As statistical prediction becomes ubiquitous in many areas of psychology, a comprehensive guide to navigating these tools is needed, one that covers topics pertinent to those in psychology and the social sciences. Prediction Statistics for Psychological Assessment, by R. Karl Hanson, is the first book to teach students and practitioners the nuts and bolts of prediction statistics, while illustrating the utility of prediction and prediction tools in applied psychological practice. This valuable resource uses real-world examples, helpful explanations and practice exercises to support the use of prediction tools in psychological assessment. Actuarial risk assessment evaluators need to know how prediction tools work, how to evaluate them, and how to interpret their results in applied assessments. Written in a clear and accessible manner, this user-friendly book helps readers understand how to evaluate and interpret different kinds of prediction tools, appreciate the numeric information used in risk communication, and utilize prediction tools to inform evidence-based decision-making.
R. Karl Hanson, PhD was a researcher with Public Safety Canada between 1991 and 2017, and is currently an adjunct research professor at Carleton University in Ottawa. His research concerns risk assessment and rehabilitation for individuals in the criminal justice and forensic mental health systems, with a particular focus on sexual offenders. He has a strong interest in the statistical methods used to quantify risk and to evaluate change over time. Follow @rkarlhanson and visit carleton.ca/psychology.
Preface Part I: Background and Overview Chapter 1: Introduction Chapter 2: The Nature of Probability Chapter 3: Overview of the Statistics Chapters Part II: Statistics for Describing Likelihoods Chapter 4: Proportions Chapter 5: Discrete Time Survival Analysis Chapter 6: Kaplan-Meier Survival Analysis Part III: Discrimination and Relative Risk Chapter 7: Dichotomous Predictors Chapter 8: Area Under the Curve Chapter 9: Cohen's d Chapter 10: Cox Regression Chapter 11: Logistic Regression Part IV: Calibration Chapter 12: Chi-Square Goodness-of-Fit Chapter 13: The E/O Index Chapter 14: Meta-Analysis Chapter 15: Calibration Plots Part V: Percentile Ranks Chapter 16: Percentiles Part VI: Practice Considerations Chapter 17: Estimating the Quality of Prediction Tools Chapter 18: Standardizing Risk Communication Chapter 19: Going Even Further References Appendix A: Glossary Appendix B: Useful Algebra and Notation
