Decolonizing The Mind

Machine generated contents note: 1.1. Changing Field -- 1.2. Importance of Context -- 1.3. Basic Terminology -- 1.4. Selection among Statistical Procedures -- 1.5. Using Computers -- 1.6. Summary -- 1.7. Quick Review -- 1.8. Exercises -- 2.1. Scales of Measurement -- 2.2. Variables -- 2.3. Random Sampling -- 2.4. Notation -- 2.5. Summary -- 2.6. Quick Review -- 2.7. Exercises -- 3.1. Plotting Data -- 3.2. Stem-and-Leaf Displays -- 3.3. Reading Graphs -- 3.4. Alternative Methods of Plotting Data -- 3.5. Describing Distributions -- 3.6. Using SPSS to Display Data -- 3.7. Summary -- 3.8. Quick Review -- 3.9. Exercises -- 4.1. Mode -- 4.2. Median -- 4.3. Mean -- 4.4. Relative Advantages and Disadvantages of the Mode, the Median, and the Mean -- 4.5. Obtaining Measures of Central Tendency Using SPSS and R -- 4.6. Simple Demonstration-Seeing Statistics -- 4.7. Summary -- 4.8. Quick Review -- 4.9. Exercises -- 5.1. Range -- 5.2. Interquartile Range and Other Range Statistics -- 5.3. Average Deviation -- 5.4. Variance -- 5.5. Standard Deviation -- 5.6. Computational Formulae for the Variance and the Standard Deviation -- 5.7. Mean and the Variance as Estimators -- 5.8. Boxplots: Graphical Representations of Dispersion and Extreme Scores -- 5.9. Return to Trimming -- 5.10. Obtaining Measures of Dispersion Using SPSS & R -- 5.11. Moon Illusion -- 5.12. Seeing Statistics -- 5.13. Summary -- 5.14. Quick Review -- 5.15. Exercises -- 6.1. Normal Distribution -- 6.2. Standard Normal Distribution -- 6.3. Setting Probable Limits on an Observation -- 6.4. Measures Related to z -- 6.5. Seeing Statistics -- 6.6. Summary -- 6.7. Quick Review -- 6.8. Exercises -- 7.1. Probability -- 7.2. Basic Terminology and Rules -- 7.3. Application of Probability to Controversial Issues -- 7.4. Writing Up the Results -- 7.5. Discrete Versus Continuous Variables -- 7.6. Probability Distributions for Discrete Variables -- 7.7. Probability Distributions for Continuous Variables -- 7.8. Summary -- 7.9. Quick Review -- 7.10. Exercises -- 8.1. Sampling Distributions and the Standard Error -- 8.2. Two More Examples Involving Course Evaluations and Human Decision Making -- 8.3. Hypothesis Testing -- 8.4. Null Hypothesis -- 8.5. Test Statistics and Their Sampling Distributions -- 8.6. Using the Normal Distribution to Test Hypotheses -- 8.7. Type I and Type II Errors -- 8.8. One- and Two-Tailed Tests -- 8.9. Seeing Statistics -- 8.10. Final Example -- 8.11. Back to Course Evaluations and Sunk Costs -- 8.12. Summary -- 8.13. Quick Review -- 8.14. Exercises -- 9.1. Scatter Diagrams -- 9.2. Example: The Relationship Between the Pace of Life and Heart Disease -- 9.3. Covariance -- 9.4. Pearson Product-Moment Correlation Coefficient (r) -- 9.5. Correlations with Ranked Data -- 9.6. Factors That Affect the Correlation -- 9.7. Beware Extreme Observations -- 9.8. Correlation and Causation -- 9.9. If Something Looks Too Good to Be True, Perhaps It Is -- 9.10. Testing the Significance of a Correlation Coefficient -- 9.11. Confidence Intervals on Correlation Coefficients -- 9.12. Intercorrelation Matrices -- 9.13. Other Correlation Coefficients -- 9.14. Using SPSS to Obtain Correlation Coefficients -- 9.15. r2 and the Magnitude of an Effect -- 9.16. Seeing Statistics -- 9.17. Review: Does Rated Course Quality Relate to Expected Grade? -- 9.18. Summary -- 9.19. Quick Review -- 9.20. Exercises -- 10.1. Relationship Between Stress and Health -- 10.2. Basic Data -- 10.3. Regression Line -- 10.4. Accuracy of Prediction -- 10.5. Influence of Extreme Values -- 10.6. Hypothesis Testing in Regression -- 10.7. Computer Solution Using SPSS -- 10.8. Seeing Statistics -- 10.9. Final Example for Review -- 10.10. Regression Versus Correlation -- 10.11. Summary -- 10.12. Quick