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                  between these codes is a complex process involving the evolving construction of an
                  understanding of the data. Iterative review of the data is often a key part of the pro-
                  cess, as identification of new codes and categories might lead you to rereview docu-
                  ments from the perspective of codes identified in later documents. This rereview
                  might also suggest multiple categorizations or types of relations between codes.
                     In the last stage, theory formulation, we aim at creating inferential and predictive
                  statements about the phenomena recorded in the data. More specifically, we develop
                  explicit causal connections or correlations between the concepts and categories iden-
                  tified in the previous stages. This process might be followed by selective coding, in
                  which previously coded data might be revisited from the context of the emerging
                  theory. Of course, further iteration and identification of open codes or axial codes is
                  also possible.
                     A study of the issues involved in building information technology support for
                  palliative care provides an instructive example of the use of emergent coding and
                  grounded theory. Noting that palliative care differs significantly from other forms
                  of medical care in a focus on the individual needs of each patient, Kuziemsky and
                  colleagues conducted a grounded theory analysis of multiple data sources, including
                  50 hours of interviews with seven professionals (nurses, physicians, and counselors),
                  patient charts, and research literature. Figure 11.2 provides an example of open and
                  axial codes used in this analysis. This coding process was used to form a more de-
                  tailed map of relationships between factors important to palliative care (Kuziemsky
                  et al., 2007).
                     While conducting research using grounded theory, it is important to fully un-
                  derstand the advantages and limitations of this research method. Grounded theory
                  obviously has a number of advantages. First, it provides a systematic approach to an-
                  alyzing qualitative, mostly text-based data, which is impossible using the traditional
                  experimental approach. Second, compared to the other qualitative research methods,
                  grounded theory allows researchers to generate theory out of qualitative data that
                  can be backed up by ample evidence as demonstrated in the thorough coding. This
                  is one of the major attractions of the grounded theory and even novice users found
                  the procedure intuitive to follow. Third, grounded theory encourages researchers to
                  study the data early on and formulate and refine the theory through constant interplay
                  between data collection and analysis (Myers, 2013).
                     On the other hand, the advantages of grounded theory can become disadvantages
                  at times. It is not uncommon for novices to find themselves overwhelmed during the
                  coding stage. The emphasis on detailed and thorough coding can cause researchers to
                  be buried in details and feel lost in the data, making it difficult to identify the higher-
                  level concepts and themes that are critical for theory formulation. In addition, theories
                  developed using this method may be hard to evaluate. Unlike the traditional experi-
                  mental approach in which the hypothesis is clearly supported or rejected by quantita-
                  tive data collected through well-controlled, replicable experiments, grounded theory
                  starts from textual information and undergoes multiple rounds of data collection and
                  coding before the theory fully emerges from the data. The evaluation of the outcome
                  depends on measures that are less direct, such as the chain of evidence between the
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