## Introduction to SPSS in Psychology

(2017)

### Abstract

Introduction to SPSS in Psychology, 7th edition is the essential step by step guide to SPSS for students taking their first course in statistics.  This well-established text provides a clear and comprehensive coverage of how to carry out statistical analyses using SPSS.  Full colour SPSS screenshots, clear explanation and a wide ranging coverage make it the perfect companion for students who want to be able to analyse data with confidence.

Section Title Page Action Price
Cover Cover
Half-Title Page i
Title Page iii
Summary of contents v
Contents vii
Guided tour xxii
Introduction xxvi
Acknowledgements xxix
Part 1 Introduction to SPSS in Psychology 1
1 Brief introduction to statistics 3
Overview 3
1.1 Basic statistical concepts essential in SPSS analyses 4
1.2 Basic research designs: comparative versus correlational designs 4
1.3 Different types of variables in statistics 7
1.4 Descriptive and inferential statistics compared 9
1.5 Related versus unrelated designs 11
1.6 Quick summaries of statistical analyses 12
1.7 Which procedure or test to use 12
2 Basics of SPSS data entry and statistical analysis 17
Overview 17
2.1 What is SPSS? 18
2.2 Accessing SPSS 18
2.3 Entering data 20
2.4 Moving within a window with the mouse 21
2.5 Moving within a window using the keyboard keys with the mouse 21
2.6 Saving data to memory device 22
2.7 Opening up a data file 23
2.8 Using â€˜Variable Viewâ€™ to create and label variables 24
2.9 More on â€˜Data Viewâ€™ 26
2.10 Simple statistical calculation with SPSS 28
2.11 SPSS output 29
Summary of SPSS steps for a statistical analysis 29
Part 2 Descriptive statistics 31
3 Describing variables tabularly 33
Overview 33
3.1 What are tables? 34
3.2 When to use tables 35
3.3 When not to use tables 35
3.4 Data requirements for tables 35
3.5 Problems in the use of tables 35
3.6 Data to be analysed 36
3.7 Entering summarised categorical or frequency data by weighting 36
3.8 Percentage frequencies 38
3.9 Interpreting the output 38
3.10 Reporting the output 39
Summary of SPSS steps for frequency tables 39
4 Describing variables diagrammatically 40
Overview 40
4.1 What are diagrams? 41
4.2 When to use diagrams 42
4.3 When not to use diagrams 42
4.4 Data requirements for diagrams 42
4.5 Problems in the use of diagrams 42
4.6 Data to be analysed 43
4.7 Entering summarised categorical or frequency data by weighting 43
4.8 Pie diagram of category data 46
4.9 Adding labels to the pie diagram and removing the legend and label 47
4.10 Changing the colour of a pie-diagram slice to a black-and-white pattern 49
4.11 Bar chart of category data 51
4.12 Histograms 52
Summary of SPSS steps for charts 54
5 Describing variables numerically: Averages, variation and spread 55
Overview 55
5.1 What are averages, variation and spread? 56
5.2 When to use averages, variation and spread 60
5.3 When not to use averages, variation and spread 60
5.4 Data requirements for averages, variation and spread 60
5.5 Problems in the use of averages, variation and spread 60
5.6 Data to be analysed 61
5.7 Entering the data 61
5.8 Mean, median, mode, standard deviation, variance and range 62
5.9 Interpreting the output 63
5.10 Other features 63
5.11 Reporting the output 64
Summary of SPSS steps for descriptive statistics 64
6 Shapes of distributions of scores 65
Overview 65
6.1 What are the different shapes of scores? 66
6.2 When to use histograms and frequency tables of scores 69
6.3 When not to use histograms and frequency tables of scores 70
6.4 Data requirements for using histograms and frequency tables of scores 70
6.5 Problems in using histograms and frequency tables of scores 70
