glaserian-grounded-theory

# Glaserian (Classic) Grounded Theory Classic grounded theory (CGT), as developed and refined by Barney Glaser, is an inductive methodology for generating **conceptual theory** that explains patterns of social or social-psychological behavior in a substantive area. The product is an **integrated set of conceptually related hypotheses** grounded in systematically analyzed data—not a description, not a list of themes, and not a verification of a prior model. Use this skill when you need end-to-end guidance for CGT: study design boundaries, analytic procedures, quality criteria, and write-up expectations. --- ## Historical context and lineage - **Glaser & Strauss (1967)** introduced grounded theory as a reaction to dominant hypothetico-deductive social science. They emphasized discovery through comparative analysis of qualitative data and the generation of theory “grounded” in data. - **Divergence**: Over time, Glaser and Strauss (and Strauss with Corbin) moved in different directions. Glaser insisted on **emergence**, **minimal preconception**, and **researcher autonomy in conceptualization**. Other approaches (often labeled “Straussian” or “constructivist”) may permit more a priori framing, axial coding templates, or paradigm models. **This skill follows Glaser’s classic line.** When in doubt, privilege: emergence, comparison, memoing, and theoretical sampling directed by the emerging theory. --- ## The complete GT process (ten integrated stages) These stages are **iterative**, not strictly linear. You cycle among them throughout a study. 1. **Preparation and entry** Clarify a broad **substantive area of interest** (not a forced research question). Secure ethics permissions. Set up data management and audit trails. 2. **Data collection (initial)** Begin with **purposive** (not theoretically sampled) first interviews/observations to get rich incidents. Treat everything as potential data. 3. **Open coding** Break data into **incidents** and label them with **substantive codes**. Compare incident to incident. Write **memos** capturing ideas. 4. **Constant comparison** Continuously compare **incidents to incidents**, **incidents to concepts**, and **concepts to concepts**. Refine code definitions, properties, and dimensions. 5. **Memoing** **Never** treat memoing as optional. Memos are the record of **conceptual leaps**—hypotheses about relationships, conditions, consequences, and processes. 6. **Selective coding** Once a **core category** emerges and earns centrality, **delimit** the study: code only for what relates to the core and its story. 7. **Theoretical coding** Relate categories using **theoretical codes** (coding families) to build an **integrated theoretical outline**. 8. **Theoretical sampling** Collect new data **to develop emerging categories**—not for representativeness alone. Sample for **variation, depth, and theoretical completeness**. 9. **Theoretical saturation** Stop sampling for a category when **new data no longer yield new properties or relationships** relevant to the emerging theory. 10. **Theory write-up** Present a **substantive theory**: core category, related categories, and **theoretical statements** (hypotheses) about relationships, often centered on a **basic social process** or core pattern. --- ## Key principles (non-negotiable in classic GT) ### Emergence Categories and hypotheses must **earn their way** from comparative analysis. If a code “doesn’t work,” discard or modify it. **Forcing** pre-existing frameworks onto data violates the logic of the method. ### All is data Interviews, observations, documents, artifacts, field notes, **researcher reflections**, and later **literature** (introduced at the right time) can be treated as data. Nothing is a priori “off-limits,” though ethics and scope still bound what you *should* collect. ### Constant comparison Comparison is the **engine** of analysis. If you are not comparing, you are likely drifting into description or affirmation of prior beliefs. ### No preconception (disciplined openness) Enter the substantive area without a **pre-rehearsed conceptual framework**. **Theoretical sensitivity** is cultivated through analytic work and broad reading **outside** the substantive area—not by importing a ready-made model of the phenomenon. ### Fit, work, relevance, modifiability Glaser’s theory criteria (see `substantive-theory` skill): - **Fit**: categories fit the data used to generate them. - **Work**: theory explains variation and the main pattern. - **Relevance**: addresses what participants actually resolve (their “main concern”). - **Modifiability**: open to refinement with new data—not brittle dogma. --- ## Coding procedures in depth ### Open coding - **Fracture** narratives into discrete **incidents** (meaningful chunks). - Ask: “What is this data a study of?” “What category does this incident indicate?” - Use **gerunds** when helpful to keep **process** visible (e.g., *managing*, *balancing*, *negotiating*). - Generate **in vivo** codes (participants’ language) when they crystallize meaning. - Write **memos** immediately when ideas spark. ### Selective coding - Identify the **core category**: recurrent, central, relates to most other categories, explains variation. - **Delimit**: stop coding everything; focus on the **story** that integrates the core with related categories. - Aim for **theoretical completeness** around the core, not encyclopedic