and Sampling Distributions7.1 Selecting Samples From Populations7.2 Selecting a Sample: Who’s In and Who’s Out?7.3 Sampling Distributions: The Mean7.4 Sampling Distributions: The Variance7.5 The Standard Error of the Mean7.6 Factors That Decrease Standard Error7.7 SPSS in Focus: Estimating the Standard Error of the Mean7.8 Standard Normal Transformations With Sampling Distributions8. Hypothesis Testing: Significance, Effect Size, Estimation, and Power8.1 The Informativeness of Evaluating Effects in Science8.2 Inferential Statistics and Applying the Steps to Hypothesis Testing8.3 Making a Decision: Types of Error8.4 Testing for Significance: Examples Using the z Test8.5 Measuring the Size of an Effect: Cohen’s d8.6 Confidence Intervals for the One-Sample z Test8.7 Factors That Influence Power8.8 Assumptions of Parametric Testing: Normality and Nonparametric Alternatives8.9 SPSS in Focus: A Preview for Analyzing Inferential StatisticsPART III. MAKING INFERENCES ABOUT ONE OR TWO MEANS9. Testing Means: One-Sample t Test With Confidence Intervals9.1 Going From z to t9.2 The Degrees of Freedom9.3 Reading the t Table9.4 Computing the One-Sample t Test9.5 Effect Size for the One-Sample t Test9.6 Confidence Intervals for the One-Sample t Test9.7 Inferring Significance and Effect Size From a Confidence Interval9.8 SPSS in Focus: One-Sample t Test and Confidence Intervals10. Testing Means: Two-Independent-Sample t Tests With Confidence Intervals10.1 Introduction to the Between-Subjects Design10.2 Selecting Two Independent Samples10.3 Variability and Comparing Differences Between Two Groups10.4 Computing the Two-Independent-Sample t Test10.5 Effect Size for the Two-Independent-Sample t Test10.6 Confidence Intervals for the Two-Independent-Sample t Test10.7 Inferring Significance and Effect Size From a Confidence Interval10.8 SPSS in Focus: Two-Independent-Sample t Test and Confidence Intervals11. Testing Means: The Related-Samples t Test With Confidence Intervals11.1 Selecting Related Samples11.2 Advantages of Selecting Related Samples11.3 Introduction to the Related-Samples t Test11.4 Computing the Related-Samples t Test11.5 Measuring Effect Size for the Related-Samples t Test11.6 Confidence Intervals for the Related-Samples t Test11.7 Inferring Significance and Effect Size From a Confidence Interval11.8 SPSS in Focus: Related-Samples t Test and Confidence IntervalsPART IV. MAKING INFERENCES ABOUT THE VARIABILITY OF TWO OR MORE MEANS12. Analysis of Variance: One-Way Between-Subjects Design12.1 Introduction to Analysis of Variance12.2 Selecting Two or More Independent Samples12.3 The Test Statistic and Sources of Variation12.4 Degrees of Freedom12.5 The One-Way Between-Subjects ANOVA12.6 Post Hoc Tests12.7 Measuring Effect Size12.8 SPSS in Focus: The One-Way Between-Subjects ANOVA13. Analysis of Variance: One-Way Within-Subjects (Repeated-Measures) Design13.1 Analysis of Variance for a Within-Subjects Factor13.2 The Test Statistic and Sources of Variation13.3 Degrees of Freedom13.4 The One-Way Within-Subjects ANOVA13.5 Post Hoc Comparisons: Bonferroni Procedure13.6 Measuring Effect Size13.7 SPSS in Focus: The One-Way Within-Subjects ANOVA13.8 The Within-Subjects Design: Consistency and Power14. Analysis of Variance: Two-Way Between-Subjects Factorial Design14.1 Analysis of Variance With Two Factors14.2 Designs for the Two-Way ANOVA14.3 Describing Variability: Main Effects and Interactions14.4 The Two-Way Between-Subjects ANOVA14.5 Analyzing Main Effects and Interactions14.6 Measuring Effect Size14.7 SPSS in Focus: The Two-Way Between-Subjects ANOVAPART V. MAKING INFERENCES ABOUT PATTERNS, FREQUENCIES, AND ORDINAL DATA15. Correlation15.1 The Structure of a Correlational Design15.2 The Pearson Test Statistic and Sources of Variability15.3 Assumptions for the Pearson Correlation Coefficient15.4 Pearson Correlation Coefficient15.5 Effect Size: The Coefficient of Determination15.6 SPSS in Focus: Pearson Correlation Coefficient15.7 Limitations in Interpretation: Causality, Outliers, and Restriction of Range15.8 Alternative to Pearson’s r: Spearman Correlation