Limitations of difference-in-difference for measuring convergence

Linguistic convergence is the phenomenon in which interlocutors' speech characteristics become more similar to each other's. One of the methods frequently used to measure convergence is the difference-in-difference (DID) approach, comparing change in absolute distance between a subject and an interlocutor or model talker. We show that this approach is not a reliable measure of convergence when the starting values of the subject and the interlocutor or model talker are close, which can result in the measurement of apparent divergence, while extreme starting points can result in overestimation of convergence. These biases are of particular concern in studies that look for individual differences in convergence. We propose an alternative approach, linear combination, which does not have the same biases, and demonstrate the advantages of this method using data from convergence studies of four linguistic characteristics and simulated data.