IBM SPSS statistics 18 made simple:
Gespeichert in:
| Hauptverfasser: | , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hove [u.a.]
Psychology Press
2011
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVI, 655 S. graph. Darst. |
| ISBN: | 9781848720473 |
Internformat
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| 245 | 1 | 0 | |a IBM SPSS statistics 18 made simple |c Paul R. Kinnear & Colin D. Gray |
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Datensatz im Suchindex
| _version_ | 1804143500486246400 |
|---|---|
| adam_text | Titel: IBM SPSS statistics 18 made simple
Autor: Kinnear, Paul R
Jahr: 2011
Contents
Preface xv
CHAPTER 1 Introduction 1
1.1.1 Variables: quantitative and qualitative 1
1.1.2 Levels of measurement: scale, ordinal and nominal data /
1.1.3 A grey area: ratings 2
1.1.4 Univariate, bivariate and multivariate data sets 2
1.2.1 A simple experiment 3
1.2.2 A more complex experiment 4
1.2.3 Correlational research 5
1.2.4 The Pearson correlation coefficient 7
1.2.5 Correlation and causation 8
1.2.6 Ouasi-exDeriments 8
1.3.1 Considerations in choosing a Statistical test 9
1.3.2 Five common research situations 9
1.4.1 The design ofthe experiment: independent versus related samples 10
1.4.2 Flow chart for selecting a suitable test for differences between means / /
1.5.1 Flow chart for selecting a suitable test for association 13
1.5.2 Measuring association in ordinal data 14
1.5.3 Measuring association in nominal data: Contingency tables 14
1.5.4 Multi-way contingency tables 75
1.6.1 Flow chart for predicting a score or category membership 15
1.6.2 Simple regression 16
1.6.3 Multiple regression 16
1.6.4 Predicting category membership: Discriminant analysis and logistic
regression / 7
1.7.1 Flow chart for selecting the appropriate one-sample test / 7
1.7.2 Goodness-of-fit: nominal data 18
1.7.3 Inferences about the mean of a Single population 18
m
Contents
Recommended reading 20
CHAPTER 2 Getting started with IBM SPSS Statistics 18 21
2.1.1 Entering the data 21
2.1.2 Selecting the exploratory and Statistical procedures 22
2.1.3 Examining the Output 22
2.1.4 A simple experiment 22
2.1.5 Preparing data for SPSS 23
2.3.1 Working in Variable View 25
2.3.2 Working in Data View 30
2.3.3 Entering the data 31
2.4.1 An example: Computing the group means 34
2.4.2 Keeping more than one application open 38
Exercise 1 Some simple Operations with SPSS Statistics 18 39
Exercise 2 Questionnaire data 39
CHAPTER 3 Editing data sets 40
3.1.1 Working in Variable View 40
3.1.2 Working in Data View 4 7
3.2.1 Editing the Output 55
3.2.2 More advanced editing 57
3.2.3 Tutorials in SPSS 61
3.3.1 Selecting cases 61
3.3.2 Aggregating data 64
3.3.3 Sorting data 66
3.3.4 Merging files 67
3.3.5 Transposing the rows and columns of a data set 72
3.4.1 Importing data from other applications 74
3.4.2 Copying Output 76
3.5.1 Printing Output from the Viewer 78
3.5.2 Printing from the Data Editor 82
3.5.3 Transferring data from the Viewer for printing 82
Exercise 3 Merging files - Adding cases variables 84
Contents
CHAPTER 4 Describing and exploring your data 85
4.2.1 Describing nominal data relating to one attribute 87
4.2.2 Obtaining contingency tables 90
4.3.1 Histograms ofthe distributions of height and weight for the males and
females 95
A3.2 Obtaining scatterplots of weight against height 97
4.3.3 Obtaining the statistics ofthe height distributions of males and females 99
4.4.1 Stem-and-leaf displays 102
4.4.2 Boxplots 103
4.4.3 Exploring a small data set 104