Coefficient15.9 SPSS in Focus: Spearman Correlation Coefficient15.10 Alternative to Pearson’s r: Point-Biserial Correlation Coefficient15.11 SPSS in Focus: Point-Biserial Correlation Coefficient15.12 Alternative to Pearson’s r: Phi Correlation Coefficient15.13 SPSS in Focus: Phi Correlation Coefficient16. Linear Regression and Multiple Regression16.1 The Structure of Linear Regression16.2 What Makes the Regression Line the Best-Fitting Line?16.3 The Slope and y-Intercept of a Straight Line16.4 Using the Method of Least Squares to Find the Best Fit16.5 Evaluating Significance Using Analysis of Regression16.6 Using the Standard Error of Estimate to Measure Accuracy16.7 SPSS in Focus: Analysis of Regression16.8 Introduction to Multiple Regression16.9 Evaluating Significance Using Multiple Regression16.10 The ß Coefficient for Multiple Regression16.11 Evaluating Significance for the Relative Contribution of Each Predictor Variable16.12 SPSS in Focus: Multiple Regression Analysis17. Nonparametric Tests: Chi-Square Tests17.1 Introduction to the Chi-Square Test17.2 Comparing Observed and Expected Frequencies for the Goodness-of-Fit Test17.3 The Test Statistic and Degrees of Freedom for the Goodness-of-Fit Test17.4 Computing the Chi-Square Goodness-of-Fit Test17.5 Interpreting the Chi-Square Goodness-of-Fit Test17.6 SPSS in Focus: The Chi-Square Goodness-of-Fit Test17.7 Introduction to the Chi-Square Test for Independence17.8 Computing the Chi-Square Test for Independence17.9 The Relationship Between Chi-Square and the Phi Coefficient17.10 Measures of Effect Size17.11 SPSS in Focus: The Chi-Square Test for Independence18. Nonparametric Tests: Tests for Ordinal Data18.1 Tests for Ordinal Data18.2 The Sign Test18.3 SPSS in Focus: Computing the Related-Samples Sign Test18.4 The Wilcoxon Signed-Ranks T Test18.5 SPSS in Focus: Computing the Wilcoxon Signed-Ranks T Test18.6 The Mann-Whitney U Test18.7 SPSS in Focus: Computing the Mann-Whitney U Test18.8 The Kruskal-Wallis H Test18.9 SPSS in Focus: Computing the Kruskal-Wallis H Test18.10 The Friedman Test18.11 SPSS in Focus: Computing the Friedman TestAppendix B. Basic Math Review and Summation NotationAppendix A. Overview of Core Statistical Concepts in the Behavioral SciencesAppendix C. SPSS General Instructions Guide With Steps for Evaluating Assumptions for Inferential StatisticsAppendix D. Statistical TablesAppendix E. Chapter Solutions for Even-Numbered ProblemsGlossary ReferencesIndex

Gregory J. Privitera is a three-time national-award-winning author and a professor of psychology at St. Bonaventure University where he is a recipient of its highest teaching honor, The Award for Professional Excellence in Teaching, and its highest honor for scholarship, The Award for Professional Excellence in Research and Publication. Dr. Privitera received his PhD in behavioral neuroscience in the field of psychology at the State University of New York at Buffalo and continued with his postdoctoral research at Arizona State University. His texts span diverse topics in psychology and the behavioral sciences and include an introductory psychology text, three statistics texts, two research methods texts, and multiple other texts bridging knowledge creation across health, health care, and analytics. In addition, Dr. Privitera has authored more than three dozen peer-reviewed papers aimed at advancing our understanding of health, health literacy, and informing policy in health care. His research has earned recognition by the American Psychological Association and in media to include Oprah’s Magazine, Time Magazine and the Wall Street Journal. He mentors a variety of undergraduate research projects at St. Bonaventure University, where dozens of students, many of whom have gone on to earn graduate and doctoral degrees at various institutions, have coauthored and presented research work. In addition to his teaching, research, and advisement, Dr. Privitera is a veteran of the U.S. Marine Corps, is an identical twin, and is married with two daughters, Grace Ann and Charlotte Jane, and two sons, Aiden Andrew and Luca James.

