Abstract

This article locates story completion (SC), a novel and underutilised qualitative data collection tool, as a valuable adjunct to traditional qualitative methods for pastoral psychology. In contrast to traditional self-reporting qualitative interviews or surveys, SC necessitates a researcher writing a story “stem” or “cue” – or, more specifically, an opening sentence – which participants are asked to continue in their own words. Uniquely, for SC, it is the stories themselves (which may be either first- or third-person perceptions, or constructions, of a phenomenon) which are subject to data analysis. Story completion has the benefit of being able to target implicit perceptions, or social constructions, depending on the epistemological framework chosen. However, this method has seldom been used in pastoral psychology, despite its potential significance. In this article, I demarcate SC as a prospective qualitative research tool for use in pastoral psychology, distinguishing it from other qualitative methodologies. I trace its emergence from psychoanalytic thought to its current usage in qualitative psychology research. I argue that SC has profound potential, especially for those looking to examine stigmatised topics or populations with sensitivity. Nevertheless, studies which utilise SC need to be theoretically cognizant and align fully with the ontological or epistemological assumptions of the researcher. I introduce and expound on varied epistemological frameworks that can be used in conjunction with story completion, further discussing their relative merits and potential drawbacks for pastoral psychology. I propose that, methodologically, SC is beneficial for accessing sociocultural discourses and broader representations surrounding religiously and culturally complex topics. I offer a case study of one recent research study, which used SC within the context of mental health and religion, to demonstrate its merit and applicability to the field. In doing so, I provide three contrasting epistemological readings of the data to show how these might be applied in practice.

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