Application of Latent Semantic Analysis to Divergent Thinking is Biased by Elaboration

Abstract

AbstractScoring divergent‐thinking response sets has always been challenging because such responses are not only open‐ended in terms of number of ideas, but each idea may also be expressed by a varying number of concepts and, thus, by a varying number of words (elaboration). While many current studies have attempted to score the semantic distance in divergent‐thinking responses by applying latent semantic analysis (LSA), it is known from other areas of research that LSA‐based approaches are biased according to the number of words in a response. Thus, the current article aimed to identify and demonstrate this elaboration bias in LSA‐based divergent‐thinking scores by means of a simulation. In addition, we show that this elaboration bias can be reduced by removing the stop words (for example, and, or, for and so forth) prior to analysis. Furthermore, the residual bias after stop word removal can be reduced by simulation‐based corrections. Finally, we give an empirical illustration for alternate uses and consequences tasks. Results suggest that when both stop word removal and simulation‐based bias correction are applied, convergent validity should be expected to be highest.