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BK

First published

Los Angeles ; London ; New Delhi ; Singapore ; Washington ; Melbourne : Sage, 2016

xvi, 746 stran : barevné ilustrace ; 25 cm

ISBN 978-1-4462-1045-1 (brožováno)

Terminologický slovník

Obsahuje bibliografické odkazy a rejstřík

001650456

HOW TO USE THIS BOOK XV // Who is the book aimed at? xv // How do I teach with a book that has a fictional narrative? xv // Can I dip into the book? xv’ // What online resources are there? XVI // PROLOGUE: THE DYING STARS 1 // 1 WHY YOU NEED SCIENCE: THE BEGINNING AND THE END 7 // 1.1. Will you love me now? 10 // 1.2. How science works 14 // 1.2.1. The research process 14 // 1.2.2. Science as a life skill 21 // 1.3. Research methods 21 // 1.3.1. Correlational research methods 22 // 1.3.2. Experimental research methods 24 // 1.3.3. Practice, order and randomization 27 // 1.3.4. Piecing it all together 31 // 1.4. Why we need science 34 // Key terms 36 // JIG:SAW’s puzzles 36 // 2 REPORTING RESEARCH, VARIABLES AND MEASUREMENT: BREAKING THE LOW 39 // 2.1. Writing up research 43 // 2.2. Maths and statistical notation 49 // 2.3. Variables and measurement 55 // 2.3.1. The conspiracy unfolds 55 // 2.3.2. Qualitative and quantitative data 57 // 2.3.3. Levels of measurement 60 // 2.3.4. Measurement error 66 // 2.3.5. Validity and reliability 68 // Key terms 70 // JIGrSAW’s puzzles 70 // 3 SUMMARIZING DRTfl: SHE LOVES ME NOT? 75 // 3.1. Frequency distributions 81 // 3.1.1. Tabulated frequency distributions 81 // 3.1.2. Grouped frequency distributions 89 // 3.1.3. Graphical frequency distributions 94 // 3.1.4. Idealized distributions 100 // 3.1.5. Histograms for nominal and ordinal data 100 // 3.2. Throwing shapes ?? // Key terms ?7 // JIG:SAW’s puzzles ?7 // 4 FITTING MODELS [CENTRAL TENDENCY]: SOMEWHERE IN THE MIDDLE // 111 // 4.1. Statistical models // 4.1.1. From the dead // 4.1.2. Why do we need statistical models? // 4.1.3. Sample size // 4.1.4. The one and only statistical model // 4.2. Central tendency // 4.2.1. The mode // 4.2.2. The median // 4.2.3. The mean // 4.3. The ‘fit’ of the mean: variance // 4.3.1. The fit of the mean // 4.3.2. Estimating the fit of the mean from a sample //

4.3.3. Outliers and variance // 4.4. Dispersion // 4.4.1. The standard deviation as an indicator of dispersion // 4.4.2. The range and interquartile range // Key terms JIG:SAW’s puzzles // 5 PRESENTING DATA: AGGRESSIVE PERFECTOR 157 // 5.1. Types of graphs 161 // 5.2. Another perfect day 162 // 5.3. The art of presenting data 166 // 5.3.1. What makes a good graph? 166 // 5.3.2. Bar graphs 170 // 5.3.3. Line graphs 172 // 5.3.4. Boxplots (box-whisker diagrams) 173 // 5.3.5. Graphing relationships: the scatterplot 176 // 5.3.6. Pie charts 177 // Key terms 181 // JIGiSAW’s puzzles 182 // b Z-SCORES: THE WOLF IS LOOSE 189 // 6.1. Interpreting raw scores 193 // 6.2. Standardizing a score 196 // 6.3. Using z-scores to compare distributions 200 // 6.4. Using z-scores to compare scores 206 // 6.5. z-scores for samples 209 // Key terms 212 // JIGiSAW’s puzzles 212 // 7 PROBRBILITY: THE BRIDGE OF DERTH 215 // 7.1. Probability 218 // 7.1.1. Classical probability 219 // 7.1.2. Empirical probability 225 // 7.2. Probability and frequency distributions 228 // 7.2.1. The discs of death 228 // 7.2.2. Probability density functions 230 // 7.2.3. Probability and the normal distribution 233 // 7.2.4. The probability of a score greater than x 236 // 7.2.5. The probability of a score less than x: The tunnels of death 238 // 7.2.6. The probability of a score between two values: // The catapults of death 242 // 7.3. Conditional probability: Deathscotch 248 // Key terms 255 // JIGiSAW’s puzzles 255 // 8 INFERENTIRLSTRTISTICS: GOING BEYOND THE DRTR: HUMILIRTIVE 257 // 8.1. Estimating parameters 262 // 8.2. How well does a sample represent the population? 267 // 8.2.1. Sampling distributions 267 // 8.2.2. The standard error 271 // 8.2.3. The central limit theorem 274 // 8.3. Confidence intervals 278 // 8.3.1. Calculating confidence intervals 281 // 8.3.2. Calculating other confidence intervals 286 //

