Discovering statistics using SPSS: (and sex and drugs and rock'n'roll)
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| New Title: | Field, Andy Discovering statistics using IBM SPSS statistics |
|---|---|
| Main Author: | |
| Format: | Book |
| Language: | English |
| Published: |
Los Angeles ; London ; New Delhi
Sage
[2009]
|
| Edition: | Third edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | xxxiii, 821 Seiten Illustrationen, Diagramme |
| 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 Third edition | ||
| 264 | 1 | |a Los Angeles ; London ; New Delhi |b Sage |c [2009] | |
| 300 | |a xxxiii, 821 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
| 630 | 0 | 4 | |a SPSS for Windows |
| 650 | 4 | |a Sozialwissenschaften | |
| 650 | 4 | |a Social sciences |x Statistical methods |x Computer programs | |
| 650 | 0 | 7 | |a SPSS |0 (DE-588)4056588-9 |2 gnd |9 rswk-swf |
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| 689 | 0 | |5 DE-604 | |
| 785 | 0 | 0 | |i 4. Aufl. u.d.T. |a Field, Andy |t Discovering statistics using IBM SPSS statistics |w (DE-604)BV040707649 |
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| adam_text |
CONTENTS
Preface
xix
How to use this book
xxiv
Acknowledgements
xxviii
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
vi DISCOVERING
STATISTICS USING SPSS
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 of 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
©
54
2.6.3.
Type
1
and Type II errors
©
55
2.6.4.
Effect sizes
©
56
2.6.5.
Statistical power
©
58
What have
1
discovered about statistics?
©
59
Key terms that I've discovered
59
Smart Alex's stats quiz
59
Further reading
60
Interesting real research
60
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
φ
82
3.8.
Saving files
©
83
3.9.
Retrieving a file
©
84
What have
1
discovered about statistics?
©
85
Key terms that I've discovered
85
Smart Alex's tasks
85
Further reading
86
Online tutorials
86
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
Vìi
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 Koimogorov-Smirnov test on SPSS
© 145
5.5.2.
Output from the explore procedure
© 146
5.5.3.
Reporting the K-S test
φ
148
5.6.
Testing for homogeneity of variance
© 149
5.6.1.
Levene's test
© 150
5.6.2.
Reporting Levene's test
© 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 ail 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
viii
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
forr®
172
6.3.5.
A word of warning about interpretation: causality
© 173
6.4.
Data entry for correlation analysis using SPSS
© 174
6.5.
Divariate
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,
fì
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,
fì
and R2
© 211
7.5.3.
Methods of regression
© 212
7.6.
How accurate is my regression model?
© 214
CONTENTS ix
7.6.1.
Assessing the regression model I: diagnostics
© 214
7.6.2.
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.
Casewlse 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 /?2
© 268
8.3.3.
Assessing the contribution of predictors: the
Wald
statistic
© 269
8.3.4.
The odds ratio: Exp(B)
φ
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?
φ
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
ŕ-test
© 324
9.3.1.
Rationale for the f-test
© 325
9.3.2.
Assumptions of the f-test
© 326
9.4.
The dependent
ŕ-test
© 326
9.4.1.
Sampling distributions and the standard error
© 327
9.4.2.
The dependent f-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
ŕ-test
φ
330
9.4.6.
Calculating the effect size
© 332
9.4.7.
Reporting the dependent
ŕ-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
φ
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 f-test as a general linear model
© 342
9.8.
What if my data are not normally distributed?
© 344
CONTENTS
XI
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.
Post 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 Alex'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.
What is ANCOVA?
© 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
XII
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
1
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
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 (SSM)
©
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.
Post 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
©
441
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
1
discovered about statistics?
©
454
Key terms that I've discovered
455
Smart Alex's tasks
455
CONTENTS xiii
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 (SST)
© 464
13.3.2.
The within-participant (SSW)
© 465
13.3.3.
The model sum of squares (SSM)
© 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-partlcipant 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.
Post 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
χ
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
(MÁNOVA)
16.1.
What will this chapter tell me?
©
16.2.
When to use
MÁNOVA
©
16.3.
Introduction: similarities and differences to ANOVA
©
16.3.1.
Words of warning
©
16.3.2.
The example for this chapter
©
16.4.
Theory of
MÁNOVA ®
16.4.1.
Introduction to matrices
φ
16.4.2.
Some important matrices and their functions
©
16.4.3.
Calculating
MÁNOVA
by hand: a worked example
©
16.4.4.
Principle of the
MÁNOVA
test statistic
©
16.5.