Review -- 10.13. Exercises -- 11.1. Overview -- 11.2. Funding Our Schools -- 11.3. Multiple Regression Equation -- 11.4. Residuals -- 11.5. Hypothesis Testing -- 11.6. Refining the Regression Equation -- 11.7. Special Section: Using R to Solve a Multiple Regression Problem -- 11.8. Second Example: What Makes a Confident Mother? -- 11.9. Third Example: Psychological Symptoms in Cancer Patients -- 11.10. Summary -- 11.11. Quick Review -- 11.12. Exercises -- 12.1. Sampling Distribution of the Mean -- 12.2. Testing Hypotheses about Means when (Sv (B Is Known -- 12.3. Testing a Sample Mean When (Sv (B Is Unknown (The One-Sample t Test) -- 12.4. Factors That Affect the Magnitude of t and the Decision about H0 -- 12.5. Second Example: The Moon Illusion -- 12.6. How Large Is Our Effect? -- 12.7. Confidence Limits on the Mean -- 12.8. Using SPSS and R to Run One-Sample t Tests -- 12.9. Good Guess Is Better than Leaving It Blank -- 12.10. Seeing Statistics -- 12.11. Confidence Intervals Can Be Far More Important than a Null Hypothesis Test -- 12.12. Summary -- 12.13. Quick Review -- 12.14. Exercises -- 13.1. Related Samples -- 13.2. Student's t Applied to Difference Scores -- 13.3. Crowd Within Is Like the Crowd Without -- 13.4. Advantages and Disadvantages of Using Related Samples -- 13.5. How Large an Effect Have We Found?-Effect Size -- 13.6. Confidence Limits on Change -- 13.7. Using SPSS and R for t Tests on Related Samples -- 13.8. Writing Up the Results -- 13.9. Summary -- 13.10. Quick Review -- 13.11. Exercises -- 14.1. Distribution of Differences Between Means -- 14.2. Heterogeneity of Variance -- 14.3. Nonnormality of Distributions -- 14.4. Second Example with Two Independent Samples -- 14.5. Effect Size Again -- 14.6. Confidence Limits on P1 -- P2 -- 14.7. Confidence Limits on Effect Size -- 14.8. Plotting the Results -- 14.9. Writing Up the Results -- 14.10. Do Lucky Charms Work? -- 14.11. Seeing Statistics -- 14.12. Summary -- 14.13. Quick Review -- 14.14. Exercises -- 15.1. Basic Concept of Power -- 15.2. Factors Affecting the Power of a Test -- 15.3. Calculating Power the Traditional Way -- 15.4. Power Calculations for the One-Sample t Test -- 15.5. Power Calculations for Differences Between Two Independent Means -- 15.6. Power Calculations for the t Test for Related Samples -- 15.7. Power Considerations in Terms of Sample Size -- 15.8. You Don't Have to Do It by Hand -- 15.9. Post-hoc (Retrospective) Power -- 15.10. Summary -- 15.11. Quick Review -- 15.12. Exercises -- 16.1. General Approach -- 16.2. Logic of the Analysis of Variance -- 16.3. Calculations for the Analysis of Variance -- 16.4. Unequal Sample Sizes -- 16.5. Multiple Comparison Procedures -- 16.6. Violations of Assumptions -- 16.7. Size of the Effects -- 16.8. Writing Up the Results -- 16.9. Final Worked Example -- 16.10. Seeing Statistics -- 16.11. Summary -- 16.12. Quick Review -- 16.13. Exercises -- 17.1. Factorial Designs -- 17.2. Eysenck Study -- 17.3. Interactions -- 17.4. Simple Effects -- 17.5. Measures of Association and Effect Size -- 17.6. Reporting the Results -- 17.7. Unequal Sample Sizes -- 17.8. Masculine Overcompensation Thesis: It's a Male Thing -- 17.9. Using SPSS for Factorial Analysis of Variance -- 17.10. Seeing Statistics -- 17.11. Summary -- 17.12. Quick Review -- 17.13. Exercises -- 18.1. Example: Depression as a Response to an Earthquake -- 18.2. Multiple Comparisons -- 18.3. Effect Size -- 18.4. Assumptions Involved in Repeated-Measures Designs -- 18.5. Advantages and Disadvantages of Repeated-Measures Designs -- 18.6. Writing Up The Results -- 18.7. Final Worked Example -- 18.8. Summary -- 18.9. Quick Review -- 18.10. Exercises -- 19.1. One Classification Variable: The Chi-Square Goodness-of-Fit Test -- 19.2. Two Classification Variables: