Intransigent compassion: Human and non-human animal self-similarity and meat avoidance intent dataset

The dataset provided with this article is related to Lowering Barriers to Plant-based Diets: The Effect of Human and Non-Human Animal Self-Similarity on Meat Avoidance Intent and Sensory Food Satisfaction [1]. The connection between compassion and adherence to plant-based diets is intuitive. The first dataset is a sample of 372 participants in the United States that was collected online. Trait compassion, measured using the Santa Clara Brief Compassion Scale [2], is positively associated with intent to avoid dietary meat consumption. The second set of data, collected online from 131 participants in the United States, provides evidence for the underlying psychological process: the relationship between trait compassion and meat avoidance intent is serially mediated by perceived similarity to other human animals and non-human animals. Similarity scores were measured inversely as perceived distance using heat-map type questionnaire items based on inclusion-of-other-in-the-self (IOS, [3]) and relational closeness scales [4]. Demographic information, physical characteristics, and measurement of athletic identity are provided [5]. These data can be used in psychology research on food studies specifically and to glean more insight on human's connection with other animals in general [6,7]. The supplementary data on participants' physical characteristics such as BMI, combined with measurement of athletic identity, can inform sports and nutrition science. Survey print-outs, two datasets including scale items, and scripts for analysis are provided. (C) 2021 The Author. Published by Elsevier Inc.

Automated summary

The dataset provided in this article explores the relationship between trait compassion and meat avoidance intent, finding that perceived similarity to other human and non-human animals plays a mediating role. These findings have implications for psychology research and understanding of human-animal connections. Additionally, the dataset includes participants' physical characteristics and measurement of athletic identity, potentially informing sports and nutrition science.