Undergraduate students of research methods in psychology and the behavioral sciences

"Privitera does an EXCELLENT job of balancing clarity with depth." -- Ronald W. Stoffey, Kutztown University of Pennsylvania

"The writing style and the presentation of material are not only ENJOYABLE TO READ but are EASY TO FOLLOW and understand."

-- Joshua J. Dobias, Rutgers University

"Privitera ties research methods, SPSS, and statistics together in a SEAMLESS fashion."

-- Walter M. Yamada, Azusa Pacific University

"I like the objectives, the readability of the text, the straightforwardness of the presentations of concepts, the problems that are quite APPROPRIATE ON MANY LEVELS (computation, theory, etc.), and the emphasis on SPSS."

-- Ted R. Bitner, DePauw University

Gregory J. Privitera is 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. He is a national award-winning author and research scholar. His textbooks span across diverse topics in psychology and the behavioral sciences, including two introductory psychology texts (one upcoming), four statistics texts, two research methods texts, and multiple other texts bridging knowledge creation across health, health care, and well-being. In addition, Dr. Privitera has authored more than three dozen peer-reviewed papers aimed at advancing our understanding of health and well-being. He research has earned recognition by the American Psychological Association, and in media and press to include Oprah's Magazine, Time Magazine, and the Wall Street Journal. 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 three children: a daughter, Grace Ann, and two sons, Aiden Andrew and Luca James.

