Introductory Statistics for the Behavioral Sciences

The accessible, hands-on statistics textbook that behavioral science students and instructors trust Introductory Statistics for the Behavioral Sciences is a respected, practical textbook that offers carefully crafted exercises to support the teaching and learning of statistics. This revised eighth edition presents all the topics students in the behavioral sciences need in a uniquely accessible format, making statistics feel relevant and approachable. With fictitious yet realistic examples that reappear throughout the chapter, students can follow a continuous narrative that helps them engage with and internalize the content. User-friendly integration with SPSS software enables readers to gain hands-on experience with the application of theoretical concepts. Exercises at the end of each chapter, with additional practice in the online study guide, give students the repetition they need to fully comprehend the material. After working through this textbook, students will understand, not only the what, but also the why of statistical analysis. Get plain-English explanations of statistical concepts and procedures important in behavioral sciences research Learn from relatable examples and exercises focused on psychology, sociology, and other behavioral science Work through well-crafted exercises designed to enhance your understanding of the material Get clear instructions on how to perform statistical procedures with the industry-standard SPSS software Online resources for instructors include a test bank, chapter quizzes, and PowerPoint slides. Introductory Statistics for the Behavioral Sciences also includes a student website containing additional basic math coverage, math review exercises, a study guide, a set of additional SPSS exercises, and downloadable data sets.

