出版社: Princeton University Press
副标题: Scientific Inference in Qualitative Research
出版年: 1994
页数: 300
定价: USD 37.50
装帧: Paperback
ISBN: 9780691034713
内容简介 · · · · · ·
While heated arguments between practitioners of qualitative and quantitative research have begun to test the very integrity of the social sciences, Gary King, Robert Keohane, and Sidney Verba have produced a farsighted and timely book that promises to sharpen and strengthen a wide range of research performed in this field. These leading scholars, each representing diverse acade...
While heated arguments between practitioners of qualitative and quantitative research have begun to test the very integrity of the social sciences, Gary King, Robert Keohane, and Sidney Verba have produced a farsighted and timely book that promises to sharpen and strengthen a wide range of research performed in this field. These leading scholars, each representing diverse academic traditions, have developed a unified approach to valid descriptive and causal inference in qualitative research, where numerical measurement is either impossible or undesirable. Their book demonstrates that the same logic of inference underlies both good quantitative and good qualitative research designs, and their approach applies equally to each. Providing precepts intended to stimulate and discipline thought, the authors explore issues related to framing research questions, measuring the accuracy of data and uncertainty of empirical inferences, discovering causal effects, and generally improving qualitative research. Among the specific topics they address are interpretation and inference, comparative case studies, constructing causal theories, dependent and explanatory variables, the limits of random selection, selection bias, and errors in measurement. Mathematical notation is occasionally used to clarify concepts, but no prior knowledge of mathematics or statistics is assumed. The unified logic of inference that this book explicates will be enormously useful to qualitative researchers of all traditions and substantive fields.
作者简介 · · · · · ·
Gary King is Professor of Government and Director of the Harvard Data Center at Harvard University.
Robert O. Keohane is Stanfield Professor of International Peace at Harvard University.
Sidney Verba is Carl H. Pforzheimer University Professor and Director of the Harvard University Library.
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Designing Social Inquiry的书评 · · · · · · ( 全部 9 条 )
读书笔记:Designing Social Inquiry
KKV is one of the greatest text books on methodology~~~
✌️我终于把这本必读给看完了
这篇书评可能有关键情节透露
本书由kkv合著,三位都是国关学界的大牛,金与维巴都是哈佛大学的教授,基欧汉则是普林斯顿的教授。 《社会科学中的研究设计》以定性研究为主题,讨论了定性研究与定量研究两种风格所共享的研究方法。本书指导读者如何做出科学的研究设计,从而在数据有限的情况下做出有限的推... (展开)Does KKV claim too much regarding selection bias in the case study?
可能对大多数社科研究者而言不适用
> 更多书评 9篇
论坛 · · · · · ·
请问有电子版吗? | 来自暴君托克维尔 | 4 回应 | 2013-08-21 18:01:19 |
Canonical Textbook in Political science | 来自ramsay | 2010-09-25 23:02:53 |
这本书的其他版本 · · · · · · ( 全部6 )
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谁读这本书? · · · · · ·
二手市场
· · · · · ·
- 在豆瓣转让 有446人想读,手里有一本闲着?
订阅关于Designing Social Inquiry的评论:
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1 有用 还是小葵酱 2015-12-30 23:44:38
大四了才讀KKV也是殘念><
0 有用 margot 2013-09-12 12:29:10
写得很好,但没有传说中的那么好,尤其是从理论上解释“为什么定性研究可以被用于实证”还是不够有力。这是最核心的基础,但简单两页纸就没了。
1 有用 Spurrr 2015-03-03 02:48:55
每周memo备受折磨。
4 有用 瓜 2016-09-25 04:13:23
所有方法论教科书都以为做研究是个无需积累不用模仿只要按图索骥的技术活。照着书里正确的废话设计个研究倒是试试看!
0 有用 苏芒 2012-05-03 08:27:51
what is causality; how to prove causality. Mission impossible
0 有用 江风吹酥眠 2023-11-24 22:05:13 北京
引发的争议恰恰证明了这是经典。完全不觉得是“正确的废话”,相反更体会到“沙堡”比喻的精准:如果没有一套规范严谨的范式,社会科学的“理论”就是风都会吹散的东西。投入人力物力心血,最后得出完全不可靠甚至带有私人倾向性的结论,这太可怕了,脚手架一样的书很重要。
0 有用 澈之 2022-02-09 05:18:51
这几年被反反复复被挑出来看了好多次,但是仍是值得多读几遍的方法入门经典书目,主要是其中的例子很经典值得反复把玩以及对于定性研究的formal model,第五章what to avoid很关键。友邻说这是葵花宝典感觉很贴切lol
0 有用 Lane 2021-12-14 11:10:08
上课材料 只读过第一章 但是真的可以用quantitative 囊括 qualitative 吗?
0 有用 鐘磬 2021-11-10 23:37:53
5.4 Endogeneity部分还不够深,不过强调内生性偏差就有意义
0 有用 kiyo 2021-09-25 23:09:09
以後來者角度對KKV做太多批判很cunning,卻依舊只能說它很聰明,但是不夠 qual&quant同軌處理的嘗試很亮眼但又很糟糕(做到最後就是quant 2.0):九成qual work用回歸做組合,而這種迴歸結果又極為脆弱:terms+examples用以表示causal effect,這種scientific的嘗試有時是很可疑的,如unit homogeneity/conditional d... 以後來者角度對KKV做太多批判很cunning,卻依舊只能說它很聰明,但是不夠 qual&quant同軌處理的嘗試很亮眼但又很糟糕(做到最後就是quant 2.0):九成qual work用回歸做組合,而這種迴歸結果又極為脆弱:terms+examples用以表示causal effect,這種scientific的嘗試有時是很可疑的,如unit homogeneity/conditional dependence薄弱的實證檢驗變量。對observation的強調同樣怪異:自身就在可觀測和適宜值之間直接反復橫跳,又不在乎causal inference和data本體的合理性(語焉不詳),方法選擇案例的出路就是成為數據與樣本的囚徒 不可否認的是KKV的貢獻,這也體現在對後續方法設計的啟發和推薦 (展开)