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Prediction Statistics for Psychological Assessment.
・ISBN 978-1-4338-3641-1 paper US$ 76.99
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★★★
| 著者・編者 | Hanson, R. Karl, |
|---|---|
| 出版社 | (American Psychological Association, US) |
| 出版年月 | 2021.12 |
| ページ数 | 440 pp. |
| 言語 | ENG |
| ニュース番号 | ＜668-1707 668-279＞ |
解説
At last, a comprehensive guide that meets psychologists and other social scientists' growing need to master the tools of statistical prediction.
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.
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.