Table of Contents:
Preface xi Acknowledgments xv Glossary of Symbols xvii Part I Descriptive Statistics 1 Chapter 1 Introduction 3 Why Study Statistics? 4 Descriptive and Inferential Statistics 5 Populations, Samples, Parameters, and Statistics 5 Measurement Scales 6 Independent and Dependent Variables 8 Summation Notation 10 Jackson’s Study 14 Summary 15 Exercises 16 Thought Questions 19 Computer Exercises 19 Bridge to SPSS 20 Chapter 2 Frequency Distributions and Graphs 22 Purpose of Descriptive Statistics 23 Regular Frequency Distributions 23 Cumulative Frequency Distributions 25 Grouped Frequency Distributions 27 Real and Apparent Limits 28 Graphic Representations 29 Skewing of Frequency Distributions 32 Summary 34 Exercises 35 Thought Questions 36 Computer Exercises 37 Bridge to SPSS 37 Chapter 3 Measures of Central Tendency and Variability 40 Introduction 41 The Mode 42 The Median 43 The Mean 44 The Concept of Variability 47 The Range 49 The Standard Deviation and Variance 50 Summary 55 Exercises 57 Thought Questions 58 Computer Exercises 58 Bridge to SPSS 59 Chapter 4 Standardized Scores and the Normal Distribution 61 Interpreting a Raw Score Revisited 62 Rules for Changing μ and σ 63 Standard Scores (z-Scores) 64 T Scores, SAT Scores, and IQ Scores 67 The Normal Distribution 68 Table of the Standard Normal Distribution 70 Illustrative Examples 71 Summary 77 Exercises 78 Thought Questions 80 Computer Exercises 80 Bridge to SPSS 81 Part II Basic Inferential Statistics 83 Chapter 5 Introduction to Statistical Inference 85 Introduction 86 The Goals of Inferential Statistics 87 Sampling Distributions 87 The Standard Error of the Mean 93 The z-Score for Sample Means 95 Null Hypothesis Testing 96 Assumptions Required by the Statistical Test for the Mean of a Single Population 103 Why is Null Hypothesis Testing So Misunderstood? 104 Summary 105 Exercises 107 Thought Questions 108 Computer Exercises 108 Bridge to SPSS 109 Chapter 6 One-Sample t Test and Interval Estimation 110 Introduction 110 Statistical Test for the Mean of a Single Population When σ Is Not Known: The t Distributions 111 Interval Estimation 115 Computation 116 Working with Proportions 118 Summary 121 Exercises 122 Thought Questions 123 Computer Exercises 123 Bridge to SPSS 124 Chapter 7 Testing Hypotheses About the Difference Between the Means of Two Populations 126 The Standard Error of the Difference 127 Estimating the Standard Error of the Difference 130 The t Test for Two Independent Sample Means 131 Confidence Intervals for μ 1 − μ 2 135 Measuring the Size of an Effect for a Difference Between Two Independent Samples 136 Reporting the Results of a t Test with a CI and a Measure of Effect Size 137 The Assumptions Underlying the Proper Use of the t Test for Two Sample Means 138 The t Test for Matched Samples 140 Summary 146 Exercises 148 Thought Questions 150 Computer Exercises 151 Bridge to SPSS 151 Chapter 8 Nonparametric Tests for the Difference Between Two Means 155 Introduction 156 The Difference Between the Locations of Two Independent Samples: The Rank-Sum Test 159 The Difference Between the Locations of Two Matched Samples: The Wilcoxon Test 163 Summary 167 Exercises 169 Thought Questions 172 Computer Exercises 172 Bridge to SPSS 172 Chapter 9 Linear Correlation 175 Introduction 176 Describing the Linear Relationship Between Two Variables 178 Interpreting the Magnitude of a Pearson r 184 When Is It Important That Pearson’s r Be Large? 190 Testing the Significance of the Correlation Coefficient 191 The Relationship Between Two Ranked Variables: The Spearman Rank-Order Correlation Coefficient 192 Summary 195 Exercises 196 Thought Questions 199 Computer Exercises 200 Bridge to SPSS 200 Appendix: Equivalence of the Various Formulas for r 203 Chapter 10 Prediction and Linear Regression 205 Introduction 206 Using Linear Regression to Make Predictions 206 Measuring Prediction Error: The Standard Error of Estimate 212 The Connection Between Correlation and the t Test 214 Estimating the Proportion of Variance Accounted for in the Population 219 Summary 220 Exercises 222 Thought Questions 224 Computer Exercises 224 Bridge to SPSS 225 Chapter 11 Introduction to Power Analysis 228 Introduction 229 Concepts of Power Analysis 230 Power Analysis for the Mean of a Single Population 231 Power Analysis for the Proportion of a Single Population 235 Power Analysis for a Pearson r 236 Power Analysis for the Difference Between Independent Means 237 Power Analysis for the Difference Between the Means of Two Matched Populations 241 Choosing a Value for d for a Power Analysis Involving Independent Means 242 Using Power Analysis Concepts to Interpret the Results of Null Hypothesis Tests 243 The Null Hypothesis Testing Controversy Revisited 244 Summary 245 Exercises 248 Thought Questions 250 Computer Exercises 250 Bridge to SPSS 251 Chapter 12 Beyond Traditional Null Hypothesis Testing 254 More on Criticisms of NHT (and Some Rebuttals) 254 Improving NHT with Robust Statistics 257 p Hacking, HARKing, and the “File Drawer Problem” 260 The Replication Crisis in Psychological Research and Possible Solutions 262 Alternatives to NHT (The “New” Statistics) 264 Summary 266 Thought Questions 267 Appendix: A Brief Introduction to the Use of Bayesian Statistics 268 Part III Analysis of Variance Methods 271 Chapter 13 One-Way Analysis of Variance 273 Introduction 274 The General Logic of ANOVA 275 Computational Procedures 278 Testing the F Ratio for Statistical Significance 281 Calculating the One-Way ANOVA From Means and Standard Deviations 282 Comparing the One-Way ANOVA With the t Test 284 A Simplified ANOVA Formula for Equal Sample Sizes 284 Effect Size for the One-Way ANOVA 285 Some Comments on the Use of ANOVA 287 A Nonparametric Alternative to the One-Way ANOVA: The Kruskal–Wallis H Test 289 Summary 291 Exercises 294 Thought Questions 297 Computer Exercises 297 Bridge to SPSS 297 Appendix: Proof That the Total Sum of Squares Is Equal to the Sum of the Between-Group and the Within-Group Sum of Squares 301 Chapter 14 Multiple Comparisons 302 Introduction 303 Fisher’s Protected t Tests and the Least Significant Difference (LSD) 303 Tukey’s Honestly Significant Difference (HSD) 307 Other Multiple Comparison Procedures 310 Planned and Complex Comparisons 311 Nonparametric Multiple Comparisons: The Protected Rank-Sum Test 313 Summary 314 Exercises 315 Thought Questions 316 Computer Exercises 316 Bridge to SPSS 317 Chapter 15 Introduction to Factorial Design: Two-Way Analysis of Variance 319 Introduction 320 Computational Procedures 321 The Meaning of Interaction 328 Following Up on a Significant Interaction 330 Measuring Effect Size in a Factorial ANOVA 332 Summary 333 Exercises 337 Thought Questions 339 Computer Exercises 339 Bridge to SPSS 340 Chapter 16 Repeated-Measures ANOVA 344 Introduction 345 Calculating the One-Way RM ANOVA 345 Rationale for the RM ANOVA Error Term 348 Assumptions and Other Considerations Involving the RM ANOVA 349 The RM Versus RB Design: An Introduction to the Issues of Experimental Design 351 The Two-Way Mixed Design 354 Summary 359 Exercises 363 Thought Questions 365 Computer Exercises 365 Bridge to SPSS 365 Part IV Nonparametric Statistics for Categorical Data 371 Chapter 17 Probability of Discrete Events and the Binomial Distribution 373 Introduction 373 Probability 374 The Binomial Distribution 377 The Sign Test for Matched Samples 381 Summary 382 Exercises 383 Exercises 385 Computer Exercises 385 Bridge to SPSS 385 Chapter 18 Chi-Square Tests 389 Introduction 389 Chi-Square and the Goodness of Fit: One-Variable Problems 390 Chi-Square as a Test of Independence: Two-Variable Problems 394 Measures of Strength of Association in Two-Variable Tables 399 Summary 401 Exercises 402 Thought Questions 404 Computer Exercises 404 Bridge to SPSS 405 Appendix 409 Statistical Tables 411 Answer Key 426 Data from Jackson’s Experiment 434 Glossary of Terms 435 References 444 Index 000

About the Author :
R. BROOKE LEA, PhD, is DeWitt Wallace Professor of Psychology and Director of Cognitive Science at Macalester College in Saint Paul, MN, where he has taught psychological statistics for more than 25 years. He is a cognitive psychologist who studies reasoning and language processing, with a special interest in the role that poetic devices—such as rhyme, alliteration, and meter—play in the comprehension of poetry. BARRY H. COHEN, Ph.D. earned a B.S. in Physics and a Ph.D in experimental psychology. Until his retirement, he was the director of the MA program in psychology at NYU, and taught statistics and research design at the graduate level there for more than 30 years. He is now spending his active retirement by collaborating on meditation research.