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E-raamat: Introduction to SPSS in Psychology

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  • Ilmumisaeg: 25-May-2017
  • Kirjastus: Pearson Education Limited
  • Keel: eng
  • ISBN-13: 9781292186702
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Introduction to SPSS in PsychologySuurem pilt
  • Formaat: 1 pages
  • Ilmumisaeg: 25-May-2017
  • Kirjastus: Pearson Education Limited
  • Keel: eng
  • ISBN-13: 9781292186702
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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.
Introduction xxvi
Acknowledgements xxx
Part 1 Introduction to SPSS in Psychology
1(30)
1 Brief introduction to statistics
3(14)
Overview
3(1)
1.1 Basic statistical concepts essential in SPSS analyses
4(1)
1.2 Basic research designs: comparative versus correlational designs
4(3)
1.3 Different types of variables in statistics
7(2)
1.4 Descriptive and inferential statistics compared
9(2)
1.5 Related versus unrelated designs
11(1)
1.6 Quick summaries of statistical analyses
12(1)
1.7 Which procedure or test to use
12(5)
2 Basics of SPSS data entry and statistical analysis
17(14)
Overview
17(1)
2.1 What is SPSS?
18(1)
2.2 Accessing SPSS
18(2)
2.3 Entering data
20(1)
2.4 Moving within a window with the mouse
21(1)
2.5 Moving within a window using the keyboard keys with the mouse
21(1)
2.6 Saving data to memory device
22(1)
2.7 Opening up a data file
23(1)
2.8 Using `Variable View' to create and label variables
24(2)
2.9 More on `Data View'
26(2)
2.10 Simple statistical calculation with SPSS
28(1)
2.11 SPSS output
29(2)
Summary of SPSS steps for a statistical analysis
29(2)
Part 2 Descriptive statistics
31(94)
3 Describing variables tabularly
33(7)
Overview
33(1)
3.1 What are tables?
34(1)
3.2 When to use tables
35(1)
3.3 When not to use tables
35(1)
3.4 Data requirements for tables
35(1)
3.5 Problems in the use of tables
35(1)
3.6 Data to be analysed
36(1)
3.7 Entering summarised categorical or frequency data by weighting
36(2)
3.8 Percentage frequencies
38(1)
3.9 Interpreting the output
38(1)
3.10 Reporting the output
39(1)
Summary of SPSS steps for frequency tables
39(1)
4 Describing variables diagrammatically
40(15)
Overview
40(1)
4.1 What are diagrams?
41(1)
4.2 When to use diagrams
42(1)
4.3 When not to use diagrams
42(1)
4.4 Data requirements for diagrams
42(1)
4.5 Problems in the use of diagrams
42(1)
4.6 Data to be analysed
43(1)
4.7 Entering summarised categorical or frequency data by weighting
43(3)
4.8 Pie diagram of category data
46(1)
4.9 Adding labels to the pie diagram and removing the legend and label
47(2)
4.10 Changing the colour of a pie-diagram slice to a black-and-white pattern
49(2)
4.11 Bar chart of category data
51(1)
4.12 Histograms
52(3)
Summary of SPSS steps for charts
54(1)
5 Describing variables numerically: Averages, variation and spread
55(10)
Overview
55(1)
5.1 What are averages, variation and spread?
56(4)
5.2 When to use averages, variation and spread
60(1)
5.3 When not to use averages, variation and spread
60(1)
5.4 Data requirements for averages, variation and spread
60(1)
5.5 Problems in the use of averages, variation and spread
60(1)
5.6 Data to be analysed
61(1)
5.7 Entering the data
61(1)
5.8 Mean, median, mode, standard deviation, variance and range
62(1)
5.9 Interpreting the output
63(1)
5.10 Other features
63(1)
5.11 Reporting the output
64(1)
Summary of SPSS steps for descriptive statistics
64(1)
6 Shapes of distributions of scores
65(11)
Overview
65(1)
6.1 What are the different shapes of scores?
66(3)
6.2 When to use histograms and frequency tables of scores
69(1)
6.3 When not to use histograms and frequency tables of scores
70(1)
6.4 Data requirements for using histograms and frequency tables of scores
70(1)
6.5 Problems in using histograms and frequency tables of scores
70(1)
6.6 Data to be analysed
70(1)
6.7 Entering the data
71(1)
6.8 Frequency tables
71(1)
6.9 Interpreting the output
72(1)
6.10 Histograms
73(1)
6.11 Interpreting the output
74(2)
Summary of SPSS steps for frequency distributions
75(1)
7 Relationships between two or more variables Tables
76(10)
Overview
76(1)
