Basic Statistics for Psychologists
Description
Written by an experienced teacher of statistics, the new edition of this accessible yet authoritative textbook covers all areas of undergraduate statistics and provides a firm foundation upon which students can build their own knowledge.
Featuring new chapters on Bayesian and multiple regression analysis, this book gives students a working understanding of how to conduct reliable and methodical research using statistics. Brysbaert illustrates the key concepts using examples from psychological research, with clear formulas and explanations for calculations. With helpful chapter-by-chapter guidance for carrying out tests using SPSS, as well as coverage of jamovi and JASP software, this book aims to develop students' confidence in statistical analysis, and to take the fear out of the topic. It offers an easily navigable layout filled with features that help learners to avoid common pitfalls and check their understanding along the way.This engaging and informative guide is essential reading for undergraduate psychology students taking courses in research methods and statistics.
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Content
1. Using statistics in psychology research.- 2. Summarising data using the frequency distribution.- 3. Summarising data using measures of central tendency.- 4. Summarising data using measures of variability.- 5. Standardised scores, normal distribution and probability.- 6. Using the t-test to measure the difference between independent groups.- 7. Interpreting the results of a statistical test: The traditional approach.- 8. Interpreting the results of a statistical test: The Bayesian approach.- 9. Non-parametric tests of difference between independent groups.- 10. Using the t-test to measure change in related samples.- 11. Non-parametric tests to measure changes in related samples.- 12. Improving predictions through the Pearson correlation coefficient.- 13. Improving predictions through non-parametric tests.- 14. Using analysis of variance as an extension of t-tests.- 15. Using analysis of variance for designs with more than one independent variable.- 16. More than one predictor in correlational studies: Multiple regression.- 17. More than one observation per condition per participant: Mixed-effects analysis.
