定性比较分析
计算机科学
学位(音乐)
变化(天文学)
集合(抽象数据类型)
模糊集
结果(博弈论)
数据科学
管理科学
模糊逻辑
数学
人工智能
数理经济学
机器学习
工程类
程序设计语言
物理
天体物理学
声学
出处
期刊:Utrecht University - Utrecht University Repository
日期:2018-11-01
被引量:10
摘要
Analyzing relationships of necessity is important for both scholarly and applied research questions in the social sciences. An often-used technique for identifying such relationships—fuzzy set Qualitative Comparative Analysis (fsQCA)—has limited ability to make the most out of the data used. The set-theoretical technique fsQCA makes statements in kind (e.g., “a condition or configuration is necessary or not for an outcome”), thereby ignoring the variation in degree. We propose to apply a recently developed technique for identifying relationships of necessity that can make both statements in kind and in degree, thus making full use of variation in the data: Necessary Condition Analysis (NCA). With its ability to also make statements in degree (“a specific level of a condition is necessary or not for a specific level of the outcome”), NCA can complement the in kind analysis of necessity with fsQCA.
科研通智能强力驱动
Strongly Powered by AbleSci AI