Discovering statistics using SPSS: (and sex and drugs and rock'n'roll)
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Los Angeles [u.a.]
Sage
2011
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| Ausgabe: | 3. ed., reprint. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXXIII, 821 S. Ill., graph. Darst. |
| ISBN: | 9781847879066 9781847879073 |
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| 100 | 1 | |a Field, Andy |d 1973- |e Verfasser |0 (DE-588)128714581 |4 aut | |
| 245 | 1 | 0 | |a Discovering statistics using SPSS |b (and sex and drugs and rock'n'roll) |c Andy Field |
| 250 | |a 3. ed., reprint. | ||
| 264 | 1 | |a Los Angeles [u.a.] |b Sage |c 2011 | |
| 300 | |a XXXIII, 821 S. |b Ill., graph. Darst. | ||
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| 338 | |b nc |2 rdacarrier | ||
| 630 | 0 | 4 | |a SPSS for Windows |
| 650 | 4 | |a Sozialwissenschaften | |
| 650 | 4 | |a Social sciences |x Statistical methods |x Computer programs | |
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Datensatz im Suchindex
| _version_ | 1804148552361836544 |
|---|---|
| adam_text | Titel: Discovering statistics using SPSS
Autor: Field, Andy P.
Jahr: 2011
CONTENTS
Preface xix
How to use this book xxiv
Acknowledgements xxvüi
Dedication xxx
Symbols used in this book xxxi
Some maths revision xxxiii
1 Why is my evil lecturer forcing me to learn statistics? 1
1.1. What will this chapter tell me? © 1
1.2. What the hell am I doing here? I don t belong here © 2
1.2.1. The research process © 3
1.3. Initial observation: finding something that needs explaining © 3
1.4. Generating theories and testing them © 4
1.5. Data collection 1 : what to measure © 7
1.5.1. Variables © 7
1.5.2. Measurement error© 10
1.5.3. Validity and reliability © 11
1.6. Data collection 2: how to measure © 12
1.6.1. Correlational research methods © 12
1.6.2. Experimental research methods © 13
1.6.3. Randomization © 17
1.7. Analysing data © 18
1.7.1. Frequency distributions © 18
1.7.2. The centre of a distribution © 20
1.7.3. The dispersion in a distribution © 23
1.7.4. Using a frequency distribution to go beyond the data © 24
1.7.5. Fitting statistical models to the data © 26
What have I discovered about statistics? © 28
Key terms that I ve discovered 28
Smart Alex s stats quiz 29
Further reading 29
Interesting real research 30
DISCOVERING STATISTICS USING SPSS
2 Everything you ever wanted to know about statistics
(well, sort of) 31
2.1. What will this chapter tell me? © 31
2.2. Building statistical models © 32
2.3. Populations and samples © 34
2.4. Simple statistical models © 35
2.4.1. The mean: a very simple statistical model © 35
2.4.2. Assessing the fit ot the mean: sums of squares, variance and standard
deviations © 35
2.4.3. Expressing the mean as a model © 38
2.5. Going beyond the data © 40
2.5.1. The standard error © 40
2.5.2. Confidence intervals © 43
2.6. Using statistical models to test research questions © 48
2.6.1. Test statistics © 52
2.6.2. One- and two-tailed tests 0 54
2.6.3. Type I and Type II errors © 55
2.6.4. Effect sizes © 56
2.6.5. Statistical power © 58
What have I discovered about statistics? © 59
Key terms that I ve discovered 59
Smart Alex s stats quiz 59
Further reading 60
Interesting real research 60
3 The SPSS environment 61
3.1. What will this chapter tell me? © 61
3.2. Versions of SPSS © 62
3.3. Getting started © 62
3.4. The data editor © 63
3.4.1. Entering data into the data editor © 69
3.4.2. The Variable View © 70
3.4.3. Missing values © 77
3.5. The SPSS viewer © 78
3.6. The SPSS SmartViewer © 81
3.7. The syntax window © 82
3.8. Saving files © 83
3.9. Retrieving a file © 84
What have I discovered about statistics? © 85
Key terms that I ve discovered 85
Smart Alex s tasks 85
