ArticleJournal of personality2025
Can an Algorithm Tell How Spiritual You Are? Using Generative Pretrained Transformers for Sophisticated Forms of Text Analysis.
Article in Journal of personality, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- The use and methodological reporting of large language models in qualitative research: a scoping review.BMC medical research methodology · 2026Article
- An AI-powered research assistant in the lab: A practical guide for text analysis through iterative collaboration with LLMs.Behavior research methods · 2026Article
- Sexuality in Positive Psychology: Toward the Integration of a Neglected Component of Human Flourishing.The journal of positive psychology · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveText analysis is a form of psychological assessment that involves converting qualitative information (text) into quantitative data. We tested whether automated text analysis using Generative Pre-trained Transformers (GPTs) can match the "gold standard" of manual text analysis, even when assessing a highly nuanced construct like spirituality.
methodIn Study 1, N = 2199 US undergraduates wrote about their goals (N = 6597 texts) and completed self-reports of spirituality and theoretically related constructs (religiousness and mental health). In Study 2, N = 357 community adults wrote short essays (N = 714 texts) and completed trait self-reports, 5 weeks of daily diaries, and behavioral measures of spirituality. Trained research assistants and GPTs then coded the texts for spirituality.
resultsThe GPTs performed just as well as human raters. Human- and GPT-generated scores were remarkably consistent and showed equivalent associations with other measures of spirituality and theoretically related constructs.
conclusionsGPTs can match the gold standard set by human raters, even in sophisticated forms of text analysis, but require a fraction of the time and labor.
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