The clarity with which the information is covered and the variety of ways that the information is presented are extremely impressive. For every concept, there is the text, figures, examples, and applications.

• PART I. INTRODUCTION AND DESCRIPTIVE STATISTICS1. Introduction to Statistics1.1 The Use of Statistics in Science1.2 Descriptive and Inferential Statistics1.3 Research Methods and Statistics1.4 Scales of Measurement1.5 Types of Variables for Which Data Are Measured1.6 SPSS in Focus: Entering and Defining Variables2. Summarizing Data: Frequency Distributions in Tables and Graphs2.1 Why Summarize Data?2.2 Simple Frequency Distributions for Grouped Data2.3 Other Ways of Summarizing Grouped Data in Frequency Distributions2.4 Identifying Percentile Points and Percentile Ranks2.5 SPSS in Focus: Frequency Distributions for Quantitative Data2.6 Frequency Distributions for Ungrouped Data2.7 SPSS in Focus: Frequency Distributions for Categorical Data2.8 Pictorial Frequency Distributions2.9 Graphing Distributions: Continuous Data2.10 Stem-and-Leaf Displays2.11 Graphing Distributions: Discrete and Categorical Data2.12 SPSS in Focus: Histograms, Bar Charts, Pie Charts, and Stem-and-Leaf Displays3. Summarizing Data: Central Tendency3.1 Introduction to Central Tendency3.2 Measures of Central Tendency: The Mean3.3 Measures of Central Tendency: The Weighted Mean3.4 Measures of Central Tendency: The Median and the Mode3.5 Characteristics of the Mean3.6 Choosing an Appropriate Measure of Central Tendency3.7 SPSS in Focus: Mean, Median, and Mode4. Summarizing Data: Variability4.1 Introduction to Variability4.2 The Range4.3 Quartiles and Interquartiles4.4 The Variance4.5 The Computational Formula for Variance4.6 Explaining Variance for Populations and Samples4.7 The Standard Deviation4.8 The Informativeness of Standard Deviation4.9 SPSS in Focus: Range, Quartiles, Variance, and Standard DeviationPART II. PROBABILITY AND THE FOUNDATIONS OF INFERENTIAL STATISTICS5. Probability5.1 Introduction to Probability5.2 Probability and Relative Frequency5.3 The Relationship Between Multiple Outcomes5.4 Conditional Probabilities and Bayes’s Theorem5.5 SPSS in Focus: Probability Tables5.6 Probability Distributions5.7 The Mean of a Probability Distribution and Expected Value5.8 The Variance and Standard Deviation of a Probability Distribution5.9 Expected Value and the Binomial Distribution6. Probability, Normal Distributions, and z Scores6.1 Characteristics of the Normal Distribution6.2 The Standard Normal Distribution and the z Transformation6.3 The Unit Normal Table: A Brief Introduction6.4 Locating Proportions6.5 Locating Scores6.6 SPSS in Focus: Converting Raw Scores to Standard z Scores6.7 The Normal Approximation to the Binomial Distribution7. Probability and Sampling Distributions7.1 Selecting Samples From Populations7.2 Selecting a Sample: Who’s In and Who’s Out?7.3 Sampling Distributions: The Mean7.4 Sampling Distributions: The Variance7.5 The Standard Error of the Mean7.6 Factors That Decrease Standard Error7.7 SPSS in Focus: Estimating the Standard Error of the Mean7.8 Standard Normal Transformations With Sampling Distributions8. Hypothesis Testing: Significance, Effect Size, Estimation, and Power8.1 The Informativeness of Evaluating Effects in Science8.2 Inferential Statistics and Applying the Steps to Hypothesis Testing8.3 Making a Decision: Types of Error8.4 Testing for Significance: Examples Using the z Test8.5 Measuring the Size of an Effect: Cohen’s d8.6 Confidence Intervals for the One-Sample z Test8.7 Factors That Influence Power8.8 Assumptions of Parametric Testing: Normality and Nonparametric Alternatives8.9 SPSS