Introductory Statistics for the Behavioral Sciences

A comprehensive and user-friendly introduction to statistics for behavioral science students?revised and updated Refined over seven editions by master teachers, this book gives instructors and students alike clear examples and carefully crafted exercises to support the teaching and learning of statistics for both manipulating and consuming data. One of the most popular and respected statistics texts in the behavioral sciences, the Seventh Edition of Introductory Statistics for the Behavioral Sciences has been fully revised. The new edition presents all the topics students in the behavioral sciences need in a uniquely accessible and easy-to-understand format, aiding in the comprehension and implementation of the statistical analyses most commonly used in the behavioral sciences. The Seventh Edition features: A continuous narrative that clearly explains statistics while tracking a common data set throughout, making the concepts unintimidating and memorable, and providing a framework that connects all of the topics and allows for easy comparison of different statistical analyses Coverage of important aspects of research design throughout the text, such as the "correlation is not causality" principle Updated and annotated SPSS output at the end of each chapter with step-by-step instructions Updated examples and exercises An expanded website, at www.wiley.com/go/welkowitz, with test bank, chapter quizzes, and PowerPoint slides for instructors, as well as a second website for students with additional basic math coverage, math review exercises, a study guide, a set of additional SPSS exercises, and more downloadable data sets

Table of Contents:
Preface xv Acknowledgments xix Glossary of Symbols xxi Part I Descriptive Statistics 1 Chapter 1 Introduction 3 Why Study Statistics? 4 Descriptive and Inferential Statistics 5 Populations, Samples, Parameters, and Statistics 6 Measurement Scales 7 Independent and Dependent Variables 10 Summation Notation 12 Ihno’s Study 16 Summary 18 Exercises 19 Thought Questions 23 Computer Exercises 23 Bridge to SPSS 24 Chapter 2 Frequency Distributions and Graphs 26 The Purpose of Descriptive Statistics 27 Regular Frequency Distributions 28 Cumulative Frequency Distributions 30 Grouped Frequency Distributions 31 Real and Apparent Limits 33 Interpreting a Raw Score 34 Definition of Percentile Rank and Percentile 34 Computational Procedures 35 Deciles, Quartiles, and the Median 38 Graphic Representations 39 Shapes of Frequency Distributions 43 Summary 45 Exercises 47 Thought Questions 49 Computer Exercises 49 Bridge to SPSS 50 Chapter 3 Measures of Central Tendency and Variability 53 Introduction 54 The Mode 56 The Median 56 The Mean 58 The Concept of Variability 62 The Range 65 The Standard Deviation and Variance 66 Summary 73 Exercises 75 Thought Questions 76 Computer Exercises 77 Bridge to SPSS 78 Chapter 4 Standardized Scores and the Normal Distribution 81 Interpreting a Raw Score Revisited 82 Rules for Changing μ and σ 84 Standard Scores (z Scores) 85 T Scores, SAT Scores, and IQ Scores 88 The Normal Distribution 90 Table of the Standard Normal Distribution 93 Illustrative Examples 95 Summary 101 Exercises 103 Thought Questions 105 Computer Exercises 106 Bridge to SPSS 106 Part II Basic Inferential Statistics 109 Chapter 5 Introduction to Statistical Inference 111 Introduction 113 The Goals of Inferential Statistics 114 Sampling Distributions 114 The Standard Error of the Mean 119 The z Score for Sample Means 122 Null Hypothesis Testing 124 Assumptions Required by the Statistical Test for the Mean of a Single Population 132 Summary 133 Exercises 135 Thought Questions 137 Computer Exercises 138 Bridge to SPSS 138 Appendix: The Null Hypothesis Testing Controversy 139 Chapter 6 The One-Sample t Test and Interval Estimation 142 Introduction 143 The Statistical Test for the Mean of a Single Population When σ Is Not Known: The t Distributions 144 Interval Estimation 148 The Standard Error of a Proportion 152 Summary 155 Exercises 156 Thought Questions 157 Computer Exercises 158 Bridge to SPSS 158 Chapter 7 Testing Hypotheses About the Difference Between the Means of Two Populations 160 The Standard Error of the Difference 162 Estimating the Standard Error of the Difference 166 The t Test for Two Sample Means 167 Confidence Intervals for μ1 − μ2 172 The Assumptions Underlying the Proper Use of the t Test for Two Sample Means 175 Measuring the Size of an Effect 176 The t Test for Matched Samples 178 Summary 185 Exercises 187 Thought Questions 190 Computer Exercises 191 Bridge to SPSS 191 Chapter 8 Nonparametric Tests for the Difference Between Two Means 194 Introduction 195 The Difference Between the Locations of Two Independent Samples: The Rank-Sum Test 199 The Difference Between the Locations of Two Matched Samples: The Wilcoxon Test 205 Summary 210 Exercises 212 Thought Questions 215 Computer Exercises 216 Bridge to SPSS 216 Chapter 9 Linear Correlation 218 Introduction 219 Describing the Linear Relationship Between Two Variables 222 Interpreting the Magnitude of a Pearson r 229 When Is It Important That Pearson’s r Be Large? 