MARC状态：待编 文献类型：电子图书 浏览次数：27
- 题名/责任者:
- The Measurement of Association A Permutation Statistical Approach / by Kenneth J. Berry, Janis E. Johnston, Paul W. Mielke, Jr.
- 版本说明:
- 1st ed. 2018.
- 出版发行项:
- Cham : Springer International Publishing : Imprint: Springer, 2018.
- ISBN:
- 9783319989266
- 其它标准号:
- 10.1007/978-3-319-98926-6
- 载体形态项:
- XX, 647 p. 22 illus. online resource.
- 主文献:
- Springer eBooks
- 其他载体形态:
- Printed edition: 9783319989259
- 其他载体形态:
- Printed edition: 9783319989273
- 个人责任者:
- Berry, Kenneth J. author.
- 附加个人名称:
- Johnston, Janis E. author.
- 附加个人名称:
- Mielke, Jr., Paul W. author.
- 附加团体名称:
- SpringerLink (Online service)
- 论题主题:
- Statistics .
- 论题主题:
- Combinatorics.
- 论题主题:
- Biostatistics.
- 论题主题:
- Combinatorics.
- 论题主题:
- Biostatistics.
- 内容附注:
- 1 Introduction -- 2 Permutation Statistical Methods -- 3 Nominal Level Variables, I -- 4 Nominal Level Variables, II -- 5 Ordinal Level Variables, I -- 6 Ordinal Level Variables, II -- 7 Interval-level Variables -- 8 Mixed-level Variables -- 9 Fourfold Contingency Tables, I -- 10 Fourfold Contingency Tables, II -- Epilogue -- References -- Index.
- 摘要附注:
- This research monograph utilizes exact and Monte Carlo permutation statistical methods to generate probability values and measures of effect size for a variety of measures of association. Association is broadly defined to include measures of correlation for two interval-level variables, measures of association for two nominal-level variables or two ordinal-level variables, and measures of agreement for two nominal-level or two ordinal-level variables. Additionally, measures of association for mixtures of the three levels of measurement are considered: nominal-ordinal, nominal-interval, and ordinal-interval measures. Numerous comparisons of permutation and classical statistical methods are presented. Unlike classical statistical methods, permutation statistical methods do not rely on theoretical distributions, avoid the usual assumptions of normality and homogeneity of variance, and depend only on the data at hand. This book takes a unique approach to explaining statistics by integrating a large variety of statistical methods, and establishing the rigor of a topic that to many may seem to be a nascent field. This topic is relatively new in that it took modern computing power to make permutation methods available to those working in mainstream research. Written for a statistically informed audience, it is particularly useful for teachers of statistics, practicing statisticians, applied statisticians, and quantitative graduate students in fields such as psychology, medical research, epidemiology, public health, and biology. It can also serve as a textbook in graduate courses in subjects like statistics, psychology, and biology.
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