7.1 What tables are used to show relationships between variables?
77(2)
7.2 When to use tables to show relationships between variables
79(1)
7.3 When not to use tables to show relationships between variables
79(1)
7.4 Data requirements for tables to show relationships between variables
80(1)
7.5 Problems in the use of tables to show relationships between variables
80(1)
7.6 Data to be analysed
80(1)
7.7 Entering the data
81(1)
7.8 Weighting the data
82(1)
7.9 Crosstabulation with frequencies
83(1)
7.10 Displaying frequencies as a percentage of the total number
84(1)
7.11 Displaying frequencies as a percentage of the column total
85(1)
Summary of SPSS steps for contingency tables
85(1)
8 Relationships between two or more variables Diagrams
86(13)
Overview
86(1)
8.1 What diagrams are used to show relationships between variables?
87(3)
8.2 When to use diagrams to show relationships between variables
90(1)
8.3 When not to use diagrams to show relationships between variables
90(1)
8.4 Data requirements for diagrams to show relationships between variables
90(1)
8.5 Problems in the use of diagrams to show relationships between variables
91(1)
8.6 Data to be analysed
91(1)
8.7 Entering the data
92(1)
8.8 Weighting the data
93(1)
8.9 Compound (stacked) percentage bar chart
94(2)
8.10 Compound (clustered) bar chart
96(3)
Summary of SPSS steps for bar charts
98(1)
9 Correlation coefficients: Pearson's correlation and Spearman's rho
99(14)
Overview
99(1)
9.1 What is a correlation coefficient?
100(3)
9.2 When to use Pearson and Spearman rho correlation coefficients
103(1)
9.3 When not to use Pearson and Spearman rho correlation coefficients
103(1)
9.4 Data requirements for Pearson and Spearman rho correlation coefficients
103(1)
9.5 Problems in the use of correlation coefficients
104(1)
9.6 Data to be analysed
104(1)
9.7 Entering the data
105(1)
9.8 Pearson's correlation
105(1)
9.9 Interpreting the output
106(1)
9.10 Spearman's rho
107(1)
9.11 Interpreting the output
107(1)
9.12 Scatter diagram
108(2)
9.13 Interpreting the output
110(1)
9.14 Scattergram with more than one case with the same two values
110(3)
Summary of SPSS steps for correlation
112(1)
10 Regression: Prediction with precision
113(12)
Overview
113(1)
10.1 What is simple regression?
114(2)
10.2 When to use simple regression
116(1)
10.3 When not to use simple regression
116(1)
10.4 Data requirements for simple regression
116(1)
10.5 Problems in the use of simple regression
117(1)
10.6 Data to be analysed
117(1)
10.7 Entering the data
118(1)
10.8 Simple regression
118(1)
10.9 Interpreting the output
119(1)
10.10 Regression scatterplot
120(3)
10.11 Interpreting the output
123(2)
Summary of SPSS steps for simple regression
124(1)
Part 3 Significance testing and basic inferential tests
125(66)
11 Related t-test: Comparing two samples of correlated/related/paired scores
127(9)
Overview
127(1)
11.1 What is the related t-test?
128(2)
11.2 When to use the related t-test
130(1)
11.3 When not to use the related t-test
131(1)
11.4 Data requirements for the related t-test
131(1)
11.5 Problems in the use of the related t-test
131(1)
11.6 Data to be analysed
132(1)
11.7 Entering the data
132(1)
11.8 Related t-test
133(1)
11.9 Interpreting the output
133(3)
Summary of SPSS steps for related t-test
135(1)
12 Unrelated t-test: Comparing two groups of unrelated/uncorrelated scores
136(8)
Overview
136(1)
12.1 What is the unrelated t-test?
137(1)
12.2 When to use the unrelated t-test
138(1)
12.3 When not to use the unrelated r-test
138(1)
12.4 Data requirements for the unrelated t-test
139(1)
12.5 Problems in the use of the unrelated t-test
139(1)
12.6 Data to be analysed
139(1)
12.7 Entering the data
139(2)
12.8 Unrelated t-test
141(1)
12.9 Interpreting the output
141(3)
Summary of SPSS steps for unrelated t-test
143(1)
13 Confidence intervals
144(4)
Overview
144(1)