Practical issues when conducting
MÁNOVA
©
16.5.1.
Assumptions and how to check them ®
16.5.2.
Choosing a test statistic ®
16.5.3.
Follow-up analysis
©
16.6.
MÁNOVA
on SPSS
©
16.6.1.
The main analysis
©
16.6.2.
Multiple comparisons in
MÁNOVA
©
16.6.3.
Additional options ®
16.7.
Output from
MÁNOVA
©
16.7.1.
Preliminary analysis and testing assumptions ®
16.7.2.
MÁNOVA
test statistics
©
16.7.3.
Univariate test statistics
©
16.7.4.
SSCP Matrices
©
16.7.5.
Contrasts
©
584
584
585
585
587
587
588
588
590
591
598
603
603
604
605
605
606
607
607
608
608
608
609
611
613
xvi
DISCOVERING
STATISTICS USING SPSS
16.8.
Reporting results from
MÁNOVA
© 614
16.9.
Following up
MÁNOVA
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
MÁNOVA
© 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
ι
use factor analysis
©
17.3.
Factors
©
17.3.1.
Graphical representation of factors
©
17.3.2.
Mathematical representation of factors
©
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 xvii
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.
Yates'
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
φ
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
loglinear
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 multiievel 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
©
19.5.3.
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 (D
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
АЛ.
A.2.
A.3.
A.4.
References
Index
Table of the standard normal distribution
Critical values of the r-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 |
| adam_txt |
CONTENTS
Preface
xix
How to use this book
xxiv
Acknowledgements
xxviii
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
vi DISCOVERING
STATISTICS USING SPSS
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 of 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
©
54
2.6.3.
Type
1
and Type II errors
©
55
2.6.4.
Effect sizes
©
56
2.6.5.
Statistical power
©
58
What have
1
discovered about statistics?
©
59
Key terms that I've discovered
59
Smart Alex's stats quiz
59
Further reading
60
Interesting real research
60
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
φ
82
3.8.
Saving files
©
83
3.9.
Retrieving a file
©
84
What have
1
discovered about statistics?
©
85
Key terms that I've discovered
85
Smart Alex's tasks
85
Further reading
86
Online tutorials
86
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
Vìi
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 Koimogorov-Smirnov test on SPSS
© 145
5.5.2.
Output from the explore procedure
© 146
5.5.3.
Reporting the K-S test
φ
148
5.6.
Testing for homogeneity of variance
© 149
5.6.1.
Levene's test
© 150
5.6.2.
Reporting Levene's test
© 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 ail 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
viii
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
forr®
172
6.3.5.
A word of warning about interpretation: causality
© 173
6.4.
Data entry for correlation analysis using SPSS
© 174
6.5.
Divariate
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,
fì
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,
fì
and R2
© 211
7.5.3.
Methods of regression
© 212
7.6.
How accurate is my regression model?
© 214
CONTENTS ix
7.6.1.
Assessing the regression model I: diagnostics
© 214
7.6.2.
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.
Casewlse 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 /?2
© 268
8.3.3.
Assessing the contribution of predictors: the
Wald
statistic
© 269
8.3.4.
The odds ratio: Exp(B)
φ
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?
φ
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
ŕ-test
© 324
9.3.1.
Rationale for the f-test
© 325
9.3.2.
Assumptions of the f-test
© 326
9.4.
The dependent
ŕ-test
© 326
9.4.1.
Sampling distributions and the standard error
© 327
9.4.2.
The dependent f-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
ŕ-test
φ
330
9.4.6.
Calculating the effect size
© 332
9.4.7.
Reporting the dependent
ŕ-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
φ
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 f-test as a general linear model
© 342
9.8.
What if my data are not normally distributed?
© 344
CONTENTS
XI
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.
Post 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 Alex'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.
What is ANCOVA?
© 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
XII
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
1
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
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 (SSM)
©
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.
Post 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
©
441
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
1
discovered about statistics?
©
454
Key terms that I've discovered
455
Smart Alex's tasks
455
CONTENTS xiii
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 (SST)
© 464
13.3.2.
The within-participant (SSW)
© 465
13.3.3.
The model sum of squares (SSM)
© 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-partlcipant 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.
Post 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
χ
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
(MÁNOVA)
16.1.
What will this chapter tell me?
©
16.2.
When to use
MÁNOVA
©
16.3.
Introduction: similarities and differences to ANOVA
©
16.3.1.
Words of warning
©
16.3.2.
The example for this chapter
©
16.4.
Theory of
MÁNOVA ®
16.4.1.
