semanticfa

Semantic Factor Analysis of Language Model Embeddings.

semanticfa performs exploratory factor analysis on language model embeddings of psychological scale items, recovering latent factor structure entirely from item text — no human response data required.

Installation

# install.packages("devtools")
devtools::install_github("devon7y/semanticfa")

Quick start

library(semanticfa)
data(big5)

fit <- sfa(
  big5$items,
  nfactors   = 5,
  embeddings = big5$embeddings,
  scoring    = big5$scoring
)
print(fit)
plot(fit, type = "scree")

Features

• Multiple encoding methods: atomic reversed (Guenole et al.), SQuID centering (Pellert et al. 2026), mean-centered Pearson (Pokropek 2026)
• Embedding-adapted parallel analysis: random unit vector null distribution (no sample size needed)
• Unified retention diagnostics: sfa_nfactors() runs parallel analysis, Kaiser, TEFI, and EGA in one call
• psych-compatible output: $loadings works with psych::factor.congruence(), psych::fa.sort(), and all standard tools
• Pluggable embedding backends: sentence-BERT (default), OpenAI API, custom functions, or precomputed matrices
• Fit diagnostics: KMO, TEFI, RMSR, CAF, McDonald’s omega, DAAL
• Structure comparison: Tucker phi, NMI, ARI, Frobenius, disattenuated correlation via sfa_congruence()

Encoding methods

References

• Milano, N., Luongo, M., Ponticorvo, M., & Marocco, D. (2025). Semantic analysis of test items through large language model embeddings predicts a-priori factorial structure of personality tests. Current Research in Behavioral Sciences, 8, 100168. doi:10.1016/j.crbeha.2025.100168
• Casella, M., Luongo, M., Marocco, D., Milano, N., & Ponticorvo, M. (2024). LLM embeddings on test items predict post hoc loadings in personality tests. Ital-IA 2024, CEUR Workshop Proceedings.
• Guenole, N., D’Urso, E. D., Samo, A., Sun, T., & Haslbeck, J. M. B. (preprint). Enhancing Scale Development: Pseudo Factor Analysis of Language Embedding Similarity Matrices. OSF: https://osf.io/3mpzb/
• Pellert, M., Lechner, C. M., Sen, I., & Strohmaier, M. (2026). Neural network embeddings recover value dimensions from psychometric survey items on par with human data (SQuID). Findings of the ACL: EACL 2026, 5738–5752.
• Pokropek, A. (2026). From keyword-based text measures to latent variables: Confirmatory factor analysis with word embeddings. EPJ Data Science. doi:10.1140/epjds/s13688-026-00654-1
• Kmetty, Z., Koltai, J., & Rudas, T. (2021). The presence of occupational structure in online texts based on word embedding NLP models. EPJ Data Science, 10, 55. doi:10.1140/epjds/s13688-021-00311-9
• Christensen, A. P., Garrido, L. E., & Golino, H. (2023). Unique Variable Analysis: A network psychometrics method to detect local dependence. Multivariate Behavioral Research, 58(6), 1165–1182. doi:10.1080/00273171.2023.2194606
• Golino, H. (2026). Optimizing the landscape of LLM embeddings with Dynamic Exploratory Graph Analysis for generative psychometrics. arXiv:2601.17010.
• Wulff, D. U., & Mata, R. (2025). Semantic embeddings reveal and address taxonomic incommensurability in psychological measurement. Nature Human Behaviour, 9(5), 944–954. doi:10.1038/s41562-024-02089-y
• Wulff, D. U., & Mata, R. (2026). Escaping the jingle-jangle jungle: Increasing conceptual clarity in psychology using large language models. Current Directions in Psychological Science, 35(2), 59–65. doi:10.1177/09637214251382083
• Hommel, B. E., & Arslan, R. C. (2025). Language models accurately infer correlations between psychological items and scales from text alone. Advances in Methods and Practices in Psychological Science, 8(4). doi:10.1177/25152459251377093

License

GPL (>= 3)