Further reading 86
Online tutorials 86
4 Exploring data with graphs 87
4.1. What will this chapter tell me? © 87
4.2. The art of presenting data © 88
4.2.1. What makes a good graph? © 88
4.2.2. Lies, damned lies, and ... erm ... graphs © 90
CONTENTS
4.3. The SPSS Chart Builder© 91
4.4. Histograms: a good way to spot obvious problems © 93
4.5. Boxplots (box-whisker diagrams) © 99
4.6. Graphing means: bar charts and error bars© 103
4.6.1. Simple bar charts for independent means © 105
4.6.2. Clustered bar charts for independent means © 107
4.6.3. Simple bar charts for related means © 109
4.6.4. Clustered bar charts for related means © 111
4.6.5. Clustered bar charts for mixed designs © 113
4.7. Line charts© 115
4.8. Graphing relationships: the scatterplot © 116
4.8.1. Simple scatterplot© 117
4.8.2. Grouped scatterplot© 119
4.8.3. Simple and grouped 3-D scatterplots © 121
4.8.4. Matrix scatterplot© 123
4.8.5. Simple dot plot or density plot© 125
4.8.6. Drop-line graph © 126
4.9. Editing graphs© 126
What have I discovered about statistics? © 129
Key terms that I ve discovered 130
Smart Alex s tasks 130
Further reading 130
Online tutorial 130
Interesting real research 130
5 Exploring assumptions 131
5.1. What will this chapter tell me? © 131
5.2. What are assumptions?© 132
5.3. Assumptions of parametric data © 132
5.4. The assumption of normality© 133
5.4.1. Oh no, it s that pesky frequency distribution again: checking
normality visually © 134
5.4.2. Quantifying normality with numbers © 136
5.4.3. Exploring groups of data © 140
5.5. Testing whether a distribution is normal © 144
5.5.1. Doing the Kolmogorov-Smimov test on SPSS © 145
5.5.2. Output from the explore procedure © 146
5.5.3. Reporting the K-S test © 148
5.6. Testing for homogeneity of variance © 149
5.6.1. Levene stest© 150
5.6.2. Reporting Levene stest© 152
5.7. Correcting problems in the data © 153
5.7.1. Dealing with outliers © 153
5.7.2. Dealing with non-normality and unequal variances© 153
5.7.3. Transforming the data using SPSS © 156
5.7.4. When it all goes horribly wrong © 162
What have I discovered about statistics? © 164
Key terms that I ve discovered 164
Smart Alex s tasks 165
Online tutorial 165
Further reading 165
DISCOVERING STATISTICS USING SPSS
6 Correlation 166
6.1. What will this chapter tell me? © 166
6.2. Looking at relationships © 167
6.3. How do we measure relationships? © 167
6.3.1. A detour into the murky world of covariance © 167
6.3.2. Standardization and the correlation coefficient © 169
6.3.3. The significance of the correlation coefficient © 171
6.3.4. Confidence intervals for r © 172
6.3.5. A word of warning about interpretation: causality © 173
6.4. Data entry for correlation analysis using SPSS © 174
6.5. Bivariate correlation © 175
6.5.1. General procedure for running correlations on SPSS © 175
6.5.2. Pearson s correlation coefficient © 177
6.5.3. Spearman s correlation coefficient © 179
6.5.4. Kendall s tau (non-parametric) © 181
6.5.5. Biserial and point-biserial correlations ® 182
6.6. Partial correlation © 186
6.6.1. The theory behind part and partial correlation © 186
6.6.2. Partial correlation using SPSS © 188
6.6.3. Semi-partial (or part) correlations © 190
6.7. Comparing correlations ® 191
6.7.1. Comparing independent rs ® 191
6.7.2. Comparing dependent rs ® 191
6.8. Calculating the effect size © 192
6.9. How to report correlation coefficents © 193
What have I discovered about statistics? © 195
Key terms that I ve discovered 195
Smart Alex s tasks 195
Further reading 196
Online tutorial 196
Interesting real research 196
7 Regression 197
7.1. What will this chapter tell me? © 197
7.2. An introduction to regression © 198
7.2.1. Some important information about straight lines © 199
