Reactivity in measuring depression

If a human subject knows they are being measured, this knowledge may affect their attitudes and behaviour to such an extent that it affects the measurement results as well. This broad range of effects is shared under the term ‘reactivity’. Although reactivity is often seen by methodologists as a problem to overcome, in this paper I argue that some quite extreme reactive changes may be legitimate, as long as we are measuring phenomena that are not simple biological regularities. Legitimate reactivity is reactivity which does not undermine the accuracy of a measure; I show that if such reactivity were corrected for, this would unjustifiably ignore the authority of the research subject. Applying this argument to the measurement of depression, I show that under the most commonly accepted models of depression there is room for legitimate reactivity. In the first part of the paper, I provide an inventory of the different types of reactivity that exist in the literature, as well as the different types of phenomena that one could measure. In the second part, I apply my argument to the measurement of depression with the PHQ-9 survey. I argue that depending on what kind of phenomenon we consider depression to be (a disease, a social construction, a harmful dysfunction, or a practical kind), we will accept different kinds of reactivity. I show that both under the harmful dysfunction model and the practical kinds model, certain reactive changes in measuring depression are best seen as legitimate recharacterizations of the underlying phenomenon, and define what legitimate means in this context. I conclude that in both models, biological aspects constrain characterization, but the models are not so strict that only one concept is acceptable, leaving room for reactivity.