234 Testing the Significance of the Correlation Coefficient 236 The Relationship Between Two Ranked Variables: The Spearman Rank-Order Correlation Coefficient 239 Summary 242 Exercises 244 Thought Questions 247 Computer Exercises 248 Bridge to SPSS 248 Appendix: Equivalence of the Various Formulas for r 251 Chapter 10 Prediction and Linear Regression 253 Introduction 254 Using Linear Regression to Make Predictions 254 Measuring Prediction Error: The Standard Error of Estimate 263 The Connection Between Correlation and the t Test 265 Estimating the Proportion of Variance Accounted for in the Population 271 Summary 273 Exercises 275 Thought Questions 277 Computer Exercises 277 Bridge to SPSS 278 Chapter 11 Introduction to Power Analysis 281 Introduction 282 Concepts of Power Analysis 283 The Significance Test of the Mean of a Single Population 285 The Significance Test of the Proportion of a Single Population 290 The Significance Test of a Pearson r 292 Testing the Difference Between Independent Means 293 Testing the Difference Between the Means of Two Matched Populations 297 Choosing a Value for d for a Power Analysis Involving Independent Means 299 Using Power Analysis Concepts to Interpret the Results of Null Hypothesis Tests 301 Summary 304 Exercises 306 Thought Questions 308 Computer Exercises 309 Bridge to SPSS 310 Part III Analysis of Variance Methods 313 Chapter 12 One-Way Analysis of Variance 315 Introduction 317 The General Logic of ANOVA 318 Computational Procedures 321 Testing the F Ratio for Statistical Significance 326 Calculating the One-Way ANOVA From Means and Standard Deviations 328 Comparing the One-Way ANOVA With the t Test 329 A Simplified ANOVA Formula for Equal Sample Sizes 330 Effect Size for the One-Way ANOVA 331 Some Comments on the Use of ANOVA 333 A Nonparametric Alternative to the One-Way ANOVA: The Kruskal-Wallis H Test 336 Summary 339 Exercises 343 Thought Questions 346 Computer Exercises 346 Bridge to SPSS 346 Appendix: Proof That the Total Sum of Squares Is Equal to the Sum of the Between-Group and the Within-Group Sum of Squares 348 Chapter 13 Multiple Comparisons 349 Introduction 350 Fisher’s Protected t Tests and the Least Significant Difference (LSD) 351 Tukey’s Honestly Significant Difference (HSD) 355 Other Multiple Comparison Procedures 360 Planned and Complex Comparisons 362 Nonparametric Multiple Comparisons: The Protected Rank-Sum Test 365 Summary 366 Exercises 368 Thought Questions 369 Computer Exercises 370 Bridge to SPSS 370 Chapter 14 Introduction to Factorial Design: Two-Way Analysis of Variance 372 Introduction 373 Computational Procedures 374 The Meaning of Interaction 384 Following Up a Significant Interaction 387 Measuring Effect Size in a Factorial ANOVA 390 Summary 392 Exercises 395 Thought Questions 398 Computer Exercises 399 Bridge to SPSS 399 Chapter 15 Repeated-Measures ANOVA 402 Introduction 403 Calculating the One-Way RM ANOVA 403 Rationale for the RM ANOVA Error Term 408 Assumptions and Other Considerations Involving the RM ANOVA 408 The RM Versus RB Design: An Introduction to the Issues of Experimental Design 411 The Two-Way Mixed Design 415 Summary 423 Exercises 428 Thought Questions 430 Computer Exercises 430 Bridge to SPSS 431 Part IV Nonparametric Statistics for Categorical Data 435 Chapter 16 Probability of Discrete Events and the Binomial Distribution 437 Introduction 438 Probability 439 The Binomial Distribution 442 The Sign Test for Matched Samples 448 Summary 450 Exercises 451 Thought Questions 453 Computer Exercises 453 Bridge to SPSS 454 Chapter 17 Chi-Square Tests 457 Chi Square and the Goodness of Fit: One-Variable Problems 458 Chi Square as a Test of Independence: Two-Variable Problems 464 Measures of Strength of Association in Two-Variable Tables 470 Summary 472 Exercises 474 Thought Questions 476 Computer Exercises 477 Bridge to SPSS 478 Appendix 481 Statistical Tables 483 Answers to Odd-Numbered Exercises 499 Data From Ihno’s Experiment 511 Glossary of Terms 515 References 525 Index 527