Assessing the self-reported honesty threshold in adolescent epidemiological research: comparing supervised machine learning and inferential statistical techniques.

ABSTRACT:

Background

Epidemiological surveys offer essential data on adolescent substance use. Nevertheless, the precision of these self-report-based surveys often faces mistrust from researchers and the public. We evaluate the efficacy of a direct method to assess data quality by asking adolescents if they were honest. The main goal of our study was to assess the accuracy of a self-report honesty item and designate an optimal threshold for it, allowing us to better account for its impact on point estimates.

Methods

The participants were from the 2020 Illinois Youth Survey, a self-report school-based survey. We divided the primary dataset into subsets based on responses to an honesty item. Then, for each dataset, we examined two distinct data analysis methodologies: supervised machine

SUBMITTER: Kosgolla JV 

PROVIDER: S-EPMC10512612 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature