Abstract
The smallest and most commonly used words in English are pronouns, articles, and other function words. Almost invisible to the reader or writer, function words can reveal ways people think and approach topics. A computerized text analysis of over 50,000 college admissions essays from more than 25,000 entering students found a coherent dimension of language use based on eight standard function word categories. The dimension, which reflected the degree students used categorical versus dynamic language, was analyzed to track college grades over students' four years of college. Higher grades were associated with greater article and preposition use, indicating categorical language (i.e., references to complexly organized objects and concepts). Lower grades were associated with greater use of auxiliary verbs, pronouns, adverbs, conjunctions, and negations, indicating more dynamic language (i.e., personal narratives). The links between the categorical-dynamic index (CDI) and academic performance hint at the cognitive styles rewarded by higher education institutions.
Highlights
The ways we use words reflect how we think
Function words can be categorized in slightly different ways, the current project focused on eight broad dimensions as measured by the computerized text analysis program, LIWC: personal pronouns (e.g., I, her, they), impersonal pronouns, auxiliary verbs, articles (a, an, the), prepositions, conjunctions, negations, and common adverbs
The eight function word categories represented a total of approximately 370 words and accounted for 57.1% (SD53.58%) of all words used in the essays
Summary
The ways we use words reflect how we think. In trying to assess people’s intellectual potential, common sense might dictate that we should pay attention to their use of long words or obscure references. The current study suggests that scholarly aptitude is better reflected in the ways people use short words. Following from previous literature showing how small word use reflects psychological states and cognitive processing, we applied computerized text analysis on a large corpus of college admissions essays with associated data on scholarship. PLOS ONE | DOI:10.1371/journal.pone.0115844 December 31, 2014
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