6.6 Data to be analysed 70
6.7 Entering the data 71
6.8 Frequency tables 71
6.9 Interpreting the output 72
6.10 Histograms 73
6.11 Interpreting the output 74
Summary of SPSS steps for frequency distributions 75
7 Relationships between two or more variables Tables 76
Overview 76
7.1 What tables are used to show relationships between variables? 77
7.2 When to use tables to show relationships between variables 79
7.3 When not to use tables to show relationships between variables 79
7.4 Data requirements for tables to show relationships between variables 80
7.5 Problems in the use of tables to show relationships between variables 80
7.6 Data to be analysed 80
7.7 Entering the data 81
7.8 Weighting the data 82
7.9 Crosstabulation with frequencies 83
7.10 Displaying frequencies as a percentage of the total number 84
7.11 Displaying frequencies as a percentage of the column total 85
Summary of SPSS steps for contingency tables 85
8 Relationships between two or more variables Diagrams 86
Overview 86
8.1 What diagrams are used to show relationships between variables? 87
8.2 When to use diagrams to show relationships between variables 90
8.3 When not to use diagrams to show relationships between variables 90
8.4 Data requirements for diagrams to show relationships between variables 90
8.5 Problems in the use of diagrams to show relationships between variables 91
8.6 Data to be analysed 91
8.7 Entering the data 92
8.8 Weighting the data 93
8.9 Compound (stacked) percentage bar chart 94
8.10 Compound (clustered) bar chart 96
Summary of SPSS steps for bar charts 98
9 Correlation coefficients: Pearsonâ€™s correlation and Spearmanâ€™s rho 99
Overview 99
9.1 What is a correlation coefficient? 100
9.2 When to use Pearson and Spearman rho correlation coefficients 103
9.3 When not to use Pearson and Spearman rho correlation coefficients 103
9.4 Data requirements for Pearson and Spearman rho correlation coefficients 103
9.5 Problems in the use of correlation coefficients 104
9.6 Data to be analysed 104
9.7 Entering the data 105
9.8 Pearsonâ€™s correlation 105
9.9 Interpreting the output 106
9.10 Spearmanâ€™s rho 107
9.11 Interpreting the output 107
9.12 Scatter diagram 108
9.13 Interpreting the output 110
9.14 Scattergram with more than one case with the same two values 110
Summary of SPSS steps for correlation 112
10 Regression: Prediction with precision 113
Overview 113
10.1 What is simple regression? 114
10.2 When to use simple regression 116
10.3 When not to use simple regression 116
10.4 Data requirements for simple regression 116
10.5 Problems in the use of simple regression 117
10.6 Data to be analysed 117
10.7 Entering the data 118
10.8 Simple regression 118
10.9 Interpreting the output 119
10.10 Regression scatterplot 120
10.11 Interpreting the output 123
Summary of SPSS steps for simple regression 124
Part 3 Significance testing and basic inferential tests 125
11 Related t-test: Comparing two samples of correlated/related/paired scores 127
Overview 127
11.1 What is the related t-test? 128
11.2 When to use the related t-test 130
11.3 When not to use the related t-test 131
11.4 Data requirements for the related t-test 131
11.5 Problems in the use of the related t-test 131
11.6 Data to be analysed 132
11.7 Entering the data 132
11.8 Related t-test 133
11.9 Interpreting the output 133
Summary of SPSS steps for related t-test 135
12 Unrelated t-test: Comparing two groups of unrelated/uncorrelated scores 136
Overview 136
12.1 What is the unrelated t-test? 137
12.2 When to use the unrelated t-test 138
12.3 When not to use the unrelated t-test 138
12.4 Data requirements for the unrelated t-test 139
