更改

合适地应用定量方法来推断因果关系的相关争论导致了对研究'''<font color = '#ff8000'>可重复性reproducibility</font>'''的更多关注。对广泛被使用的方法持批评态度的人认为，研究人员利用'''<font color = '#ff8000'>数据挖掘Data dredging</font>'''或'''<font color = '#ff8000'>p-hacking</font>'''技术以在虚假相关的基础上发表文章<ref>{{Cite news|url=https://www.nytimes.com/2017/10/18/magazine/when-the-revolution-came-for-amy-cuddy.html|title=When the Revolution Came for Amy Cuddy|last=Dominus|first=Susan|date=18 October 2017|work=The New York Times|access-date=2019-03-02|language=en-US|issn=0362-4331}}</ref>。为了避免这种情况的发生，一些人主张研究人员在进行研究之前'''<font color = '#ff8000'>预注册preregister</font>'''他们的研究设计，这样他们就不会无意中过分强调一项不可复制的发现，这项发现并非最初的调查对象，但在数据分析中被发现具有统计意义<ref>{{Cite web|url=https://www.americanscientist.org/article/the-statistical-crisis-in-science|title=The Statistical Crisis in Science|date=6 February 2017|website=American Scientist|language=en|access-date=2019-04-18}}</ref>。社会科学内部关于方法论和可重复性的争论有时是尖锐的{{Citation needed|date=May 2019}}。

合适地应用定量方法来推断因果关系的相关争论导致了对研究'''<font color = '#ff8000'>可重复性reproducibility</font>'''的更多关注。对广泛被使用的方法持批评态度的人认为，研究人员利用'''<font color = '#ff8000'>数据挖掘Data dredging</font>'''或'''<font color = '#ff8000'>p-hacking</font>'''技术以在虚假相关的基础上发表文章<ref>{{Cite news|url=https://www.nytimes.com/2017/10/18/magazine/when-the-revolution-came-for-amy-cuddy.html|title=When the Revolution Came for Amy Cuddy|last=Dominus|first=Susan|date=18 October 2017|work=The New York Times|access-date=2019-03-02|language=en-US|issn=0362-4331}}</ref>。为了避免这种情况的发生，一些人主张研究人员在进行研究之前'''<font color = '#ff8000'>预注册preregister</font>'''他们的研究设计，这样他们就不会无意中过分强调一项不可复制的发现，这项发现并非最初的调查对象，但在数据分析中被发现具有统计意义<ref>{{Cite web|url=https://www.americanscientist.org/article/the-statistical-crisis-in-science|title=The Statistical Crisis in Science|date=6 February 2017|website=American Scientist|language=en|access-date=2019-04-18}}</ref>。社会科学内部关于方法论和可重复性的争论有时是尖锐的{{Citation needed|date=May 2019}}。

   --[[用户:ZC|ZC]]([[用户讨论:ZC|讨论]])  【审校】

   --[[用户:ZC|ZC]]([[用户讨论:ZC|讨论]])  【审校】“技术以在虚假相关的基础上发表文章”一句中“技术”改为“黑客技术”

While much of the emphasis remains on statistical inference in the potential outcomes framework, social science methodologists have developed new tools to conduct causal inference with both qualitative and quantitative methods, sometimes called a “mixed methods” approach.<ref>{{Cite book|url=https://books.google.com/books/about/Designing_and_Conducting_Mixed_Methods_R.html?id=YcdlPWPJRBcC|title=Designing and Conducting Mixed Methods Research|last=Creswell|first=John W.|last2=Clark|first2=Vicki L. Plano|date=2011|publisher=SAGE Publications|isbn=9781412975179|language=en}}</ref><ref>{{Cite book|url=https://www.cambridge.org/core/books/multimethod-social-science/286C2742878FBCC6225E2F10D6095A0C|title=Multi-Method Social Science by Jason Seawright|last=Seawright|first=Jason|date=September 2016|website=Cambridge Core|language=en|access-date=2019-04-18|doi=10.1017/CBO9781316160831|isbn=9781316160831}}</ref> Advocates of diverse methodological approaches argue that different methodologies are better suited to different subjects of study. Sociologist Herbert Smith and Political Scientists James Mahoney and Gary Goertz have cited the observation of Paul Holland, a statistician and author of the 1986 article “Statistics and Causal Inference,” that statistical inference is most appropriate for assessing the “effects of causes” rather than the “causes of effects.”<ref>{{Cite journal|last=Smith|first=Herbert L.|date=10 February 2014|title=Effects of Causes and Causes of Effects: Some Remarks from the Sociological Side|journal=Sociological Methods and Research|volume=43|issue=3|pages=406–415|doi=10.1177/0049124114521149|pmid=25477697|pmc=4251584}}</ref><ref>{{Cite journal|last=Goertz|first=Gary|last2=Mahoney|first2=James|date=2006|title=A Tale of Two Cultures: Contrasting Quantitative and Qualitative Research|journal=Political Analysis|language=en|volume=14|issue=3|pages=227–249|doi=10.1093/pan/mpj017|issn=1047-1987}}</ref> Qualitative methodologists have argued that formalized models of causation, including process tracing and fuzzy set theory, provide opportunities to infer causation through the identification of critical factors within case studies or through a process of comparison among several case studies.<ref name=":0" /> These methodologies are also valuable for subjects in which a limited number of potential observations or the presence of confounding variables would limit the applicability of statistical inference.{{Citation needed|date=May 2019}}