AAA Some ofthe Statistical Output from Explore 105
4.5.1 Data for the Multiple Response procedure 108
4.5.2 Creating a multiple response set HO
4.5.3 Obtaining the crosstabulations 112
4.5.4 Finding the frequencies of specific transport profiles 116
4.6.1 Reducing and transforming data 120
4.6.2 The COMPUTE VARIABLE procedure 120
4.6.3 The RECODE procedure 127
Exercise 4 Correcting and preparing your data 131
Exercise 5 Preparing your data (continued) 131
CHAPTER 5 Graphs and Charts 132
5.1.1 Graphs and Charts on SPSS 132
5.1.2 Viewing a chart 134
5.1.3 Editing Charts and saving templates 135
5.2.1 Simple bar Charts 135
5.2.2 Clustered bar Charts 139
5.2.3 Panelled bar Charts 141
5.2.4 3-D Charts 142
5.2.5 Editing a bar chart 144
5.2.6 Chart templates 145
5.6.1 The Visual Binning procedure 154
5.6.2 Plotting line graphs 157
VI
Contents
5.7.1 Scatterplots 159
5.7.2 Dot plots 161
5.10.1 The SPSS ROC curve 168
5.10.2 The d statistic 7 77
Exercise 6 Charts and graphs / 72
Exercise 7 Recoding data; selecting cases; line graph / 72
CHAPTER 6 Comparing averages: Two-sample and one-
sample tests 173
6.2.1 Preparing the data file 174
6.2.2 Exploring the data 175
6.2.3 Running the t test 177
6.2.4 Interpreting the Output 179
6.2.5 Two-tailed and one-tailed/?-values 180
6.2.6 The effects of extreme scores and outliers in a small data set 181
6.2.7 Measuring effect size 181
6.2.8 Reporting the results of a Statistical test 183
6.3.1 Preparing the data file 185
6.3.2 Exploring the data 185
6.3.3 Running the t test 186
6.3.4 Interpreting the Output 187
6.3.5 Measuring effect size 188
6.3.6 Reporting the results of the test 188
6.3.7 A one-sample test 189
6.4.1 Nonparametric tests in SPSS 189
6.4.2 Independent samples: The Mann-Whitney (/test 190
6.4.3 Output for the Mann-Whitney C/test 192
6.4.4 Effect size 194
6.4.5 The report 195
6.5.1 The Wilcoxon matched-pairs tests in SPSS 195
6.5.2 The Output 196
6.5.3 Effect size 197
6.5.4 The report 197
6.6.1 The sign test in SPSS 199
6.6.2 Bernoulli trials: the binomial test 201
6.7.1 How many participants shall I need in my experiment? 207
Contents
Exercise 8 Comparing the averages of two independent samples of data 208
Exercise 9 Comparing the averages of two related samples of data 208
Exercise 10 One-sample tests 208
CHAPTER 7 The one-way ANOVA 209
7.1.1 A more complex drug experiment 209
7.1.2 ANOVA modeis 210
7.1.3 The one-way ANOVA 210
7.2.1 Entering the data 217
7.2.2 Running the one-way ANOVA 220
7.2.3 The Output 220
IIA Effect size 221
7.2.5 Report ofthe primary analysis 224
7.2.6 The two-group case: equivalence of F and 1223
7.3.1 Factors with fixed and random effects 225
7.3.2 The analysis of covariance (ANCOVA) 226
7.3.3 Univariate versus multivariate Statistical tests 226
7.3.4 The one-way ANOVA with GLM 226
7.3.5 The GLM Output 227
7.3.6 Requesting additional items 229
7.3.7 Additional Output from GLM 231
IAA Planned and unplanned comparisons 234
7.4.2 Linear contrasts 238
7.5.1 Polynomials 249
7.6.1 How many participants shall I need? Using G*Power 3 251
1.1.1 The Kruskal-Wallis k-sample test 253
1.1.2 Dichotomous nominal data: the chi-square test 260
Recommended reading 261
Exercise 11 One-factor between subjects ANOVA 261
Appendix 7.4.2.6 Partition of the between groups sum of Squares into the sums of Squares
ofthe contrasts in an orthogonal set 261
Appendix 7.5.1 An illustration of trend analysis 262
CHAPTER 8 Between subjects factorial experiments 267
8.1.1 An experiment with two treatment factors 267
8.1.2 Main effects and interactions 269
viii Contents
