R C Arslan, A K Reitz, J C Driebe, T M Gerlach, and L Penke (2019)

Routinely randomize potential sources of measurement reactivity to estimate and adjust for biases in subjective reports

Preprint on PsyArXiv.

With the advent of online and app-based studies, researchers in psychology are making
increasing use of repeated subjective reports. The new methods open up opportunities to study behavior in the field and to map causal processes, but they also pose new challenges. Recent work has added initial elevation bias to the list of common pitfalls; here, higher negative states (i.e., thoughts and feelings) are reported on the first day of assessment than on later days. This article showcases a new approach to addressing this and other measurement reactivity biases. Specifically, we employed a planned missingness design in a daily diary study of more than 1,200 individuals who were assessed over a period of up to 70 days to estimate and adjust for measurement reactivity biases. We found that day of first item presentation, item order, and item number were associated with only negligible bias: items were not answered differently depending on when and where they were shown. Initial elevation bias may thus be more limited than has previously been reported or it may act only at the level of the survey, not at the item level. We encourage researchers to make design choices that will allow them to routinely assess measurement reactivity biases in their studies. Specifically, we advocate the routine randomization of item display and order, as well as of the timing and frequency of measurement. Randomized planned missingness makes it possible to empirically gauge how fatigue, familiarity, and learning interact to bias responses.

preprints and author's version postprints, open analysis code
Preprint available at https://psyarxiv.com/va8bx. Open analysis code available at https://rubenarslan.github.io/initial_elevation_bias/.