Introduction to matrices
φ
16.4.2.
Some important matrices and their functions
©
16.4.3.
Calculating
MÁNOVA
by hand: a worked example
©
16.4.4.
Principle of the
MÁNOVA
test statistic
©
16.5.
Practical issues when conducting
MÁNOVA
©
16.5.1.
Assumptions and how to check them ®
16.5.2.
Choosing a test statistic ®
16.5.3.
Follow-up analysis
©
16.6.
MÁNOVA
on SPSS
©
16.6.1.
The main analysis
©
16.6.2.
Multiple comparisons in
MÁNOVA
©
16.6.3.
Additional options ®
16.7.
Output from
MÁNOVA
©
16.7.1.
Preliminary analysis and testing assumptions ®
16.7.2.
MÁNOVA
test statistics
©
16.7.3.
Univariate test statistics
©
16.7.4.
SSCP Matrices
©
16.7.5.
Contrasts
©
584
584
585
585
587
587
588
588
590
591
598
603
603
604
605
605
606
607
607
608
608
608
609
611
613
xvi
DISCOVERING
STATISTICS USING SPSS
16.8.
Reporting results from
MÁNOVA
© 614
16.9.
Following up
MÁNOVA
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
MÁNOVA
© 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
ι
use factor analysis
©
17.3.
Factors
©
17.3.1.
Graphical representation of factors
©
17.3.2.
Mathematical representation of factors
©
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 xvii
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.
Yates'
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
φ
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
loglinear
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 multiievel 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
©
19.5.3.
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 (D
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
АЛ.
A.2.
A.3.
A.4.
References
Index
Table of the standard normal distribution
Critical values of the r-distribution
Critical values of the F-distribution
Critical values of the chi-square distribution
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797
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816 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Field, Andy 1973- |
| author_GND | (DE-588)128714581 |
| author_facet | Field, Andy 1973- |
| author_role | aut |
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| author_variant | a f af |
| building | Verbundindex |
| bvnumber | BV035011416 |
| callnumber-first | H - Social Science |
| callnumber-label | HA32 |
| callnumber-raw | HA32 QA273 |
| callnumber-search | HA32 QA273 |
| callnumber-sort | HA 232 |
| callnumber-subject | HA - Statistics |
| classification_rvk | AP 13900 CM 4000 CM 4400 MR 2200 SK 830 ST 601 ST 610 QH 231 |
| classification_tum | MAT 620 DAT 307 SOZ 720 |
| ctrlnum | (OCoLC)610778762 (DE-599)BVBBV035011416 |
| dewey-full | 519.5'02855362222 519.50285536 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5'0285536 22 22 519.50285536 |
| dewey-search | 519.5'0285536 22 22 519.50285536 |
| dewey-sort | 3519.5 6285536 222 222 |
| dewey-tens | 510 - Mathematics |
| discipline | Allgemeines Informatik Soziologie Psychologie Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Allgemeines Informatik Soziologie Psychologie Mathematik Wirtschaftswissenschaften |
| edition | Third edition |
| format | Book |
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| id | DE-604.BV035011416 |
| illustrated | Illustrated |
| index_date | 2024-07-02T21:43:55Z |
| indexdate | 2024-07-20T07:52:52Z |
| institution | BVB |
| isbn | 9781847879066 9781847879073 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016680662 |
| oclc_num | 610778762 |
| open_access_boolean | |
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| physical | xxxiii, 821 Seiten Illustrationen, Diagramme |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Sage |
| record_format | marc |
| spelling | Field, Andy 1973- Verfasser (DE-588)128714581 aut Discovering statistics using SPSS (and sex and drugs and rock'n'roll) Andy Field Third edition Los Angeles ; London ; New Delhi Sage [2009] xxxiii, 821 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke SPSS for Windows Sozialwissenschaften Social sciences Statistical methods Computer programs SPSS (DE-588)4056588-9 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf SPSS (DE-588)4056588-9 s Statistik (DE-588)4056995-0 s DE-604 4. Aufl. u.d.T. Field, Andy Discovering statistics using IBM SPSS statistics (DE-604)BV040707649 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016680662&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| 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 SPSS (DE-588)4056588-9 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4056588-9 (DE-588)4056995-0 |
| 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_exact_search_txtP | 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_new | Field, Andy Discovering statistics using IBM SPSS statistics |
| 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 SPSS (DE-588)4056588-9 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | SPSS for Windows Sozialwissenschaften Social sciences Statistical methods Computer programs SPSS Statistik |
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