Qualitative Coding for beginners - coding Surface vs Deep meaning

Qualitative coding doesn’t have to stop at what participants literally say; it can also capture the “surface meaning” and the “implicit meaning” behind responses. Still, surface-meaning coding should remain the backbone of analysis because qualitative research’s core value is grounded in participants’ expressed opinions, beliefs, and attitudes. Coding only what researchers assume participants meant would be unfair to participants and risks distorting the data.

The distinction becomes clear through a bullying study example. Interviewing people who bully, researchers ask why they did it. One participant says, “I didn’t know this would hurt him,” another claims, “I wasn’t myself that day,” and a third insists, “It’s not my fault—he made me angry.” If researchers code strictly for reasons based on what was said, these responses naturally map to categories like lack of awareness of consequences, situational loss of self-control, or anger-driven justification.

But the same answers can also be read as attempts to avoid accountability. The “I didn’t know” response can be treated as an excuse that contradicts what the participant likely understood. “I wasn’t myself” can be interpreted as shifting responsibility away from the participant. “It’s not my fault” and victim-trigger explanations can be coded as blaming the victim or externalizing blame. In this framing, the key analytic category isn’t “reasons for bullying” but “barriers to helping,” since avoiding responsibility would make intervention harder.

The practical takeaway is that implicit-meaning coding is a judgment call, not a mechanical transcription of interview text. Qualitative analysis is flexible enough to support both approaches—researchers can start with codes that reflect what participants say, then later refine toward broader interpretive categories, or they can use a combined coding scheme. For instance, “avoiding responsibility” could be treated as a single overarching code or broken into subtypes such as blaming the victim.

That flexibility comes with a warning: researchers can’t endlessly interpret data to fit their assumptions. Imposing interpretations without control is risky, especially when coding for what researchers think participants “really meant.” The safer path is to remain conscious of assumptions, document how coding decisions were made, and clearly report that some codes were derived from participants’ expressed views while others relied on analytic interpretation of what those views represent.

In the end, the method isn’t about choosing between literal and interpretive coding forever—it’s about using surface meaning as the predominant foundation while applying implicit meaning selectively, transparently, and with disciplined awareness of researcher bias. If researchers do use interpretation, they should make that choice explicit when presenting findings, so readers understand where participants’ words end and analytic inference begins.