13.1 What are confidence intervals?
145(1)
13.2 Relationship between significance and confidence intervals
146(1)
13.3 Confidence intervals and limits in SPSS
147(1)
14 Chi-square: Differences between unrelated samples of frequency data
148(15)
Overview
148(1)
14.1 What is chi-square?
149(2)
14.2 When to use chi-square
151(1)
14.3 When not to use chi-square
151(1)
14.4 Data requirements for chi-square
152(1)
14.5 Problems in the use of chi-square
152(1)
14.6 Data to be analysed
153(1)
14.7 Entering the data using the `Weighting Cases' procedure
153(1)
14.8 Entering the data case by case
154(1)
14.9 Chi-square
155(1)
14.10 Interpreting the output for chi-square
156(2)
14.11 Fisher's exact test
158(1)
14.12 Interpreting the output for Fisher's exact test
158(1)
14.13 One-sample chi-square
159(2)
14.14 Interpreting the output for a one-sample chi-square
161(1)
14.15 Chi-square without ready-made tables
161(2)
Summary of SPSS steps for chi-square
162(1)
15 McNemar's test: Differences between related samples of frequency data
163(7)
Overview
163(1)
15.1 What is McNemar's test?
164(1)
15.2 When to use McNemar's test
164(1)
15.3 When not to use McNemar's test
165(1)
15.4 Data requirements for McNemar's test
165(1)
15.5 Problems in the use of McNemar's test
165(1)
15.6 Data to be analysed
165(1)
15.7 Entering the data using the `Weighting Cases' procedure
166(1)
15.8 Entering the data case by case
167(1)
15.9 McNemar's test
167(1)
15.10 Interpreting the output for McNemar's test
168(2)
Summary of SPSS steps for McNemar's test
169(1)
16 Ranking tests for two groups: Non-parametric statistics
170(11)
Overview
170(1)
16.1 What are non-parametric tests?
171(2)
16.2 When to use non-parametric tests
173(1)
16.3 When not to use non-parametric tests
173(1)
16.4 Data requirements for non-parametric tests
173(1)
16.5 Problems in the use of non-parametric tests
173(1)
16.6 Data to be analysed
174(1)
16.7 Entering the data
174(1)
16.8 Related scores: Sign test
175(1)
16.9 Interpreting the output for the sign test
175(1)
16.10 Related scores: Wilcoxon test
176(1)
16.11 Interpreting the output for the Wilcoxon test
176(1)
16.12 Unrelated scores: Mann-Whitney U-test
177(1)
16.13 Entering the data
177(1)
16.14 Mann-Whitney U-test
178(1)
16.15 Interpreting the output for the Mann-Whitney U-test
179(2)
Summary of SPSS steps for non-parametric tests for two groups
180(1)
17 Ranking tests for three or more groups: Non-parametric statistics
181(10)
Overview
181(1)
17.1 What are ranking tests?
182(1)
17.2 When to use ranking tests
183(1)
17.3 When not to use ranking tests
183(1)
17.4 Data requirements for ranking tests
183(1)
17.5 Problems in the use of ranking tests
183(1)
17.6 Data to be analysed
183(1)
17.7 Friedman three or more related samples test
184(1)
17.8 Entering the data for the Friedman test
184(1)
17.9 Friedman test
185(1)
17.10 Interpreting the output for the Friedman test
185(1)
17.11 Kruskal-Wallis three or more unrelated samples test
186(1)
17.12 Entering the data for the Kruskal-Wallis test
187(1)
17.13 Kruskal-Wallis test
188(1)
17.14 Interpreting the output for the Kruskal-Wallis test
189(2)
Summary of SPSS steps for non-parametric tests for three or more groups
189(2)
Part 4 Analysis of variance
191(84)
18 One-way analysis of variance (ANOVA) for unrelated or uncorrected scores
193(8)
Overview
193(1)
18.1 What is one-way unrelated ANOVA?
194(1)
18.2 When to use one-way unrelated ANOVA
195(1)
18.3 When not to use one-way unrelated ANOVA
196(1)
18.4 Data requirements for one-way unrelated ANOVA
196(1)
18.5 Problems in the use of one-way unrelated ANOVA
196(1)
18.6 Data to be analysed
196(1)
18.7 Entering the data
197(1)
18.8 One-way unrelated ANOVA
197(1)
18.9 Interpreting the output
198(3)
Summary of SPSS steps for one-way unrelated ANOVA
199(2)
19 One-way analysis of variance for correlated scores or repeated measures
201(9)
Overview
201(1)
