Existential Analysis Codebooks for LLM-Assisted Qualitative Analysis
Two open-source codebooks supporting the WCET4 (2026) paper "Do You Understand?!" Best Practices using Artificial Intelligence in Research (and Life) by Graham Nelson-Zutter, MSc. Both are downloadable, citable, and licensed CC BY-NC-ND 4.0 (attribution, non-commercial, no derivatives) for use in your own research.
Both codebooks score participant testimony against Längle's existential framework: the Four Fundamental Motivations (4FM), each of which decomposes into three Fundamental Existential Prerequisites (FEPs) - twelve in all. The 4FM codebook scores at the motivational level; the 12FEP codebook scores at the finer-grained prerequisite level.
1FM · The World
Support
Space
Protection
2FM · Life
Relationship
Time
Closeness
3FM · The Person
Attention
Justice
Appreciation
4FM · Meaning
Field of Activity
Structural Context
Value to be Realized
A structured codebook for scoring participant testimony against Längle's Four Fundamental Motivations - the foundational framework. Built by multi-LLM cross-validation across Claude Sonnet 4.5, ChatGPT 5 Thinking, and Gemini 2.5 Turbo, using four primary Längle sources (1992, 2002, 2003, 2011). Every code traces to a verbatim Längle quote with citation.
A structured codebook for scoring participant testimony against Längle's Twelve Fundamental Existential Prerequisites - derived from the 4FM framework, decomposing each Fundamental Motivation into three Prerequisites for finer-grained scoring. Built by the same multi-LLM consensus methodology, against two primary Längle sources (2002, 2011b).
Each codebook is a JSON artifact that constrains an LLM to score participant testimony within Längle's framework, rather than letting the LLM draw on generic prior knowledge. They are not fine-tuned models and not prompts in themselves - they are structured references you provide to your LLM of choice alongside testimony.
instructions_for_llm / coding_guidelines block. The codebook requires the LLM to anchor every code in a verbatim Längle quote plus a verbatim testimony quote.The companion paper (below) walks through the three core techniques in detail - multi-LLM cross-validation, quote-anchored source verification, and iterative multi-pass with re-anchoring - that underlie both the construction and the application of these codebooks.
Nelson-Zutter, G. (2026, June). "Do You Understand?!" Best Practices using Artificial Intelligence in Research (and Life) [Conference presentation]. World Congress for Existential Therapy 4 (WCET4), Denver, CO, United States.
Publish date: TBD · Methodology paper DOI: forthcoming (post-conference deposit).
Both codebooks were authored by Graham Nelson-Zutter, MSc student in Existential Analysis & Logotherapy at the University of Salzburg, in the course of his MSc thesis on LLM-assisted qualitative analysis of psychedelic-assisted therapy participant experiences.
The LLMs used in the methodology (Claude Sonnet 4.5, ChatGPT 5 Thinking, Gemini 2.5 Turbo, and others) are research instruments, not co-authors. See each codebook's README.md and docs/lineage.md for the full multi-LLM build provenance.
The codebooks anchor every code in verified quotations from Längle's primary works:
Längle, A. (1992). What are we looking for when we search for meaning? Ultimate Reality and Meaning, 15(4), 306–314. https://doi.org/10.3138/uram.15.4.306
Längle, A. (2002). Existential fundamental motivation. In Motivation, Consciousness and Self-Regulation (pp. 27–42). Nova Science Publishers. https://laengle.info/userfile/doc/FM-+-motivation---Leontev-Mosc-2002-2011-publ.pdf
Längle, A. (2003). The art of involving the person – Fundamental existential motivations as the structure of the motivational process. European Psychotherapy, 4(1), 25–36.
Längle, A. (2011). The existential fundamental motivations structuring the motivational process. In D. A. Leontiev (Ed.), Motivation, Consciousness and Self-Regulation (pp. 27–42). Nova Science Publishers. https://laengle.info/userfile/doc/FM-+-motivation---Leontev-Mosc-2002-2011-publ.pdf
Anchor citations for the best-practice techniques and limitations in the companion AI-methodology paper:
Balt, E., Salmi, S., Bhulai, S., Vrinzen, S., Eikelenboom, M., Gilissen, R., Creemers, D., Popma, A., & Mérelle, S. (2025). Deductively coding psychosocial autopsy interview data using a few-shot learning large language model. Frontiers in Public Health, 13, 1512537. https://doi.org/10.3389/fpubh.2025.1512537
Charani, A., Nelson-Zutter, G., van Deurzen, E., Wagner, A., Sahu, S., Icoz, J., & Miari, E. (2026, June 5). Bridge Builder Group (Group 3)—AI, Authenticity, and the Future of Existential Therapy. Symposium with Breakout Groups (Organized by Ammar Charani). 4th World Congress for Existential Therapy.
Head, K. (2025). Minds in crisis: How the AI revolution is impacting mental health. Journal of Mental Health & Clinical Psychology, 9(3). https://www.mentalhealthjournal.org/articles/minds-in-crisis-how-the-ai-revolution-is-impacting-mental-health.html
Jayawardene, V., & Ewing, M. T. (2026). Generative AI-augmented thematic analysis. International Journal of Market Research, 68(2), 162–193. https://doi.org/10.1177/14707853251405043
Linardon, J., Jarman, H. K., McClure, Z., Anderson, C., Liu, C., & Messer, M. (2025). Influence of topic familiarity and prompt specificity on citation fabrication in mental health research using large language models: Experimental study. JMIR Mental Health, 12, e80371. https://doi.org/10.2196/80371
Maes, S. (2025). Fixing reference hallucinations of LLMs [Preprint]. OSF. https://doi.org/10.31219/osf.io/u38w4_v2
Mathis, W. S., Zhao, S., Pratt, N., Weleff, J., & De Paoli, S. (2024). Inductive thematic analysis of healthcare qualitative interviews using open-source large language models: How does it compare to traditional methods? Computer Methods and Programs in Biomedicine, 255, 108356. https://doi.org/10.1016/j.cmpb.2024.108356
McBain, R. K., Cantor, J. H., Zhang, L. A., Baker, O., Zhang, F., Burnett, A., Kofner, A., Breslau, J., Stein, B. D., Mehrotra, A., & Yu, H. (2025). Evaluation of alignment between large language models and expert clinicians in suicide risk assessment. Psychiatric Services. https://doi.org/10.1176/appi.ps.20250086
Raile, P. (2024). The usefulness of ChatGPT for psychotherapists and patients. Humanities and Social Sciences Communications, 11(1), 47. https://doi.org/10.1057/s41599-023-02567-0
Sharma, A., Cochrane, K., & Wallace, J. R. (2025). DeTAILS: Deep thematic analysis with iterative LLM support [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2510.17575
Both codebooks are archived on Zenodo (CC BY-NC-ND 4.0). Full APA citation, with RIS / BibTeX downloads for each:
Nelson-Zutter, G. (2026). EA 4FM Codebook for LLM-Assisted Qualitative Analysis [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.20483207
Nelson-Zutter, G. (2026). EA 12FEP Codebook for LLM-Assisted Qualitative Analysis [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.20481464
Building on these codebooks, or on the multi-LLM methodology, in your own research? Share your email below and I'll be in touch about collaborating.