in Focus: A Preview for Analyzing Inferential StatisticsPART III. MAKING INFERENCES ABOUT ONE OR TWO MEANS9. Testing Means: One-Sample t Test With Confidence Intervals9.1 Going From z to t9.2 The Degrees of Freedom9.3 Reading the t Table9.4 Computing the One-Sample t Test9.5 Effect Size for the One-Sample t Test9.6 Confidence Intervals for the One-Sample t Test9.7 Inferring Significance and Effect Size From a Confidence Interval9.8 SPSS in Focus: One-Sample t Test and Confidence Intervals10. Testing Means: Two-Independent-Sample t Tests With Confidence Intervals10.1 Introduction to the Between-Subjects Design10.2 Selecting Two Independent Samples10.3 Variability and Comparing Differences Between Two Groups10.4 Computing the Two-Independent-Sample t Test10.5 Effect Size for the Two-Independent-Sample t Test10.6 Confidence Intervals for the Two-Independent-Sample t Test10.7 Inferring Significance and Effect Size From a Confidence Interval10.8 SPSS in Focus: Two-Independent-Sample t Test and Confidence Intervals11. Testing Means: The Related-Samples t Test With Confidence Intervals11.1 Selecting Related Samples11.2 Advantages of Selecting Related Samples11.3 Introduction to the Related-Samples t Test11.4 Computing the Related-Samples t Test11.5 Measuring Effect Size for the Related-Samples t Test11.6 Confidence Intervals for the Related-Samples t Test11.7 Inferring Significance and Effect Size From a Confidence Interval11.8 SPSS in Focus: Related-Samples t Test and Confidence IntervalsPART IV. MAKING INFERENCES ABOUT THE VARIABILITY OF TWO OR MORE MEANS12. Analysis of Variance: One-Way Between-Subjects Design12.1 Introduction to Analysis of Variance12.2 Selecting Two or More Independent Samples12.3 The Test Statistic and Sources of Variation12.4 Degrees of Freedom12.5 The One-Way Between-Subjects ANOVA12.6 Post Hoc Tests12.7 Measuring Effect Size12.8 SPSS in Focus: The One-Way Between-Subjects ANOVA13. Analysis of Variance: One-Way Within-Subjects (Repeated-Measures) Design13.1 Analysis of Variance for a Within-Subjects Factor13.2 The Test Statistic and Sources of Variation13.3 Degrees of Freedom13.4 The One-Way Within-Subjects ANOVA13.5 Post Hoc Comparisons: Bonferroni Procedure13.6 Measuring Effect Size13.7 SPSS in Focus: The One-Way Within-Subjects ANOVA13.8 The Within-Subjects Design: Consistency and Power14. Analysis of Variance: Two-Way Between-Subjects Factorial Design14.1 Analysis of Variance With Two Factors14.2 Designs for the Two-Way ANOVA14.3 Describing Variability: Main Effects and Interactions14.4 The Two-Way Between-Subjects ANOVA14.5 Analyzing Main Effects and Interactions14.6 Measuring Effect Size14.7 SPSS in Focus: The Two-Way Between-Subjects ANOVAPART V. MAKING INFERENCES ABOUT PATTERNS, FREQUENCIES, AND ORDINAL DATA15. Correlation15.1 The Structure of a Correlational Design15.2 The Pearson Test Statistic and Sources of Variability15.3 Assumptions for the Pearson Correlation Coefficient15.4 Pearson Correlation Coefficient15.5 Effect Size: The Coefficient of Determination15.6 SPSS in Focus: Pearson Correlation Coefficient15.7 Limitations in Interpretation: Causality, Outliers, and Restriction of Range15.8 Alternative to Pearson’s r: Spearman Correlation Coefficient15.9 SPSS in Focus: Spearman Correlation Coefficient15.10 Alternative to Pearson’s r: Point-Biserial Correlation Coefficient15.11 SPSS in Focus: Point-Biserial Correlation Coefficient15.12 Alternative to Pearson’s r: Phi Correlation Coefficient15.13 SPSS in Focus: Phi Correlation Coefficient16. Linear Regression and Multiple Regression16.1 The Structure of Linear Regression16.2 What Makes the Regression Line the Best-Fitting