14 Qualitative Analysis
Learning Outcomes
By the end of this chapter, learners will:
- Understand basic terminology associated with qualitative analysis,
- Describe different qualitative methods,
- Apply a three-step coding process to their research.
Introduction
Qualitative data can provide more depth than quantitative methods. It is a rich source of descriptive data that can give an in-depth understanding into different perspectives and thought processes. It can also complement quantitative data by providing insights into why certain observations may be happening and providing context. For example, quantitative data can reveal how many folks may be hesitant to use OER. Qualitative data can explain why folks are hesitant.
In OER work, qualitative data can look like:
- Interviewing instructors about their perceptions of OER,
- Asking instructors who receive OER Grants open ended questions about their experience,
- Surveying students and asking for their feedback on a new OER being used in a classroom,
- Surveying students and asking open ended questions about the impact of purchasing expensive course materials on their academic goals and overall wellbeing.
The goal of qualitative analysis is to help a researcher identify key themes and important findings in this data.
Qualitative Analysis
The term ‘qualitative analysis’ can encompass several methodologies. In general, these methodologies can be organized into two categories: deductive analysis and inductive analysis.
In deductive analysis, the researcher is using a set of already established codes or themes as the framework for analysis. This method requires an already established framework. In inductive analysis, the goal is to allow themes to emerge from the data collected. It is likely that you will be using inductive methods to analyze data.
Some different methods of qualitative analysis include:
- Constant comparative analysis: comparing data within the dataset to highlight similarities and differences. This is an inductive, structured approach that is meant to help conceptualize the relationships within the data (Pickard, 2013).
- Thematic analysis: there are several types of thematic analysis. The goal of each is to identify themes within the data, either through a limited framework or more open ended and broad theme analysis.
- Content analysis: this methodology is most ideally done with a team. Each member of the team analyzes the dataset and creates their own set of codes. The members then come together and compare analyses and coding to see where observations overlapped. Common themes and overlapping ideas are used as the basis for the final analysis (Lawal, 2009).
Coding
Coding is a common tool used in qualitative analysis and can be a part of many of these methodologies. It refers to the process of deconstructing a dataset, identifying emerging ideas, and organizing these ideas thematically to highlight the main ideas in a dataset. This process is meant to be collaborative and iterative. Coding relies on personal interpretation and can therefore carry the biases and perspectives of the analyst. As such, having multiple individuals analyze the data and then share analyses can be a means of minimizing bias.
There are several phases to the coding process: open coding, axial coding, and selective coding. These stages are part of an iterative process used to identify key themes in a dataset. It can be beneficial to return to a previous phase to refine the analysis and look for any key points that might have been missed.
Open Coding
Open coding refers to the preliminary stages of the coding process where the goal is to deconstruct the data. The dataset is reviewed, and codes are applied to anything that the analyst deems significant. It can be helpful to have a set of guiding questions for this process. Consider:
- Has this point been raised by other participants?
- Is there a specific theme that this point might relate to?
- Does this comment highlight a particular perspective on a given topic?
At this point, there is no need to refine the codes or categorize them. Instead, the goal is to make note of all observations that could be significant. Ideally, several individuals work through this process on their own, and then come together to compare results in the following two phases.
Axial Coding
This phase is an analysis and reflection on the open coding that has been conducted. The goal is to look for repetition and connections between codes and consider any context in which certain codes have arisen. At this stage, categories start to form. It can also be useful to look for any variations or contradictions in the data.
Examples
- When asked about their perceptions of OER, some instructors speak positively about OER whereas some show hesitancy and mention concerns about quality and peer review. These contradictions and differing perspectives are noted.
- Many students emphasize the benefit of OER for when they are sick and cannot attend lectures. This repetition is noted, and the different code words used in each response are unified under a single code called “accommodations”.
- When asked why OER are important, students give a range of responses that require many codes. Multiple codes are related to improved learning and are put in a category together.
If multiple individuals have completed their own open coding, this is also the time to compare codes and reflect on the different interpretations of the data. Particular attention should be paid to any similarities in open coding.
Selective Coding
This final phase begins at the point of “saturation,” or when the open and axial coding processes have been exhausted. It is a process of refining the analysis by linking categories to a few core themes from the data. At this point, it can also be beneficial to remove some of the coding and analysis depending on the research goals. For example, if the goal was to understand the benefits of a particular OER, but there is a small set of codes that focus on improvements that could be made, it might be worth removing these codes since they do not contribute to the research question. Instead, this point could be noted as an area for future questions or analysis.
During this process it can be useful to return to the previous phases of coding to refine the analysis. Sometimes, looking at themes might highlight gaps in the current analysis that could be addressed by the data (Pickard 2013).
Technologies for Qualitative Analysis
Qualitative analysis, and coding specifically, are processes that do not require specific tools per se. Coding can be done by printing out transcripts and marking up the text with analysis, or by using the comments feature in Microsoft word. However, there are specific tools that can be used to facilitate the coding process, and they can be especially useful for the axial and selective coding processes. Read the chapter on Tools and Technologies to learn more about these options.
Conclusion
Qualitative analysis is an iterative process that can provide deeper insights into a topic. There are several circumstances in which qualitative data can be valuable to collect. OER user stories, student experiences, and instructor perspectives can all be valuable tools in OER advocacy. There are several methods for qualitative analysis, of which coding is most common. Coding, particularly when done using inductive methods, allows for key themes to arise from the data. Once this process is done, it is important to share this data.
References
Ayton, D. (2023). Thematic analysis. In D. Ayton, T. Tsindos, & D. Berkovic (Eds.), Qualitative research: A practical guide for health and social care researchers and practitioners. Pressbooks. https://oercollective.caul.edu.au/qualitative-research/chapter/__unknown__-22/
Berkovic, D. (2023). Content analysis. In D. Ayton, T. Tsindos, & D. Berkovic (Eds.), Qualitative research: A practical guide for health and social care researchers and practitioners. Pressbooks. https://oercollective.caul.edu.au/qualitative-research/chapter/__unknown__-21/
Lawal, I. (2009). Library and information science research in the 21st century: A guide for practicing librarians and students. Elsevier Science.
Pickard, A. J. (2013). Research methods in information (2nd ed.). Neal-Schuman.
Tsindos, T. (2023). Coding approaches. In D. Ayton, T. Tsindos, & D. Berkovic (Eds.), Qualitative research: A practical guide for health and social care researchers and practitioners. Pressbooks. https://oercollective.caul.edu.au/qualitative-research/chapter/__unknown__-20/