Challenges When Using Grounded Theory

Abstract

The grounded theory (GT) method is widely applied, yet frequently misunderstood. We outline the main variants of GT and dispel the most common myths associated with GT. We argue that the different variants of GT incorporate a core set of shared procedures that can be put to work by any researcher or team from their chosen ontological and epistemological perspective. This “shared core” of the GT method is articulated as the principles of (1) taking the word “grounded” seriously, (2) capturing and explaining context-related social processes, (3) pursuing theory through engagement with data, and (4) pursuing theory through theoretical sampling. In this article, we have put forward, in a nutshell, a distillation of core principles underpinning existing GT approaches that can aid further engagement with the different variants of GT. We are motivated by the wish to make GT more comprehensible and accessible, especially for researchers who are new to the method.

"The Most Trustworthy Coin"

“…We approached our data analysis utilizing grounded theory. We centered our approach based on Timonen, Foley, and Conlon's [63] synthesis of the most common and valuable principles of grounded theory in practice. These authors identified four "core principles" that we followed in our approach.…”

“…The first is that the research is actually grounded, meaning that any hypotheses are derived from the data, with existing knowledge being 'put in dialogue' with any emergent directions. Another core principle is identifying social processes and interactions specifically in terms of the action they have in the context of the field site [63]. Next, is the process of engaging in constant comparison of the data to other data in order to identify differences, similarities, and patterns across the corpus [63].…”

“…Another core principle is identifying social processes and interactions specifically in terms of the action they have in the context of the field site [63]. Next, is the process of engaging in constant comparison of the data to other data in order to identify differences, similarities, and patterns across the corpus [63]. The final principle identified by Timonen et al is theoretical sampling, where new theories are derived from concepts in the data that lead to a cycle of more focused questioning and concept clarification.…”

“…We followed a three-step coding process [63]. We began with "open-coding" in which we spent time exploring r/bitcoin.…”

“…We began with "open-coding" in which we spent time exploring r/bitcoin. The first author spent time getting "a feel for the data" [63] by following r/bitcoin for several weeks, and reading the most popular threads. During this time, she openly coded concepts that emerged repeatedly across time and interactions, including trust, social inclusion/exclusion, defining true bitcoiners, and approximately two dozen other descriptive codes related to social life on the subreddit.…”