Language-based assessments for experienced well-being: accuracy and external validity across behaviors, traits, and states

Abstract While language is central to human communication, psychological science has relied on self-reported rating scales for decades because it was difficult to quantify language effectively. Large language models (LLMs) have changed that in recent years. Here, we used LLMs with machine learning to compare traditional ratings against language-based responses (short essays and descriptive words) of experienced well-being over 12 weeks from 764 individuals from an overburdened population highly exposed to emotional experience, U.S. hospitality workers. While accuracies were comparable by internal criterion (across 9 affective traits; words r = .53 vs. .51 for ratings), both language formats doubled the ratings’ accuracy (up to r = .18 and .24 for words and essays, respectively) across 24 external criteria: traits, demographics, and behavior. Beyond improved accuracy, language responses offered psychological insights: words provided concise emotional nuances, whereas essays encompassed broad life contexts. Though each language-based format differed in strengths and weaknesses, both demonstrated greater information-richness than traditional rating scales.