Uncovering themes in qualitative data can be daunting and difficult. Summarizing a quantitative study is relatively clear: you scored 25% better than the competition, let’s say. But how do you summarize a collection of qualitative observations?
In the early stages of a project, exploratory research is often carried out. This research often produces a lot of qualitative data, which can include:
Qualitative attitudinal data,如人们的思想，信仰和自我报告的需求，从用户访谈中获得，焦点小组甚至日记研究
Thematic analysis, which anyone can do, renders important aspects of qualitative data visible and makes uncovering themes easier.
Definition: Thematic analysisis a systematic method of breaking down and organizing rich data from qualitative research by tagging individual observations and quotations with appropriate codes, to facilitate the discovery of significant themes.
As the name implies, a thematic analysis involves findingthemes.
- is a description of a belief, practice, need, or another phenomenon that is discovered from the data
- emerges when related findings appear multiple times across participants or data sources
Challenges with Analyzing Qualitative Data
Many researchers feel overwhelmed by qualitative data from exploratory research conducted in the early stages of a project. The table below highlights some common challenges and resulting issues.
Large quantity of data:Qualitative research results in long transcripts and extensive field notes that can be time-consuming to read; you may have a hard time seeing patterns and remembering what’s important.
Superficial analysis:Analysis is often done very superficially, just skimming topics, focusing on only memorable events and quotes, and missing large sections of notes.
分析成为许多细节的描述：The analysis simply becomes a regurgitation of what participants’ may have said or done, without any analytical thinking applied to it.
与数据相矛盾：Sometimes the data from different participants or even from the same participant contains contradictions that researchers have to make sense of.
Findings are not definitive:分析并非明确，因为参与者反馈是冲突，或者更糟糕的是，不适合研究员的信仰的观点被忽略。
|No goals set for the analysis:最初的目的数据收集丢失because researchers can easily become too absorbed in the detail.||Wasted time and misdirected analysis:The analysis lacks focus and the research reports on the wrong thing.|
Without some form of systematic process, the problems outlined easily arise when analyzing qualitative data. Thematic analysis keeps researchers organized and focused and gives them a general process to follow when analyzing qualitative data.
Tools and Methods for Conducting Thematic Analysis
A thematic analysis can be done in many different ways. The best tool or method for this process is determined based on the:
- context and constraints of the data-analysis phase
- Using software
- Using affinity diagramming techniques
To analyze large amounts of qualitative data, qualitative researchers often use software, known as CAQDAS (Computer-Aided Qualitative-Data–Analysis software) — pronounced “cak∙das”.Researchers upload transcripts and field notes into a software program and then analyze the text systematically through formal coding. The software helps with the discovery of themes by offering various visualization tools, such as word trees or word clouds, that allow the coded data to be manipulated in many different ways.
- Time-consuming, as it results in many codes which need to be condensed into a small, manageable list
- Hard to analyze with others synchronously
- Requires some learning of the software
Writing thought processes and ideas you have about a text is common among researchers practicing grounded-theory methodology. Journaling as a form of thematic analysis is based on this methodology and involves manual annotation and highlighting of the data, followed by writing down the researchers’ ideas and thought processes. The notes are known as memos(not to be confused with the office memo delivering news to employees).
- Hard to do collaboratively
The data is highlighted, cut out physically or digitally, and reassembled into meaningful groups until themes emerge on a physical or digital board. (See avideo demonstrating affinity-diagramming.)
- Quick arriving at themes
- Visual, and supports an iterative-analysis process
- Hard to do when data is very varied, or there is a lot of data
Codes and Coding
Definition: A codeis a word or phrase that acts as a label for a segment of text.
代码描述了文本是什么，是一个更复杂的信息的速记。（一个良好的类比是代码描述了关键字的数据描述了一篇文章或类似物描述了推文。）通常，定性研究人员不仅具有每个代码的名称，还将描述代码均值的描述和examples of text that fit or don’t fit the code. These descriptions and examples are especially useful if more than one person is responsible for coding the data or if coding is done over a longer period of time.
Definition: Codingrefers to the process of labeling segments of text with the appropriate codes.
Once codes are assigned, it’s easy to identify and compare segments of text that are about the same thing. The codes allow us to sort information easily and to analyze data to uncover similarities, differences, and relationships among segments. We can then arrive at an understanding of the essential themes.
Codes can be:
- Descriptive:They describe what the data is about
- Interpretive:They are an analytical reading of the data, adding the researcher’s interpretive lens to it.
“I was petrified about facilitating a meeting and my company offered a day-and-a-half– long course. So, I went in there and the instructor did something that I felt was horrible at the time, but I've since really come to appreciate it. The first thing that we did was we filled out a sheet of paper with our name and wrote down our worst fear of moderating or facilitating and we turned it in and then he said, okay, tomorrow you're going to act out this situation (…) the next day we came back and I would leave the room while the rest of the team read, they read my worst fear, figured out how they'd act it out, and then I'd walk in and facilitate for 10 minutes with that. And that really helped me realize that there isn't anything to be afraid of, that our fears are really in our head most of the time and facing that made me realize I can handle these situations.”
Here are possible descriptive and interpretive codes for the text above:
Descriptive code:how skills are acquired
Rationale behind the code label: Participants were asked to describe how they came to possess certain skills.
