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BK

3rd ed.

Los Angeles : Sage, c2009

xxxiii, 821 s. : il., portréty ; 28 cm

ISBN 978-1-84787-906-6 (váz.)

ISBN 978-1-84787-907-3 (brož.)

Terminologický slovník

Obsahuje bibliografie a rejstřík

000132732

Preface xix // How to use this book xxiv // Acknowledgements xxviii // 1 Why is my evil lecturer forcing me to learn statistics? 1 // 1.1 What will this chapter tell me? // 1.2 What the hell am I doing here? I don’t belong here // 1.3 Initial observation: finding something that needs explaining //1.4 Generating theories and testing them //1.5 Data collection 1: what to measure // 1.6 Data collection 2: how to measure // 1.7 Analysing data // 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 // 2.5. Going beyond the data 40 // 2.6. Using statistical models to test research questions 48 // 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 // 3.5. The SPSS viewer 78 // 3.6. The SPSS SmartViewer 81 // 3.7. The syntax window 82 // 3.8. Saving files 83 // 3.9. Retrieving a file 84 // 4 Exploring data with graphs 87 // 4.1. What will this chapter tell me? 87 // 4.2. The art of presenting data 88 // 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 // 4.7. Line charts 1 15 // 4.8. Graphing relationships: the scatterplot 116 // 5 Exploring assumptions 131 // 5.1. 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 // 5.5. Testing whether a distribution is normal 144 // 5.6. Testing for homogeneity of variance 149 // 5.7. Correcting problems in the data 153 // 6 Correlation 166 // 6.1. What will this chapter tell me? O // 6.2. Looking at relationships //

6.3. How do we measure relationships? // 6.4. Data entry for correlation analysis using SPSS // 6.5. Bivariate correlation // 6.6. Partial correlation // 6.7. Comparing correlations // 7 Regression 197 // 7.1. What will this chapter tell me? 197 // 7.2. An introduction to regression 198 // 7.3. Doing simple regression on SPSS 205 // 7.4. Interpreting a simple regression 206 // 7.5. Multiple regression: the basics 209 // 7.6. How accurate is my regression model? 214 // 7.7. How to do multiple regression using SPSS 225 // 7.8. Interpreting multiple regression 233 // 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 // 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 // 8.4. Assumptions and things that can go wrong 273 // 8.5. Binary logistic regression: an example that will make you feel eel 277 // 8.6. Interpreting logistic regression 282 // 8.7. How to report logistic regression 294 // 8.8. Testing assumptions: another example 294 // 8.9. Predicting several categories: multinomial logistic regression 300 // 9 Comparing two means 316 // 9.1. What will this chapter tell me? 316 // 9.2. Looking at differences 317 // 9.3. The t-test 324 // 9.4. The dependent t-test 326 // 9.5. The independent t-test 334 // 9.6. Between groups or repeated measures? 342 // 9.7. The t-test as a general linear model 342 // 9.8. What if my data are not normally distributed? 344 // 10 Comparing several means: ANOVA (GLM 1) 347 // 10.1. What will this chapter tell me? 347 // 10.2. The theory behind ANOVA 348 // 10.3. Running one-way ANOVA on SPSS 375 // 10.4. Output from one-way ANOVA 381 // 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 // 11 Analysis of covariance, ANCOVA (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 // 11.4. Conducting ANCOVA on SPSS 399 // 11.5. Interpreting the output from ANCOVA 404 // 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 // 12 Factorial ANOVA (GLM 3) 421 // 12.1. What will this chapter tell me? // 12.2. Theory of factorial ANOVA (between-groups) // 12.3. Factorial ANOVA using SPSS // 12.4. Output from factorial ANOVA // 12.5. Interpreting interaction graphs // 12.6. Calculating effect sizes // 12.7. Reporting the results of two-way ANOVA // 12.8. Factorial ANOVA as regression // 12.9. What to do when assumptions are violated in factorial ANOVA // 13 Repeated-measures designs (GLM 4) 457 // 13.1. What will this chapter tell me? 457 // 13.2. Introduction to repeated-measures designs 458 // 13.3. Theory of one-way repeated-measures ANOVA 462 // 13.4. One-way repeated-measures ANOVA using SPSS 468 // 13.5. Output for one-way repeated-measures ANOVA 474 // 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 // 13.9. Output for factorial repeated-measures ANOVA 492 // 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 // 14 Mixed design ANOVA (GLM 5) 506 //

