Religious exemplars’ experience of indebtedness to God: employing innovative machine learning to explore a novel construct

Abstract

ABSTRACT Acquiring gratitude and self-transcendence are critical to positive socioemotional development. Indebtedness to God may provide a pathway for this healthy development, yet it remains a largely unexplored construct. The purpose of the present work was to use two innovative qualitative methods; 1) exemplar sampling method (N = 15) and 2) cutting-edge machine learning and language models to visually describe the data (N = 5,598) and provide semantic maps. The thematic coding analyses resulted in 11 inquiry-driven themes (five describing the experience of indebtedness to God and six describing the outcomes of indebtedness to God). The experience of indebtedness to God was transcendent and positive. The machine learning language model analysis resulted in 1,920 unique tags and over 100 independent triangulated semantic themes, and several topographical views of the data to spur future research on this novel construct.