Line?16.3 The Slope and y-Intercept of a Straight Line16.4 Using the Method of Least Squares to Find the Best Fit16.5 Evaluating Significance Using Analysis of Regression16.6 Using the Standard Error of Estimate to Measure Accuracy16.7 SPSS in Focus: Analysis of Regression16.8 Introduction to Multiple Regression16.9 Evaluating Significance Using Multiple Regression16.10 The ß Coefficient for Multiple Regression16.11 Evaluating Significance for the Relative Contribution of Each Predictor Variable16.12 SPSS in Focus: Multiple Regression Analysis17. Nonparametric Tests: Chi-Square Tests17.1 Introduction to the Chi-Square Test17.2 Comparing Observed and Expected Frequencies for the Goodness-of-Fit Test17.3 The Test Statistic and Degrees of Freedom for the Goodness-of-Fit Test17.4 Computing the Chi-Square Goodness-of-Fit Test17.5 Interpreting the Chi-Square Goodness-of-Fit Test17.6 SPSS in Focus: The Chi-Square Goodness-of-Fit Test17.7 Introduction to the Chi-Square Test for Independence17.8 Computing the Chi-Square Test for Independence17.9 The Relationship Between Chi-Square and the Phi Coefficient17.10 Measures of Effect Size17.11 SPSS in Focus: The Chi-Square Test for Independence18. Nonparametric Tests: Tests for Ordinal Data18.1 Tests for Ordinal Data18.2 The Sign Test18.3 SPSS in Focus: Computing the Related-Samples Sign Test18.4 The Wilcoxon Signed-Ranks T Test18.5 SPSS in Focus: Computing the Wilcoxon Signed-Ranks T Test18.6 The Mann-Whitney U Test18.7 SPSS in Focus: Computing the Mann-Whitney U Test18.8 The Kruskal-Wallis H Test18.9 SPSS in Focus: Computing the Kruskal-Wallis H Test18.10 The Friedman Test18.11 SPSS in Focus: Computing the Friedman TestAppendix B. Basic Math Review and Summation NotationAppendix A. Overview of Core Statistical Concepts in the Behavioral SciencesAppendix C. SPSS General Instructions Guide With Steps for Evaluating Assumptions for Inferential StatisticsAppendix D. Statistical TablesAppendix E. Chapter Solutions for Even-Numbered ProblemsGlossary ReferencesIndex

Gregory J. Privitera is a three-time national-award-winning author and a professor of psychology at St. Bonaventure University where he is a recipient of its highest teaching honor, The Award for Professional Excellence in Teaching, and its highest honor for scholarship, The Award for Professional Excellence in Research and Publication. Dr. Privitera received his PhD in behavioral neuroscience in the field of psychology at the State University of New York at Buffalo and continued with his postdoctoral research at Arizona State University. His texts span diverse topics in psychology and the behavioral sciences and include an introductory psychology text, three statistics texts, two research methods texts, and multiple other texts bridging knowledge creation across health, health care, and analytics. In addition, Dr. Privitera has authored more than three dozen peer-reviewed papers aimed at advancing our understanding of health, health literacy, and informing policy in health care. His research has earned recognition by the American Psychological Association and in media to include Oprah’s Magazine, Time Magazine and the Wall Street Journal. He mentors a variety of undergraduate research projects at St. Bonaventure University, where dozens of students, many of whom have gone on to earn graduate and doctoral degrees at various institutions, have coauthored and presented research work. In addition to his teaching, research, and advisement, Dr. Privitera is a veteran of the U.S. Marine Corps, is an identical twin, and is married with two daughters, Grace Ann and Charlotte Jane, and two sons, Aiden Andrew and Luca James.