12.5 Problems in the use of the unrelated t-test 139
12.6 Data to be analysed 139
12.7 Entering the data 139
12.8 Unrelated t-test 141
12.9 Interpreting the output 141
Summary of SPSS steps for unrelated t-test 143
13 Confidence intervals 144
Overview 144
13.1 What are confidence intervals? 145
13.2 Relationship between significance and confidence intervals 146
13.3 Confidence intervals and limits in SPSS 147
14 Chi-square: Differences between unrelated samples of frequency data 148
Overview 148
14.1 What is chi-square? 149
14.2 When to use chi-square 151
14.3 When not to use chi-square 151
14.4 Data requirements for chi-square 152
14.5 Problems in the use of chi-square 152
14.6 Data to be analysed 153
14.7 Entering the data using the â€˜Weighting Casesâ€™ procedure 153
14.8 Entering the data case by case 154
14.9 Chi-square 155
14.10 Interpreting the output for chi-square 156
14.11 Fisherâ€™s exact test 158
14.12 Interpreting the output for Fisherâ€™s exact test 158
14.13 One-sample chi-square 159
14.14 Interpreting the output for a one-sample chi-square 161
Summary of SPSS steps for chi-square 162
15 McNemarâ€™s test: Differences between related samples of frequency data 163
Overview 163
15.1 What is McNemarâ€™s test? 164
15.2 When to use McNemarâ€™s test 164
15.3 When not to use McNemarâ€™s test 165
15.4 Data requirements for McNemarâ€™s test 165
15.5 Problems in the use of McNemarâ€™s test 165
15.6 Data to be analysed 165
15.7 Entering the data using the â€˜Weighting Casesâ€™ procedure 166
15.8 Entering the data case by case 167
15.9 McNemarâ€™s test 167
15.10 Interpreting the output for McNemarâ€™s test 168
Summary of SPSS steps for McNemarâ€™s test 169
16 Ranking tests for two groups: Non-parametric statistics 170
Overview 170
16.1 What are non-parametric tests? 171
16.2 When to use non-parametric tests 173
16.3 When not to use non-parametric tests 173
16.4 Data requirements for non-parametric tests 173
16.5 Problems in the use of non-parametric tests 173
16.6 Data to be analysed 174
16.7 Entering the data 174
16.8 Related scores: Sign test 175
16.9 Interpreting the output for the sign test 175
16.10 Related scores: Wilcoxon test 176
16.11 Interpreting the output for the Wilcoxon test 176
16.12 Unrelated scores: Mannâ€“Whitney U-test 177
16.13 Entering the data 177
16.14 Mannâ€“Whitney U-test 178
16.15 Interpreting the output for the Mannâ€“Whitney U-test 179
Summary of SPSS steps for non-parametric tests for two groups 180
17 Ranking tests for three or more groups: Non-parametric statistics 181
Overview 181
17.1 What are ranking tests? 182
17.2 When to use ranking tests 183
17.3 When not to use ranking tests 183
17.4 Data requirements for ranking tests 183
17.5 Problems in the use of ranking tests 183
17.6 Data to be analysed 183
17.7 Friedman three or more related samples test 184
17.8 Entering the data for the Friedman test 184
17.9 Friedman test 185
17.10 Interpreting the output for the Friedman test 185
17.11 Kruskalâ€“Wallis three or more unrelated samples test 186
17.12 Entering the data for the Kruskalâ€“Wallis test 187
17.13 Kruskalâ€“Wallis test 188
17.14 Interpreting the output for the Kruskalâ€“Wallis test 189
Summary of SPSS steps for non-parametric tests for three or more groups 189
Part 4 Analysis of variance 191
18 One-way analysis of variance (ANOVA) for unrelated or uncorrelated scores 193
Overview 193
18.1 What is one-way unrelated ANOVA? 194
18.2 When to use one-way unrelated ANOVA 195
18.3 When not to use one-way unrelated ANOVA 196
18.4 Data requirements for one-way unrelated ANOVA 196
18.5 Problems in the use of one-way unrelated ANOVA 196
18.6 Data to be analysed 196
18.7 Entering the data 197
