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E-raamat: Introduction to Statistics with SPSS for Social Science

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  • Formaat: 496 pages
  • Ilmumisaeg: 19-Sep-2014
  • Kirjastus: Routledge
  • Keel: eng
  • ISBN-13: 9781317861836
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Introduction to Statistics with SPSS for Social ScienceSuurem pilt
  • Formaat: 496 pages
  • Ilmumisaeg: 19-Sep-2014
  • Kirjastus: Routledge
  • Keel: eng
  • ISBN-13: 9781317861836
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This is a complete guide to statistics and SPSS for social science students. Statistics with SPSS for Social Science provides a step-by-step explanation of all the important statistical concepts, tests and procedures. It is also a guide to getting started with SPSS, and includes screenshots to illustrate explanations. With examples specific to social sciences, this text is essential for any student in this area.

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A complete guide to statistics and SPSS for social science students, Statistics with SPSS for Social Science provides a step-by-step explanation of all the important statistical concepts, tests and procedures and how to get started with SPSS.
Guided tour xvi
Introduction xvii
List of figures
xxi
List of tables
xxiii
List of boxes
xxviii
List of calculation boxes
xxix
Acknowledgements xxx
Part 1 Descriptive statistics
1(134)
1 Why you need statistics: types of data
3(17)
Overview
3(1)
1.1 Introduction
4(1)
1.2 Variables and measurement
4(2)
1.3 Statistical significance
6(1)
1.4 SPSS guide: an introduction
7(13)
Key points
19(1)
2 Describing variables: tables and diagrams
20(20)
Overview
20(1)
2.1 Introduction
21(1)
2.2 Choosing tables and diagrams
22(7)
2.3 Errors to avoid
29(1)
2.4 SPSS analysis
29(5)
2.5 Pie diagram of category data
34(2)
2.6 Bar chart of category data
36(2)
2.7 Histograms
38(2)
Key points
39(1)
3 Describing variables numerically: averages, variation and spread
40(15)
Overview
40(1)
3.1 Introduction: mean, median and mode
41(3)
3.2 Comparison of mean, median and mode
44(1)
3.3 The spread of scores: variability
45(4)
3.4 Probability
49(2)
3.5 Confidence intervals
51(1)
3.6 SPSS analysis
51(4)
Key points
54(1)
4 Shapes of distributions of scores
55(12)
Overview
55(1)
4.1 Introduction
56(1)
4.2 Histograms and frequency curves
56(1)
4.3 The normal curve
57(1)
4.4 Distorted curves
58(2)
4.5 Other frequency curves
60(3)
4.6 SPSS analysis
63(4)
Key points
66(1)
5 Standard deviation, z-scores and standard error: the standard unit of measurement in statistics
67(14)
Overview
67(1)
5.1 Introduction
68(1)
5.2 What is standard deviation?
68(2)
5.3 When to use standard deviation
70(1)
5.4 When not to use standard deviation
71(1)
5.5 Data requirements for standard deviation
71(1)
5.6 Problems in the use of standard deviation
72(1)
5.7 SPSS analysis
72(4)
5.8 Standard error: the standard deviation of the means of samples
76(1)
5.9 When to use standard error
77(1)
5.10 When not to use standard error
77(1)
5.11 SPSS analysis for standard error
77(4)
Key points
80(1)
6 Relationships between two or more variables: diagrams and tables
81(18)
Overview
81(1)
6.1 Introduction
82(1)
6.2 The principles of diagrammatic and tabular presentation
82(1)
6.3 Type A: both variables numerical scores
83(1)
6.4 Type B: both variables nominal categories
84(2)
6.5 Type C: one variable nominal categories, the other numerical scores
86(2)
6.6 SPSS analysis
88(11)
Key points
98(1)
7 Correlation coefficients: the Pearson correlation and Spearman's rho
99(19)
Overview
99(1)
7.1 Introduction
100(1)
7.2 Principles of the correlation coefficient
100(6)
7.3 Some rules to check out
106(1)
7.4 Coefficient of determination
107(1)
7.5 Data requirements for correlation coefficients
108(1)
7.6 SPSS analysis
108(2)
7.7 Spearman's rho - another correlation coefficient
110(4)
7.8 SPSS analysis for Spearman's rho
114(1)
7.9 Scatter diagram using SPSS
115(2)
7.10 Problems in the use of correlation coefficients
117(1)
Key points
117(1)
8 Regression and standard error
118(17)
Overview
118(1)
8.1 Introduction
119(2)
8.2 Theoretical background and regression equations
121(4)
8.3 When and when not to use simple regression
125(1)
8.4 Data requirements for simple regression
125(1)
8.5 Problems in the use of simple regression
125(1)
8.6 SPSS analysis
126(3)
8.7 Regression scatterplot
129(3)
