Four Text‐Mining Methods for Measuring Elaboration

Abstract

AbstractWhen individuals engage in divergent thinking, they vary on their Elaboration, or the degree to which they explain and embellish their responses. Although Elaboration has been considered relevant to creativity research for decades, its measurement has remained under‐developed. Here, we leverage technical and methodological perspectives from the text‐mining literature to posit four methods for quantifying elaboration: Unweighted Word Count, Stoplisted Inclusion, Part of Speech Inclusion, and Inverse Frequency Weighting. Although the Unweighted Word Count method is becoming typical in the field, more complex weighting methods appear to better fit the conceptualization of Elaboration. We explain the benefits of each of the included methods and demonstrate their application to responses from the Alternate Uses Task: showing that all four of these text‐mining methods produced Elaboration scores with high levels of reliability, but the Stoplisted Inclusion method appeared to maximize the score validity both in terms of criteria correlations and power to discriminate among creative experts and non‐experts. We offer an open‐access module for creativity researchers to apply these methods to their own data via our laboratory website [https://openscoring.du.edu/].