Grounded Theory

Building theory from the data

Grounded theory aims to generate theory inductively from data through systematic analysis rather than testing a predetermined hypothesis. Open, axial, and selective coding are combined with constant comparison, memoing, and theoretical sampling. Analysis proceeds in iterative cycles until theoretical saturation is reached, allowing concepts and their relationships to emerge directly from the data.

Concept and Core Principles

Grounded theory is a qualitative research design introduced by Barney Glaser and Anselm Strauss in 1967. Its core principle is that theory is derived inductively from the data itself, not imposed from a pre-existing framework. The approach is especially suited for understanding social processes and human experiences. Strauss and Corbin (1990) later proposed more structured coding procedures, while Glaser (1992) advocated allowing concepts to emerge naturally; this divergence gave rise to two distinct schools of thought over subsequent decades.

How It Works: Coding, Sampling, and Saturation

The process begins by conducting data collection and analysis simultaneously. In open coding, concepts are extracted from raw data; in axial coding, causal and contextual relationships among concepts are established; in selective coding, a core category is identified to construct an integrated theoretical pattern. Constant comparison requires each new data unit to be compared with all previous ones. Theoretical sampling involves purposefully selecting participants and settings to develop emerging categories further. Analysis concludes when theoretical saturation is reached — the point at which new data no longer contributes to existing categories.

A Concrete Example

Consider a researcher studying how new doctoral students develop academic identities. Open coding of interview and observation data might yield concepts such as "isolation," "supervisor expectations," and "conforming to academic norms." Axial coding links how the supervisor relationship either constrains or supports identity development. Theoretical sampling brings in students from different disciplines; selective coding identifies "identity construction through negotiation" as the core category, resulting in a grounded theory of academic identity formation.

Common Pitfalls and Best Practice

A common mistake is conducting an extensive literature review at the outset, fixing a conceptual framework before data collection — this forecloses meaning from the data prematurely. Another error is ending analysis before theoretical saturation is genuinely achieved. Neglecting memo writing causes loss of analytical reasoning. In best practice the researcher remains reflexive throughout, brackets prior assumptions, keeps data collection and analysis cyclical, and ultimately presents a theoretical model with genuine explanatory power for the phenomenon as a whole.

Key terms

Theoretical Sampling: Purposeful selection of participants to develop emerging theoretical categories.
Constant Comparison: Systematically comparing each new data unit with all previously collected data.
Theoretical Saturation: The point at which new data no longer adds to existing categories.
Open Coding: Initial coding phase where concepts are freely extracted from raw data.
Memoing: Researcher's written notes capturing theoretical ideas as analysis unfolds.