19.1 What is one-way repeated-measures ANOVA?
202(1)
19.2 When to use repeated-measures ANOVA
203(1)
19.3 When not to use one-way repeated-measures ANOVA
203(1)
19.4 Data requirements for one-way repeated-measures ANOVA
204(1)
19.5 Problems in the use of one-way repeated-measures ANOVA
204(1)
19.6 Data to be analysed
204(1)
19.7 Entering the data
204(1)
19.8 One-way repeated-measures ANOVA
205(1)
19.9 Interpreting the output
206(4)
Summary of SPSS steps for one-way repeated-measures ANOVA
209(1)
20 Two-way analysis of variance for unrelated/uncorrelated scores
210(13)
Overview
210(1)
20.1 What is two-way unrelated ANOVA?
211(3)
20.2 When to use two-way unrelated AM OVA
214(1)
20.3 When not to use two-way unrelated ANOVA
214(1)
20.4 Data requirements for two-way unrelated ANOVA
214(1)
20.5 Problems in the use of two-way unrelated ANOVA
215(1)
20.6 Data to be analysed
216(1)
20.7 Entering the data
216(1)
20.8 Two-way unrelated ANOVA
217(1)
20.9 Interpreting the output
218(2)
20.10 Editing the graph
220(3)
Summary of SPSS steps for two-way unrelated ANOVA
221(2)
21 Multiple comparisons in ANOVA
223(8)
Overview
223(1)
21.1 What is multiple-comparisons testing?
224(1)
21.2 When to use multiple-comparisons tests
225(1)
21.3 When not to use multiple-comparisons tests
225(1)
21.4 Data requirements for multiple-comparisons tests
225(1)
21.5 Problems in the use of multiple-comparisons tests
226(1)
21.6 Data to be analysed
226(1)
21.7 Entering the data
227(1)
21.8 Multiple-comparisons tests
227(1)
21.9 Interpreting the output
228(1)
21.10 Reporting the output
229(2)
Summary of SPSS steps for multiple-comparison tests
230(1)
22 Two-way analysis of variance for correlated scores or repeated measures
231(13)
Overview
231(1)
22.1 What is two-way repeated-measures ANOVA?
232(2)
22.2 When to use two-way repeated-measures ANOVA
234(1)
22.3 When not to use two-way repeated-measures AIMOVA
235(1)
22.4 Data requirements for two-way related-measures ANOVA
235(1)
22.5 Problems in the use of two-way repeated-measures ANOVA
235(1)
22.6 Data to be analysed
235(1)
22.7 Entering the data
236(1)
22.8 Two-way repeated-measures ANOVA
236(2)
22.9 Interpreting the output
238(4)
22.10 Reporting the output
242(2)
Summary of SPSS steps for two-way repeated-measures ANOVA
242(2)
23 Two-way mixed analysis of variance
244(10)
Overview
244(1)
23.1 What is two-way mixed ANOVA?
245(1)
23.2 When to use two-way mixed ANOVA
245(1)
23.3 When not to use two-way mixed ANOVA
246(1)
23.4 Data requirements for two-way mixed ANOVA
247(1)
23.5 Problems in the use of two-way mixed ANOVA
247(1)
23.6 Data to be analysed
247(1)
23.7 Entering the data
247(1)
23.8 Two-way mixed ANOVA
248(2)
23.9 Interpreting the output
250(1)
23.10 Reporting the output
251(3)
Summary of SPSS steps for mixed ANOVA
252(2)
24 One-way analysis of covariance (ANCOVA)
254(11)
Overview
254(1)
24.1 What is one-way analysis of covariance (ANCOVA)?
255(1)
24.2 When to use one-way ANCOVA
256(1)
24.3 When not to use one-way ANCOVA
256(1)
24.4 Data requirements for one-way ANCOVA
257(1)
24.5 Problems in the use of one-way ANCOVA
257(1)
24.6 Data to be analysed
257(1)
24.7 Entering the data
257(1)
24.8 One-way ANCOVA
258(1)
24.9 Testing that the slope of the regression line within cells is similar
259(1)
24.10 Interpreting the output
259(1)
24.11 Testing the full model
260(2)
24.12 Interpreting the output
262(1)
24.13 Reporting the output
263(2)
Summary of SPSS steps for one-way ANCOVA 2
63(202)
25 One-way multivariate analysis of variance (MANOVA)
265(10)
Overview
265(1)
