This is an excellent approach to qualitative data analysis! Coding data at multiple levels of abstraction is a cornerstone of rigorous qualitative research, moving you from specific observations to broader themes and theoretical insights.

Let's break down how to start with descriptive codes close to the data and then build up.

Coding Data at Multiple Levels of Abstraction

The process of multi-level coding involves moving from emic (insider, participant-driven) and descriptive codes to more etic (researcher-driven) and interpretive/conceptual codes. It's often an iterative process, meaning you'll go back and forth between levels.

Here's a structured approach, starting exactly where you requested: with descriptive codes.

Level 1: Descriptive / In Vivo Coding (Close to Data)

Goal: To capture the essence of what is being said or observed in the data without imposing your own interpretations or theoretical frameworks. These codes are often short phrases, keywords, or actual words used by participants. They answer the question: "What is happening here?" or "What are they talking about?"

Characteristics:

• Highly Specific: Tied directly to a small segment of data.


• Phenomenological: Reflects the participants' lived experience or direct observations.


• Minimal Interpretation: You're simply labeling what you see/hear.


• Often "In Vivo": Using the exact words or phrases of the participants.


• Numerous: You'll generate many codes at this level.

• Read Through the Data (First Pass): Read your interview transcripts, field notes, documents, etc., actively and attentively.


• Segment the Data: Break your data into meaningful chunks – sentences, phrases, paragraphs. What constitutes a "chunk" depends on your data and research question, but at this stage, err on the side of smaller segments.


• Ask "What is this about?": For each segment, assign a short, concrete label.


• Use In Vivo Codes: When a participant uses a particularly evocative or clear phrase, use their exact words as the code.


• Use Descriptive Codes: If an in vivo code isn't suitable, create a short, descriptive label for the segment, focusing on actions, events, feelings, or topics.


• Be Open and Generative: Don't worry about redundancy or overlap yet. Code everything that seems relevant or interesting. The goal is to open up the data, not to close it down.


• Avoid Premature Categorization: Resist the urge to group things too early. Just label what's there.

Example Data Snippet:

Let's imagine you're interviewing an employee about their experience with a new project management system.

"Honestly, it's been a nightmare. I spent three hours just trying to log in last Tuesday. The training session was super brief, only like 30 minutes, and it didn't cover half of what we actually need to do. My supervisor said, 'just figure it out,' but it's taking so much time away from my actual tasks. I feel really overwhelmed, and I'm worried about missing my deadlines. Plus, the interface is so clunky, it's hard to find anything."

Level 1: Descriptive / In Vivo Codes:

Here's how you might code that snippet at Level 1, along with the rationale:

Moving Forward (Brief Glimpse of Subsequent Levels):

Once you have a robust set of Level 1 codes, you would then move to:

Level 2: Pattern / Category / Thematic Coding (Grouping and Identifying Patterns)

• Goal: To group similar descriptive codes into broader, more abstract categories or themes. You're looking for recurring patterns, shared meanings, and initial analytical distinctions. You start asking: "What are the common ideas or experiences across these descriptive codes?"


• Example from above:


• Brief training, Training gaps could group into Insufficient Training.


• "a nightmare", Time spent logging in, Time away from tasks, "feel overwhelmed", Worried about deadlines, Clunky interface, Hard to find things (system) could point to System Usability Issues AND Employee Frustration/Stress.

• Goal: To move beyond simply describing and grouping to interpreting and explaining. You're developing abstract concepts that help explain the relationships between your categories and contribute to a broader understanding or theory. You ask: "What does this mean? Why are these patterns occurring? How do these categories relate to each other?"


• Example from above:


• Insufficient Training and System Usability Issues both contribute to Decreased Productivity and Increased Employee Strain. This might lead to concepts like "Technology Adoption Barriers" or "Organizational Readiness for Change." These concepts could then be linked to existing theories (e.g., Technology Acceptance Model, Stress-Coping Theory).

Practical Tips for Level 1 Coding:

• Use CAQDAS (Computer Assisted Qualitative Data Analysis Software): Tools like NVivo, ATLAS.ti, Dedoose, MAXQDA, or even basic functions in Word/Excel can help you highlight, segment, and tag data efficiently.


• NVivo/ATLAS.ti/Dedoose/MAXQDA: Best for managing codes, memos, and visual relationships. You can highlight a segment and immediately assign a code.


• Spreadsheets (Excel/Google Sheets): You can create columns for "Data Segment," "Level 1 Code," "Memos," etc.


• Word Processor: Use comments or the highlight function to mark segments, then keep a separate document for your codes.


• Memo Writing: As you code, make notes (memos) to yourself. Why did you use that code? What does it make you think of? This helps record your early interpretations and connections, which will be vital for moving to higher levels of abstraction.


• Codebook (Emergent): Keep a running list of your codes and a brief definition for each. This will evolve, but it's crucial for consistency.


• Iterative Process: Don't expect to get it "right" on the first pass. You'll read, code, reflect, refine codes, re-code. This is normal and expected.


• Start Small: If you have a lot of data, don't try to code everything at once. Code a few transcripts, then review your codes, and only then proceed to more data.