ABSTRACT
This essay first introduced seven qualitative technique tools and discussed their application, then summarized the NVivo techniques of a grounded theory research project and discussed its implications.
KEYWORDS
NVivo techniques, grounded theory, qualitative analysis.
INTRODUCTION
As data analysis in grounded theory research is time-consuming and laborious, using Computer Assisted Qualitative Data Analysis Software (CAQDAS) in an appropriate way can help lessen such burden. This essay aims to discuss how to use NVivo appropriately in grounded theory research. After reviewing qualitative technique tools and their application, this essay introduced a grounded theory research project which used NVivo to facilitate data analysis, discussing its implications for future research.
QUALITATIVE TECHNIQUE TOOLS
Leech and Onwuegbuzie introduced seven qualitative technique tools (see Table 1), they argued that to strengthen qualitative analysis, researchers should use at least two qualitative techniques to ensure representation and legitimation (Leech & Onwuegbuzie, 2007). Representation refers the ability to extract the exact meaning of the text, which can be enhanced by within-method complementary, within-method expansion and within-method development. Legitimation refers to the trustworthiness, credibility, dependability, confirmability and transferability of the inferences made, which can be enhanced by within-method initiation and within-method triangulation (Leech & Onwuegbuzie, 2007).
A GROUNDED THEORY RESEARCH EXAMPLE
This section introduces a project whose researchers used NVivo to facilitate grounded theory research (Hutchisona et al., 2010). It summarizes all techniques in this project into two folds:
Manage data and thoughts with memo, sets, node structure and casebook
The management of research data and thoughts is the basis of qualitative analysis. To manage interview data for further analysis, the researchers created a branch of descriptive nodes for different dimensions of concepts, and utilized sets to regroup nodes based on potentially meaningful relationships without destroying the node structure. By these strategies, they can explore different dimensions of concepts with code queries. They also create a casebook for constant comparison, only adding potentially important attributes they emerged during coding
To manage thoughts and other materials such as literature, the researchers designed a specific memo structure for their project, including research diaries, reflective memos, conceptual memos, emergent questions and explanatory memos. Specially, they imported related literature and create literature-related memos to summarize their reading, then linked these memos to imported literatures and emergent question memos, which can help them identify appropriate research questions, methods and sampling strategies in the early stage. They also created memos for each generated node recoding analytical ideas, which can encourage further sampling and finding answers to emergent questions.
Facilitate analysis with coding strip, (matrix) code queries, relationship nodes and models
To identify higher order concepts, researchers need to understand relationships between nodes and concepts as comprehensively as possible. In this project, the researchers explored relationships between different concepts with coding stripe, which presents all codes from one sentence. By checking coding stripe, researchers can find out new dimensions to recategorize nodes by creating new nodes or node sets. They also used coding queries to extract content appearing in different nodes or node sets to identify higher order concepts, or matrix coding queries to identify concepts distinguishing different patterns.
To recognize the complexity of potential relationships, the researchers used relationships nodes to record particular connections between different items.
To identify the gaps in the emergent understanding and summarize findings, the researchers used models linked to explanatory memos, which can represent the core categories and their various dimensions, visualize the evolvement of analysis (when automatically updated) and summary results in a static model.
CONCLUSION AND DISCUSSION
The project mentioned above used multiple NVivo techniques when analyzing data, which can help enhance its representation. Furthermore, it provides insights on the application of NVivo in grounded theory research, including:
Good data organization is the half of a good research
In grounded theory research, researchers need to analyze data iteratively and thoroughly to get a comprehensive view of data, during which they can generate a lot of research data including nodes, memos and annotations. Utilize tools such as memos, sets and casebook can help researchers organize data effectively and review them in multiple dimensions, putting more energy on creative thinking. To do that, researchers should design an appropriate data structure at first based on research need.
Use tools considering research need
Although NVivo function “relationship node” is suitable for axial coding, which encourage researchers to fit data into a limited framework and contradict with epistemological perspectives of grounded theory (Hutchisona et al., 2010), the researchers in the project mentioned above used relationship nodes to present the complexity of potential relationships and stimulate in-depth analysis in grounded theory research. The use of tools can be flexible, as researchers identify research needs clearly and understand functions of tools well, they can utilize tools with flexibility to solve their problems.
However, the researchers of this project discussed little about ensuring legitimation with NVivo techniques. For example, if there are more than one coder in this project, how did they record, manage and solve contradictions in coding with NVivo? Did they use techniques such as Kappa to exam credibility? If so, what is the satisfying Kappa value from their perspective? How will they deal with conditions that Kappa value is lower than their expectation? Solving these questions can consummate this project.
REFERENCES
Chen, X. (2015). 扎根理论在中国教育研究中的运用探索[The Exploration of Grounded Theory Application in Chinese Education Science Research]. 北京大学教育评论[Peking University Education Review], 13(1), 2–15.
Hutchisona, A. J., Johnstonb, L. H., & Breckona, J. D. (2010). Using QSR-NVivo to facilitate the development of a grounded theory project: An account of a worked example. International Journal of Social Research Methodology, 13(4), 283–302. https://doi.org/10.1080/13645570902996301
Leech, N. L., & Onwuegbuzie, A. J. (2007). An Array of Qualitative Data Analysis Tools: A Call for Data Analysis Triangulation. School Psychology Quarterly, 22(4), 557–584. https://doi.org/10.1037/1045-3830.22.4.557