8.1.3 Profile plots 269
8.2.1 The two-way ANOVA 271
8.2.2 Degrees offreedom 272
8.2.3 The two-way ANOVA summary table 273
8.3.1 Entering the data into the Data Editor 275
8.3.2 Exploring the data: boxplots 276
8.3.3 Choosing a factorial ANOVA 277
8.3.4 Output for a factorial ANOVA 278
8.3.5 Measuring effect size in the two-way ANOVA 280
8.3.6 Reporting the results ofthe two-way ANOVA 283
8.4.1 The danger with multiple comparisons 284
8.4.2 Unpacking significant main effects: post hoc tests 284
8.4.3 The analysis of interactions 285
8.5.1 The Syntax Editor 287
8.5.2 Building syntax files automatically 288
8.5.3 Using the MANOVA command to run the univariate ANOVA 288
8.7.1 Three-way interactions 297
8.7.2 The three-way ANOVA 298
8.7.3 How the three-way ANOVA works 299
8.7.4 Measures of effect size in the three-way ANOVA 301
8.7.5 How many participants shall I need? 301
8.7.6 The three-way ANOVA with SPSS 301
8.7.7 Follow-up analysis following a significant three-way interaction 304
8.7.8 Using SPSS syntax to test for simple interactions and simple, simple main
effects 305
8.7.9 Unplanned multiple comparisons following a significant three-way
interaction 308
Recommended reading 311
Exercise 12 Between subjects factorial ANOVA (two-way ANOVA) 311
CHAPTER 9 Within subjects experiments 312
9.1.1 Rationale of a within subjects experiment 312
9.1.2 How the within subjects ANOVA works 313
9.1.3 A within subjects experiment on the effect of target shape on shooting
accuracy 316
9.1.4 Order effects: counterbalancing 577
9.1.5 Assumptions underlying the within subjects ANOVA: homogeneity of
covariance 317
Contents
9.2.1 Entering the data 319
9.2.2 Exploring the data: Boxplots for within subjects factors 319
9.2.3 Running the within subjects ANOVA 321
9.2.4 Output for a one-factor within subjects ANOVA 325
9.2.5 Effect size in the within subjects ANOVA 329
9.4.1 The Friedman test for ordinal data 332
9.4.2 Cochran s Q test for nominal data 335
9.5.1 Preparing the data set 338
9.5.2 Running the two-factor within subjects ANOVA 338
9.5.3 Output for a two-factor within subjects ANOVA 341
9.5.4 Unpacking a significant interaction with multiple comparisons 345
Recommended reading 348
Exercise 13 One-factor within subjects (repeated measures) ANOVA 348
Exercise 14 Two-factor within subjects ANOVA 348
CHAPTER 10 Mixed factorial experiments 349
10.1.1 A mixed factorial experiment 349
10.1.2 Classifying mixed factorial designs 350
10.1.3 Rationale ofthe mixed ANOVA 351
10.2.1 Preparing the SPSS data set 353
10.2.2 Exploring the results: Boxplots 354
10.2.3 Running the ANOVA 355
10.2.4 Output for the two-factor mixed ANOVA 357
10.2.5 Simple effects analysis with syntax 363
10.3.1 The two three-factor designs 367
10.3.2 Two within subjects factors 368
10.3.3 Using syntax to test for simple effects 3 73
10.3.4 One within subjects factor and two between subjects factors: the AxBx(C)
mixed factorial design 377
10.4.1 What the MANOVA does 384
10.4.2 How the MANOVA works 386
10.4.3 Assumptions of MANOVA 389
10.4.4 Application of MANOVA to the shape recognition experiment 390
Recommended reading 394
Exercise 15 Mixed ANOVA: two-factor experiment 394
Exercise 16 Mixed ANOVA: three-factor experiment 394
x Contents
CHAPTER 11 Measuring Statistical association 395
11.1.1 A correlational study 396
11.1.2 Linear relationships 397
11.1.3 Error in measurement 397
11.2.1 Formula for the Pearson correlation 398
11.2.2 The ränge of values ofthe Pearson correlation 399
11.2.3 The sign of a correlation 399
