An adventure in statistics: the reality enigma
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| Format: | Book |
| Language: | English |
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Los Angeles ; London ; New Delhi ; Singapore ; Washington, DC ; Melbourne
Sage
2016
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| Series: | Sage edge
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xvi, 746 Seiten Illustrationen, Diagramme |
| ISBN: | 9781446210444 9781446210451 |
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| 245 | 1 | 0 | |a An adventure in statistics |b the reality enigma |c Andy Field ; illustrated by James Iles |
| 246 | 1 | 3 | |a Statistics |
| 264 | 1 | |a Los Angeles ; London ; New Delhi ; Singapore ; Washington, DC ; Melbourne |b Sage |c 2016 | |
| 300 | |a xvi, 746 Seiten |b Illustrationen, Diagramme | ||
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| adam_text | CONTENTS
HOW TO USE THIS BOOK XV
PROLOGUE: THE DYING STHRS 1
1 WHY YOU NEED SCIENCE 7
2 REPORTING RESERRCH, VHRIHBLES RND MEASUREMENT 39
3 SUMMARIZING ORTH 75
4 FITTING MODELS [CENTRAL TENDENCY] 111
5 PRESENTING DRTR 157
6 Z-SCORES 189
7 PROBABILITY 215
8 INFERENTIAL STATISTICS: GOING BEYOND THE DATA 257
9 ROBUST ESTIMATION 297
10 HYPOTHESIS TESTING 331
11 MODERN APPROACHES TO THEORY TESTING 361
12 ASSUMPTIONS 395
13 RELATIONSHIPS 429
THE GENERAL LINEAR MODEL 477
15 COMPARING TWO MERNS 527
16 COMPARING SEVERAL MEANS 567
17 FACTORIAL DESIGNS 633
EPILOGUE: THE GENIAL NIGHT 677
RPPENDIX 685
GLOSSARY 699
REFERENCES 727
INDEX 733
ACKNOWLEDGEMENTS 743
EXTENDED CONTENTS
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? xvi
What online resources are there? xvi
PROLOGUE: THE DYING STARS 1
1 WHY YOU NEED SCIENCE: THE BEGINNING RND 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
JIGiSAW’s puzzles 36
2 REPORTING RESEARCH, VRRIHBLES RND MERSUREMENT: BREAKING THE LAW 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
JIG:SAW’s puzzles 70
3 SUMMARIZING DATA: SHE LOVES ME NOT? 75
3.1. Frequency distributions 61
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 103
Key terms 107
JIG:SAW’s puzzles 107
4 FITTING MODELS (CENTRAL TENDENCY]: SOMEWHERE IN THE MIDDLE 111
4.1. Statistical models 116
4.1.1. From the dead 116
4.1.2. Why do we need statistical models? 117
4.1.3. Sample size 118
4.1.4. The one and only statistical model 120
4.2. Central tendency 124
4.2.1. The mode 126
4.2.2. The median 128
4.2.3. The mean 129
4.3. The‘fit’of the mean: variance 135
4.3.1. The fit of the mean 136
4.3.2. Estimating the fit of the mean from a sample 141
4.3.3. Outliers and variance 148
4.4. Dispersion 149
4.4.1. The standard deviation as an indicator of dispersion 149
4.4.2. The range and interquartile range 151
Key terms 155
JIG:SAW’s puzzles 155
5 PRESENTING DRTR: 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 131
JIG:SAW’s puzzles 132
6 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
JIG:SAW’s puzzles 212
7 PROBABILITY: 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 catapu Its of death 242
7.3. Conditional probability: Deathscotch 248
Key terms 255
JIG:SAW’s puzzles 255
8 INFERENTIAL STATISTICS: 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
JIG:SAW’s puzzles 296
9 ROBUST ESTIMATION: MRN WITHOUT FRITH 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
JIGrSAW’s puzzles 329
I o HYPOTHESIS TESTING: IN REALITY ALL IS VOID 33I
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. Inflated 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
JIG:SAW’s puzzles 353
II MODERN RPPRORCHES TO THEORY TESTING: R CAREWORN HEART 361
11.1. Problems with NHST 354
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 334
11.4.2. Bayes’theorem revisited 386
11.4.3. Comparing hypotheses 388
11.4.4. Benefits of Bayesian approaches 390
Key terms 393
JIG:SAW’s puzzles 393
12 ASSUMPTIONS: STHRBLIND 395
12.1. Fitting models: bringing it all together 399
12.2. Assumptions 404
12.2.1. Additivity and linearity 405
12.2.2. Independent errors 407
12.2.3. Homoscedasticity/homogeneity of variance 408
12.2.4. Normally distributed something or other 415
12.2.5. External variables 419
12.2.6. Variable types 421
12.2.7. Multicollinearity 421
12.2.8. Non-zero variance 423