7.2.2. The method of least squares © 200
7.2.3. Assessing the goodness of fit: sums of squares, R and R2 © 201
7.2.4. Assessing individual predictors © 204
7.3. Doing simple regression on SPSS © 205
7.4. Interpreting a simple regression © 206
7.4.1. Overall fit of the model © 206
7.4.2. Model parameters © 207
7.4.3. Using the model © 208
7.5. Multiple regression: the basics © 209
7.5.1. An example of a multiple regression model © 210
7.5.2. Sums of squares, R and R2 © 211
7.5.3. Methods of regression © 212
7.6. How accurate is my regression model? © 214
CONTENTS
7.6.1. Assessing the regression model I: diagnostics © 214
7.5.2. Assessing the regression model II: generalization © 220
7.7. How to do multiple regression using SPSS © 225
7.7.1. Some things to think about before the analysis © 225
7.7.2. Main options © 225
7.7.3. Statistics© 227
7.7.4. Regression plots © 229
7.7.5. Saving regression diagnostics © 230
7.7.6. Further options © 231
7.8. Interpreting multiple regression © 233
7.8.1. Descriptives © 233
7.8.2. Summary of model © 234
7.8.3. Model parameters © 237
7.8.4. Excluded variables© 241
7.8.5. Assessing the assumption of no multicollinearity © 241
7.8.6. Casewise diagnostics © 244
7.8.7. Checking assumptions © 247
7.9. What if I violate an assumption? © 251
7.10. How to report multiple regression © 252
7.11. Categorical predictors and multiple regression ® 253
7.11.1. Dummy coding© 253
7.11.2. SPSS output for dummy variables © 256
What have I discovered about statistics? © 261
Key terms that I ve discovered 261
Smart Alex s tasks 262
Further reading 263
Online tutorial 263
Interesting real research 263
8 Logistic regression 264
8.1. What will this chapter tell me? © 264
8.2. Background to logistic regression © 265
8.3. What are the principles behind logistic regression? ® 265
8.3.1. Assessing the model: the log-likelihood statistic® 267
8.3.2. Assessing the model: R and FP © 268
8.3.3. Assessing the contribution of predictors: the Wald statistic © 269
8.3.4. The odds ratio: Exp(B) © 270
8.3.5. Methods of logistic regression © 271
8.4. Assumptions and things that can go wrong © 273
8.4.1. Assumptions © 273
8.4.2. Incomplete information from the predictors © 273
8.4.3. Complete separation © 274
8.4.4. Overdispersion © 276
8.5. Binary logistic regression: an example that will make you feel eel © 277
8.5.1. The main analysis © 278
8.5.2. Method of regression © 279
8.5.3. Categorical predictors © 279
8.5.4. Obtaining residuals © 280
8.5.5. Further options © 281
8.6. Interpreting logistic regression © 282
DISCOVERING STATISTICS USING SPSS
8.6.1. The initial model © 282
8.6.2. Step 1 : intervention © 284
8.6.3. Listing predicted probabilities © 291
8.6.4. Interpreting residuals © 292
8.6.5. Calculating the effect size © 294
8.7. How to report logistic regression © 294
8.8. Testing assumptions: another example © 294
8.8.1. Testing for linearity of the logit ® 296
8.8.2. Testing for multicollinearity ® 297
8.9. Predicting several categories: multinomial logistic regression © 300
8.9.1. Running multinomial logistic regression in SPSS © 301
8.9.2. Statistics © 304
8.9.3. Other options ® 305
8.9.4. Interpreting the multinomial logistic regression output ® 306
8.9.5. Reporting the results 312
What have I discovered about statistics? © 313
Key terms that I ve discovered 313
Smart Alex s tasks 313
Further reading 315
Online tutorial 315
Interesting real research 315
9 Comparing two means 316
9.1. What will this chapter tell me? © 316
9.2. Looking at differences © 317
9.2.1. A problem with error bar graphs of repeated-measures designs © 317
9.2.2. Step 1 : calculate the mean for each participant © 320
9.2.3. Step 2: calculate the grand mean © 320
9.2.4. Step 3: calculate the adjustment factor © 322