Analysis of Contingency Tables -- 19.3. Possible Improvements on Standard Chi-Square -- 19.4. Chi-Square for Larger Contingency Tables -- 19.5. Problem of Small Expected Frequencies -- 19.6. Use of Chi-Square as a Test on Proportions -- 19.7. Measures of Effect Size -- 19.8. Final Worked Example -- 19.9. Second Example of Writing up Results -- 19.10. Seeing Statistics -- 19.11. Summary -- 19.12. Quick Review -- 19.13. Exercises -- 20.1. Traditional Nonparametric Tests -- 20.2. Randomization Tests -- 20.3. Measures of Effect Size -- 20.4. Bootstrapping -- 20.5. Writing up the Results of the Study of Maternal Adaptation -- 20.6. Summary -- 20.7. Quick Review -- 20.8. Exercises -- 21.1. Meta-Analysis -- 21.2. Brief Review of Effect Size Measures -- 21.3. Example[—]Child and Adolescent Depression -- 21.4. Second Example[—]Nicotine Gum and Smoking Cessation -- 21.5. Quick Review -- 21.6. Exercises. Cover 1 ES2 2 Title 3 Statement 4 Copyright 5 Dedication 6 Brief Contents 8 Contents 10 Preface 16 Ch 1: Introduction 26 Ch 1: Introduction 26 1.1: A Changing Field 28 1.2: The Importance of Context 30 1.3: Basic Terminology 31 1.4: Selection among Statistical Procedures 35 1.5: Using Computers 37 1.6: Summary 39 1.7: A Quick Review 40 1.8: Exercises 41 Ch 2: Basic Concepts 43 Ch 2: Introduction 43 2.1: Scales of Measurement 44 2.2: Variables 49 2.3: Random Sampling 51 2.4: Notation 52 2.5: Summary 55 2.6: A Quick Review 55 2.7: Exercises 56 Ch 3: Displaying Data 60 Ch 3: Introduction 60 3.1: Plotting Data 62 3.2: Stem-and-Leaf Displays 67 3.3: Reading Graphs 72 3.4: Alternative Methods of Plotting Data 74 3.5: Describing Distributions 77 3.6: Using SPSS to Display Data 79 3.7: Summary 80 3.8: AQuick Review 81 3.9: Exercises 82 Ch 4: Measures of Central Tendency 89 Ch 4: Introduction 89 4.1: The Mode 90 4.2: The Median 90 4.3: The Mean 91 4.4: Relative Advantages and Disadvantages of the Mode, the M edian, and the Mean 92 4.5: Obtaining Measures of Central Tendency Using SPSS and 94 4.6: ASimple Demonstration—Seeing Statistics 97 4.7: Summary 100 4.8: AQuick Review 101 4.9: Exercises 101 Ch 5: Measures of Variability 105 Ch 5: Introduction 105 5.1: Range 108 5.2: Interquartile Range and Other Range Statistics 109 5.3: The Average Deviation 110 5.4: The Variance 110 5.5: The Standard Deviation 112 5.6: Computational Formulae for the Variance and the Standard Deviation 113 5.7: The Mean and the Variance as Estimators 115 5.8: Boxplots: Graphical Representations of Dispersion and E xtreme Scores 116 5.9: AReturn to Trimming 120 5.10: Obtaining Measures of Dispersion Using SPSS & R 121 5.11: The Moon Illusion 124 5.12: Seeing Statistics 126 5.13: Summary 128 5.14: A Quick Review 129 5.15: Exercises 130 Ch 6: The Normal Distribution 133 Ch 6: Introduction 133 6.1: The Normal Distribution 136 6.2: The Standard Normal Distribution 140 6.3: Setting Probable Limits on an Observation 147 6.4: Measures Related to z 148 6.5: Seeing Statistics 149 6.6: Summary 150 6.7: A Quick Review 151 6.8: Exercises 151 Ch 7: Basic Concepts of Probability 155 Ch 7: Introduction 155 7.1: Probability 156 7.2: Basic Terminology and Rules 158 7.3: The Application of Probability to Controversial Issues 163 7.4: Writing Up the Results 165 7.5: Discrete Versus Continuous Variables 166 7.6: Probability Distributions for Discrete Variables 167 7.7: Probability Distributions for Continuous Variables 168 7.8: Summary 170 7.9: A Quick Review 172 7.10: Exercises 172 Ch 8: Sampling Distributions and Hypothesis Testing 175 Ch 8: Introduction 175 8.1: Sampling Distributions and the Standard Error 176 8.2: Two More Examples Involving Course Evaluations and Human Decision Making 178 8.3: Hypothesis Testing 181 8.4: The Null Hypothesis 184 8.5: Test Statistics and Their Sampling Distributions 