Correcting for measurement error in categorical, longitudinal data using hidden Markov models

This project focuses on the problem of measurement error and investigates the feasibility of using hidden Markov models (HMMs) to correct for such error in categorical, longitudinal data. In doing so, we have first illustrate how measurement error poses a substantial threat to the validity and accuracy of estimates. We then demonstrate the need to use multiple-indicator HMM specifications, which can account for the nonignorable presence of systematic/dependent errors. Finally, we show that the use of such extended models is feasible. That is, even though such HMMs require record linkage, linkage error is largely not a problem. Furthermore, while their implementation process is complex and time-consuming, it can be simplified because error parameters can be re-used for a number of years.