9.2.5. Step 4: create adjusted values for each variable © 323
9.3. The t-test © 324
9.3.1. Rationale for the i-test © 325
9.3.2. Assumptions of the i-test © 326
9.4. The dependent f-test © 326
9.4.1. Sampling distributions and the standard error © 327
9.4.2. The dependent i-test equation explained © 327
9.4.3. The dependent f-test and the assumption of normality © 329
9.4.4. Dependent f-tests using SPSS © 329
9.4.5. Output from the dependent f-test © 330
9.4.6. Calculating the effect size © 332
9.4.7. Reporting the dependent f-test © 333
9.5. The independent f-test © 334
9.5.1. The independent f-test equation explained © 334
9.5.2. The independent f-test using SPSS © 337
9.5.3. Output from the independent f-test © 339
9.5.4. Calculating the effect size © 341
9.5.5. Reporting the independent f-test © 341
9.6. Between groups or repeated measures? © 342
9.7. The i-test as a general linear model © 342
9.8. What if my data are not normally distributed? © 344
CONTENTS
What have I discovered about statistics? © 345
Key terms that I ve discovered 345
Smart Alex s task 346
Further reading 346
Online tutorial 346
Interesting real research 346
10 Comparing several means: ANOVA (GLM 1) 347
10.1. What will this chapter tell me? © 347
10.2. The theory behind ANOVA© 348
10.2.1. Inflated error rates© 348
10.2.2. Interpreting F© 349
10.2.3. ANOVA as regression© 349
10.2.4. Logic of the F-ratio © 354
10.2.5. Total sum of squares (SST)© 356
10.2.6. Model sum of squares (SSM) © 356
10.2.7. Residual sum of squares (SSR)@ 357
10.2.8. Mean squares© 358
10.2.9. The F-ratio© 358
10.2.10. Assumptions of ANOVA® 359
10.2.11. Planned contrasts© 360
10.2.12. Posi hoc procedures© 372
10.3. Running one-way ANOVA on SPSS © 375
10.3.1. Planned comparisons using SPSS© 376
10.3.2. Post hoc tests in SPSS © 378
10.3.3. Options© 379
10.4. Output from one-way ANOVA© 381
10.4.1. Output for the main analysis © 381
10.4.2. Output for planned comparisons © 384
10.4.3. Output for post hoc tests © 385
10.5. Calculating the effect size© 389
10.6. Reporting results from one-way independent ANOVA © 390
10.7. Violations of assumptions in one-way independent ANOVA© 391
What have I discovered about statistics? © 392
Key terms that I ve discovered 392
Smart Aiex s tasks 393
Further reading 394
Online tutorials 394
Interesting real research 394
11 Analysis of covariance, ANC0VA (GLM 2) 395
11.1. What will this chapter tell me? © 395
11.2. WhatisANCOVA?© 396
11.3. Assumptions and issues in ANCOVA® 397
11.3.1. Independence of the covariate and treatment effect® 397
11.3.2. Homogeneity of regression slopes © 399
11.4. Conducting ANCOVA on SPSS© 399
11.4.1. Inputting data© 399
11.4.2. Initial considerations: testing the independence of the independent
variable and covariate© 400
x¡¡ DISCOVERING STATISTICS USING SPSS
11.4.3. The main analysis © 401
11.4.4. Contrasts and other options © 401
11.5. Interpreting the output from ANCOVA © 404
11.5.1. What happens when the covariate is excluded? © 404
11.5.2. The main analysis © 405
11.5.3. Contrasts © 407
11.5.4. Interpreting the covariate © 408
11.6. ANCOVA run as a multiple regression © 408
11.7. Testing the assumption of homogeneity of regression slopes ® 413
11.8. Calculating the effect size © 415
11.9. Reporting results © 417
11.10. What to do when assumptions are violated in ANCOVA ® 418
What have I discovered about statistics? © 418
Key terms that I ve discovered 419
Smart Alex s tasks 419
Further reading 420
Online tutorials 420
Interesting real research 420
12 Factorial ANOVA (GLM 3) 421
12.1. What will this chapter tell me? © 421
12.2. Theory of factorial ANOVA (between-groups) © 422
12.2.1. Factorial designs © 422
12.2.2. An example with two independent variables © 423
12.2.3. Total sums of squares (SST) © 424
12.2.4. The model sum of squares (SSJ © 426
12.2.5. The residual sum of squares (SSR) © 428
12.2.6. The F-ratios © 429
12.3. Factorial ANOVA using SPSS © 430
12.3.1. Entering the data and accessing the main dialog box © 430
12.3.2. Graphing interactions © 432
12.3.3. Contrasts © 432
12.3.4. Posi hoc tests© 434
12.3.5. Options © 434
12.4. Output from factorial ANOVA © 435
12.4.1. Output for the preliminary analysis © 435
12.4.2. Levene s test © 436
12.4.3. The main ANOVA table© 436
12.4.4. Contrasts © 439
12.4.5. Simple effects analysis ® 440
12.4.6. Post hoc analysis © 44I
12.5. Interpreting interaction graphs © 443
12.6. Calculating effect sizes ® 446
12.7. Reporting the results of two-way ANOVA © 448
12.8. Factorial ANOVA as regression © 450
12.9. What to do when assumptions are violated in factorial ANOVA © 454
What have I discovered about statistics? © 454
Key terms that I ve discovered 455
Smart Alex s tasks 455
CONTENTS xiü
Further reading 456
Online tutorials 456
Interesting real research 456
13 Repeated-measures designs (GLM 4) 457
13.1. What will this chapter tell me? © 457
13.2. Introduction to repeated-measures designs © 458
13.2.1. The assumption of sphericity © 459
13.2.2. How is sphericity measured? © 459
13.2.3. Assessing the severity of departures from sphericity © 460
13.2.4. What is the effect of violating the assumption of sphericity? ® 460
13.2.5. What do you do if you violate sphericity? © 461
13.3. Theory of one-way repeated-measures ANOVA © 462
13.3.1. The total sum of squares (SS,.) © 464
13.3.2. The within-participant (SSW) © 465
13.3.3. The model sum of squares (SSJ © 466
13.3.4. The residual sum of squares (SSR) © 467
13.3.5. The mean squares © 467
13.3.6. The F-ratio © 467
13.3.7. The between-participant sum of squares © 468
13.4. One-way repeated-measures ANOVA using SPSS © 468
13.4.1. The main analysis © 468
13.4.2. Defining contrasts for repeated-measures © 471
13.4.3. Posi hoc tests and additional options ® 471
13.5. Output for one-way repeated-measures ANOVA © 474
13.5.1. Descriptives and other diagnostics © 474
13.5.2. Assessing and correcting for sphericity: Mauchly s test © 474
13.5.3. The main ANOVA© 475
13.5.4. Contrasts © 477
13.5.5. Post hoc tests© 478
13.6. Effect sizes for repeated-measures ANOVA © 479
13.7. Reporting one-way repeated-measures ANOVA © 481
13.8. Repeated-measures with several independent variables © 482
13.8.1. The main analysis © 484
13.8.2. Contrasts © 488
13.8.3. Simple effects analysis © 488
13.8.4. Graphing interactions © 490
13.8.5. Other options © 491
13.9. Output for factorial repeated-measures ANOVA © 492
13.9.1. Descriptives and main analysis © 492
13.9.2. The effect of drink © 493
13.9.3. The effect of imagery © 495
13.9.4. The interaction effect (drink x imagery) © 496
13.9.5. Contrasts for repeated-measures variables © 498
13.10. Effect sizes for factorial repeated-measures ANOVA © 501
13.11. Reporting the results from factorial repeated-measures ANOVA © 502
13.12. What to do when assumptions are violated in repeated-measures ANOVA © 503
What have I discovered about statistics? © 503
Key terms that I ve discovered 504
x¡v DISCOVERING STATISTICS USING SPSS
Smart Alex s tasks 504
Further reading 505
Online tutorials 505
Interesting real research 505
14 Mixed design ANOVA (GLM 5) 506
14.1. What will this chapter tell me? © 506
14.2. Mixed designs © 507
14.3. What do men and women look for in a partner? © 508
14.4. Mixed ANOVA on SPSS © 508
14.4.1. The main analysis © 508
14.4.2. Other options © 513
14.5. Output for mixed factorial ANOVA: main analysis ® 514
14.5.1. The main effect of gender © 517
14.5.2. The main effect of looks © 518
14.5.3. The main effect of charisma © 520
14.5.4. The interaction between gender and looks © 521
