菜单
Use when conducting classic grounded theory research following Glaser's methodology. Covers the complete GT process from entering the field through theory write-up, including open/selective/theoretical coding, constant comparison, memoing, theoretical sampling, and saturation.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when writing academic prose for qualitative research — findings, methods, discussion sections with appropriate voice and conventions.
Use when conducting participatory action research (PAR) involving collaborative inquiry, cycles of action and reflection.
Use when formatting academic work in APA 7th edition style — citations, references, headings, tables, and manuscript structure.
Use when designing or conducting case study research following Yin's multiple case study methodology or Stake's approach.
Use when developing and densifying categories with properties, dimensions, conditions, and consequences.
Use when formatting academic work in Chicago/Turabian style — notes-bibliography or author-date systems.
| name | glaserian-grounded-theory |
| description | Use when conducting classic grounded theory research following Glaser's methodology. Covers the complete GT process from entering the field through theory write-up, including open/selective/theoretical coding, constant comparison, memoing, theoretical sampling, and saturation. |
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.
When in doubt, privilege: emergence, comparison, memoing, and theoretical sampling directed by the emerging theory.
These stages are iterative, not strictly linear. You cycle among them throughout a study.
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.
Data collection (initial)
Begin with purposive (not theoretically sampled) first interviews/observations to get rich incidents. Treat everything as potential data.
Open coding
Break data into incidents and label them with substantive codes. Compare incident to incident. Write memos capturing ideas.
Constant comparison
Continuously compare incidents to incidents, incidents to concepts, and concepts to concepts. Refine code definitions, properties, and dimensions.
Memoing
Never treat memoing as optional. Memos are the record of conceptual leaps—hypotheses about relationships, conditions, consequences, and processes.
Selective coding
Once a core category emerges and earns centrality, delimit the study: code only for what relates to the core and its story.
Theoretical coding
Relate categories using theoretical codes (coding families) to build an integrated theoretical outline.
Theoretical sampling
Collect new data to develop emerging categories—not for representativeness alone. Sample for variation, depth, and theoretical completeness.
Theoretical saturation
Stop sampling for a category when new data no longer yield new properties or relationships relevant to the emerging theory.
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.
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.
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.
Comparison is the engine of analysis. If you are not comparing, you are likely drifting into description or affirmation of prior beliefs.
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.
Glaser’s theory criteria (see substantive-theory skill):
theoretical-coding skill).Memos are analytic narratives: they explain what you think is going on conceptually, propose relationships, note puzzles, and log methodological decisions.
Rules of thumb:
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.
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.
A classic GT write-up foregrounds:
Avoid “theme lists” without integration. Avoid over-quotes with thin conceptual lift.
| 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 |
If your project must integrate another tradition, document that choice in your audit trail and clarify what you borrowed and why.
open-coding, selective-coding, theoretical-codingconstant-comparison, memo-writingtheoretical-sampling, theoretical-saturation, theoretical-sensitivitysubstantive-theory, formal-theoryAI 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.