Preface | p. viii |
Tour of book | p. xii |
Symbols and abbreviations | p. xiv |
Using statistics in psychology research | p. 1 |
The need for reliable and valid empirical studies | p. 2 |
The population and the sample | p. 3 |
Different types of research in psychology | p. 5 |
Which statistics should we calculate? | p. 15 |
Answers to chapter questions | p. 21 |
Summarising data using the frequency distribution | p. 25 |
Introduction | p. 26 |
Frequency distribution tables | p. 27 |
Frequency distribution graphs | p. 33 |
The shape of a frequency distribution | p. 39 |
Making a frequency distribution table and a frequency distribution graph in SPSS | p. 43 |
Going further: continuous variables, real limits and theoretical distributions | p. 55 |
Answers to chapter questions | p. 56 |
Learning check solutions | p. 57 |
Summarising data using measures of central tendency | p. 56 |
The need for summary data and the danger of them | p. 60 |
The mean | p. 61 |
The median | p. 64 |
The mode | p. 66 |
Which measure of central tendency to use? | p. 67 |
Comparing the different measures of central tendency | p. 70 |
Calculating measures of central tendency in SPSS | p. 70 |
Going further: using interpolation to find a more exact value of the median | p. 72 |
Answers to chapter questions | p. 74 |
Learning check solutions | p. 75 |
Summarising data using measures of variability | p. 76 |
The underestimated importance of variability | p. 77 |
The range | p. 78 |
Standard deviation and variance | p. 79 |
Calculating the range and standard deviation with SPSS | p. 87 |
Going further a computational formula for the standard deviation | p. 88 |
Answers to chapter questions | p. 90 |
Learning check solutions | p. 91 |
Standardised scores, normal distribution and probability | p. 92 |
The need for standardised scores | p. 93 |
Transforming raw scores into z-scores | p. 93 |
Interpreting z-scores | p. 94 |
Transforming z-scores into raw scores | p. 95 |
The normal distribution | p. 96 |
Probability | p. 103 |
Z-scores, normal distributions, probabilities and percentiles | p. 107 |
Calculating z-scores and probabilities in SPSS | p. 115 |
Going further defining the shape of the normal distribution | p. 119 |
Answers to chapter questions | p. 119 |
Learning check solutions | p. 121 |
Using the t-test to measure the difference between independent groups | p. 122 |
Few differences between groups can be spotted with the naked eye | p. 123 |
The standard error of the mean | p. 128 |
The t-statistic for independent samples | p. 137 |
Hypothesis testing on the basis of the t-statistic | p. 141 |
Calculating a t-test for independent samples with SPSS | p. 152 |
Going further: unequal sample sizes and unequal variances | p. 156 |
Answers to chapter questions | p. 158 |
Learning check solutions | p. 161 |
Interpreting the results of a statistical test | p. 162 |
Confidence intervals and their relation to statistical tests | p. 164 |
More on the interpretation of significant effects | p. 170 |
The effect size | p. 177 |
How to interpret non-significant effects | p. 183 |
How many participants should I include in my experiment? | p. 184 |
Adding confidence intervals to your graphs in SPSS | p. 188 |
Going further: one- and two-tailed tests, and a mathematical summary | p. 192 |
Answers to chapter questions | p. 194 |
Learning check solutions | p. 197 |
Non-parametric tests of difference between independent groups | p. 199 |
The Mann-Whitney U-test for ordinal data | p. 200 |
The one-way chi-square test for nominal data | p. 217 |
Answers to chapter questions | p. 219 |
Learning check solutions | p. 221 |
Using the t-test to measure change in related samples | p. 223 |
The t-statistic for repeated measures | p. 224 |
The effect size, likelihood ratio and power of a t-test with repeated measures | p. 228 |
The confidence interval for a design with repeated measures | p. 232 |
Step by step: a t-test for repeated measures | p. 235 |
Running a t-test with repeated measures in SPSS | p. 239 |
Going further: the relationship between SDD, SD1 and SD2 | p. 240 |
Answers to chapter questions | p. 241 |
Learning check solutions | p. 242 |
Non-parametric tests to measure changes in related samples | p. 243 |
The Wilcoxon signed-rank statistic | p. 244 |
The Wilcoxon signed-rank test | p. 248 |
How to report a Wilcoxon signed-rank test | p. 249 |
Adding a confidence interval to your graph | p. 249 |
The Wilcoxon signed-rank test as an alternative to the t-test for related samples | p. 252 |
Step by step: the Wilcoxon signed-rank test | p. 254 |
The Wilcoxon signed-rank test in SPSS | p. 256 |
Answers to chapter questions | p. 259 |
Improving predictions through the Pearson correlation coefficient | p. 261 |
The Pearson product-moment correlation coefficient | p. 262 |
Significance of the Pearson product-moment correlation coefficient | p. 280 |
Calculating the Pearson product-moment correlation in SPSS | p. 286 |
Going further: using the t-distribution to calculate the p-value of a Pearson correlation and using Pearson's correlation as an effect size | p. 292 |
Answers to chapter questions | p. 295 |
Improving predictions through non-parametric tests | p. 298 |
The Spearman rank correlation for ordinal data and interval/ratio data | p. 299 |
The chi-square test of independence for nominal data | p. 307 |
Answers to chapter questions | p. 317 |
Using analysis of variance for multiple independent variables | p. 319 |
Using analysis of variance to compare groups of people | p. 320 |
Using analysis of variance to compare conditions within people | p. 334 |
Analysis of variance with one between-groups variable and one repeated measure | p. 343 |
Answers to chapter questions | p. 355 |
Going further: more than two levels and multiple predictors | p. 357 |
Analysing a study with three groups of participants | p. 358 |
Analysing a repeated measures study with more than two levels | p. 371 |
Working with more than one predictor in a regression analysis | p. 381 |
Answers to chapter questions | p. 390 |
References | p. 391 |
Appendices | p. 393 |
Index | p. 407 |
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