14.5.5. The interaction between gender and charisma © 523
14.5.6. The interaction between attractiveness and charisma © 524
14.5.7. The interaction between looks, charisma and gender © 527
14.5.8. Conclusions ® 530
14.6. Calculating effect sizes © 531
14.7. Reporting the results of mixed ANOVA © 533
14.8. What to do when assumptions are violated in mixed ANOVA ® 536
What have I discovered about statistics? © 536
Key terms that I ve discovered 537
Smart Alex s tasks 537
Further reading 538
Online tutorials 538
Interesting real research 538
15 Non-parametric tests 539
15.1. What will this chapter tell me? © 539
15.2. When to use non-parametric tests © 540
15.3. Comparing two independent conditions: the Wilcoxon rank-sum test and
Mann-Whitney test © 540
15.3.1. Theory © 542
15.3.2. Inputting data and provisional analysis © 545
15.3.3. Running the analysis © 546
15.3.4. Output from the Mann-Whitney test © 548
15.3.5. Calculating an effect size © 550
15.3.6. Writing the results © 550
15.4. Comparing two related conditions: the Wilcoxon signed-rank test © 552
15.4.1. Theory of the Wilcoxon signed-rank test © 552
15.4.2. Running the analysis © 554
15.4.3. Output for the ecstasy group © 556
15.4.4. Output for the alcohol group © 557
15.4.5. Calculating an effect size © 558
15.4.6. Writing the results © 558
CONTENTS xv
15.5. Differences between several independent groups: the Kruskal-Wallis test © 559
15.5.1. Theory of the Kruskal-Wallis test© 560
15.5.2. Inputting data and provisional analysis © 562
15.5.3. Doing the Kruskal-Wallis test on SPSS © 562
15.5.4. Output from the Kruskal-Wallis test © 564
15.5.5. Post hoc tests for the Kruskal-Wallis test© 565
15.5.6. Testing for trends: the Jonckheere-Terpstra test © 568
15.5.7. Calculating an effect size © 570
15.5.8. Writing and interpreting the results © 571
15.6. Differences between several related groups: Friedman s ANOVA © 573
15.6.1. Theory of Friedman s ANOVA © 573
15.6.2. Inputting data and provisional analysis © 575
15.6.3. Doing Friedman s ANOVA on SPSS © 575
15.6.4. Output from Friedman s ANOVA© 576
15.6.5. Post hoc tests for Friedman s ANOVA © 577
15.6.6. Calculating an effect size © 579
15.6.7. Writing and interpreting the results © 580
What have I discovered about statistics? © 581
Key terms that I ve discovered 582
Smart Alex s tasks 582
Further reading 583
Online tutorial 583
Interesting real research 583
16 Multivariate analysis of variance (MANOVA) 584
16.1. What will this chapter tell me? © 584
16.2. When to use MANOVA © 585
16.3. Introduction: similarities and differences to ANOVA © 585
16.3.1. Words of warning © 587
16.3.2. The example for this chapter © 587
16.4. Theory of MANOVA © 588
16.4.1. Introduction to matrices © 588
16.4.2. Some important matrices and their functions © 590
16.4.3. Calculating MANOVA by hand: a worked example ® 591
16.4.4. Principle of the MANOVA test statistic © 598
16.5. Practical issues when conducting MANOVA © 603
16.5.1. Assumptions and how to check them © 603
16.5.2. Choosing a test statistic © 604
16.5.3. Follow-up analysis © 605
16.6. MANOVA on SPSS © 605
16.6.1. The main analysis © 606
16.6.2. Multiple comparisons in MANOVA© 607
16.6.3. Additional options © 607
16.7. Output from MANOVA® 608
16.7.1. Preliminary analysis and testing assumptions © 608
16.7.2. MANOVA test statistics® 608
16.7.3. Univariate test statistics © 609
16.7.4. SSCP Matrices ® 611
16.7.5. Contrasts® 613
xv¡ DISCOVERING STATISTICS USING SPSS
16.8. Reporting results from MANOVA © 614
16.9. Following up MANOVA with discriminant analysis ® 615
16.10. Output from the discriminant analysis © 618
16.11. Reporting results from discriminant analysis © 621
16.12. Some final remarks © 622
16.12.1. The final interpretation © 622
