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.