Part I: Introduction and Descriptive Statistics Chapter 1: Introduction to Statistics 1.1 The Use of Statistics in Science 1.2 Descriptive and Inferential Statistics 1.3 Research Methods and Statistics 1.4 Scales of Measurement 1.5 Types of Data 1.6 Research in Focus: Types of Data and Scales of Measurement 1.7 SPSS in Focus: Entering and Defining Variables Chapter 2: Summarizing Data: Frequency Distributions in Tables and Graphs 2.1 Why Summarize Data? 2.2 Frequency Distributions for Grouped Data 2.3 Identifying Percentile Points and Percentile Ranks 2.4 SPSS in Focus: Frequency Distributions for Quantitative Data 2.5 Frequency Distributions for Ungrouped Data 2.6 Research in Focus: Summarizing Demographic Information 2.7 SPSS in Focus: Frequency Distributions for Categorical Data 2.8 Pictorial Frequency Distributions 2.9 Graphing Distributions: Continuous Data 2.10 Graphing Distributions: Discrete and Categorical Data 2.11 Research in Focus: Frequencies and Percents 2.12 SPSS in Focus: Histograms, Bar Charts, and Pie Charts Chapter 3: Summarizing Data: Central Tendency 3.1 Introduction to Central Tendency 3.2 Measures of Central Tendency 3.3 Characteristics of the Mean 3.4 Choosing an Appropriate Measure of Central Tendency 3.5 Research in Focus: Describing Central Tendency 3.6 SPSS in Focus: Mean, Median, and Mode Chapter 4: Summarizing Data: Variability 4.1 Measuring Variability 4.2 The Range 4.3 Research in Focus: Reporting the Range 4.4 Quartiles and Intequartiles 4.5 The Variance 4.6 Explaining Variance for Populations and Samples 4.7 The Computational Formula for Variance 4.8 The Standard Deviation 4.9 What Does the Standard Deviation Tell Us? 4.10 Characteristics of the Standard Deviation 4.11 SPSS in Focus: Range, Variance, and Standard Deviation Part II: Probability and the Foundations of Inferential Statistics Chapter 5: Probability 5.1 Introduction to Probability 5.2 Calculating Probability 5.3 Probability and Relative Frequency 5.4 The Relationship Between Multiple Outcomes 5.5 Conditional Probabilities and Bayes' Theorem 5.6 SPSS in Focus: Probability Tables 5.7 Probability Distributions 5.8 The Mean of a Probability Distribution and Expected Value 5.9 Research in Focus: When Are Risks Worth Taking? 5.10 The Variance and Standard Deviation of a Probability Distribution 5.11 Expected Value and the Binomial Distribution 5.12 A Final Thought on the Likelihood of Random Behavioral Outcomes Chapter 6: Probability, Normal Distributions, and z Scores 6.1 The Normal Distribution in Behavioral Science 6.2 Characteristics of the Normal Distribution 6.3 Research in Focus: The Statistical Norm 6.4 The Standard Normal Distribution 6.5 The Unit Normal Table: A Brief Introduction 6.6 Locating Proportions 6.7 Locating Scores 6.8 SPSS in Focus: Converting Raw Scores to Standard z Scores 6.9 Going From Binomial to Normal 6.10 The Normal Approximation to the Binomial Distribution Chapter 7: Probability and Sampling Distributions 7.1 Selecting Samples From Populations 7.2 Selecting a Sample: Who's in and Who's out? 7.3 Sampling Distributions: The Mean 7.4 Sampling Distributions: The Variance 7.5 The Standard Error of the Mean 7.6 Factors that Decrease Standard Error 7.7 SPSS in Focus: Estimating the Standard Error of the Mean 7.8 APA in Focus: Reporting the Standard Error 7.9 Standard Normal Transformations With Sampling Distributions Part III: Probability and the Foundations of Inferential Statistics Chapter 8: Hypothesis Testing: Significance, Effect Size, and Power 8.1 Inferential Statistics and Hypothesis Testing 8.2 Four Steps to Hypothesis Testing 8.3 Hypothesis Testing and Sampling Distributions 8.4 Making a Decision: Types of Error 8.5 Testing for Significance: Examples Using the z Test 8.6 Research in Focus: Directional Versus Nondirectional Tests 8.7 Measuring the Size of an Effect: Cohen's d 8.8 Effect Size, Power, and Sample Size 8.9 Additional Factors That Increase Power 8.10 SPSS in Focus: A Preview for Chapters 9 to 18 8.11 APA in Focus: Reporting the Test Statistic and Effect Size Chapter 9: Testing Means: One-Sample and Two-Independent Sample t Tests 9.1 Going From z to t 9.2 The Degrees of Freedom 9.3 Reading the t Table 9.4 One Sample t Test 9.5 Effect Size for the One Sample t Test 9.6 SPSS in Focus: One Sample t Test 9.7 Two-Independent Sample t Test 9.8 Effect Size for the Two-Independent Sample t Test 9.9 SPSS in Focus: Two-Independent Sample t Test 9.10 APA in Focus: Reporting the t Statistic and Effect Size Chapter 10: Testing Means: Related Samples t Test 10.1 Related and Independent Samples 10.2 Introduction to the Related Samples t Test 10.3 Related Samples t Test: Repeated-Measures Design 10.4 SPSS in Focus: The Related Samples t Test 10.5 Related Samples t Test: Matched-Pairs Design 10.6 Measuring Effect Size for the Related Samples t Test 10.7 Advantages for Selecting Related Samples 10.8 APA in Focus: Reporting the t Statistic and Effect Size for Related Samples Chapter 11: Estimation and Confidence Intervals 11.1 Point Estimation and Interval Estimation 11.2 The Process of Estimation 11.3 Estimation for the One-Sample z Test 11.4 Estimation for the One-Sample t Test 11.5 SPSS in Focus: Confidence Intervals for the One-Sample t Test 11.6 Estimation for the Two-Independent Sample t Test 11.7 SPSS