186 8.6: Using the Normal Distribution to Test Hypotheses 187 8.7: Type I and Type II Errors 192 8.8: Oneand Two-Tailed Tests 196 8.9: Seeing Statistics 200 8.10: A Final Example 201 8.11: Back to Course Evaluations and Sunk Costs 203 8.12: Summary 203 8.13: A Quick Review 204 8.14: Exercises 205 Ch 9: Correlation 208 Ch 9: Introduction 208 9.1: Scatter Diagrams 209 9.2: An Example: The Relationship Between the Pace of Life and Heart Disease 215 9.3: The Covariance 216 9.4: The Pearson Product-Moment Correlation Coefficient (r) 217 9.5: Correlations with Ranked Data 219 9.6: Factors That Affect the Correlation 221 9.7: Beware Extreme Observations 225 9.8: Correlation and Causation 226 9.9: If Something Looks Too Good to Be True, Perhaps It Is 228 9.10: Testing the Significance of a Correlation Coefficient 229 9.11: Confidence Intervals on Correlation Coefficients 232 9.12: Intercorrelation Matrices 234 9.13: Other Correlation Coefficients 236 9.14: Using SPSS to Obtain Correlation Coefficients 237 9.15: r2 and the Magnitude of an Effect 237 9.16: Seeing Statistics 239 9.17: A Review: Does Rated Course Quality Relate to Expected Grade? 243 9.18: Summary 245 9.19: A Quick Review 246 9.20: Exercises 247 Ch 10: Regression 251 Ch 10: Introduction 251 10.1: The Relationship Between Stress and Health 252 10.2: The Basic Data 254 10.3: The Regression Line 256 10.4: The Accuracy of Prediction 264 10.5: The Influence of Extreme Values 270 10.6: Hypothesis Testing in Regression 271 10.7: Computer Solution Using SPSS 272 10.8: Seeing Statistics 274 10.9: A Final Example for Review 278 10.10: Regression Versus Correlation 282 10.11: Summary 282 10.12: A Quick Review 284 10.13: Exercises 284 Ch 11: Multiple Regression 290 Ch 11: Introduction 290 11.1: Overview 291 11.2: Funding Our Schools 294 11.3: The Multiple Regression Equation 300 11.4: Residuals 307 11.5: Hypothesis Testing 308 11.6: Refining the Regression Equation 309 11.7: Special Section: Using R to Solve a Multiple Regression Problem 311 11.8: A Second Example: What Makes a Confident Mother? 312 11.9: Third Example: Psychological Symptoms in Cancer Patients 315 11.10: Summary 318 11.11: A Quick Review 319 11.12: Exercises 319 Ch 12: Hypothesis Tests Applied to Means: One Sample 324 Ch 12: Introduction 324 12.1: Sampling Distribution of the Mean 326 12.2: Testing Hypotheses about Means when Is Known 328 12.3: Testing a Sample Mean when Is Unknown (The One-Sample Test) 333 12.4: Factors That Affect the Magnitude of and the Decision about 340 12.5: A Second Example: The Moon Illusion 340 12.6: How Large Is Our Effect? 341 12.7: Confidence Limits on the Mean 342 12.8: Using SPSS and to Run One-Sample Tests 345 12.9: A Good Guess Is Better than Leaving It Blank 347 12.10: Seeing Statistics 349 12.11: Confidence Intervals Can Be Far More Important than a Null Hypothesis Test 353 12.12: Summary 355 12.13: A Quick Review 356 12.14: Exercises 356 Ch 13: Hypothesis Tests Applied to Means: Two Related Samples 359 Ch 13: Introduction 359 13.1: Related Samples 360 13.2: Student’s Applied to Difference Scores 361 13.3: The Crowd Within Is Like the Crowd Without 364 13.4: Advantages and Disadvantages of Using Related Samples 366 13.5: How Large an Effect Have We Found?