Finding the experts in the crowd: Validity and reliability of crowdsourced measures of children’s gradient speech contrasts

Abstract

Perceptual ratings aggregated across multiple non-expert listeners can be used to measure covert contrast in child speech. Online crowdsourcing provides access to a large pool of raters, but for practical purposes, researchers may wish to use smaller samples. The ratings obtained from these smaller samples may not maintain the high levels of validity seen in larger samples.

This study aims to measure the validity and reliability of crowdsourced continuous ratings of child speech, obtained through Visual Analog Scaling, and to identify ways to improve these measurements. We first assess overall validity and interrater reliability for measurements obtained from a large set of raters. Second, we investigate two rater-level measures of quality, individual validity and intrarater reliability, and examine the relationship between them. Third, we show these estimates may be used to establish guidelines for the inclusion of raters, thus impacting the quality of results obtained when smaller samples are used.