8.8 Standard error: how accurate are the predicted score and the regression equations?
132(3)
Key points
133(2)
Part 2 Inferential statistics
135(62)
9 The analysis of a questionnaire/survey project
137(8)
Overview
137(1)
9.1 Introduction
138(1)
9.2 The research project
138(1)
9.3 The research hypothesis
139(1)
9.4 Initial variable classification
140(1)
9.5 Further coding of data
141(1)
9.6 Data cleaning
142(1)
9.7 Data analysis
142(2)
9.8 SPSS analysis
144(1)
Key points
144(1)
10 The related t-test: comparing two samples of correlated/related scores
145(13)
Overview
145(1)
10.1 Introduction
146(1)
10.2 Dependent and independent variables
147(1)
10.3 Theoretical considerations
148(5)
10.4 SPSS analysis
153(3)
10.5 A cautionary note
156(2)
Key points
157(1)
11 The unrelated t-test: comparing two samples of unrelated/uncorrelated scores
158(18)
Overview
158(1)
11.1 Introduction
159(1)
11.2 Theoretical considerations
160(4)
11.3 Standard deviation and standard error
164(6)
11.4 A cautionary note
170(1)
11.5 Data requirements for the unrelated t-test
170(1)
11.6 When not to use the unrelated t-test
170(1)
11.7 Problems in the use of the unrelated t-test
171(1)
11.8 SPSS analysis
171(5)
Key points
175(1)
12 Chi-square: differences between samples of frequency data
176(21)
Overview
176(1)
12.1 Introduction
177(1)
12.2 Theoretical considerations
178(5)
12.3 When to use chi-square
183(1)
12.4 When not to use chi-square
183(1)
12.5 Data requirements for chi-square
183(1)
12.6 Problems in the use of chi-square
184(1)
12.7 SPSS analysis
184(5)
12.8 The Fisher exact probability test
189(3)
12.9 SPSS analysis for the Fisher exact test
192(1)
12.10 Partitioning chi-square
193(2)
12.11 Important warnings
195(1)
12.12 Alternatives to chi-square
195(1)
12.13 Chi-square and known populations
196(1)
Key points
196(1)
Recommended further reading
196(1)
Part 3 Introduction to analysis of variance
197(78)
13 Analysis of variance (ANOVA): introduction to one-way unrelated or uncorrelated ANOVA
199(13)
Overview
199(1)
13.1 Introduction
200(1)
13.2 Theoretical considerations
200(4)
13.3 Degrees of freedom
204(1)
13.4 When to use one-way ANOVA
204(1)
13.5 When not to use one-way ANOVA
205(1)
13.6 Data requirements for one-way ANOVA
205(1)
13.7 Problems in the use of one-way ANOVA
205(1)
13.8 SPSS analysis
205(3)
13.9 Computer analysis for one-way unrelated ANOVA
208(4)
Key points
211(1)
14 Two-way analysis of variance for unrelated/uncorrelated scores: two studies for the price of one?
212(28)
Overview
212(1)
14.1 Introduction
213(1)
14.2 Theoretical considerations
214(1)
14.3 Steps in the analysis
215(5)
14.4 When to use two-way ANOVA
220(1)
14.5 When not to use two-way ANOVA
220(1)
14.6 Data requirements for two-way ANOVA
220(1)
14.7 Problems in the use of two-way ANOVA
220(1)
14.8 SPSS analysis
221(6)
14.9 Computer analysis for two-way unrelated ANOVA
227(6)
14.10 Three or more independent variables
233(1)
14.11 Multiple-comparisons testing in ANOVA
234(6)
Key points
239(1)
15 Analysis of covariance (ANCOVA): controlling for additional variables
240(18)
Overview
240(1)
15.1 Introduction
241(1)
15.2 Example of the analysis of covariance
241(9)
15.3 When to use ANCOVA
250(1)
15.4 When not to use ANCOVA
250(1)
15.5 Data requirements for ANCOVA
250(1)
15.6 SPSS analysis
250(8)
Key points
257(1)
Recommended further reading
257(1)
16 Multivariate analysis of variance (MANOVA)
258(17)
Overview
258(1)
16.1 Introduction
259(1)
16.2 Questions for MANOVA
260(1)
16.3 MANOVA's two stages
261(1)
16.4 Doing MANOVA
262(4)
16.5 When to use MANOVA
266(1)
16.6 When not to use MANOVA
267(1)
16.7 Data requirements for MANOVA
267(1)
16.8 Problems in the use of MANOVA
268(1)
16.9 SPSS analysis
268(7)
Key points
273(1)
Recommended further reading
273(2)
Part 4 More advanced statistics and techniques
275(132)
17 Partial correlation: spurious correlation, third or confounding variables (control variables), suppressor variables
277(11)
Overview
277(1)
17.1 Introduction
278(1)
17.2 Theoretical considerations
278(2)
17.3 The calculation
280(2)
17.4 Multiple control variables
282(1)
17.5 Suppressor variables
282(1)
17.6 An example from the research literature
282(2)
17.7 When to use partial correlation
284(1)
17.8 When not to use partial correlation
284(1)
17.9 Data requirements for partial correlation
284(1)
17.10 Problems in the use of partial correlation
284(1)
17.11 SPSS analysis
284(4)
Key points
287(1)
18 Factor analysis: simplifying complex data
288(20)
Overview
288(1)
18.1 Introduction
289(1)
18.2 Data issues in factor analysis
290(1)
18.3 Concepts in factor analysis
291(2)
18.4 Decisions, decisions, decisions
293(5)
18.5 When to use factor analysis
298(1)
18.6 When not to use factor analysis
298(1)
18.7 Data requirements for factor analysis
299(1)
18.8 Problems in the use of factor analysis
299(1)
18.9 SPSS analysis
299(9)
Key points
306(1)
Recommended further reading
307(1)
19 Multiple regression and multiple correlation
308(24)
Overview
308(1)
19.1 Introduction
309(1)
19.2 Theoretical considerations
309(5)
19.3 Stepwise multiple regression example
314(3)
19.4 Reporting the results
317(1)