11.2.4 Testing an obtained value of r for significance 400
11.2.5 A word of warning about the correlation coefficient 401
11.2.6 Effect size 401
11.3.1 Preparing the SPSS data set 403
11.3.2 Obtaining the scatterplot 403
11.3.3 Obtaining the Pearson correlation 404
11.3.4 Output for the Pearson correlation 405
11.4.1 Spearman s rank correlation 406
11.4.2 Kendall s tau statistics 407
11.4.3 Rank correlations with SPSS 407
11.5.1 The approximate chi-square goodness-of-fit test with three or more
categories 409
11.5.2 Running a chi-square goodness-of-fit test on SPSS 410
11.5.3 Measuring effect size following a chi-square test of goodness-of-fit 413
11.5.4 Testing for association between two qualitative variables in a contingency
table 415
11.5.5 Analysis of contingency tables with SPSS 420
11.5.6 Getting help with the Output 425
11.5.7 Some cautions and caveats 426
11.5.8 Other problems with traditional chi-square analyses 431
11.7.1 Correlation does not imply causation 434
11.7.2 Meaning of partial correlation 436
11.8.1 Reliability and number of items: coefficient alpha 440
11.8.2 Measuring agreement among judges: the intraclass correlation 441
11.8.3 Reliability analysis with SPSS 442
Recommended reading 445
Exercise 17 The Pearson correlation 445
Exercise 18 Other measures of association 445
Exercise 19 The analysis of nominal data 445
Contents
CHAPTER 12 Regression 446
12.1.1 Simple, two-variable regression 446
12.1.2 Residualst
12.1.3 The least Squares criterion for the best-fitting l me,449
12.1.4 Regression and correlation 450
12.1.5 The coefficient of determination revisited 451
12.1.6 Shrinkage with resampling 454
12.1.7 Beta-weights 454
12.1.8 Effects of linear transformations on the correlation and regression
Coefficients 455
12.1.9 Significance testing in simple regression 455
12.2.1 Drawing scatterplots with regression lines 456
12.2.2 A problem in simple regression 459
12.2.3 Procedure for simple regression 459
12.2.4 Output for simple regression 462
12.3.1 The multiple correlation coefficient R 468
12.3.2 Significance testing in multiple regression 469
12.3.3 Partial and semipartial correlation 469
12.4.1 Simultaneous multiple regression 476
12.4.2 Stepwise multiple regression 480
12.6.1 A path diagram 484
12.6.2 Path analysis with AMOS 487
12.6.3 A brief look at a saturated model 502
Recommended reading 506
Exercise 20 Simple, two-variable regression 507
Exercise 21 Multiple regression 507
CHAPTER 13 Analyses of multiway frequency tables 508
13.2.1 Loglinear modeis and ANOVA modeis 509
13.2.2 Model-building and the hierarchical principle 511
13.2.3 The main-effects-only loglinear model and the traditional chi-square test for
association 513
13.2.4 Analysis ofthe residuals 514
13.3.1 SPSS procedures for loglinear analysis 515
13.3.2 Fitting an unsaturated model 521
13.3.3 Summary 525
Contents
13.4.1 Exploring the data 526
13.4.2 Loglinear analysis ofthe data on gender and helpfulness 527
13.4.3 The main-effects-only model and the traditional chi-square test 531
13.4.4 Collapsing a multi-way table: the requirement of conditional independence 533
13.4.5 An alternative data set for the gender and helpfulness experiment 535
13.4.6 Reporting the results of a loglinear analysis 538
Recommended reading 539
Exercise 22 Loglinear analysis 539
CHAPTER 14 Discriminant analysis and logistic regression 540
14.1.1 Discriminant analysis 541
14.1.2 Types of discriminant analysis 542
14.1.3 Stepwise discriminant analysis 542
14.1.4 AssumDtions of discriminant analvsis 543
14.2.1 Accessing the data 544