—Effect Size 367 13.6: Confidence Limits on Change 369 13.7: Using SPSS and for Tests on Related Samples 370 13.8: Writing Up the Results 371 13.9: Summary 371 13.10: A Quick Review 372 13.11: Exercises 373 Ch 14: Hypothesis Tests Applied to Means: Two Independent Samples 376 Ch 14: Introduction 376 14.1: Distribution of Differences between Means 377 14.2: Heterogeneity of Variance 385 14.3: Nonnormality of Distributions 387 14.4: A Second Example with Two Independent Samples 387 14.5: Effect Size Again 390 14.6: Confidence Limits on 391 14.7: Confidence Limits on Effect Size 392 14.8: Plotting the Results 393 14.9: Writing Up the Results 394 14.10: Do Lucky Charms Work? 394 14.11: Seeing Statistics 397 14.12: Summary 398 14.13: A Quick Review 399 14.14: Exercises 400 Ch 15: Power 403 Ch 15: Introduction 403 15.1: The Basic Concept of Power 406 15.2: Factors Affecting the Power of a Test 407 15.3: Calculating Power the Traditional Way 410 15.4: Power Calculations for the One-Sample t Test 412 15.5: Power Calculations for Differences between Two Independent Means 415 15.6: Power Calculations for the t Test for Related Samples 419 15.7: Power Considerations in Terms of Sample Size 420 15.8: You Don’t Have to Do It by Hand 420 15.9: Post-hoc (Retrospective) Power 422 15.10: Summary 424 15.11: A Quick Review 424 15.12: Exercises 425 Ch 16: One-Way Analysis of Variance 428 Ch 16: Introduction 428 16.1: The General Approach 429 16.2: The Logic of the Analysis of Variance 432 16.3: Calculations for the Analysis of Variance 437 16.4: Unequal Sample Sizes 446 16.5: Multiple Comparison Procedures 448 16.6: Violations of Assumptions 456 16.7: The Size of the Effects 457 16.8: Writing Up the Results 459 16.9: A Final Worked Example 460 16.10: Seeing Statistics 463 16.11: Summary 464 16.12: A Quick Review 466 16.13: Exercises 466 Ch 17: Factorial Analysis of Variance 472 Ch 17: Introduction 472 17.1: Factorial Designs 473 17.2: The Eysenck Study 475 17.3: Interactions 479 17.4: Simple Effects 481 17.5: Measures of Association and Effect Size 485 17.6: Reporting the Results 488 17.7: Unequal Sample Sizes 489 17.8: Masculine Overcompensation Thesis: It’s a Male Thing 489 17.9: Using SPSS for Factorial Analysis of Variance 492 17.10: Seeing Statistics 493 17.11: Summary 494 17.12: A Quick Review 495 17.13: Exercises 496 Ch 18: RepeatedMeasures Analysis of Variance 501 Ch 18: Introduction 501 18.1: An Example: Depression as a Response to an Earthquake 502 18.2: Multiple Comparisons 508 18.3: Effect Size 510 18.4: Assumptions Involved in Repeated-Measures Designs 511 18.5: Advantages and Disadvantages of Repeated-Measures Designs 512 18.6: Writing Up the Results 513 18.7: A Final Worked Example 514 18.8: Summary 515 18.9: A Quick Review 516 18.10: Exercises 517 Ch 19: Chi-Square 520 Ch 19: Introduction 520 19.1: One Classification Variable: The Chi-Square Goodness-of-Fit Test 522 19.2: Two Classification Variables: Analysis of Contingency Tables 527 19.3: Possible Improvements on Standard Chi-Square 530 19.4: Chi-Square for Larger Contingency Tables 532 19.5: The Problem of Small Expected Frequencies 534 19.6: The Use of Chi-Square as a Test on Proportions 534 19.7: Measures of Effect Size 536 19.8: A Final Worked Example 539 19.9: A Second Example of Writing up Results 541 19.10: Seeing Statistics 541 19.11: Summary 542 19.12: A Quick Review 543 19.13: Exercises 544 Ch 20: Nonparametric and DistributionFree Statistical Tests 549 Ch 20: Introduction 549 20.1: Traditional Nonparametric Tests 550 20.2: Randomization Tests 559 20.3: Measures of Effect Size 561 20.4: Bootstrapping 562 20.5: Writing Up the Results of the Study of Maternal Adaptation 562 20.6: Summary 563 20.7: A Quick Review 563 20.8: Exercises 564 Ch 21: Meta-Analysis 569 Ch 21: Introduction 569 21.1: Meta-Analysis1 570 21.2: A Brief Review of Effect Size Measures 571 21.3: An Example—Child and Adolescent Depression 575 21.4: A Second Example—Nicotine Gum and Smoking Cessation 580 21.5: A Quick Review 584 21.6: Exercises 584 Appendix A: Arithmetic Review 587 Appendix B: Symbols and Notation 594 Appendix C: Basic Statistical Formulae 597 Appendix D: Data Set 601 Appendix E: Statistical Tables 605 Glossary 623 References 629 Answers to Exercises 635 Index 660 ES5 676 ES6 677 ES7 678 WCN:,02-200-202 WCN: 02-200-202