19.5 What is stepwise multiple regression?
317(1)
19.6 When to use stepwise multiple regression
318(1)
19.7 When not to use stepwise multiple regression
318(1)
19.8 Data requirements for stepwise multiple regression
319(1)
19.9 Problems in the use of stepwise multiple regression
319(1)
19.10 SPSS analysis
319(5)
19.11 What is hierarchical multiple regression?
324(1)
19.12 When to use hierarchical multiple regression
325(1)
19.13 When not to use hierarchical multiple regression
325(1)
19.14 Data requirements for hierarchical multiple regression
325(1)
19.15 Problems in the use of hierarchical multiple regression
326(1)
19.16 SPSS analysis
326(6)
Key points
330(1)
Recommended further reading
331(1)
20 Multinomial logistic regression: distinguishing between several different categories or groups
332(25)
Overview
332(1)
20.1 Introduction
333(2)
20.2 Dummy variables
335(1)
20.3 What can multinomial logistic regression do?
335(2)
20.4 Worked example
337(1)
20.5 Accuracy of the prediction
338(1)
20.6 How good are the predictors?
339(3)
20.7 The prediction
342(2)
20.8 What have we found?
344(1)
20.9 Reporting the results
345(1)
20.10 When to use multinomial logistic regression
345(1)
20.11 When not to use multinomial logistic regression
345(1)
20.12 Data requirements for multinomial logistic regression
346(1)
20.13 Problems in the use of multinomial logistic regression
346(1)
20.14 SPSS analysis
346(11)
Key points
356(1)
21 Binomial logistic regression
357(21)
Overview
357(1)
21.1 Introduction
358(1)
21.2 Simple logistic regression
358(4)
21.3 Typical example
362(3)
21.4 Applying the logistic regression procedure
365(3)
21.5 The regression formula
368(2)
21.6 Reporting the results
370(1)
21.7 When to use binomial logistic regression
370(1)
21.8 When not to use binomial logistic regression
370(1)
21.9 Data requirements for binomial logistic regression
370(1)
21.10 Problems in the use of binomial logistic regression
371(1)
21.11 SPSS analysis
371(7)
Key points
377(1)
22 Log-linear methods: the analysis of complex contingency tables
378(29)
Overview
378(1)
22.1 Introduction
379(2)
22.2 A two-variable example
381(7)
22.3 A three-variable example
388(10)
22.4 Reporting the results
398(1)
22.5 When to use log-linear analysis
399(1)
22.6 When not to use log-linear analysis
399(1)
22.7 Data requirements for log-linear analysis
400(1)
22.8 Problems in the use of log-linear analysis
400(1)
22.9 SPSS analysis
400(7)
Key points
405(1)
Recommended further reading
405(2)
Appendices
407(38)
A Testing for excessively skewed distributions
409(3)
A.1 Skewness
409(1)
A.2 Standard error of skewness
410(2)
B Extended table of significance for the Pearson correlation coefficient
412(4)
C Table of significance for the Spearman correlation coefficient
416(4)
D Extended table of significance for the t-test
420(4)
E Table of significance for chi-square
424(1)
F Extended table of significance for the sign test
425(4)
G Table of significance for the Wilcoxon matched pairs test
429(4)
H Tables of significance for the Mann-Whitney U-test
433(3)
I Tables of significant values for the F-distribution
436(3)
J Table of significant values of t when making multiple t-tests
439(4)
K Some other statistics in SPSS Statistics
443(2)
Glossary 445(8)
References 453(1)
Index 454
Faiza Qureshi
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