14.1. What will this chapter tell me? 506 // 14.2. Mixed designs 507 // 14.3. What do men and women look for in a partner? 508 // 14.4. Mixed ANOVA on SPSS 508 // 14.5. Output for mixed factorial ANOVA: main analysis 514 // 14.6. Calculating effect sizes 531 // 14.7. Reporting the results of mixed ANOVA 533 // 14.8. What to do when assumptions are violated in mixed ANOVA 536 // 15 Non-parametric tests 539 // 15.1. What will this chapter tell me? 539 // 15.2. When to use non-parametric tests 540 // 15.3. Comparing two independent conditions: the Wilcoxon rank-sum test and Mann-Whitney test 540 // 15.4. Comparing two related conditions: the Wilcoxon signed-rank test 552 // 15.5. Differences between several independent groups: the Kruskal-Wallis test 559 // 15.6. Differences between several related groups: Friedman’s ANOVA 573 // 16 Multivariate analysis of variance (MANOVA) 584 // 16.1. What will this chapter tell me? 584 // 16.2. When to use MANOVA 585 // 16.3. Introduction: similarities and differences to ANOVA 585 // 16.4. Theory of MANOVA 588 // 16.5. Practical issues when conducting MANOVA 603 // 16.6. MANOVA on SPSS 605 // 16.7. Output from MANOVA 608 // 16.8. Reporting results from MANOVA 614 // 16.9. Following up MANOVA with discriminant analysis 615 // 16.10. Output from the discriminant analysis 618 // 16.11. Reporting results from discriminant analysis 621 // 16.12. Some final remarks 622 // 17 Exploratory factor analysis 627 // 17.1. What will this chapter tell me? 627 // 17.2. When to use factor analysis 628 // 17.3. Factors 628 // 17.4. Discovering factors 636 // 17.5. Research example 645 // 17.6. Running the analysis 650 // 17.7. Interpreting output from SPSS 655 // 17.8. How to report factor analysis 671 // 17.9. Reliability analysis 673 // 17.10. How to report reliability analysis 681 // 18 Categorical data 686 //

18.1. What will this chapter tell me? 686 // 18.2. Analysing categorical data 687 // 18.3. Theory of analysing categorical data 687 // 18.4. Assumptions of the chi-square test 691 // 18.5. Doing chi-square on SPSS 692 18.6. Several categorical variables: loglinear analysis 702 // 18.7. Assumptions in loglinear analysis 710 // 18.8. Loglinear analysis using SPSS 711 // 18.9. Output from loglinear analysis 714 // 18.10. Following up loglinear analysis 719 // 18.11. Effect sizes in loglinear analysis 720 // 18.12. Reporting the results of loglinear analysis 721 // 19 Multilevel linear models 725 // 19.1. What will this chapter tell me? 725 // 19.2. Hierarchical data 726 // 19.3. Theory of multilevel linear models 730 // 19.4. The multilevel model 734 // 19.5. Some practical issues 739 // 19.6. Multilevel modelling on SPSS 741 // 19.7. Growth models 761 // Epilogue 779 // Glossary 781 // Appendix 797 // A.1. Table of the standard normal distribution 797 // A.2. Critical values of the t-distribution 803 // A.3. Critical values of the F-distribution 804 // A.4. Critical values of the chi-square distribution 808 // References 809 // Index 816