The clarity with which the information is covered and the variety of ways that the information is presented are extremely impressive. For every concept, there is the text, figures, examples, and applications.

• PART I. INTRODUCTION AND DESCRIPTIVE STATISTICS1. Introduction to Statistics1.1 The Use of Statistics in Science1.2 Descriptive and Inferential Statistics1.3 Research Methods and Statistics1.4 Scales of Measurement1.5 Types of Variables for Which Data Are Measured1.6 SPSS in Focus: Entering and Defining Variables2. Summarizing Data: Frequency Distributions in Tables and Graphs2.1 Why Summarize Data?2.2 Simple Frequency Distributions for Grouped Data2.3 Other Ways of Summarizing Grouped Data in Frequency Distributions2.4 Identifying Percentile Points and Percentile Ranks2.5 SPSS in Focus: Frequency Distributions for Quantitative Data2.6 Frequency Distributions for Ungrouped Data2.7 SPSS in Focus: Frequency Distributions for Categorical Data2.8 Pictorial Frequency Distributions2.9 Graphing Distributions: Continuous Data2.10 Stem-and-Leaf Displays2.11 Graphing Distributions: Discrete and Categorical Data2.12 SPSS in Focus: Histograms, Bar Charts, Pie Charts, and Stem-and-Leaf Displays3. Summarizing Data: Central Tendency3.1 Introduction to Central Tendency3.2 Measures of Central Tendency: The Mean3.3 Measures of Central Tendency: The Weighted Mean3.4 Measures of Central Tendency: The Median and the Mode3.5 Characteristics of the Mean3.6 Choosing an Appropriate Measure of Central Tendency3.7 SPSS in Focus: Mean, Median, and Mode4. Summarizing Data: Variability4.1 Introduction to Variability4.2 The Range4.3 Quartiles and Interquartiles4.4 The Variance4.5 The Computational Formula for Variance4.6 Explaining Variance for Populations and Samples4.7 The Standard Deviation4.8 The Informativeness of Standard Deviation4.9 SPSS in Focus: Range, Quartiles, Variance, and Standard DeviationPART II. PROBABILITY AND THE FOUNDATIONS OF INFERENTIAL STATISTICS5. Probability5.1 Introduction to Probability5.2 Probability and Relative Frequency5.3 The Relationship Between Multiple Outcomes5.4 Conditional Probabilities and Bayes’s Theorem5.5 SPSS in Focus: Probability Tables5.6 Probability Distributions5.7 The Mean of a Probability Distribution and Expected Value5.8 The Variance and Standard Deviation of a Probability Distribution5.9 Expected Value and the Binomial Distribution6. Probability, Normal Distributions, and z Scores6.1 Characteristics of the Normal Distribution6.2 The Standard Normal Distribution and the z Transformation6.3 The Unit Normal Table: A Brief Introduction6.4 Locating Proportions6.5 Locating Scores6.6 SPSS in Focus: Converting Raw Scores to Standard z Scores6.7 The Normal Approximation to the Binomial Distribution7. Probability and Sampling Distributions7.1 Selecting Samples From Populations7.2 Selecting a Sample: Who’s In and Who’s Out?7.3 Sampling Distributions: The Mean7.4 Sampling Distributions: The Variance7.5 The Standard Error of the Mean7.6 Factors That Decrease Standard Error7.7 SPSS in Focus: Estimating the Standard Error of the Mean7.8 Standard Normal