The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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

Method	Description
`"atomic_reversed"`	Sign-flip by keying, L2-normalize, cosine similarity
`"atomic"`	L2-normalize, cosine similarity (no sign-flip)
`"squid"`	Subtract questionnaire-mean embedding, then cosine
`"mean_centered_pearson"`	Mean-center → cosine = Pearson correlation

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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.