Methodological Foundations

Doksa.io implements established research methodologies from the qualitative and systematic review literature. Here's how we maintain academic rigor.

Important Disclaimer

Doksa.io is not affiliated with, endorsed by, or sponsored by any of the authors, researchers, or publishers referenced on this page. All methodological references are provided for educational and transparency purposes to demonstrate the academic foundations of our platform's features. Proper citations are included to honor the original authors' work.

Qualitative Coding Methods

When starting qualitative analysis, you'll choose a coding method that matches your research goals. Each method offers a different analytical lens. Click any method to learn when to use it.

Elemental Methods

Foundation coding approaches for initial data analysis

Descriptive Coding Assigns topic labels to data segments

Best for: Getting started with coding, topic mapping, any data type. Ideal when you need a clear topical index of your data.

Produces: Short noun or noun-phrase labels (e.g., "communication challenges", "resource constraints")

In Vivo Coding Uses participants' own words as codes

Best for: Interview data, honoring participant voice, studies prioritizing emic perspective. Essential for phenomenological research.

Produces: Verbatim phrases from participants (e.g., "just surviving day to day", "feeling invisible")

Process Coding Uses gerunds (-ing words) to capture action

Best for: Observational data, narratives with action sequences, process-focused research questions. Excellent for understanding how things happen.

Produces: Action-oriented gerund codes (e.g., "negotiating boundaries", "building trust", "adapting strategies")

Affective Methods

Approaches focused on emotions, values, and subjective experience

Emotion Coding Labels emotions and feelings in data

Best for: Understanding emotional responses, wellbeing research, healthcare studies, any research where affect is central.

Produces: Emotion labels (e.g., "frustration", "hope", "anxiety", "relief")

Values Coding Identifies values, attitudes, and beliefs

Best for: Cultural studies, organizational research, belief systems, worldview exploration. Ideal for understanding what matters to participants.

Produces: Value statements (e.g., "prioritizing family", "valuing autonomy", "belief in fairness")

Evaluation Coding Captures judgments and assessments

Best for: Program evaluation, quality assessments, policy research, studies examining how people judge or assess things.

Produces: Evaluative statements with valence (e.g., "positive: efficient process", "negative: lack of support")

Key References

Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. SAGE Publications.
Corbin, J., & Strauss, A. (2015). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (4th ed.). SAGE Publications.
Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods, 16(1), 15-31.
Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative Data Analysis: A Methods Sourcebook (3rd ed.). SAGE Publications.
Patton, M. Q. (2015). Qualitative Research & Evaluation Methods (4th ed.). SAGE Publications.
Saldaña, J. (2021). The Coding Manual for Qualitative Researchers (4th ed.). SAGE Publications.

Three-Cycle Analysis Process

Our qualitative analysis follows a structured three-cycle approach informed by established coding methodology (Saldaña, 2015). Each cycle builds systematically toward theoretical understanding.

Initial Coding

The selected coding method (Descriptive, In Vivo, Process, Values, etc.) is applied systematically to your data segments.

AI maintains coding consistency across all documents
Each code linked to supporting text segments
Code emergence tracked for analysis

Pattern Analysis

Cycle 1 codes are analyzed to identify patterns spanning multiple segments and grouped into meaningful categories.

Pattern validity tested through counter-evidence search
Confidence levels assessed based on evidence consistency
Alternative interpretations documented

Theoretical Integration

Patterns are synthesized into a theoretical framework that addresses your research question.

Core categories identified to organize patterns
Theoretical propositions developed
Conceptual model showing relationships

Key Principle: Theory emerges from your data, not imposed on it. AI provides systematic pattern identification while you control all interpretive decisions and theoretical conclusions.

Literature Review Frameworks

When analyzing literature, you'll choose an extraction framework that matches your research domain. Each framework structures what information is systematically extracted from each paper.

Health & Medical Research

PICO Framework

The gold standard for clinical and evidence-based research

Population: Who was studied (patients, participants, problem)
Intervention: What treatment, exposure, or action was examined
Comparison: Control group or alternative intervention
Outcome: What was measured or observed

Reference: Richardson, W. S., et al. (1995). The well-built clinical question. ACP Journal Club, 123, A12-13.

SPIDER Framework

Adapted for qualitative and mixed-methods health research

Sample: Who or what was studied
Phenomenon of Interest: Central focus of the research
Design: Research methodology used
Evaluation: How quality/outcomes were assessed
Research type: Qualitative, quantitative, or mixed

Reference: Cooke, A., Smith, D., & Booth, A. (2012). Beyond PICO. Qualitative Health Research, 22(10), 1435-1443.

Business & Management Research

TCCM Framework

Comprehensive framework for management literature reviews

Theory: Theoretical foundations and frameworks used
Context: Settings, industries, and conditions studied
Characteristics: Key variables, constructs, and factors
Methodology: Research approaches and methods employed

Reference: Paul, J., & Criado, A. R. (2020). The art of writing literature review. International Business Review, 29(4), 101717.

CIMO Framework

For design science and intervention-based research

Context: Environmental conditions and constraints
Intervention: What action or change was implemented
Mechanism: How and why the intervention worked
Outcome: Results achieved

Reference: Denyer, D., Tranfield, D., & van Aken, J. E. (2008). Developing design propositions through research synthesis. Organization Studies, 29(3), 393-413.

ADO Framework

For decision-making and causal analysis research

Antecedents: What came before, causes and drivers
Decisions: Choices, actions, or processes examined
Outcomes: Consequences and results

Reference: Paul, J., & Benito, G. R. G. (2018). A review of research on outward FDI from emerging market multinationals. International Business Review, 27(1), 197-210.

General Purpose

5W Framework

Universal framework for basic systematic extraction

Who: Subjects, participants, or actors involved
What: Main topic, phenomenon, or intervention
When: Time period, duration, or temporal context
Where: Geographic or contextual setting
Why: Purpose, rationale, or research motivation

Best for: Quick analysis, exploratory reviews, and interdisciplinary research where domain-specific frameworks don't apply.

Tip: Choose a framework that matches your research domain. The same framework should be applied consistently across all papers in your review for comparable extraction.

How We Implement These Methods

The Human-AI Division of Labor

Doksa.io uses AI to provide systematic structure based on these established methodologies, while you maintain complete control over:

Code definitions and refinements: You decide what each code means
Theme identification and interpretation: You identify patterns and meanings
Theoretical connections: You make connections to existing literature
Final conclusions: You draw the scholarly conclusions

Every coding decision is visible, reviewable, and editable. You're not accepting a black box. You're working with a research assistant that shows its work.

Designed for Academic Integrity

Our tools are designed to be:

Defensible in methodology sections - Full transparency about AI-assisted structure with human-led analysis
Reviewable by committees - All coding decisions documented and visible
Compliant with academic integrity standards - You maintain intellectual control over interpretation and conclusions

Example Methodology Section

Here's how you might describe using Doksa.io in your methodology section:

"Qualitative data was analyzed using a three-cycle coding approach informed by Saldaña (2015). In Cycle 1, [coding method] was applied to generate initial codes from data segments. Cycle 2 involved pattern analysis to identify themes spanning multiple codes, with pattern validity tested through examination of counter-evidence. Cycle 3 synthesized patterns into a theoretical framework with core categories and propositions. AI-assisted analysis (Doksa.io) maintained coding consistency while all interpretive decisions and theoretical conclusions remained with the research team."

Questions About Our Approach?

We're committed to transparency about how our tools work and how they fit into rigorous academic research.

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