| Preface |
|
ix | |
| Acknowledgements |
|
xiii | |
| Tour of book |
|
xiv | |
| Symbols and abbreviations |
|
xvi | |
|
Chapter 1 Using statistics in psychology research |
|
|
1 | (23) |
|
1.1 The need for reliable and valid empirical studies |
|
|
2 | (1) |
|
1.2 The population and the sample |
|
|
3 | (2) |
|
1.3 Different types of research in psychology |
|
|
5 | (9) |
|
1.4 Which statistics should we calculate? |
|
|
14 | (6) |
|
1.5 Answers to chapter questions |
|
|
20 | (4) |
|
Chapter 2 Summarising data using the frequency distribution |
|
|
24 | (36) |
|
|
|
25 | (1) |
|
2.2 Frequency distribution tables |
|
|
26 | (6) |
|
2.3 Frequency distribution graphs |
|
|
32 | (5) |
|
2.4 The shape of a frequency distribution |
|
|
37 | (3) |
|
2.5 Making a frequency distribution table and a frequency distribution graph in SPSS |
|
|
40 | (13) |
|
2.6 Making a frequency distribution graph with jamovi |
|
|
53 | (3) |
|
2.7 Going further: continuous variables, real limits and theoretical distributions |
|
|
56 | (1) |
|
2.8 Answers to chapter questions |
|
|
56 | (2) |
|
2.9 Learning check solutions |
|
|
58 | (2) |
|
Chapter 3 Summarising data using measures of central tendency |
|
|
60 | (16) |
|
3.1 The need for summary data and the danger of them |
|
|
61 | (1) |
|
|
|
62 | (2) |
|
|
|
64 | (2) |
|
|
|
66 | (2) |
|
3.5 Which measure of central tendency to use? |
|
|
68 | (1) |
|
3.6 Comparing the different measures of central tendency |
|
|
69 | (1) |
|
3.7 Calculating measures of central tendency in SPSS |
|
|
69 | (2) |
|
3.8 Calculating measures of central tendency in jamovi |
|
|
71 | (2) |
|
3.9 Going further: using interpolation to find a more exact value of the median |
|
|
73 | (1) |
|
3.10 Answers to chapter questions |
|
|
74 | (1) |
|
3.11 Learning check solutions |
|
|
75 | (1) |
|
Chapter 4 Summarising data using measures of variability |
|
|
76 | (18) |
|
4.1 The underestimated importance of variability |
|
|
77 | (1) |
|
|
|
78 | (1) |
|
4.3 Standard deviation and variance |
|
|
79 | (8) |
|
4.4 Calculating the range and standard deviation with SPSS |
|
|
87 | (1) |
|
4.5 Calculating the range and standard deviation with jamovi |
|
|
88 | (2) |
|
4.6 Going further: a computational formula for the standard deviation |
|
|
90 | (2) |
|
4.7 Answers to chapter questions |
|
|
92 | (1) |
|
4.8 Learning check solutions |
|
|
92 | (2) |
|
Chapter 5 Standardised scores, normal distribution and probability |
|
|
94 | (29) |
|
5.1 The need for standardised scores |
|
|
95 | (1) |
|
5.2 Transforming raw scores into z-scores |
|
|
95 | (1) |
|
5.3 Interpreting z-scores |
|
|
96 | (1) |
|
5.4 Transforming z-scores into raw scores |
|
|
97 | (1) |
|
5.5 The normal distribution |
|
|
98 | (6) |
|
|
|
104 | (4) |
|
5.7 Z-scores, normal distributions, probabilities and percentiles |
|
|
108 | (7) |
|
5.8 Calculating z-scores and probabilities in SPSS |
|
|
115 | (4) |
|
5.9 Finding percentiles in jamovi |
|
|
119 | (1) |
|
5.10 Going further: defining the shape of the normal distribution |
|
|
120 | (1) |
|
5.11 Answers to chapter questions |
|
|
120 | (2) |
|
5.12 Learning check solutions |
|
|
122 | (1) |
|