Transformations With Sampling Distributions8. Hypothesis Testing: Significance, Effect Size, Estimation, and Power8.1 The Informativeness of Evaluating Effects in Science8.2 Inferential Statistics and Applying the Steps to Hypothesis Testing8.3 Making a Decision: Types of Error8.4 Testing for Significance: Examples Using the z Test8.5 Measuring the Size of an Effect: Cohen’s d8.6 Confidence Intervals for the One-Sample z Test8.7 Factors That Influence Power8.8 Assumptions of Parametric Testing: Normality and Nonparametric Alternatives8.9 SPSS in Focus: A Preview for Analyzing Inferential StatisticsPART III. MAKING INFERENCES ABOUT ONE OR TWO MEANS9. Testing Means: One-Sample t Test With Confidence Intervals9.1 Going From z to t9.2 The Degrees of Freedom9.3 Reading the t Table9.4 Computing the One-Sample t Test9.5 Effect Size for the One-Sample t Test9.6 Confidence Intervals for the One-Sample t Test9.7 Inferring Significance and Effect Size From a Confidence Interval9.8 SPSS in Focus: One-Sample t Test and Confidence Intervals10. Testing Means: Two-Independent-Sample t Tests With Confidence Intervals10.1 Introduction to the Between-Subjects Design10.2 Selecting Two Independent Samples10.3 Variability and Comparing Differences Between Two Groups10.4 Computing the Two-Independent-Sample t Test10.5 Effect Size for the Two-Independent-Sample t Test10.6 Confidence Intervals for the Two-Independent-Sample t Test10.7 Inferring Significance and Effect Size From a Confidence Interval10.8 SPSS in Focus: Two-Independent-Sample t Test and Confidence Intervals11. Testing Means: The Related-Samples t Test With Confidence Intervals11.1 Selecting Related Samples11.2 Advantages of Selecting Related Samples11.3 Introduction to the Related-Samples t Test11.4 Computing the Related-Samples t Test11.5 Measuring Effect Size for the Related-Samples t Test11.6 Confidence Intervals for the Related-Samples t Test11.7 Inferring Significance and Effect Size From a Confidence Interval11.8 SPSS in Focus: Related-Samples t Test and Confidence IntervalsPART IV. MAKING INFERENCES ABOUT THE VARIABILITY OF TWO OR MORE MEANS12. Analysis of Variance: One-Way Between-Subjects Design12.1 Introduction to Analysis of Variance12.2 Selecting Two or More Independent Samples12.3 The Test Statistic and Sources of Variation12.4 Degrees of Freedom12.5 The One-Way Between-Subjects ANOVA12.6 Post Hoc Tests12.7 Measuring Effect Size12.8 SPSS in Focus: The One-Way Between-Subjects ANOVA13. Analysis of Variance: One-Way Within-Subjects (Repeated-Measures) Design13.1 Analysis of Variance for a Within-Subjects Factor13.2 The Test Statistic and Sources of Variation13.3 Degrees of Freedom13.4 The One-Way Within-Subjects ANOVA13.5 Post Hoc Comparisons: Bonferroni Procedure13.6 Measuring Effect Size13.7 SPSS in Focus: The One-Way Within-Subjects ANOVA13.8 The Within-Subjects Design: Consistency and Power14. Analysis of Variance: Two-Way Between-Subjects Factorial Design14.1 Analysis of Variance With Two Factors14.2 Designs for the Two-Way ANOVA14.3 Describing Variability: Main Effects and Interactions14.4 The Two-Way Between-Subjects