in Focus: Confidence Intervals for the Two-Independent Sample t Test 11.8 Estimation for the Related Samples t Test 11.9 SPSS in Focus: Confidence Intervals for the Related Samples t Test 11.10 Characteristics of Estimation: Precisions and Certainty 11.11: APA in Focus: Reporting Confidence Intervals Part IV: Making Inferences About the Variability of Two or More Means Chapter 12. Analysis of Variance: One-Way Between-Subjects Design 12.1 Increasing k: A Shift to Analyzing Variance 12.2 An Introduction to Analysis of Variance 12.3 Sources of Variation and the Test Statistic 12.4 Degrees of Freedom 12.5 The One-Way Between-Subjects ANOVA 12.6 What Is the Next Step? 12.7 Post Hoc Comparisons 12.8 SPSS in Focus: The One-Way Between-Subjects ANOVA 12.9 Measuring Effect Size 12.10 APA in Focus: Reporting the F Statistic, Significance, and Effect Size Chapter 13: Analysis of Variance: One-Way Within-Subjects (Repeated Measures) Design 13.1 Observing the Same Participants Across Groups 13.2 Sources of Variation and the Test Statistic 13.3 Degrees of Freedom 13.4 The One-Way Within-Subjects ANOVA 13.5 Post Hoc Comparison: Bonferroni Procedure 13.6 SPSS in Focus: The One-Way Within-Subjects ANOVA 13.7 Measuring Effect Size 13.8 The Within-Subjects Design: Consistency and Power 13.9 APA in Focus: Reporting the F Statistic, Significance, and Effect Size Chapter 14. Analysis of Variance: Two-Way Between-Subjects Factorial Design 14.1 Observing Two Factors at the Same Time 14.2 New Terminology and Notation 14.3 Designs for the Two-Way ANOVA 14.4 Describing Variability: Main Effects and Interactions 14.5 The Two-Way Between-Subjects ANOVA 14.6 Analyzing Main Effects and Interactions 14.7 Measuring Effect Size 14.8 SPSS in Focus: The Two-Way Between-Subjects ANOVA 14.9 APA in Focus: Reporting Main Effects, Interactions, and Effect Size Part V: Making Inferences About Patterns, Frequencies, and Ordinal Data Chapter 15. Correlation 15.1 The Structure of a Correlational Design 15.2 Describing a Correlation 15.3 Pearson Correlation Coefficient 15.4 SPSS in Focus: Pearson Correlation Coefficient 15.5 Assumptions of Tests for Linear Correlations 15.6 Limitations in Interpretation: Causality, Outliers, and Restrictions of Range 15.7 Alternative to Pearson r: Spearman Correlation Coefficient 15.8 SPSS in Focus: Spearman Correlation Coefficient 15.9 Alternative to Pearson r: Point-Biserial Correlation Coefficient 15.10 SPSS in Focus: Point-Biserial Correlation Coefficient 15.11 Alternative to Pearson r: Phi Correlation Coefficient 15.12 SPSS in Focus: Phi Correlation Coefficient 15.13 APA in Focus: Reporting Correlations Chapter 16: Linear Regression and Multiple Regression 16.1 From Relationships to Predictions 16.2 Fundamentals of Linear Regression 16.3 What Makes the Regression Line the Best-Fitting Line? 16.4 The Slope and y Intercept of a Straight Line 16.5 Using the Method of Least Squares to Find the Best Fit 16.6 Using Analysis of Regression to Measure Significance 16.7 SPSS in Focus: Analysis of Regression 16.8 Using the Standard Error of Estimate to Measure Accuracy 16.9 Introduction to Multiple Regression 16.10 Computing and Evaluating Significance for Multiple Regression 16.11 The Beta Coefficient for Multiple Regression 16.12 Evaluating Significance for the Relative Contribution of Each Predictor Variable 16.13 SPSS in Focus: Multiple Regression Analysis 16.14 APA in Focus: Reporting Regression Analysis Chapter 17: Nonparametric Tests: Chi-Square Tests 17.1 Tests for Nominal Data 17.2 The Chi-Square Goodness-of-Fit Test 17.3 SPSS in Focus: The Chi-Square Goodness-of-Fit Test 17.4 Interpreting the Chi-Square Goodness-of-Fit Test 17.5 Independent Observations and Expected Frequency Size 17.6 The Chi-Square Test for Independence 17.7 The Relationship Between Chi-Square and the Phi Coefficient 17.8 Measures of Effect Size 17.9 SPSS in Focus: The Chi-Square Test for Independence 17.10 APA in Focus: Reporting the Chi-Square Test Chapter 18: Nonparametric Tests: Tests for Ordinal Data 18.1 Tests for Ordinal Data 18.2 The Sign Test 18.3 SPSS in Focus: The Related Samples Sign Test 18.4. The Wilcoxon Signed-Ranks T Test 18.5 SPSS in Focus: The Wilcoxon Signed-Ranks T Test 18.6 The Mann-Whitney U Test 18.7 SPSS in Focus: The Mann-Whitney U Test 18.8 The Kruskal-Wallis H Test 18.9 SPSS in Focus: The Kruskal-Wallis H Test 18.10 The Friedman Test 18.11 SPSS in Focus: The Friedman Test 18.12 APA in Focus: Reporting Nonparametric Tests Appendix A: Basic Math Review and Summation Notation A.1 Positive and Negative Numbers A.2 Addition A.3 Subtraction A.4 Multiplication A.5 Division A.6 Fractions A.7 Decimals and Percents A.8 Exponents and Roots A.9 Order of Computation A.10 Equations: Solving for x A.11 Summation Notation Appendix B: Statistical Tables Table B.1 The Unit Normal Table Table B.2 The t Distribution Table B.3 Critical Values for F Distribution Table B.4 The Studentized Range Statistic (q) Table B.5 Critical Values for the Pearson Correlation Table B.6 Critical Values for the Spearman Correlation Table B.7 Critical Values of Chi-Square Table B.8 Distribution of Binomial Probabilities Table B.9 Wilcoxon Signed-Rank T Critical Values Table B.10 Critical Values of the Mann-Whitney U Appendix C: Chapter Solutions for Even-Numbered Problems