Arbeitspapier

Using Machine Learning and Qualitative Interviews to Design a Five-Question Women's Agency Index

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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.

Language: Englisch

Bibliographic citation: Series: CESifo Working Paper ; No. 8984

Classification: Wirtschaft
Survey Methods; Sampling Methods
Household Production and Intrahousehold Allocation
Economics of Gender; Non-labor Discrimination
Microeconomic Analyses of Economic Development

Subject: women’s empowerment
survey design
feature selection
psychometrics

Event: Geistige Schöpfung

(who): Jayachandran, Seema
Biradavolu, Monica
Cooper, Jan

Event: Veröffentlichung

(who): Center for Economic Studies and Ifo Institute (CESifo)

(where): Munich

(when): 2021

Handle: http://hdl.handle.net/10419/235354

Last update: 10.03.2025, 11:45 AM CET

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Object type

Arbeitspapier

Associated

Jayachandran, Seema
Biradavolu, Monica
Cooper, Jan
Center for Economic Studies and Ifo Institute (CESifo)

Time of origin

2021

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