A computational framework identifies more coherent themes in free-text survey data on race, gender, and sexual orientation than previous methods, with applications for survey design, explaining variation, and detecting identity discordance.
Sociology of Race and Ethnicity5(1), 55–69 (2019)
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In your own words: computationally identifying interpretable themes in free-text survey data
A computational framework identifies more coherent themes in free-text survey data on race, gender, and sexual orientation than previous methods, with applications for survey design, explaining variation, and detecting identity discordance.