[DSRP Evidence](https://dsrpevidence.org/)

# Assessing the Potential of Latent Class Modelling for Classifying Stone Artefacts and the Quantification of Technological Diversity

## Details

**Authors** Lucy Timbrell, Matt Grove, Eleanor Scerri

**Year** 2026

**Publisher** Journal of Paleolithic Archaeology

**Kind of work** article

**Discipline** Archaeology

**Applied** false

[Read it at the publisher](https://doi.org/10.1007/s41982-026-00255-4) 
10.1007/s41982-026-00255-4

## In authors' words

### Abstract

Archaeological typologies are used to determine the number and diversity of artefact forms within a prehistoric toolkit. However, objective classification is challenging, complicating cross-assemblage comparisons. We explore the potential of latent class modelling (LCM) for grouping stone tools based on their morphological and technological attributes. LCM identifies unobserved ('latent') subgroups that share certain observed characteristics, producing posterior probabilities of artefact membership to latent classes. Applied to a large dataset of Middle Stone Age and Middle Palaeolithic lithics from northern Africa and Arabia, we compare LCM results with the original typological assessment of each artefact as well as hierarchical clustering, another non-model based unsupervised technique of group classification. Our results show that, although both methods are equally (in)coherent with the original typology, LCM can group artefacts with important technological and morphological characteristics, such as diverse bifacially worked pieces and different types of unretouched Levallois products. We further evaluate LCM performance using permutation tests, which highlight that our model fits the observed data substantially better than any randomly generated structure. Using latent class proportions, we then quantify technological diversity robustly across varying sample sizes. Assemblage-level diversity patterns indicate that northern African MSA toolkits are generally variable, with only a limited number of assemblages departing significantly from null expectations. Overall, LCM offers a transparent, probabilistic framework for capturing the polythetic nature of stone tool assemblages.

### What they set out to do (purpose)

To test whether latent class modelling provides a more objective way of grouping stone tools than traditional typology, and to use it to quantify technological diversity within stone-tool assemblages.

### Who or what was studied (sample)

1,334 complete stone tools from 20 Middle Stone Age and Middle Palaeolithic sites in northern Africa and Arabia.

### How they did it (methods)

Eight manifest morphological and technological variables (e.g., laminarity, convexity, edge type, retouch) were used to fit latent class models compared against the original typological assignments and against hierarchical clustering, with model fit assessed via information criteria and permutation tests.

### What they found (results)

Latent class modelling found five statistically supported classes and showed that the traditional 'retouched flake' category, covering over 60% of the assemblage, is a poorly defined, heterogeneous grouping rather than a coherent technological category.

## Commentary

### In short

The finding shows that a widely used artifact category is not a clean distinction at all, while a different way of grouping the same parts recovers a more coherent classification structure for the whole assemblage.

**Patterns it shows** D, S

**Added** 2026-09-28

**How to cite this** Lucy Timbrell, Matt Grove, Eleanor Scerri (2026). Assessing the Potential of Latent Class Modelling for Classifying Stone Artefacts and the Quantification of Technological Diversity. Journal of Paleolithic Archaeology.