25.1 What is one-way multivariate analysis of variance (MAIMOVA)?
266(1)
25.2 When to use one-way MANOVA
267(1)
25.3 When not to use one-way MANOVA
268(1)
25.4 Data requirements for one-way MAIMOVA
269(1)
25.5 Problems in the use of one-way MANOVA
269(1)
25.6 Data to be analysed
269(1)
25.7 Entering the data
270(1)
25.8 One-way MANOVA
270(1)
25.9 Interpreting the output
271(3)
25.10 Reporting the output
274(1)
Summary of SPSS steps for one-way MANOVA
274(1)
Part 5 More advanced statistics
275(98)
26 Partial correlation
277(7)
Overview
277(1)
26.1 What is partial correlation?
278(2)
26.2 When to use partial correlation
280(1)
26.3 When not to use partial correlation
280(1)
26.4 Data requirements for partial correlation
280(1)
26.5 Problems in the use of partial correlation
280(1)
26.6 Data to be analysed
280(1)
26.7 Entering the data
281(1)
26.8 Partial correlation
281(1)
26.9 Interpreting the output
282(1)
26.11 Reporting the output
283(1)
Summary of SPSS steps for partial correlation
283(1)
27 Factor analysis
284(13)
Overview
284(1)
27.1 What is factor analysis?
285(2)
27.2 When to use factor analysis
287(1)
27.3 When not to use factor analysis
288(1)
27.4 Data requirements for factor analysis
288(1)
27.5 Problems in the use of factor analysis
288(1)
27.6 Data to be analysed
289(1)
27.7 Entering the data
289(1)
27.8 Principal components analysis with orthogonal rotation
290(3)
27.9 Interpreting the output
293(2)
27.10 Reporting the output
295(2)
Summary of SPSS steps for factor analysis
296(1)
28 Item reliability and inter-rater agreement
297(13)
Overview
297(1)
28.1 What are item reliability and inter-rater agreement?
298(2)
28.2 When to use item reliability and inter-rater agreement
300(1)
28.3 When not to use item reliability and inter-rater agreement
301(1)
28.4 Data requirements for item reliability and inter-rater agreement
301(1)
28.5 Problems in the use of item reliability and inter-rater agreement?
302(1)
28.6 Data to be analysed for item alpha reliability
302(1)
28.7 Entering the data
302(1)
28.8 Alpha reliability
303(1)
28.9 Interpreting the output
304(1)
28.10 Split-half reliability
305(1)
28.11 Interpreting the output
305(1)
28.12 Data to be analysed for inter-rater agreement (kappa)
306(1)
28.13 Entering the data
306(1)
28.14 Kappa
307(1)
28.15 Interpreting the output
308(2)
Summary of SPSS steps for reliability
309(1)
29 Stepwise multiple regression
310(11)
Overview
310(1)
29.1 What is stepwise multiple regression?
311(1)
29.2 When to use stepwise multiple regression
312(1)
29.3 When not to use stepwise multiple regression
313(1)
29.4 Data requirements for stepwise multiple regression
314(1)
29.5 Problems in the use of stepwise multiple regression
314(1)
29.6 Data to be analysed
314(1)
29.7 Entering the data
315(1)
29.8 Stepwise multiple regression analysis
315(1)
29.9 Interpreting the output
316(3)
29.10 Reporting the output
319(2)
Summary of SPSS steps for stepwise multiple regression
319(2)
30 Simultaneous or standard multiple regression
321(13)
Overview
321(1)
30.1 What is simultaneous or standard multiple regression?
322(3)
30.2 When to use simultaneous or standard multiple regression
325(1)
30.3 When not to use simultaneous or standard multiple regression
326(1)
30.4 Data requirements for simultaneous or standard multiple regression
326(1)
30.5 Problems in the use of simultaneous or standard multiple regression
327(1)
30.6 Data to be analysed
327(1)
30.7 Entering the data
327(1)
30.8 Simultaneous or standard multiple regression analysis
328(1)
30.9 Interpreting the output
329(2)
30.10 Reporting the output
331(3)
Summary of SPSS steps for simultaneous or standard multiple regression
333(1)
31 Simple mediational analysis
334(10)
Overview
334(1)
31.1 What is simple mediational analysis?
335(3)
31.2 When to use simple mediational analysis
338(1)
31.3 When not to use simple mediational analysis
338(1)
31.4 Data requirements for a simple mediational analysis
339(1)
31.5 Problems in the use of simple mediational analysis
339(1)
31.6 Data to be analysed
339(1)
31.7 Entering the data
339(1)
31.8 Simultaneous multiple regression analysis
340(1)
31.9 Interpreting the output
341(1)
31.10 Reporting the output
342(2)
Summary of SPSS steps for simultaneous or standard multiple regression
343(1)
32 Hierarchical multiple regression
344(10)
Overview
344(1)