18.8 One-way unrelated ANOVA 197
18.9 Interpreting the output 198
Summary of SPSS steps for one-way unrelated ANOVA 199
19 One-way analysis of variance for correlated scores or repeated measures 201
Overview 201
19.1 What is one-way repeated-measures ANOVA? 202
19.2 When to use repeated-measures ANOVA 203
19.3 When not to use one-way repeated-measures ANOVA 203
19.4 Data requirements for one-way repeated-measures ANOVA 204
19.5 Problems in the use of one-way repeated-measures ANOVA 204
19.6 Data to be analysed 204
19.7 Entering the data 204
19.8 One-way repeated-measures ANOVA 205
19.9 Interpreting the output 207
Summary of SPSS steps for one-way repeated-measures ANOVA 209
20 Two-way analysis of variance for unrelated/uncorrelated scores 210
Overview 210
20.1 What is two-way unrelated ANOVA? 211
20.2 When to use two-way unrelated ANOVA 214
20.3 When not to use two-way unrelated ANOVA 214
20.4 Data requirements for two-way unrelated ANOVA 214
20.5 Problems in the use of two-way unrelated ANOVA 215
20.6 Data to be analysed 216
20.7 Entering the data 216
20.8 Two-way unrelated ANOVA 217
20.9 Interpreting the output 218
20.10 Editing the graph 220
Summary of SPSS steps for two-way unrelated ANOVA 221
21 Multiple comparisons in ANOVA 223
Overview 223
21.1 What is multiple-comparisons testing? 224
21.2 When to use multiple-comparisons tests 225
21.3 When not to use multiple-comparisons tests 225
21.4 Data requirements for multiple-comparisons tests 225
21.5 Problems in the use of multiple-comparisons tests 226
21.6 Data to be analysed 226
21.7 Entering the data 227
21.8 Multiple-comparisons tests 227
21.9 Interpreting the output 228
21.10 Reporting the output 229
Summary of SPSS steps for multiple-comparison tests 230
22 Two-way analysis of variance for correlated scores or repeated measures 231
Overview 231
22.1 What is two-way repeated-measures ANOVA? 232
22.2 When to use two-way repeated-measures ANOVA 234
22.3 When not to use two-way repeated-measures ANOVA 235
22.4 Data requirements for two-way related-measures ANOVA 235
22.5 Problems in the use of two-way repeated-measures ANOVA 235
22.6 Data to be analysed 235
22.7 Entering the data 236
22.8 Two-way repeated-measures ANOVA 236
22.9 Interpreting the output 238
22.10 Reporting the output 242
Summary of SPSS steps for two-way repeated-measures ANOVA 242
23 Two-way mixed analysis of variance 244
Overview 244
23.1 What is two-way mixed ANOVA? 245
23.2 When to use two-way mixed ANOVA 245
23.3 When not to use two-way mixed ANOVA 246
23.4 Data requirements for two-way mixed ANOVA 247
23.5 Problems in the use of two-way mixed ANOVA 247
23.6 Data to be analysed 247
23.7 Entering the data 247
23.8 Two-way mixed ANOVA 248
23.9 Interpreting the output 250
23.10 Reporting the output 251
Summary of SPSS steps for mixed ANOVA 252
24 One-way analysis of covariance (ANCOVA) 254
Overview 254
24.1 What is one-way analysis of covariance (ANCOVA)? 255
24.2 When to use one-way ANCOVA 256
24.3 When not to use one-way ANCOVA 256
24.4 Data requirements for one-way ANCOVA 257
24.5 Problems in the use of one-way ANCOVA 257
24.6 Data to be analysed 257
24.7 Entering the data 257
24.8 One-way ANCOVA 258
24.9 Testing that the slope of the regression line within cells is similar 259
24.10 Interpreting the output 260
24.11 Testing the full model 260
24.12 Interpreting the output 262
24.13 Reporting the output 263
Summary of SPSS steps for one-way ANCOVA 263
25 One-way multivariate analysis of variance (MANOVA) 265
Overview 265
25.1 What is one-way multivariate analysis of variance (MANOVA)? 266
25.2 When to use one-way MANOVA 267
25.3 When not to use one-way MANOVA 268
25.4 Data requirements for one-way MANOVA 269