8.3.3. Confidence intervals in small samples 287 // 8.4. Inferential statistics 291 // Key terms 296 // JIGiSAW’s puzzles 296 // 9 ROBUST ESTIMATION: MAN WITHOUT FAITH OR TRUST // 297 // 9.1. Sources of bias 301 // 9.1.1. Extreme scores and non-normal distributions 301 // 9.1.2. The mixed normal distribution 309 // 9.2. A great mistake 311 // 9.3. Reducing bias . 314 // 9.3.1. Transforming data 315 // 9.3.2. Trimming data 319 // 9.3.3. M-estimators 321 // 9.3.4. Winsorizing 321 // 9.3.5. The bootstrap 323 // 9.4. A final point about extreme scores 326 // Key terms 329 // JIGiSAW’s puzzles 329 // 10 HYPOTHESIS TESTING: IN REALITY ALL IS VOID 331 // 10.1. Null hypothesis significance testing 336 // 10.1.1. Types of hypothesis 337 // 10.1.2. Fisher’s p-value 339 // 10.1.3. The principles of NHST 341 // 10.1.4. Test statistics 343 // 10.1.5. One-and two-tailed tests 345 // 10.1.6. Type I and Type II errors 347 // 10.1.7. Inf lated error rates 348 // 10.1.8. Statistical power 350 // 10.1.9. Confidence intervals and statistical significance 351 // 10.1.10. Sample size and statistical significance 353 // Key terms 358 // JIGiSAW’s puzzles 358 // 11 MODERN APPROACHES TO THEORY TESTING: A CAREWORN HEART 361 // 11.1. Problems with NHST 364 // 11.1.1. What can you conclude from a‘significance’test? 364 // 11.1.2. All-or-nothing thinking 366 // 11.1.3. NHST is influenced by the intentions of the scientist 368 // 11.2. Effect sizes 370 // 11.2.1. Cohen’s d 371 // 11.2.2. Pearson’s correlation coefficient, r 377 // 11.2.3. The odds ratio 379 // 11.3. Meta-analysis 380 // 11.4. Bayesian approaches 382 // 11.4.1. Asking a different question 384 // 11.4.2. Bayes’theorem revisited 386 // 11.4.3. Comparing hypotheses 388 // 11.4.4. Benefits of Bayesian approaches // Key terms JIGiSAW’s puzzles 393 //

12 ASSUMPTIONS: STARBLIND 395 // 12.1. Fitting models: bringing it all together // 12.2. Assumptions // 12.2.1. Additivity and linearity // 12.2.2. Independent errors // 12.2.3. Homoscedasticity/homogeneity of variance // 12.2.4. Normally distributed something or other // 12.2.5. External variables // 12.2.6. Variable types // 12.2.7. Multicollinearity // 12.2.8. Non-zero variance // 12.3. Turning ever towards the sun Key terms // JIGiSAW’s puzzles // 13 RELATIONSHIPS: A STRANGER’S GRAVE 429 // 13.1. Finding relationships in categorical data // 13.1.1. Pearson’s chi-square test // 13.1.2. Assumptions // 13.1.3. Fisher’s exact test // 13.1.4. Yates’s correction // 13.1.5. The likelihood ratio (G-test) // 13.1.6. Standardized residuals // 13.1.7. Calculating an effect size // 13.1.8. Using a computer // 13.1.9. Bayes factors for contingency tables // 13.1.10. Summary // 13.2. What evil lay dormant // 13.3. Modelling relationships // 13.3.1. Covariance // 13.3.2. Pearson’s correlation coefficient // 13.3.3. The significance of the correlation coefficient // 13.3.4. Confidence intervals for r // 13.3.5. Using a computer // 13.3.6. Robust estimation of the correlation // 13.3.7. Bayesian approaches to relationships between two variables // 13.3.8. Correlation and causation // 13.3.9. Calculating the effect size // 13.4. Silent sorrow in empty boats Key terms // JIGiSAW’s puzzles // 14 THE GENERHL LINERR MODEL: RED FIRE COMING OUT FROM HIS GILLS 477 // 14.1. The linear model with one predictor 481 // 14.1.1. Estimating parameters 484 // 14.1.2. Interpreting regression coefficients 491 // 14.1.3. Standardized regression coefficients 492 // 14.1.4. The standard error of b 492 // 14.1.5. Confidence Intervals for b 494 // 14.1.6. Test statistic for b 495 // 14.1.7. Assessing the goodness of fit 496 // 14.1.8. Fitting a linear model using a computer 499 //