Rationale behind the code label: The participant describes how this experience changed her beliefs about facilitation and how she reflected on her fear.
Steps to Conduct a Thematic Analysis
Regardless of which tool you use (software, journaling, or affinity diagraming), the act of conducting a thematic analysis can be broken down into 6 steps.
Step 1: Gather All Your Data
Start with the raw data, such as interview or focus-group transcripts, field notes, or日记研究entries. I recommendedtranscribing audio recordings from interviewsand using the transcriptions for analysis instead of依靠斑驳的内存.
第二步:读你所有的数据from Beginning to End
Familiarize yourself with the data before you begin the analysis, even if you were the one to perform the research. Read all your transcripts, field notes, and other data sources before analyzing them. At this step, you can involve your team in the project.Involving your teaminstills knowledge of users andempathyfor them and theirneeds.
Run aworkshop(or a series of workshops if your team is very large or you have a lot of data). Follow these steps:
- Before your team members engage with the data, write your research questions on a whiteboard or piece of flipchart paper in order to make the questions easy to refer to while working.
- Give each member a transcript or one field- or diary-study entry. Tell people to highlight anything they think is important.
- Once team members have completed reading their entries, they can pass their transcript or entry to someone else and receive a new one from another team member. This step is repeated until all team members have engaged with all the data.
- Discuss as a group what you noticed or found surprising.
While it’s best if your team observes all your research sessions, that may not be possible if you have a lot of sessions or a big team. When individual team members observe only a handful of sessions, they sometimes walk away with an incomplete understanding of the findings. The workshop can solve that problem, since everyone will read all the session transcripts.
Step 3: Code the Text Based on What It’s About
In the coding step, highlighted sections need to be categorized so that the highlighted sections can be easily compared.
At this stage, remind yourself of your research objectives. Print your research questions out. Stick them up on a wall or on a whiteboard in the room where you’re conducting the analysis.
If you have adequate time, you can involve your team in this initial coding step. If time is limited and there is a lot of data to work through, then do this step by yourself and invite your team later to review your codes and help flesh out the themes.
As you are coding, review each segment of text and ask yourself“这是关于什么的？“为片段提供描述数据（描述性代码）的名称。您还可以在此阶段添加文本的解释码。但是，这些通常会变得更容易分配。
Thecode can be created before or after you have grouped the data. The next two sections of this step describe how and when you may add the codes.
Traditional Method: Create Codes Before Grouping
Once all the text has been coded, you can group all the data that has the same code.
如果你using CAQDAS for this process, then the software automatically logs the codes you assign while coding, so you can use them again. It then provides a way for you to view all text coded with the same code.
Quick Method: Group Segments of Text, Then Assign a Code
Rather than coming up with a code when you highlight text, you cut up (physically or digitally) and cluster all the similar highlighted segments (similarly to how different stickies may be grouped in an亲和地图)。然后分组被给定一个代码。如果你doing the clustering digitally, you might pull coded sections into a new document or a visual collaboration platform.
In the pictures below, the grouping was done manually. Transcripts were cut up, fixed to stickies, and moved around the board until they fell into natural topic groups. The researcher then assigned a pink sticky with a descriptive code to the grouping.
At the end of this step, you should have data grouped by topics and codes for each topic.
After grouping the highlighted clippings from my interviews by topic, I ended up with 3 broad descriptive codes and corresponding groupings:
- Cooking experiences：与烹饪相关的令人难忘的积极和消极经验
- Pain points: anything that stops someone from cooking or makes cooking difficult (including navigating dietary restrictions, limited budgets, etc.)
- Things that help:what helps (or is believed to possibly help) someone overcome specific challenges or pain points
Step 4: Create New Codes that Encapsulate Potential Themes
Look across all the codes and explore any causal relationships, similarities, differences, or contradictions to see if you can uncover underlying themes. While doing so, some of the codes will be set aside (either archived or deleted) and new interpretive codes will be created. If you’re using a physical-mapping approach like that discussed in step 3, then some of these initial groupings may collapse or expand as you look for themes.
- What’s going on in each group?
- How are these codes related?
Returning to our cooking topic, when analyzing the text within each grouping and looking for relationships between the data, I noticed that two participants said that they liked ingredients that can be prepared in different ways and go well with other different ingredients. A third participant talked about wishing she could have a set of ingredients that can be used for many different meals throughout the week, rather than having to buy separate ingredients for each meal plan. Thus, a new theme about the flexibility of ingredients emerged. For this theme, I came up with the codeone ingredient fits all,我然后写了一个详细的描述。
Step 5: Take a Break for a Day, then Return to the Data
It almost always is a good idea to take a break and come back and look at the data with a fresh pair of eyes. Doing so sometimes helps you to see significant patterns in the data clearly and derive breakthrough insights.
Step 6: Evaluate Your Themes for Good Fit
In this step, it can be useful to have others involved to help you review your codes and emerging themes. Not only are new insights drawn out, but your conclusions can be challenged and critiqued by fresh eyes and brains. This practice reduces the potential for your interpretation to be colored by personal biases.
Put your themes under scrutiny. Ask yourself these questions:
- Is the theme saturated with lots of instances?
If the answer to these questions isno, it might mean that you need to return to the analysis board. Assuming you collected sound data, there is almost always something to be learned, so spending more time with your team repeating steps 4–6 will be worthwhile.
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