ANOVA14.5 Analyzing Main Effects and Interactions14.6 Measuring Effect Size14.7 SPSS in Focus: The Two-Way Between-Subjects ANOVAPART V. MAKING INFERENCES ABOUT PATTERNS, FREQUENCIES, AND ORDINAL DATA15. Correlation15.1 The Structure of a Correlational Design15.2 The Pearson Test Statistic and Sources of Variability15.3 Assumptions for the Pearson Correlation Coefficient15.4 Pearson Correlation Coefficient15.5 Effect Size: The Coefficient of Determination15.6 SPSS in Focus: Pearson Correlation Coefficient15.7 Limitations in Interpretation: Causality, Outliers, and Restriction of Range15.8 Alternative to Pearson’s r: Spearman Correlation Coefficient15.9 SPSS in Focus: Spearman Correlation Coefficient15.10 Alternative to Pearson’s r: Point-Biserial Correlation Coefficient15.11 SPSS in Focus: Point-Biserial Correlation Coefficient15.12 Alternative to Pearson’s r: Phi Correlation Coefficient15.13 SPSS in Focus: Phi Correlation Coefficient16. Linear Regression and Multiple Regression16.1 The Structure of Linear Regression16.2 What Makes the Regression Line the Best-Fitting Line?16.3 The Slope and y-Intercept of a Straight Line16.4 Using the Method of Least Squares to Find the Best Fit16.5 Evaluating Significance Using Analysis of Regression16.6 Using the Standard Error of Estimate to Measure Accuracy16.7 SPSS in Focus: Analysis of Regression16.8 Introduction to Multiple Regression16.9 Evaluating Significance Using Multiple Regression16.10 The ß Coefficient for Multiple Regression16.11 Evaluating Significance for the Relative Contribution of Each Predictor Variable16.12 SPSS in Focus: Multiple Regression Analysis17. Nonparametric Tests: Chi-Square Tests17.1 Introduction to the Chi-Square Test17.2 Comparing Observed and Expected Frequencies for the Goodness-of-Fit Test17.3 The Test Statistic and Degrees of Freedom for the Goodness-of-Fit Test17.4 Computing the Chi-Square Goodness-of-Fit Test17.5 Interpreting the Chi-Square Goodness-of-Fit Test17.6 SPSS in Focus: The Chi-Square Goodness-of-Fit Test17.7 Introduction to the Chi-Square Test for Independence17.8 Computing the Chi-Square Test for Independence17.9 The Relationship Between Chi-Square and the Phi Coefficient17.10 Measures of Effect Size17.11 SPSS in Focus: The Chi-Square Test for Independence18. Nonparametric Tests: Tests for Ordinal Data18.1 Tests for Ordinal Data18.2 The Sign Test18.3 SPSS in Focus: Computing the Related-Samples Sign Test18.4 The Wilcoxon Signed-Ranks T Test18.5 SPSS in Focus: Computing the Wilcoxon Signed-Ranks T Test18.6 The Mann-Whitney U Test18.7 SPSS in Focus: Computing the Mann-Whitney U Test18.8 The Kruskal-Wallis H Test18.9 SPSS in Focus: Computing the Kruskal-Wallis H Test18.10 The Friedman Test18.11 SPSS in Focus: Computing the Friedman TestAppendix B. Basic Math Review and Summation NotationAppendix A. Overview of Core Statistical Concepts in the Behavioral SciencesAppendix C. SPSS General Instructions Guide With Steps for Evaluating Assumptions for Inferential StatisticsAppendix D. Statistical TablesAppendix E. Chapter Solutions for Even-Numbered ProblemsGlossary ReferencesIndex