A Guide to Qualitative Research
Chapter 9 – Coding, Memoing, and Descriptions
Coding, Memoing, and descriptions are components of qualitative data analysis. Actually, the analysis of the data begins from the onset and continues throughout the project. Once the researcher leaves the field, the arduous task of making sense of the data, breaking it down, studying its components, and investigating its importance, and interpreting its meanings begins. According to Lofland and Lofland (1984), this arduous task can take 2 to 5 times longer than the amount time taken to collect the data. In comparison, quantitative data is analyzed using software, such as SPSS. The researcher performs statistical tests and procedures, such as t-tests, ANOVAs, etc. on the data and then the researcher makes deductions about the data. Whereas in qualitative analysis, the researcher reads pages of text multiple times, grouping and organizing the data throughout the successive reads. At the conclusion, the researcher interprets the results based on their research questions.
Many strategies for analyzing qualitative data exist. Bailey (2006) covers 10 of these strategies. In chapter 9, Bailey (2006) focuses on the Coding and Memoing strategies because they are essential to all qualitative data analysis strategies.
Bailey (2006) defines coding as the “process of organizing a large amount of data into smaller segments that, when needed, can be retrieved easily.” She distinguishes coding analysis from thematic analysis in that themes do not emerge from the data. She asserts that the themes appear at the interpretation of the researcher and the associated research questions. She describes two types of coding: initial coding and focused coding.
- Initial coding is also known as open coding.
- During initial coding, the researcher reads and codes the data.
- Only, the data that is relevant to the study purpose and research questions are coded.
- An iterative process
- Focus coding is also known as axial coding.
- Typically, it occurs after the initial or open coding.
- Involves grouping coded text into larger segments which encompasses the smaller segments
- An iterative process
Strategies for Improving Coding
- Make connections to “research on the topic, concerns of the researcher’s discipline, or theoretical concepts”
- Begin the process by being well grounded in the discipline
- Read the academic literature in the area being studied
- Discuss the research finding with other people, who are knowledgeable in the area of study or are willing to listen, exercising care not to violate the confidentiality of the study participants
- Writing notes to oneself regarding the coding, including reflections on the data
- Notes could include attempts to operationalize definitions, questions, posing hypotheses, and answers revealed in the data.
- Facilitates coding at a higher conceptual level
- Data from this process can be used for subsequent analysis
- An iterative process
C. Qualitative Analysis Software
Numerous software packages exist that can assist with qualitative analysis. These software packages are tools and do not replace the skill necessary to inform the study or elicit information from the data. A researcher may choose to use software for many reasons.
Reasons for using software
- Taking notes in the field
- Transcribing or writing up field notes
- Search and retrieval
- Linking data
- Content analysis (counting frequencies, sequencing and locating words or phrases)
- Data visualization
- Drawing conclusions
- Building theory
- Creating diagrams
- Preparing interim and final reports (p. 134)
Atlas.ti, HyperRESEARCH, MAXqda2, NVivo, N6, CDC EZ-text, Qualrus, QDA Miner, and Ethnograph
Manual vs. Software
Some researchers prefer traditional methods for analyzing qualitative data. In this case, these researchers may:
- Print, cut, and past hard copies of the data and code it with colored pencils or highlighters
- Use word processing software, such as MS Word, or spreadsheet software, such as MS Excel to code data
- Use adhesive notes, such as 3M Post-Its, in different colors to code
|Learning Curve||Minimal||Depends on the software, can be steep|
|Cost||Minimal||Can be costly|
|Flexibility||Limited||Medium to High, depending on the features available|
|Functionality||Limited, requires using other mediums or software||Medium to High, depending on the features available|
Descriptions facilitate contextualizing the data. It involves recording detailed descriptions of the setting, interactions, and observations over the duration of the study. They are likened to answering a “reporter’s questions.” As such, the descriptions should answer the 5 W’s (what, why, when, where, and who) and how. Suffice it to say, descriptions need not include every detail, such as every object in the room, but characteristics or qualities that visualize the concept being conveyed. Descriptions can be thick or thin as asserted by Geertz (1973). Additionally, descriptions should relate to the research questions.
Thick descriptions provide concrete detail about a phenomenon or concept. They are a necessity for research in the field. They provide strong visual images for the reader to conceptualize the context or concept. An example of thick description is:
Ana is between 30 and 40. Her brown hair lies limp and greasy against her head. Her eyes tend not to focus on any one thing. Her skin is riddled with pockmarks suggesting years of drug usage. She scratches her arms or head constantly. Although the weather is cold, she is wearing a tube-top and shorts. She is shoe-less and her feet are covered with soot and grime.
On the other hand, thin descriptions provide less detail. For example,
Ana has brown hair and is in her thirties.
The decision to use descriptions should depend on the research questions. Some details may appear “sexy” or exciting but may not inform the study. In such case, those details should be omitted. The purpose of using thick or thin description to facilitate the visualization of the contextual complexity of the subject being studied.
Descriptions help the readers “see the participants and the setting.” Thick descriptions are an important element of the final report. More importantly, it allows the readers to understand the importance of the concept within the context.
- How would you characterize coding, art or science, and why?
- What strategies would you use to improve your coding practices?
- How would you use research to inform your coding strategy?
- If you were learning how to code qualitative data, how would you begin?
- How would you use Memoing to inform your qualitative data analysis?
|Axial Coding||See focused coding|
|Coding||Organizing data into smaller units that are retrieved easily, when needed|
|Descriptions||Recording detailed descriptions of the setting, interactions, and observations over the duration of the study. Answer 5 W’s and How.|
|Focused Coding||Process of grouping coded text into larger segments which encompasses smaller segments|
|Initial Coding||Process of breaking up pages of text into smaller segments that can be grouped and used in the later stages of analysis|
|Memoing||Writing notes to oneself regarding the coding, including reflections on the data|
|Open Coding||See initial coding|
|Thick Descriptions||Provide concrete details about a phenomenon or concept.|
|Thin Descriptions||Provide detail about a phenomenon or concept with less detail|