32.1 What is hierarchical multiple regression?
345(2)
32.2 When to use hierarchical multiple regression
347(1)
32.3 When not to use hierarchical multiple regression
347(1)
32.4 Data requirements for hierarchical multiple regression
347(1)
32.5 Problems in the use of hierarchical multiple regression
347(1)
32.6 Data to be analysed
348(1)
32.7 Entering the data
348(1)
32.8 Hierarchical multiple regression analysis
349(1)
32.9 Interpreting the output
350(2)
32.10 Reporting the output
352(2)
Summary of SPSS steps for hierarchical multiple regression
353(1)
33 Log-linear analysis
354(9)
Overview
354(1)
33.1 What is log-linear analysis?
355(1)
33.2 When to use log-linear analysis
356(1)
33.3 When not to use log-linear analysis
357(1)
33.4 Data requirements for log-linear analysis
358(1)
33.5 Problems in the use of log-linear analysis
358(1)
33.6 Data to be analysed
358(1)
33.7 Entering the data
358(1)
33.8 Log-linear analysis
359(1)
33.9 Interpreting the output
360(2)
33.10 Reporting the output
362(1)
Summary of SPSS steps for log-linear analysis
362(1)
34 Meta-analysis
363(10)
Overview
363(1)
34.1 What is meta-analysis?
364(3)
34.2 When to use meta-analysis
367(1)
34.3 When not to use meta-analysis
368(1)
34.4 Data requirements for meta-analysis
368(1)
34.5 Problems in the use of meta-analysis
369(1)
34.6 Data to be analysed
369(1)
34.7 Meta-analysis
369(2)
34.8 Interpreting the output
371(1)
34.9 Reporting the output
371(2)
Part 6 Data handling procedures
373(44)
35 Missing values
375(7)
Overview
375(1)
35.1 What are missing values?
376(1)
35.2 Entering the data
377(1)
35.3 Defining missing values
378(1)
35.4 Pairwise and listwise options
378(1)
35.5 Sample output for pairwise exclusion
379(1)
35.6 Sample output for listwise exclusion
380(1)
35.7 Interpreting the output
380(1)
35.8 Reporting the output
381(1)
Summary of SPSS steps for handling missing values
381(1)
36 Recoding values
382(8)
Overview
382(1)
36.1 What is recoding values?
383(1)
36.2 Entering the data
383(1)
36.3 Recoding values
384(3)
36.4 Recoding missing values
387(1)
36.5 Saving the recode procedure as a syntax file
387(1)
36.6 Adding some extra cases to Table 36.1
388(1)
36.7 Running the Recode syntax command
388(2)
Summary of SPSS steps for recoding values
388(2)
37 Computing a scale score with some values missing
390(7)
Overview
390(1)
37.1 What is computing a scale score with some values missing?
391(1)
37.2 Entering the data
392(1)
37.3 Computing a scale score with some values missing
393(2)
37.4 Saving the Compute procedure as a syntax file
395(1)
37.5 Adding some extra cases to Table 37.1
395(1)
37.6 Running the Compute syntax command
396(1)
Summary of SPSS steps for computing a scale score with some missing values
396(1)
38 Computing a new group variable from existing group variables
397(7)
Overview
397(1)
38.1 What is computing a new group variable from existing group variables?
398(2)
38.2 Entering the data
400(1)
38.3 Syntax file for computing a new group variable from existing group variables
400(1)
38.4 Running the Compute syntax commands
401(1)
38.5 Computing a new group using menus and dialogue boxes
402(2)
Summary of SPSS steps for computing a new group variable from existing group variables
403(1)
39 Selecting cases
404(6)
Overview
404(1)
39.1 What is selecting cases?
405(1)
39.2 Entering the data
406(1)
39.3 Selecting cases
406(4)
Summary of SPSS steps for selecting cases
409(1)
40 Reading ASCII or text files into the `Data Editor'
410(7)
Overview
410(1)
40.1 What is an ASCII or text data file?
411(1)
40.2 Entering data into an ASCII or text data file
412(1)
40.3 Reading an ASCII or text data file
413(4)
Summary of SPSS steps for inputting an ASCII or text data file
416(1)
Glossary 417(7)
Index 424
Dennis Howitt and Duncan Cramer are with Loughborough University.
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