Abstract This article investigates the effectiveness of sigmoid trajectories (S-curves) for statistically modelling language change. In diachronic linguistics, it is customary to fit a sigmoid regression line through data, on the assumption that such a curve fits the ideal language change. A coefficient with an associated p-value significantly different from zero is taken as evidence for the presence of a change. Here, we take the inverse perspective: given a known change, how well can the S-curve predict the actual data? We look at 15 well-known changes in Late Modern Dutch, in a genre-balanced corpus. For each change we built four different models: one model based on all the data and three partly blinded models, where either the beginning, middle or end of the S-curve is omitted. We check how well the S-curve can predict or reconstruct the unknown data, i.e. the blinded parts of the S-curve. We investigate in which cases (the type of change, the part of the data that is omitted, etc.) it is easier or harder to reconstruct the missing data.
Nijs et al. (Mon,) studied this question.