Measure twice, model once: Introduction to measurement methods for better longitudinal modelling

The underlying motivation of this collection is the concern that the failure to understand the relationship between our measure of a concept and the concept itself has real consequences. It may cause us to overlook model specification problems, to unnecessarily and inappropriately change our theories, and to miss out on information that could be usefully applied to the specification and estimation of our model. In order to take modelling seriously, we first must take measurement seriously. This is most effectively done when multiple measures of the underlying concept are utilised, allowing for the application of more appropriate models with more precise estimates. The contributions to this symposium demonstrate a wide range of such modelling approaches, each of which is tailored to match the theoretical expectations for the statistical characteristics of the data being used. By way of introduction, I highlight the measurement problems that these approaches are designed to tackle.