Chapter 6 Using the t-test to measure the difference between independent groups |
|
|
123 | (40) |
|
6.1 Few differences between groups can be spotted with the naked eye |
|
|
124 | (5) |
|
6.2 The standard error of the mean |
|
|
129 | (8) |
|
6.3 The t-statistic for independent samples Kit |
|
|
137 | (4) |
|
6.4 Hypothesis testing on the basis of the t-statistic |
|
|
141 | (11) |
|
6.5 Calculating a t-test for independent samples with SPSS |
|
|
152 | (3) |
|
6.6 Calculating a t-test for independent samples with jamovi |
|
|
155 | (2) |
|
6.7 Going further: unequal sample sizes and unequal variances |
|
|
157 | (2) |
|
6.8 Answers to chapter questions |
|
|
159 | (3) |
|
6.9 Learning check solutions |
|
|
162 | (1) |
|
Chapter 7 Interpreting the results of a statistical test: the traditional approach |
|
|
163 | (48) |
|
7.1 How to interpret p-values? |
|
|
165 | (8) |
|
|
|
173 | (8) |
|
|
|
181 | (7) |
|
7.4 How to interpret non-significant effects in the traditional approach? |
|
|
188 | (1) |
|
7.5 How many participants should I include in my experiment? |
|
|
189 | (6) |
|
7.6 Bad practices and the replication crisis in psychology |
|
|
195 | (3) |
|
7.7 Adding confidence intervals to your graphs in SPSS and jamovi |
|
|
198 | (4) |
|
7.8 Going further: one- and two-tailed tests, and a mathematical summary |
|
|
202 | (2) |
|
7.9 Answers to chapter questions |
|
|
204 | (4) |
|
7.10 Learning check solutions |
|
|
208 | (3) |
|
Chapter 8 Interpreting the results of a statistical test: the Bayesian approach |
|
|
211 | (17) |
|
8.1 Frequentist v. Bayesian statistics |
|
|
212 | (1) |
|
|
|
213 | (3) |
|
|
|
216 | (5) |
|
8.4 Calculating Bayes factors in SPSS, jamovi, and JASP |
|
|
221 | (4) |
|
8.5 Answers to chapter questions |
|
|
225 | (1) |
|
8.6 Answers to learning checks |
|
|
226 | (2) |
|
Chapter 9 Non-parametric tests of difference between independent groups |
|
|
228 | (24) |
|
9.1 The Mann-Whitney U-test for ordinal data |
|
|
229 | (17) |
|
9.2 The one-way chi-square test for nominal data |
|
|
246 | (3) |
|
9.3 Answers to chapter questions |
|
|
249 | (1) |
|
9.4 Learning check solutions |
|
|
250 | (2) |
|
Chapter 10 Using the t-test to measure change in related samples |
|
|
252 | (23) |
|
10.1 The t-statistic for repeated measures |
|
|
253 | (4) |
|
10.2 The effect size, Bayes factor, and power of a t-test with repeated measures |
|
|
257 | (4) |
|
10.3 The confidence interval for a design with repeated measures |
|
|
261 | (3) |
|
10.4 Step by step: a t-test for repeated measures |
|
|
264 | (4) |
|
10.5 Running a t-test with repeated measures in SPSS, jamovi, and JASP |
|
|
268 | (4) |
|
10.6 Going further: the relationship between SDD, SD1 and SD2 |
|
|
272 | (1) |
|
10.7 Answers to chapter questions |
|
|
273 | (1) |
|
10.8 Learning check solutions |
|
|
274 | (1) |
|
Chapter 11 Non-parametric tests to measure change in related samples |
|
|
275 | (20) |
|
11.1 The Wilcoxon signed-rank statistic |
|
|
276 | (4) |
|
11.2 The Wilcoxon signed-rank test |
|
|
280 | (1) |
|
11.3 How to report a Wilcoxon signed-rank test |
|
|
281 | (1) |
|
11.4 Adding a confidence interval to your graph |
|
|
281 | (3) |
|