16.12.2. Univariate ANOVA or discriminant analysis? 624
16.13. What to do when assumptions are violated in MANOVA © 624
What have I discovered about statistics? © 624
Key terms that I ve discovered 625
Smart Alex s tasks 625
Further reading 626
Online tutorials 626
Interesting real research 626
17 Exploratory factor analysis 627
627
628
628
630
631
633
636
636
637
638
638
639
642
645
645
650
651
653
654
654
655
656
660
664
669
671
671
673
673
675
676
678
681
17.1. What will this chapter tell me? ©
17.2. When tc ) use factor analysis ©
17.3. Factors ©
17.3.1. Graphical representation of factors ©
17.3.2. Mathematical representation of lactors ©
17.3.3. Factor scores ©
17.4. Discovering factors ©
17.4.1. Choosing a method ©
17.4.2. Communality ©
17.4.3. Factor analysis vs. principal component analysis ©
17.4.4. Theory behind principal component analysis ©
17.4.5. Factor extraction: eigenvalues and the scree plot ©
17.4.6. Improving interpretation: factor rotation ©
17.5. Research example ©
17.5.1. Before you begin ©
17.6. Running the analysis ©
17.6.1. Factor extraction on SPSS ©
17.6.2. Rotation ©
17.6.3. Scores ©
17.6.4. Options ©
17.7. Interpreting output from SPSS ©
17.7.1. Preliminary analysis ©
17.7.2. Factor extraction ©
17.7.3. Factor rotation ©
17.7.4. Factor scores ©
17.7.5. Summary ©
17.8. How to report factor analysis ©
17.9. Reliability analysis ©
17.9.1. Measures of reliability ©
17.9.2. Interpreting Cronbach s a (some cautionary tales ...) ©
17.9.3. Reliability analysis on SPSS ©
17.9.4. Interpreting the output ©
17.10. How to report reliability analysis ©
CONTENTS
What have I discovered about statistics? © 682
Key terms that I ve discovered 682
Smart Alex s tasks 683
Further reading 685
Online tutorial 685
Interesting real research 685
18 Categorical data 686
18.1. What will this chapter tell me? © 686
18.2. Analysing categorical data © 687
18.3. Theory of analysing categorical data © 687
18.3.1. Pearson s chi-square test © 688
18.3.2. Fisher s exact test © 690
18.3.3. The likelihood ratio © 690
18.3.4. Yates1 correction © 691
18.4. Assumptions of the chi-square test © 691
18.5. Doing chi-square on SPSS © 692
18.5.1. Entering data: raw scores © 692
18.5.2. Entering data: weight cases © 692
18.5.3. Running the analysis © 694
18.5.4. Output for the chi-square test © 696
18.5.5. Breaking down a significant chi-square test with standardized residuals © 698
18.5.6. Calculating an effect size © 699
18.5.7. Reporting the results of chi-square © 700
18.6. Several categorical variables: loglinear analysis ® 702
18.6.1. Chi-square as regression © 702
18.6.2. Loglinear analysis © 708
18.7. Assumptions in loglinear analysis © 710
18.8. Loglinear analysis using SPSS © 711
18.8.1. Initial considerations © 711
18.8.2. The logllnear analysis © 712
18.9. Output from loglinear analysis ® 714
18.10. Following up loglinear analysis © 719
18.11. Effect sizes in loglinear analysis © 720
18.12. Reporting the results of loglinear analysis © 721
What have I discovered about statistics? © 722
Key terms that I ve discovered 722
Smart Alex s tasks - 722
Further reading 724
Online tutorial 724
Interesting real research 724
19 Multilevel linear models 725
19.1. What will this chapter tell me? © 725
19.2. Hierarchical data © 726
19.2.1. The intraclass correlation © 728
19.2.2. Benefits of multilevel models © 729
19.3. Theory of multilevel linear models ® 730
XVIII
DISCOVERING STATISTICS USING SPSS
19.3.1. An example ©
19.3.2. Fixed and random coefficients ©
19.4. The multilevel model ©
19.4.1. Assessing the fit and comparing multilevel models ©
19.4.2. Types of covariance structures ©
19.5. Some practical issues ®
19.5.1. Assumptions ©
19.5.2. Sample size and power ©
19.53. Centring variables ©