25.5 Problems in the use of one-way MANOVA 269
25.6 Data to be analysed 269
25.7 Entering the data 270
25.8 One-way MANOVA 270
25.9 Interpreting the output 271
25.10 Reporting the output 274
Summary of SPSS steps for one-way MANOVA 274
Part 5 More advanced statistics 275
26 Partial correlation 277
Overview 277
26.1 What is partial correlation? 278
26.2 When to use partial correlation 280
26.3 When not to use partial correlation 280
26.4 Data requirements for partial correlation 280
26.5 Problems in the use of partial correlation 280
26.6 Data to be analysed 280
26.7 Entering the data 281
26.8 Partial correlation 281
26.9 Interpreting the output 282
Reporting the output 283
Summary of SPSS steps for partial correlation 283
27 Factor analysis 284
Overview 284
27.1 What is factor analysis? 285
27.2 When to use factor analysis 287
27.3 When not to use factor analysis 288
27.4 Data requirements for factor analysis 288
27.5 Problems in the use of factor analysis 288
27.6 Data to be analysed 289
27.7 Entering the data 289
27.8 Principal components analysis with orthogonal rotation 290
27.9 Interpreting the output 293
27.10 Reporting the output 295
Summary of SPSS steps for factor analysis 296
28 Item reliability and inter-rater agreement 297
Overview 297
28.1 What are item reliability and inter-rater agreement? 298
28.2 When to use item reliability and inter-rater agreement 300
28.3 When not to use item reliability and inter-rater agreement 301
28.4 Data requirements for item reliability and inter-rater agreement 301
28.5 Problems in the use of item reliability and inter-rater agreement? 302
28.6 Data to be analysed for item alpha reliability 302
28.7 Entering the data 302
28.8 Alpha reliability 303
28.9 Interpreting the output 304
28.10 Split-half reliability 305
28.11 Interpreting the output 305
28.12 Data to be analysed for inter-rater agreement (kappa) 306
28.13 Entering the data 306
28.14 Kappa 307
28.15 Interpreting the output 308
Summary of SPSS steps for reliability 309
29 Stepwise multiple regression 310
Overview 310
29.1 What is stepwise multiple regression? 311
29.2 When to use stepwise multiple regression 312
29.3 When not to use stepwise multiple regression 313
29.4 Data requirements for stepwise multiple regression 314
29.5 Problems in the use of stepwise multiple regression 314
29.6 Data to be analysed 314
29.7 Entering the data 315
29.8 Stepwise multiple regression analysis 315
29.9 Interpreting the output 316
29.10 Reporting the output 319
Summary of SPSS steps for stepwise multiple regression 319
30 Simultaneous or standard multiple regression 321
Overview 321
30.1 What is simultaneous or standard multiple regression? 322
30.2 When to use simultaneous or standard multiple regression 325
30.3 When not to use simultaneous or standard multiple regression 326
30.4 Data requirements for simultaneous or standard multiple regression 326
30.5 Problems in the use of simultaneous or standard multiple regression 327
30.6 Data to be analysed 327
30.7 Entering the data 327
30.8 Simultaneous or standard multiple regression analysis 328
30.9 Interpreting the output 329
30.10 Reporting the output 331
Summary of SPSS steps for simultaneous or standard multiple regression 333
31 Simple mediational analysis 334
Overview 334
31.1 What is simple mediational analysis? 335
31.2 When to use simple mediational analysis 338
31.3 When not to use simple mediational analysis 338
31.4 Data requirements for a simple mediational analysis 339
31.5 Problems in the use of simple mediational analysis 339
31.6 Data to be analysed 339
31.7 Entering the data 339
31.8 Simultaneous multiple regression analysis 340
