Discovering statistics using SPSS: (and sex and drugs and rock'n'roll)
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Los Angeles [u.a.]
Sage
2009
|
| Ausgabe: | 3. ed., reprinted (twice) |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. 809 - 815 |
| Beschreibung: | XXXIII, 821 S. Ill., graph. Darst. |
| ISBN: | 9781847879066 9781847879073 |
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MARC
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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., reprinted (twice) | ||
| 264 | 1 | |a Los Angeles [u.a.] |b Sage |c 2009 | |
| 300 | |a XXXIII, 821 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Literaturverz. S. 809 - 815 | ||
| 650 | 0 | 7 | |a SPSS |0 (DE-588)4056588-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Statistik |0 (DE-588)4056995-0 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a SPSS |0 (DE-588)4056588-9 |D s |
| 689 | 0 | 1 | |a Statistik |0 (DE-588)4056995-0 |D s |
| 689 | 0 | |C b |5 DE-604 | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020478946&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
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Datensatz im Suchindex
| _version_ | 1804143132253618176 |
|---|---|
| adam_text | CONTENTS
J tí
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
■ .........................
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 of the mean: sums of squares, variance and standard
deviations
©
35
2.4.3.
Expressing the mean as a model
©
3°
2.5.
Going beyond the data
©
40
2.5.1.
The standard error
©
4^
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 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 (D
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
vii
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.
l. 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
φ
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-Smirnov 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 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 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
v¡¡¡
......... .......
DISCOVERING STATISTICS USING SPSS
6
Correlation 166
6.1.
What will this chapter tell me?
Φ
166
6.2.
Looking at relationships
©
167
6.3.
How do we measure relationships?
©
1
67
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
г
®
1
7^
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
© 1
75
6.5.1.
General procedure for running correlations on SPSS
© 175
6.5.2.
Pearson s correlation coefficient
© 1
77
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
гѕ
© 191
6.7.2.
Comparing dependent rs
© 191
6.8.
Calculating the effect size
© 192
6.9.
How to report correlation coeff¡cents
© 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
¡χ
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.
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 R2
© 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
©
8.8.2.
Testing for multicollinearity
©
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 f-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 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
ŕ-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
.......................................
DISCOVERING
STATISTICS USING SPSS
xii
11.4.3.
The main analysis
©
11.4.4.
Contrasts and other options
©
4u1
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
©
11.5.3.
Contrasts
©
407
11.5.4.
Interpreting the covariate
©
4^°
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 42Q
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.
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 I 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 (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.
Post hoc tests and additional options
© 471
13.5.
Output for one-way repeated-measures ANOVA
© 474
13.5.1.
Descriptives
and other diagnostics
0 474
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
|
| any_adam_object | 1 |
| author | Field, Andy 1973- |
| author_GND | (DE-588)128714581 |
| author_facet | Field, Andy 1973- |
| author_role | aut |
| author_sort | Field, Andy 1973- |
| author_variant | a f af |
| building | Verbundindex |
| bvnumber | BV036557522 |
| classification_rvk | CM 4000 CM 4400 SK 830 ST 601 |
| classification_tum | DAT 307f SOZ 720f MAT 620f |
| ctrlnum | (OCoLC)610778762 (DE-599)BVBBV036557522 |
| dewey-full | 519.50285536 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.50285536 |
| dewey-search | 519.50285536 |
| dewey-sort | 3519.50285536 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Soziologie Psychologie Mathematik |
| edition | 3. ed., reprinted (twice) |
| format | Book |
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| id | DE-604.BV036557522 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:42:48Z |
| institution | BVB |
| isbn | 9781847879066 9781847879073 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020478946 |
| oclc_num | 610778762 |
| open_access_boolean | |
| owner | DE-573 |
| owner_facet | DE-573 |
| physical | XXXIII, 821 S. Ill., graph. Darst. |
| 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 3. ed., reprinted (twice) Los Angeles [u.a.] Sage 2009 XXXIII, 821 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Literaturverz. S. 809 - 815 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 b DE-604 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020478946&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 (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_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 (DE-588)4056588-9 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | SPSS Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020478946&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT fieldandy discoveringstatisticsusingspssandsexanddrugsandrocknroll |