Mixing modes and measurement methods in longitudinal studies

Across the world longitudinal studies are facing falling response rates, at the same time cost imperatives are bringing into question the feasibility of large scale regular face-to-face data collection. While, the rapid development of communications technology and associated cultural changes is assumed to mean that study participants will increasingly expect to be able to answer surveys when and how it suits them. All of these factors are driving longitudinal studies to combine different modes of data collection both to increase response and to reduce costs. Mixing modes of data collection either across individuals at one point in time or within individuals over time, presents longitudinal researchers with a range of methodological challenges in both data collection and analysis. Within CLOSER, and beyond, studies are investigating different aspects of the implications of mixed mode data collection, and giving data users varying degrees of support and advice about issues that should be of concern. Drawing on evidence from across CLOSER’s longitudinal studies, this report reviews the latest evidence gathered on the effect of mixing modes and measurement methods on response, measurement issues and survey costs. The review also focuses on the implications for analysis of measures collected in different ways either across individuals at the same point in time or within individuals over time. Building on these reviews, we identify what further research is required in relation to both the design and analysis of mixed mode data collection. The contents of this report is based on a CLOSER workshop held in November 2016 (http://www.closer.ac.uk/event/mixing-modes-measurement-methods-longitudinal-studies/). The workshop and report were funded by a CLOSER Innovation grant awarded to Michaela Benzeval and Annette Jackle (University of Essex), and Kate Tilling and Dr Andy Skinner (University of Bristol) and are part of a series of three reports (see Jackle, Gaia, & Benzeval, 2017; Stone & Skinner, 2017).