Socially Desired Awareness of Biological Diversity? A New Approach to a Well-Known Problem with Normatively Charged Survey Topics

Biodiversity is a particular term that brings complex ideas of the abun-dance of life to a scientific formula and at the same time marks it as a desirable state. Since the early 1990s, the meta-narrative threatened biodiversity has func-tioned as a meaningful moment of international environmental policy. It is well known that the attitude surveys on social awareness of biodiversity, which the Bundesamt fur Naturschutz (Federal Agency for Nature Conservation) has had conducted every two years since 2009, are contaminated by social desirability. Since previous analyses either explicitly or implicitly assume that the form of bias is uni-form, statements regarding associations and group comparisons are at risk. Causal machine learning methods open up a new analytical approach to the underlying statistical problem. They can be used to uncover how causal effects vary and which variables are related to this variation in such a way that they can explain effect diffe-rences. In our specific case, we use causal forest models to show that the influence of social desirability on attitude patterns and behavioral intentions about biodiver-sity varies systematically, with this effect heterogeneity being significantly associated with the age of the interviewees in a non-linear manner. Furthermore, we present this procedure as a methodological innovation for quality assurance in normatively charged survey topics.

Automated summary

Biodiversity is a complex concept that represents the abundance of life and is seen as an ideal state. The meta-narrative of threatened biodiversity has played a significant role in international environmental policy since the early 1990s. However, surveys on social awareness of biodiversity are influenced by social desirability, and causal machine learning methods are needed to address this issue.