Author
Summary
Mixed modes are receiving increased interest from survey methodologists as a possible solution to saving costs while retaining high quality data. In recent years this interest has extended also to panel studies which are looking to save costs by including a cheaper mode for some of their respondents. The current presentation aims to tackle some of the issues linked to such a design. First, I analyze if using a mixed mode design will increase systematic and random error compared to a single mode CAPI survey by applying equivalence testing in a Confirmatory Factor Analysis. Secondly, I investigate if estimates of individual change are influenced by mode design by comparing latent growth models across the two designs. The first four waves of the Innovation Panel, part of Understanding Society (UKHLS), are be used for the analysis. The second wave of the data randomized respondents to either a single-mode CAPI design or to a CATI-CAPI sequential design. The SF12 health scale is used to investigate both measurement equivalence and estimates of individual change.