31.9 Interpreting the output 341
31.10 Reporting the output 342
Summary of SPSS steps for simultaneous or standard multiple regression 343
32 Hierarchical multiple regression 344
Overview 344
32.1 What is hierarchical multiple regression? 345
32.2 When to use hierarchical multiple regression 347
32.3 When not to use hierarchical multiple regression 347
32.4 Data requirements for hierarchical multiple regression 347
32.5 Problems in the use of hierarchical multiple regression 347
32.6 Data to be analysed 348
32.7 Entering the data 348
32.8 Hierarchical multiple regression analysis 349
32.9 Interpreting the output 350
32.10 Reporting the output 352
Summary of SPSS steps for hierarchical multiple regression 353
33 Log-linear analysis 354
Overview 354
33.1 What is log-linear analysis? 355
33.2 When to use log-linear analysis 356
33.3 When not to use log-linear analysis 357
33.4 Data requirements for log-linear analysis 358
33.5 Problems in the use of log-linear analysis 358
33.6 Data to be analysed 358
33.7 Entering the data 358
33.8 Log-linear analysis 359
33.9 Interpreting the output 360
33.10 Reporting the output 362
Summary of SPSS steps for log-linear analysis 362
34 Meta-analysis 363
Overview 363
34.1 What is meta-analysis? 364
34.2 When to use meta-analysis 367
34.3 When not to use meta-analysis 368
34.4 Data requirements for meta-analysis 368
34.5 Problems in the use of meta-analysis 369
34.6 Data to be analysed 369
34.7 Meta-analysis 369
34.8 Interpreting the output 371
34.9 Reporting the output 371
Part 6 Data handling procedures 373
35 Missing values 375
Overview 375
35.1 What are missing values? 376
35.2 Entering the data 377
35.3 Defining missing values 378
35.4 Pairwise and listwise options 378
35.5 Sample output for pairwise exclusion 379
35.6 Sample output for listwise exclusion 380
35.7 Interpreting the output 380
35.8 Reporting the output 381
Summary of SPSS steps for handling missing values 381
36 Recoding values 382
Overview 382
36.1 What is recoding values? 383
36.2 Entering the data 383
36.3 Recoding values 384
36.4 Recoding missing values 387
36.5 Saving the recode procedure as a syntax file 387
36.6 Adding some extra cases to Table 36.1 388
36.7 Running the Recode syntax command 388
Summary of SPSS steps for recoding values 388
37 Computing a scale score with some values missing 390
Overview 390
37.1 What is computing a scale score with some values missing? 391
37.2 Entering the data 392
37.3 Computing a scale score with some values missing 393
37.4 Saving the Compute procedure as a syntax file 395
37.5 Adding some extra cases to Table 37.1 395
37.6 Running the Compute syntax command 396
Summary of SPSS steps for computing a scale score with some missing values 396
38 Computing a new group variable from existing group variables 397
Overview 397
38.1 What is computing a new group variable from existing group variables? 398
38.2 Entering the data 400
38.3 Syntax file for computing a new group variable from existing group variables 400
38.4 Running the Compute syntax commands 401
38.5 Computing a new group using menus and dialogue boxes 402
Summary of SPSS steps for computing a new group variable from existing group variables 403
39 Selecting cases 404
Overview 404
39.1 What is selecting cases? 405
39.2 Entering the data 406
39.3 Selecting cases 406
Summary of SPSS steps for selecting cases 409
40 Reading ASCII or text files into the â€˜Data Editorâ€™ 410
Overview 410
40.1 What is an ASCII or text data file? 411
40.2 Entering data into an ASCII or text data file 412
40.3 Reading an ASCII or text data file 413
Summary of SPSS steps for inputting an ASCII or text data file 416
Glossary 417
Index 424