14.2.2 Exploring the data 544
14.2.3 Running discriminant analysis 545
14.2.4 Output for discriminant analysis 547
14.2.5 Predicting group membership 555
14.3.1 Logistic regression 557
14.3.2 How logistic regression works 559
14.3.3 An example of a binary logistic regression with quantitative independent
variables 562
14.3.4 Binary logistic regression with categorical independent variables 570
14.4.1 Running multinomial logistic regression 575
Recommended reading 579
Exercise 23 Predicting category membership: Discriminant analysis and binary logistic
regression 579
CHAPTER 15 The search for latent variables: factor
analysis 580
15.1.1 Stages in an exploratory factor analysis 582
15.1.2 The extraction of factors 583
15.1.3 The rationale of rotation 583
15.1.4 Some issues in factor analysis 583
15.1.5 Some key technical terms 584
15.1.6 Preliminaries 585
Contents x
15.2.1 Entering the data for a factor analysis 586
15.2.2 Running a factor analysis on SPSS 586
15.2.3 Output for factor analysis 589
15.3.1 Running a factor analysis with syntax 598
15.3.2 Using a correlation matrix as input for factor analysis 599
15.3.3 Progressing with SPSS syntax 603
15.4.1 Input path diagram for a confirmatory factor analysis 605
15.4.2 Drawing the input path diagram for the researcher s two-factor model 607
15.4.3 Running the analysis 610
15.4.4 Result ofthe chi-square goodness-of-fit test and the Output path diagram 611
15.4.5 Text Output from a confirmatory factor analysis 613
15.4.6 Structural equation modelling 616
Recommended reading 617
Exercise 24 Factor analysis 617
Glossary 618
References 635
Index 637
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| spelling | Kinnear, Paul R. Verfasser aut IBM SPSS statistics 18 made simple Paul R. Kinnear & Colin D. Gray Hove [u.a.] Psychology Press 2011 XVI, 655 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier SPSS (Computer file) Sozialwissenschaften Social sciences / Statistical methods / Computer programs IBM SPSS Statistics 18 (DE-588)7713265-8 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content IBM SPSS Statistics 18 (DE-588)7713265-8 s DE-604 Gray, Colin D. Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020722393&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kinnear, Paul R. Gray, Colin D. IBM SPSS statistics 18 made simple SPSS (Computer file) Sozialwissenschaften Social sciences / Statistical methods / Computer programs IBM SPSS Statistics 18 (DE-588)7713265-8 gnd |
| subject_GND | (DE-588)7713265-8 (DE-588)4151278-9 |
| title | IBM SPSS statistics 18 made simple |
| title_auth | IBM SPSS statistics 18 made simple |
| title_exact_search | IBM SPSS statistics 18 made simple |
| title_full | IBM SPSS statistics 18 made simple Paul R. Kinnear & Colin D. Gray |
| title_fullStr | IBM SPSS statistics 18 made simple Paul R. Kinnear & Colin D. Gray |
| title_full_unstemmed | IBM SPSS statistics 18 made simple Paul R. Kinnear & Colin D. Gray |
| title_short | IBM SPSS statistics 18 made simple |
| title_sort | ibm spss statistics 18 made simple |
| topic | SPSS (Computer file) Sozialwissenschaften Social sciences / Statistical methods / Computer programs IBM SPSS Statistics 18 (DE-588)7713265-8 gnd |
| topic_facet | SPSS (Computer file) Sozialwissenschaften Social sciences / Statistical methods / Computer programs IBM SPSS Statistics 18 Einführung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020722393&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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