Using Machine Learning to Uncover Hidden Heterogeneities in Survey Data.

ABSTRACT: Survey responses in public health surveys are heterogeneous. The quality of a respondent's answers depends on many factors, including cognitive abilities, interview context, and whether the interview is in person or self-administered. A largely unexplored issue is how the language used for public health survey interviews is associated with the survey response. We introduce a machine learning approach, Fuzzy Forests, which we use for model selection. We use the 2013 California Health Interview Survey (CHIS) as our training sample and the 2014 CHIS as the test sample. We found that non-English language survey responses differ substantially from English responses in reported health outcomes. We also found heterogeneity among the Asian languages suggesting that caution should be used when inte

SUBMITTER: Ramirez CM 

PROVIDER: S-EPMC6831673 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature