Using Grounded Theory in Qualitative & Mixed Method Field Studies
Determining The Basis For Grounded Theory Analysis
My research is based upon the development of grounded theory to explore boundary-spanning processes in collaborative design. Because the processes I study involve distributed cognition, I employ ethnographic data collection, and qualitative/mixed analysis methods to investigate and understand them. My approach is based on the premise that an interpretive theory of action is explored and developed through iterative cycles of grounded theory generation.
To justify the use of grounded theory, we usually review the existing theories of action pertaining to a domain. For example, in investigating collaborative learning in online courses, we explored the literature on online learning and also on the related processes of learning as part of situated, collaborative action (e.g. in organizational change). We concluded that extant theories were insufficient to explain the types of collaboration that occur when students from disparate backgrounds attempt to collaborate in problem-solving and joint learning online. That conclusion justified the use of a grounded theory approach to investigate these processes.
Data Collection
The core aspect of grounded theory studies is deciding what data to collect and analyze. Barney Glaser, one of the two originators of grounded theory, famously said that "all is data," by which he meant that everything related to a research study is data: "whatever the source, whether interview, observations, documents, in whatever combination. It is not only what is being told, how it is being told and the conditions of its being told, but also all the data surrounding what is being told. It means what is going on must be figured out exactly what it is is to be used for, that is conceptualization, not for accurate description." (Glaser, 2001, p.145)
However, the problem with online processes is that we only ever have access to trace data (Howison et al. (2011). This means that we need to follow the ‘vapor trails’ left behind by online participants in various interactions with the community platform (Latour,2005). Individuals move in and out of visibility as they participate selectively in different ways and choose to engage in different ways with different resources,technical tools, structures, and peers. This, more than most situations, requires that we consider multiple sources of data in developing a grounded theory. For our study of online learning, we analyzed course community discussions, student perceptions (gathered from online discussions and through interviews), course grades (a blunt tool for outcome assessment), student references to course resources, student references to peer-suggested resources, and evidence of other forms of student interaction with the online learning environment that were provided by the online learning platform. In general, my approach is to collect all the data you possibly can - and then decide what data is relevant to which part of your research question.
Data Analysis
Deciding what data is relevant to your research question depends on what you are trying to discover. For example, in the online learning study, we defined our research question as:
How do students engage with a community of peer-learners in ways that enhance shared and/or individual learning?
Our initial topic guide provided a short set of definitions concerning what we were looking for, based on Glaser’s (1978, p. 57) three questions to be used in generating open codes:
-
What is this data a study of?
- How and why students engage with course topics and peer-learners in asynchronous online courses. -
What category does this incident indicate?
- Unit of analysis (Glaser’s ‘incident): Individual student post to discussion board, selected to provide sufficient granularity for the analysis of individual posting behaviors, while retaining the potential to examine patterns of interaction later.
- Initial categorization focus (Glaser’s ‘category'): Student course engagement and interaction behaviors (defined as loosely as possible to avoid preconceived categorizations). -
‘What is actually happening in the data?
Our initial data sample for analysis focused on discussion board posts over a 10-week teaching quarter from a large online class (to avoid small group constraints on student interaction) taking a conceptually complex course (to select data with the potential for student engagement). We used theoretical sampling to select other data samples for analyis.
Although we predominantly followed a Glaserian approach to the grounded theory analysis method, our interpretive research philosophy led us to follow the evaluation approach of Strauss & Corbin (1998), who suggest two types of criteria:
- Criteria for the research process are presented as a set ofquestions that are concerned with the approach to theory generation. These are concerned with the grounding/rationale, and the development of criteria for theoretical sampling, data analysis, and theory-construction.
- Criteria for the empirical grounding of the study guide how the researcher may evaluate the theoretical concepts, their relationships, the conditions under which the grounded theory may vary, processes embedded in the theory, and the significance of the theory.
We used these criteria to develop reflexivity in our data analysis process, distinguishing between
- Theoretical Memos, used to record insights relating to theoretical patterns in the data, and
- Process Memos, used to record insights relating to our analysis process and its consequences.
Grounded Theory Generation
In my own work, I follow the Glaserian, or ‘classical’ method for generating Grounded Theory (Glaser & Strauss, 1967; Glaser, 1992), moving from identifying broad categories of behavior (open coding) to identifying a core category that represents the central idea or construct of the study, then employing selective categories that analyze concepts in relation to the core category (selective coding), then onto theoretical coding that generates concepts to explain the integrated set of relationships between the core category and other elements of the situation. This sounds a great deal tidier than it tends to be in practice.
The use of a systematic, evidence-based approach to theory generation ensures rigor in data analyis. The ‘troublesome trinity’ of theoretical sampling, constant comparison, and theoretical saturation generates considerable explanatory power that is not found in other approaches (Hood, 2007). I briefly define these concepts here, but explain their operationalization in our description of how the research was conductesd in my paper on the use of grounded theory to explore online learning (Gasson & Waters, 2011).
- Theoretical sampling is the selection of additional empirical data for analysis on the basis of emerging codes or concepts (Glaser & Strauss, 1967). For example, if we discovered that some students appeared not to understand course materials because they did not read widely enough, we might choose to examine learning outcomes for students who did read widely, to compare and contrast learning outcomes for these samples.
- Constant comparison is the comparison of concepts developed in one data sample with the concepts developed for similar situations in previous and ongoing data samples (Glaser & Strauss, 1967). For example, if we discovered that some students appeared not to understand course materials, we might compare these findings to other samples to discover if students who also did not understand course materials shared similar attributes (such as not reading widely enough) or different attributes (such as reading equally widely, which might indicate that the materials chosen for the course lacked some requisite attribute for student comprehension).
- Theoretical saturation is reached when additional data analysis reveals no new concepts related to the ‘core category’ (Glaser & Strauss, 1967). For example, the studies reported here revolve around the processes by which thought-leaders lead, direct, and encourage individual learning in an online course community. Our core category could be defined as ‘student role-behavior’. Theoretical saturation would be reached when no new process mechanisms, consequences, effects, types, or attributes of student role-behaviors in online learning communities are found in new data. This relies on a theoretical sampling strategy that selects appropriate new data to test and extend the emerging theory.
As a substantive theory relates to a core category that is grounded in specific mechanisms, contexts, or environments, the core category is often difficult to define. This leads many researchers to define multiple core categories, as it is so difficult to determine which categories are the most significant in early sampling iterations of grounded theory analysis (Gasson, 2009). Theoretical sampling often involves parallel streams of coding, where several competing subsets of the data are analyzed to explore relationships between various "core category" possibilities and other conceptual categories. Not only the definition of the core category, but the researcher’s understand of its meaning tend to evolve across multiple iterations of theoretical sampling as researchers construct and discard hypotheses and theoretical explanations of the phenomena that they encounter. In the study reported in Gasson & Waters (2011), we summarize four rounds of data analysis that led to our substantive theory of how students engage with their community of peer-learners in ways that enhance shared and individual learning. This took a substantial amount of time - and generated new research questions that were investigated in subsequent studies.
Overall, the use of a grounded theory approach to research is not for the faint-hearted (or those impatient to complete). It takes a passion to discover what is happening, a dedication to rigor, and the discipline to collect data about - and document - everything that is happening.
References
Gasson, S. (2009) Employing a grounded theory approach for MIS research. In Handbook of Research on Contemporary Theoretical Models in Information Systems, Eds.: Dwivedi, Y.K., Lal, B., Williams. M.D., Schneberger, S.L. and Wade, M., pp 34–56, Hershey, PA: IGI Publishing.
Gasson, S. & Waters, J. (2013). Using a grounded theory approach to study online collaboration behaviors. European Journal of Information Systems, 22(1), 95-118.
Glaser, B.G. (1978) Advances in the Methodology of Grounded Theory:Theoretical Sensitivity. Mill Valley, CA.: The Sociology Press.
Glaser, B.G. (1992) Basics of Grounded Theory Analysis: Emergence vs. Forcing. Mill Valley, CA: The Sociology Press.
Glaser, B. G. (2001). The Grounded Theory Perspective: Conceptualization Contrasted with Description Mill Valley, CA.: Sociology Press.
(http://www.groundedtheory.com/soc14.html).
Hood, J.C. (2007) Orthodoxy vs. power: the defining traits of grounded theory. In The Sage Handbook of Grounded Theory, Eds.: Bryant, A. and Charmaz, K., pp 151–164, Thousand Oaks, CA: Sage Publications.
Strauss, A.L. and Corbin, J. (1998) Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory, 2nd edn, Sage, Newbury Park, CA.
Howison, James, Andrea Wiggins, and Kevin Crowston (2011) "Validity issues in the use of social network analysis with digital trace data." Journal of the Association for Information Systems 12[12], 2.
Related Publications
Gasson, S. (2003) 'Rigor in Grounded Theory Research - An Interpretive Perspective on Generating Theory From Qualitative Field Studies', in Whitman, M. and Woszczynski, A. (Eds.), Handbook for Information Systems Research , Idea Group Publishing, Hershey PA, pp. 79-102
Gasson, S. (2009) ‘ Employing A Grounded Theory Approach For MIS Research’, in Dwivedi, Y.K., Lal, B., Williams, M.D., Schneberger, S.L., Wade, M. (Eds.), Handbook of Research on Contemporary Theoretical Models in Information Systems, IGI Publishing, Hershey PA, pp. 34-56.
Gasson, S. & Waters, J. (2013) 'Using A Grounded Theory Approach To Study Online Collaboration Behaviors,' European Journal of Information Systems (2013) 22, 95–118. doi:10.1057/ejis.2011.24.
Khazraee, E.K. & Gasson, S. (2015) 'Epistemic Objects and Embeddedness: Knowledge Construction and Narratives in Research Networks of Practice' The Information Society, 31(2), March 2015.
Shim, M., Johnson, B., Bradt, J., Gasson, S. (2020) “A Mixed Methods–Grounded Theory Design for Producing More Refined Theoretical Models.” Journal of Mixed Methods Research, June 2020. https://doi.org/10.1177/1558689820932311