While much of the emphasis remains on statistical inference in the potential outcomes framework, social science methodologists have developed new tools to conduct causal inference with both qualitative and quantitative methods, sometimes called a “mixed methods” approach.<ref>{{Cite book|url=https://books.google.com/books/about/Designing_and_Conducting_Mixed_Methods_R.html?id=YcdlPWPJRBcC|title=Designing and Conducting Mixed Methods Research|last=Creswell|first=John W.|last2=Clark|first2=Vicki L. Plano|date=2011|publisher=SAGE Publications|isbn=9781412975179|language=en}}</ref><ref>{{Cite book|url=https://www.cambridge.org/core/books/multimethod-social-science/286C2742878FBCC6225E2F10D6095A0C|title=Multi-Method Social Science by Jason Seawright|last=Seawright|first=Jason|date=September 2016|website=Cambridge Core|language=en|access-date=2019-04-18|doi=10.1017/CBO9781316160831|isbn=9781316160831}}</ref> Advocates of diverse methodological approaches argue that different methodologies are better suited to different subjects of study. Sociologist Herbert Smith and Political Scientists James Mahoney and Gary Goertz have cited the observation of Paul Holland, a statistician and author of the 1986 article “Statistics and Causal Inference,” that statistical inference is most appropriate for assessing the “effects of causes” rather than the “causes of effects.”<ref>{{Cite journal|last=Smith|first=Herbert L.|date=10 February 2014|title=Effects of Causes and Causes of Effects: Some Remarks from the Sociological Side|journal=Sociological Methods and Research|volume=43|issue=3|pages=406–415|doi=10.1177/0049124114521149|pmid=25477697|pmc=4251584}}</ref><ref>{{Cite journal|last=Goertz|first=Gary|last2=Mahoney|first2=James|date=2006|title=A Tale of Two Cultures: Contrasting Quantitative and Qualitative Research|journal=Political Analysis|language=en|volume=14|issue=3|pages=227–249|doi=10.1093/pan/mpj017|issn=1047-1987}}</ref> Qualitative methodologists have argued that formalized models of causation, including process tracing and fuzzy set theory, provide opportunities to infer causation through the identification of critical factors within case studies or through a process of comparison among several case studies.<ref name=":0" /> These methodologies are also valuable for subjects in which a limited number of potential observations or the presence of confounding variables would limit the applicability of statistical inference.{{Citation needed|date=May 2019}}

   --[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]） 学科还是对象？

   --[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]） 学科还是对象？

社会学家 Herbert Smith 和政治学家 James Mahoney 、 Gary Goertz 引用了统计学家 Paul Holland 的观察结果，Paul Holland在1986年发表了一篇名为《'''<font color = '#ff8000'>统计学和因果推断Statistics and Causal Inference</font>'''》的文章，认为推论统计学最适合于评估“结果的原因”而不是“结果的原因”。定性方法学家认为，形式化的因果关系模型，包括'''<font color = '#ff8000'>过程追踪process tracing</font>'''和'''<font color = '#ff8000'>模糊集理论fuzzy set theory</font>'''，通过在某个案例研究内识别关键因素或在几个案例研究之间比较的过程提供了推断因果关系的机会。这些方法对于那些可能的观察数量有限或存在混淆变量从而限制统计推论适用性的课题也是有价值的。

社会学家 Herbert Smith 和政治学家 James Mahoney 、 Gary Goertz 引用了统计学家 Paul Holland 的观察结果，Paul Holland在1986年发表了一篇名为《'''<font color = '#ff8000'>统计学和因果推断Statistics and Causal Inference</font>'''》的文章，认为推论统计学最适合于评估“结果的原因”而不是“结果的原因”。定性方法学家认为，形式化的因果关系模型，包括'''<font color = '#ff8000'>过程追踪process tracing</font>'''和'''<font color = '#ff8000'>模糊集理论fuzzy set theory</font>'''，通过在某个案例研究内识别关键因素或在几个案例研究之间比较的过程提供了推断因果关系的机会。这些方法对于那些可能的观察数量有限或存在混淆变量从而限制统计推论适用性的课题也是有价值的。

== See also ==

== See also ==