19.6. Multilevel modelling on SPSS ©
19.6.1. Entering the data ©
19.6.2. Ignoring the data structure: ANOVA ©
19.6.3. Ignoring the data structure: ANCOVA ©
19.6.4. Factoring in the data structure: random intercepts ©
19.6.5. Factoring in the data structure: random intercepts and slopes ©
19.6.6. Adding an interaction to the model ©
19.7. Growth models ©
19.7.1. Growth curves (polynomials) ©
19.7.2. An example: the honeymoon period ©
19.7.3. Restructuring the data ©
19.7.4. Running a growth model on SPSS ©
19.7.5. Further analysis ©
19.8. How to report a multilevel model ®
What have I discovered about statistics? ©
Key terms that I ve discovered
Smart Alex s tasks
Further reading
Online tutorial
Interesting real research
Epilogue
Glossary
Appendix
A.l.
A.2.
A.3.
A.4.
References
Index
Table of the standard normal distribution
Critical values of the i-distribution
Critical values of the F-distribution
Critical values of the chi-square distribution
730
732
734
737
737
739
739
740
740
741
742
742
746
749
752
756
761
761
761
763
767
774
775
776
777
777
778
778
778
779
781
797
797
803
804
808
809
816
|
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| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
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| dewey-search | 519.50285536 519.5'0285536 22 22 |
| dewey-sort | 3519.50285536 |
| dewey-tens | 510 - Mathematics |
| discipline | Allgemeines Informatik Soziologie Psychologie Mathematik |
| edition | 3. ed., reprint. |
| format | Book |
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| language | English |
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| spelling | Field, Andy 1973- Verfasser (DE-588)128714581 aut Discovering statistics using SPSS (and sex and drugs and rock'n'roll) Andy Field 3. ed., reprint. Los Angeles [u.a.] Sage 2011 XXXIII, 821 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier SPSS for Windows Sozialwissenschaften Social sciences Statistical methods Computer programs Statistik (DE-588)4056995-0 gnd rswk-swf SPSS (DE-588)4056588-9 gnd rswk-swf SPSS (DE-588)4056588-9 s Statistik (DE-588)4056995-0 s 1\p DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024534662&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Field, Andy 1973- Discovering statistics using SPSS (and sex and drugs and rock'n'roll) SPSS for Windows Sozialwissenschaften Social sciences Statistical methods Computer programs Statistik (DE-588)4056995-0 gnd SPSS (DE-588)4056588-9 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4056588-9 |
| title | Discovering statistics using SPSS (and sex and drugs and rock'n'roll) |
| title_auth | Discovering statistics using SPSS (and sex and drugs and rock'n'roll) |
| title_exact_search | Discovering statistics using SPSS (and sex and drugs and rock'n'roll) |
| title_full | Discovering statistics using SPSS (and sex and drugs and rock'n'roll) Andy Field |
| title_fullStr | Discovering statistics using SPSS (and sex and drugs and rock'n'roll) Andy Field |
| title_full_unstemmed | Discovering statistics using SPSS (and sex and drugs and rock'n'roll) Andy Field |
| title_short | Discovering statistics using SPSS |
| title_sort | discovering statistics using spss and sex and drugs and rock n roll |
| title_sub | (and sex and drugs and rock'n'roll) |
| topic | SPSS for Windows Sozialwissenschaften Social sciences Statistical methods Computer programs Statistik (DE-588)4056995-0 gnd SPSS (DE-588)4056588-9 gnd |
| topic_facet | SPSS for Windows Sozialwissenschaften Social sciences Statistical methods Computer programs Statistik SPSS |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024534662&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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