14.1.9. When this fails 501 // 14.2. Bias in the linear model 503 // 14.3. A general procedure for fitting linear models 506 // 14.4. Models with several predictors 507 // 14.4.1. The expanded linear model 510 // 14.4.2. Methods for entering predictors 512 // 14.4.3. Estimating parameters 513 // 14.4.4. Using a computer to build more complex models 514 // 14.5. Robust regression 522 // 14.5.1. Bayes factors for linear models 522 // Key terms 525 // JIGiSAW’s puzzles 525 // 15 COMPRRING TWO MERNS: ROCK OR BUST 527 // 15.1. Testing differences between means: The rationale 532 // 15.2. Means and the linear model 534 // 15.2.1. Estimating the model parameters 537 // 15.2.2. How the model works 540 // 15.2.3. Testing the model parameters 541 // 15.2.4. The independent Mest on a computer 545 // 15.2.5. Assumptions of the model 546 // 15.3. Everything you believe is wrong 547 // 15.4. The paired-samples r-test 549 // 15.4.1. The paired-samples Mest on a computer 553 // 15.5. Alternative approaches 556 // 15.5.1. Effect sizes 556 // 15.5.2. Robust tests of two means 558 // 15.5.3. Bayes factors for comparing two means 560 // Key terms 562 // JIGiSAW’s puzzles 562 // 16 COMPRRING SEVERRL MERNS: FRITH IN OTHERS 567 // 16.1. General procedure for comparing means 575 // 16.2. Comparing several means with the linear model 576 // 16.2.1. Dummy coding 577 // 16.2.2. The F-ratio as a test of means 580 // 16.2.3. The total sum of squares (SST) 582 // 16.2.4. The model sum of squares (SSJ 584 // 16.2.5. The residua! sum of squares (SSR) 586 // 16.2.6. Partitioning variance 587 // 16.2.7. Mean squares 588 // 16.2.8. The F-ratio 588 // 16.2.9. Comparing several means using a computer 590 // 16.3. Contrast coding 592 // 16.3.1. Generating contrasts 593 // 16.3.2. Devising weights 596 // 16.3.3. Contrasts and the linear model 597 // 16.3.4. Posf hoc procedures 602 //

16.3.5. Contrasts and post hoc tests using a computer 603 // 16.4. Storm of memories 605 // 16.5. Repeated-measures designs 609 // 16.5.1. The total sum of squares, SST 611 // 16.5.2. The within-participant variance, SSW 611 // 16.5.3. The model sum of squares, SSM 614 // 16.5.4. The residual sum of squares, SSR 614 // 16.5.5. Mean squares and the F-ratio 615 // 16.5.6. Repeated-measures designs using a computer 618 // 16.6. Alternative approaches 619 // 16.6.1. Effect sizes 619 // 16.6.2. Robust tests of several means 621 // 16.6.3. Bayesian analysis of several means 623 // 16.7. The invisible man 625 // Key terms 627 // JIGiSAW’s puzzles 628 // 17 FACTORIAL DESIGNS: PRAYER OF TRANSFORMATION 633 // 17.1. Factorial designs 638 // 17.2. General procedure and assumptions 640 // 17.3. Analysing factorial designs 640 // 17.3.1. Factorial designs and the linear model 640 // 17.3.2. The fit of the model 646 // 17.3.3. Factorial designs on a computer 656 // 17.4. From the pinnacle to the pit 658 // 17.5. Alternative approaches 658 // 17.5.1. Calculating effect sizes 658 // 17.5.2. Robust analysis of factorial designs 660 // 17.5.3. Bayes factors for factorial designs 661 // 17.6. Interpreting interaction effects 662 // Key terms 670 // JIGiSAW’s puzzles 670 // EPILOGUE: THE GENIHL NIGHT: SI MOMENTUM REQUIRIS, CIRCUMSPICE 677 // APPENDIX 685 // Al. The standard normal distribution 685 // A.2. The {-distribution 691 // A.3. Critical values of the chi-square distribution 693 // A.4. Critical values of the F-distribution 694 // GLOSSARY 699 // REFERENCES 727 // INDEX 733 // ACKNOWLEDGEMENTS 743