全球智库信息集成服务系统(G2TT): DP15961 Using machine learning and qualitative interviews to design a five-question women's agency index

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP15961
	DP15961 Using machine learning and qualitative interviews to design a five-question women's agency index
	Seema Jayachandran; Monica Biradavolu; Jan Cooper
发表日期	2021-03-25
出版年	2021
语种	英语
摘要	We propose a new method to design a short survey measure of a complex concept such as women's agency. The approach combines mixed-methods data collection and machine learning. We select the best survey questions based on how strongly correlated they are with a "gold standard" measure of the concept derived from qualitative interviews. In our application, we measure agency for 209 women in Haryana, India, first, through a semi-structured interview and, second, through a large set of close-ended questions. We use qualitative coding methods to score each woman's agency based on the interview, which we treat as her true agency. To identify the close-ended questions most predictive of the "truth," we apply statistical algorithms that build on LASSO and random forest but constrain how many variables are selected for the model (five in our case). The resulting five-question index is as strongly correlated with the coded qualitative interview as is an index that uses all of the candidate questions. This approach of selecting survey questions based on their statistical correspondence to coded qualitative interviews could be used to design short survey modules for many other latent constructs.
主题	Development Economics
关键词	Women's empowerment Survey design Feature selection Psychometrics
URL	https://cepr.org/publications/dp15961
来源智库	Centre for Economic Policy Research (United Kingdom)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/544951
推荐引用方式 GB/T 7714	Seema Jayachandran,Monica Biradavolu,Jan Cooper. DP15961 Using machine learning and qualitative interviews to design a five-question women's agency index. 2021.