coverage. ### Theoretical coding - Use **theoretical codes** (e.g., causal, conditional, strategy) as **integrative** devices—**not** as a front-loaded template. - Apply **coding families** thoughtfully (see `theoretical-coding` skill). - Build a **theoretical outline** that states relationships among categories. --- ## Memoing as the core intellectual activity Memos are **analytic narratives**: they explain what you think is going on conceptually, propose relationships, note puzzles, and log methodological decisions. Rules of thumb: - **Stop coding when a big idea hits**—memo first, then return. - **Sort memos** regularly into an emerging **outline** of the theory. - **Date memos** and link them to **data locations** (interview line, page, timestamp). - Allow memos to **contradict** earlier memos; treat contradictions as opportunities for comparison. --- ## Theoretical sampling (brief) Sampling is driven by **analysis**, not only by access or convenience. You ask: “What data do I next need to **develop** this category’s properties, dimensions, and relationships?” See `theoretical-sampling` skill for directives, probes, and pitfalls. --- ## Theoretical saturation (brief) Saturation is **about categories**, not raw repetition of stories. A category is saturated when continued sampling **does not** refine its **properties**, **dimensions**, or **relationships** in ways that matter to the emerging theory. See `theoretical-saturation` skill for checklists and common confusions. --- ## Writing the theory A classic GT write-up foregrounds: - The **main concern** and how participants continually **resolve** it. - The **core category** and its **multivariate** relations (conditions, strategies, consequences). - A **processual** account if a **basic social process** (BSP) is core. - **Conceptual** language with **illustrative** (not exhaustive) data excerpts. - **Theoretical statements** that read as hypotheses grounded in comparative evidence. Avoid “theme lists” without integration. Avoid over-quotes with thin conceptual lift. --- ## Common mistakes and how to avoid them | Mistake | Why it fails CGT | Corrective practice | |--------|-------------------|----------------------| | Front-loading a literature model | Blocks emergence; encourages forcing | Delay substantive-area literature; read widely elsewhere | | Thematic summary only | Describes; doesn’t theorize | Integrate via core category + theoretical codes | | Confusing saturation with *n* | Sample size is irrelevant as a rule | Track category properties/dimensions | | Ignoring negative cases | Misses boundaries and conditions | Purposively compare deviant instances | | Coding without memoing | Loses the trail of ideas | Memo in the same session | | “Core category” by fiat | No earned centrality | Apply core criteria; test against data | | Over-quotes | Obscures concepts | Quote to **show** fit, not to **fill** space | --- ## Comparison with other GT traditions (practical distinctions) - **Classic Glaserian**: emergence-first; theoretical codes used **after** substantive theory forms; strong stance against forced paradigms. - **Strauss & Corbin style**: often teaches structured coding steps (e.g., axial) and conditional matrix tools earlier and more prescriptively—useful for some teams, but **not identical** to Glaser’s classic GT. - **Constructivist GT** (Charmaz): shares inductive spirit; emphasizes interpretive construction and researcher-participant meaning-making—aligns ethically/politically with many projects; **epistemological language differs** from Glaser’s objectivist-realist emphasis. If your project **must** integrate another tradition, document that choice in your audit trail and clarify what you borrowed and why. --- ## Key references - Glaser, B. G., & Strauss, A. L. (1967). *The discovery of grounded theory: Strategies for qualitative research*. Aldine. - Glaser, B. G. (1978). *Theoretical sensitivity: Advances in the methodology of grounded theory*. Sociology Press. - Glaser, B. G. (1992). *Basics of grounded theory analysis: Emergence vs forcing*. Sociology Press. - Glaser, B. G. (1998). *Doing grounded theory: Issues and discussions*. Sociology Press. - Glaser, B. G. (2005). *The grounded theory perspective III: Theoretical coding*. Sociology Press. --- ## Companion skills in this library - `open-coding`, `selective-coding`, `theoretical-coding` - `constant-comparison`, `memo-writing` - `theoretical-sampling`, `theoretical-saturation`, `theoretical-sensitivity` - `substantive-theory`, `formal-theory` --- ## Quick-start checklist (classic GT) - [ ] Broad area of interest defined; ethics in place - [ ] First data collected and fractured into incidents - [ ] Open coding + incident comparison running in parallel with **memoing** - [ ] Codesheet/evolving codebook + **audit trail** of renames/splits/merges - [ ] Core category candidate tested against **fit** and **explanatory reach** - [ ] Selective coding + theoretical coding toward an **integrated outline** - [ ] Theoretical sampling to saturate **key** categories - [ ] Draft theory written in **conceptual** voice with clear hypothetical statements --- ## Notes on AI-assisted analysis AI tools can assist **fracturing**, **draft coding**, **memo prompts**, and **outline experiments**—but the researcher must **own** comparisons, **verify** fit against source data, and **maintain** an audit trail. Treat AI outputs as **provisional hooks for comparison**, not as findings.