11.5 The Wilcoxon signed-rank test as an alternative to the t-test for related samples |
|
|
284 | (2) |
|
11.6 Step by step: the Wilcoxon signed-rank test |
|
|
286 | (2) |
|
11.7 The Wilcoxon signed-rank test in SPSS and jamovi |
|
|
288 | (5) |
|
11.8 Answers to chapter questions |
|
|
293 | (2) |
|
Chapter 12 Improving predictions through the Pearson correlation coefficient |
|
|
295 | (46) |
|
12.1 The Pearson product-moment correlation coefficient |
|
|
296 | (17) |
|
12.2 Significance of the Pearson product-moment correlation coefficient |
|
|
313 | (7) |
|
12.3 The Bayesian alternative |
|
|
320 | (2) |
|
12.4 Calculating the Pearson product-moment correlation in SPSS, jamovi and JASP |
|
|
322 | (13) |
|
12.5 Going further: using the t-distribution to calculate the p-value of a Pearson correlation and using Pearson's correlation as an effect size |
|
|
335 | (3) |
|
12.6 Answers to chapter questions |
|
|
338 | (3) |
|
Chapter 13 Improving predictions through non-parametric tests |
|
|
341 | (22) |
|
13.1 The Spearman rank correlation for ordinal data and interval/ratio data |
|
|
342 | (8) |
|
13.2 The chi-square test of independence for nominal data |
|
|
350 | (11) |
|
13.3 Answers to chapter questions |
|
|
361 | (2) |
|
Chapter 14 Using analysis of variance as an extension of t-tests |
|
|
363 | (62) |
|
14.1 Using analysis of variance to compare groups of people |
|
|
364 | (18) |
|
14.2 Extending ANOVA to three groups of participants |
|
|
382 | (15) |
|
14.3 Using analysis of variance to compare conditions within people |
|
|
397 | (15) |
|
14.4 Analysing a repeated measures factor with more than two levels |
|
|
412 | (11) |
|
14.6 Answers to chapter questions |
|
|
423 | (2) |
|
Chapter 15 Using analysis of variance for designs with more than one independent variable |
|
|
425 | (28) |
|
15.1 When do we need multifactorial designs? |
|
|
426 | (2) |
|
15.2 Analysis of variance with one between-groups factor and one repeated measures factor |
|
|
428 | (22) |
|
15.3 Extensions to other multiway ANOVAs |
|
|
450 | (1) |
|
15.4 Answers to chapter questions |
|
|
451 | (2) |
|
Chapter 16 More than one predictor in correlational studies: multiple regression |
|
|
453 | (26) |
|
16.1 Working with more than one predictor in a regression analysis |
|
|
454 | (11) |
|
16.2 The importance of reliability |
|
|
465 | (4) |
|
16.3 Going further: Multiple regression analysis as an alternative to ANOVA |
|
|
469 | (9) |
|
16.4 Answers to chapter questions |
|
|
478 | (1) |
|
Chapter 17 More than one observation per condition per participant: mixed-effects analysis |
|
|
479 | (52) |
|
17.1 Collecting more than one observation per participant per condition |
|
|
480 | (5) |
|
17.2 Mixed-effects analysis of the datasets previously analysed |
|
|
485 | (14) |
|
17.3 Mixed-effects analysis with participants and stimuli as random factors |
|
|
499 | (11) |
|
17.4 Two more examples of mixed-effects analysis |
|
|
510 | (15) |
|
17.5 Analysis of accuracy data |
|
|
525 | (3) |
|
17.6 Answers to chapter questions |
|
|
528 | (3) |
| References |
|
531 | (4) |
| Appendices |
|
535 | (15) |
| Index |
|
550 | |
Lisainfo e-raamatute kohta