12.3. Turning ever towards the sun 423
Key terms 426
JIG:SAW’s puzzles 426
13 RELATIONSHIPS: A STRANGER’S GRAVE 429
13.1. Finding relationships in categorical data 434
13.1.1. Pearson’s chi-square test 436
13.1.2. Assumptions 441
13.1.3. Fisher s exact test 442
13.1.4. Yates s correction 442
13.1.5. The likelihood ratio (G-test) 443
13.1.6. Standardized residuals 444
13.1.7. Calculating an effect size 446
13.1.8. Using a computer 447
13.1.9. Bayes factors for contingency tables 448
13.1.10. Summary 450
13.2. What evil lay dormant 451
13.3. Modelling relationships 453
13.3.1. Covariance 456
13.3.2. Pearson’s correlation coefficient 461
13.3.3. The significance of the correlation coefficient 462
13.3.4. Confidence intervals for r 465
13.3.5. Using a computer 467
13.3.6. Robust estimation of the correlation 467
13.3.7. Bayesian approaches to relationships between two variables 468
13.3.8. Correlation and causation 469
13.3.9. Calculating the effect size 469
13.4. Silent sorrow in empty boats 470
Key terms 474
JIGiSAW’s puzzles 474
477
W THE GENERAL LINEAR MODEL: RED FIRE COMING OUT FROM HIS GILLS
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 Intervalsfor/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
JIG.-SAW’s puzzles 525
15 COMPARING TWO MEANS: 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 f-test 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 f-test 549
15.4.1. The paired-samples f-test 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
JIG:SAW’s puzzles 562
16 COMPARING SEVERAL MEANS: FAITH 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. TheF-ratioasatestofmeans 580
16.2.3. The total sum of squares (SST) 582
16.2.4. The model sum of squares (SSM) 584
16.2.5. The residual sum of squares (SSR) 586
16.2.6. Partitioning variance 587
16.2.7. Mean squares 588
16.2.8. TheF-ratlo 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. Post 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
JIG:SAW’s puzzles 628
17 FHCTORIHL DESIGNS: PRRYER 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 67®
JIG:SAW’s puzzles 670
EPILOGUE: THE GENIAL NIGHT: SI MOMENTUM REQUIRIS, CIRCUMSPICE 677
APPENDIX 685
A.1. 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
|
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| ctrlnum | (OCoLC)951734111 (DE-599)HBZHT018861666 |
| discipline | Pädagogik Soziologie Psychologie Mathematik Wirtschaftswissenschaften |
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| id | DE-604.BV043300163 |
| illustrated | Illustrated |
| indexdate | 2025-02-20T06:43:23Z |
| institution | BVB |
| isbn | 9781446210444 9781446210451 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028721070 |
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| physical | xvi, 746 Seiten Illustrationen, Diagramme |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Sage |
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| spellingShingle | Field, Andy 1973- An adventure in statistics the reality enigma Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4123623-3 |
| title | An adventure in statistics the reality enigma |
| title_alt | Statistics |
| title_auth | An adventure in statistics the reality enigma |
| title_exact_search | An adventure in statistics the reality enigma |
| title_full | An adventure in statistics the reality enigma Andy Field ; illustrated by James Iles |
| title_fullStr | An adventure in statistics the reality enigma Andy Field ; illustrated by James Iles |
| title_full_unstemmed | An adventure in statistics the reality enigma Andy Field ; illustrated by James Iles |
| title_short | An adventure in statistics |
| title_sort | an adventure in statistics the reality enigma |
| title_sub | the reality enigma |
| topic | Statistik (DE-588)4056995-0 gnd |
| topic_facet | Statistik Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028721070&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT fieldandy anadventureinstatisticstherealityenigma AT ilesjames anadventureinstatisticstherealityenigma AT fieldandy statistics AT ilesjames statistics |
Indexes
THWS Schweinfurt Zentralbibliothek Lesesaal
| Call Number: |
2000 QH 231 F453 |
|---|---|
| Copy 1 | ausleihbar Available Place a Hold |