Online data collection to address language sampling bias: lessons from the COVID-19 pandemic

Abstract

Abstract The COVID-19 pandemic has massively limited how linguists can collect data, and out of necessity, researchers across several disciplines have moved data collection online. Here we argue that the rising popularity of remote web-based experiments also provides an opportunity for widening the context of linguistic research by facilitating data collection from understudied populations. We discuss collecting production data from adult native speakers of Tagalog using an unsupervised web-based experiment. Compared to equivalent lab experiments, data collection went quicker, and the sample was more diverse, without compromising data quality. However, there were also technical and human issues that come with this method. We discuss these challenges and provide suggestions on how to overcome them.