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Basics Qualitative Research Grounded Theory Corbin Strauss
In a sentence
A practical guide that demystifies qualitative data analysis by providing a systematic set of techniques, grounded in Pragmatism and Interactionism, for transforming raw data into credible concepts, rich descriptions, and integrated theories.
Overwhelmed by a mountain of interviews and field notes? Basics of Qualitative Research, Third Edition, transforms that anxiety into analytic confidence. Moving beyond abstract philosophy, Juliet Corbin, building on her decades of work with grounded theory co-founder Anselm Strauss, provides a concrete set of techniques for making sense of qualitative data. This book is a masterclass in the art and science of analysis, guiding you through a systematic process of coding, memoing, diagramming, and theoretical sampling. Through an intensely practical, step-by-step demonstration analyzing the experiences of Vietnam veterans, you'll learn not just what to do, but how to think analytically to move from raw data to robust concepts and, ultimately, to a fully integrated theory. Whether your goal is rich description, concept analysis, or theory building, this book is your indispensable guide to producing high-quality, credible, and insightful qualitative research.
The four lenses
- Science
- Statistics
- Systems
- Strategy
The model
This model, inferred from the book's methodology, outlines the process for conducting qualitative research to produce credible empirical knowledge. It illustrates how researcher characteristics and the application of specific analytic procedures lead to the development of conceptual understandings (from concepts to integrated theory), which ultimately enhance professional knowledge and practice. The entire process is iterative and guided by the principle of theoretical sampling.
Researcher Aptitude and Orientationcontextual condition
The constellation of personal characteristics (curiosity, creativity, tolerance for ambiguity), professional training, and philosophical worldview (Pragmatism, Interactionism) that the researcher brings to the inquiry process.
Quality of Raw Datacontextual condition
The richness, depth, and relevance of the information captured in primary data sources such as interviews, field notes, and documents.
Application of Analytic Proceduresdesign lever
The systematic and flexible use of specific techniques (coding, comparison, questioning, memoing, diagramming) to interact with data in order to move from description to conceptualization and theory.
Theoretical Samplingdesign lever
The iterative process of data collection wherein the analyst decides what data to collect next and where to find them based on emerging concepts and theoretical questions.
Theoretical Sensitivitypsychological state
A state of heightened awareness and insight, developed through immersion in the data, that allows the researcher to perceive subtle nuances, meanings, and patterns.
Conceptualizationbehavioral pattern
The core analytic behavior of abstracting from raw data to generate concepts, develop their properties and dimensions, and group them into higher-order categories.
Analysis of Context and Processbehavioral pattern
The analytic activity of specifying the structural conditions (context) that shape a phenomenon and tracing the flow of action, interaction, and emotion over time (process).
Theoretical Integrationbehavioral pattern
The final analytic act of systematically relating major categories to a central or core category to form a dense, coherent, and explanatory theoretical framework.
Conceptual Density and Variationoutcome metric
A quality of the final analysis where the main concepts are fully developed in terms of their properties and dimensions and are specified across a wide range of conditions and manifestations.
Explanatory Power of Findingsoutcome metric
The ability of the research findings (whether descriptive or theoretical) to provide a coherent and compelling explanation for the phenomenon under investigation.
Credibility and Applicability of Findingsoutcome metric
The perceived trustworthiness, plausibility ('fit'), and usefulness ('work') of the research findings to relevant audiences, including participants and professionals.
Development of Empirical Knowledgeoutcome metric
The ultimate contribution of the research to the accumulated, evidence-based understanding within a discipline or professional field.
How they connect
- researcher aptitude and orientation → influences theoretical sensitivity
- theoretical sampling → influences quality of raw data
- quality of raw data → influences conceptualization
- application of analytic procedures → influences conceptualization
- theoretical sensitivity → influences conceptualization
- conceptualization → influences conceptual density and variation
- theoretical sampling → influences conceptual density and variation
- application of analytic procedures → influences analysis of context and process
- conceptualization → influences analysis of context and process
- analysis of context and process → influences theoretical integration
- application of analytic procedures → influences theoretical integration
- analysis of context and process → predicts explanatory power of findings
- theoretical integration → predicts explanatory power of findings
- conceptual density and variation → predicts credibility and applicability of findings
- explanatory power of findings → predicts credibility and applicability of findings
- credibility and applicability of findings → predicts development of empirical knowledge
The story
The reader A student, academic, or professional researcher who is facing a pile of qualitative data (interviews, observations, documents) and wants to produce a high-quality, credible analysis that uncovers deep insights and is more than just a superficial summary.
External problem
The researcher is overwhelmed by a mass of unstructured qualitative data and lacks a clear, step-by-step process for systematically making sense of it.
Internal problem
They feel anxious, intimidated, and uncertain about their ability to conduct a rigorous analysis, fearing their findings will be superficial or that they are 'not doing it right'.
Philosophical problem
It's just plain wrong for valuable qualitative data, representing people's lived experiences, to remain poorly analyzed, preventing the generation of new knowledge that could improve understanding and practice.
The plan
- Grasp the Pragmatist and Interactionist philosophy that underpins the method.
- Master the core analytic tools of asking questions and making constant comparisons to break open the data.
- Systematically code your data to generate concepts, developing their properties and dimensions.
- Use memos and diagrams continuously to record analytic thoughts and visualize relationships.
- Analyze for context and process to link actions and meanings to their structural conditions and temporal flow.
- Achieve integration by identifying a core category and building a coherent theoretical framework around it.
Success
- The researcher feels confident and competent in their ability to analyze qualitative data.
- They transform their messy, overwhelming data into a clear, logical, and insightful analysis (rich description, concept development, or a full theory).
- They produce credible, high-quality research that contributes new knowledge, enhances professional practice, and successfully completes their thesis, dissertation, or publication.
At stake
- The researcher remains stuck and overwhelmed, unable to make sense of their data.
- Their final research report is superficial, merely a collection of quotes or a list of themes without depth or explanatory power.
- They fail to generate meaningful knowledge from their research, wasting the opportunity to give voice to their participants' experiences.