How to Use Grounded Theory in Doctoral Dissertation Research

How to Use Grounded Theory in Doctoral Dissertation Research

Grounded theory is a qualitative research methodology used in doctoral dissertations to develop a theory that is grounded directly in data, rather than testing an existing theory. It is especially useful when little is known about a topic or when existing theories do not fully explain a social, educational, or psychological process.

In simple terms, grounded theory asks: “What theory emerges from the real experiences and data of participants?”

Unlike other approaches, you do not begin with a fixed theory—you build one from the ground up.

What Is Grounded Theory?

Grounded theory is a systematic qualitative research approach designed to:

• Collect rich qualitative data

• Analyze data simultaneously with collection

• Identify patterns and categories

• Develop a theory that explains a process or phenomenon

It is widely used in education, psychology, sociology, nursing, and organizational studies.

Key idea:

The theory emerges from the data, not before it.

When Should You Use Grounded Theory in a Dissertation?

You should use grounded theory when your research focuses on:

• Social or behavioral processes

• How people experience change over time

• Decision-making or interaction patterns

• Emerging or under-researched phenomena

• Building new theoretical explanations

Example research questions:

• How do doctoral students develop academic resilience in online programs?

• What process do teachers use to adapt to new curriculum reforms?

• How do individuals cope with long-term workplace burnout?

Grounded theory is ideal when there is no strong existing theory explaining the process.

Key Features of Grounded Theory

• Theory is inductively generated from data

• Data collection and analysis happen simultaneously

• Constant comparison of data is used

• The goal is theory development, not just description

• Sampling continues until theoretical saturation is reached

Types of Grounded Theory

1. Glaserian Grounded Theory (Classic)

Focus:

• Emergent theory from data

• Minimal researcher interference

• Pure inductive approach

Key idea:

Let the data “speak for itself.”

2. Straussian Grounded Theory

Focus:

• More structured coding process

• Uses predefined coding stages

• Emphasizes systematic analysis

3. Constructivist Grounded Theory (Charmaz)

Focus:

• Researcher actively interprets data

• Acknowledges subjectivity

• Common in education and social sciences

Key idea:

Theory is co-constructed between researcher and participants.

Step-by-Step: How to Use Grounded Theory in a Doctoral Dissertation

Step 1: Identify a Process-Oriented Research Problem

Grounded theory focuses on processes, not static descriptions.

Example:

Instead of:

• “student motivation”

Use:

• “how student motivation develops over time in online doctoral programs”

Your focus should be:

• Action

• Change

• Interaction

• Development

Step 2: Develop Open-Ended Research Questions

Grounded theory questions should explore processes.

Example:

• How do doctoral students develop academic persistence?

• What process do teachers use to integrate technology into instruction?

Avoid:

• Hypothesis-based or fixed-variable questions

Step 3: Use Theoretical Sampling

Unlike fixed sampling, grounded theory uses theoretical sampling, meaning:

• You select participants based on emerging data needs

• Sampling continues until no new insights emerge

Example:

Start with:

• 5 teachers

  Then expand to:

• 10 teachers based on emerging categories

Step 4: Collect Qualitative Data

Common methods include:

• Semi-structured interviews

• Focus groups

• Observations

• Reflective journals

• Open-ended questionnaires

Data collection and analysis occur simultaneously.

Step 5: Use Constant Comparative Analysis

This is the core of grounded theory.

You continuously compare:

• Data vs data

• Data vs codes

• Codes vs categories

This helps identify patterns and relationships.

Step 6: Conduct Coding in Three Stages

1. Open Coding

• Break data into small units

• Identify initial concepts

2. Axial Coding

• Link categories together

• Identify relationships

3. Selective Coding

• Identify core category

• Build emerging theory

Step 7: Develop a Grounded Theory

The final product is a theoretical model explaining a process.

This includes:

• Core category (central idea)

• Supporting categories

• Relationships between categories

• Process model or framework

Example:

A theory explaining how doctoral students develop academic resilience over time.

Step 8: Reach Theoretical Saturation

Data collection continues until:

• No new themes emerge

• Categories are fully developed

• Relationships are well understood

Step 9: Validate the Emerging Theory

Grounded theory uses:

• Credibility (accuracy of interpretation)

• Fit (how well theory matches data)

• Workability (explains the process clearly)

• Relevance (meaningful to real-world context)

Techniques include:

• Member checking

• Peer debriefing

• Audit trails

Common Mistakes in Grounded Theory Dissertations

Avoid:

• Starting with a pre-existing theory

• Using fixed sample sizes too early

• Treating it like thematic analysis only

• Failing to develop a theory

• Ignoring constant comparison

• Collecting all data before analysis

Strengths of Grounded Theory

• Produces new theoretical insights

• Explains complex processes

• Flexible and data-driven

• Strong for understudied topics

• Widely used in education and psychology

Limitations of Grounded Theory

• Time-intensive

• Requires iterative data collection

• Can be methodologically complex

• Risk of losing focus without strong coding discipline

Final Thoughts on How to Use Grounded Theory in Doctoral Dissertation Research

Grounded theory is a powerful methodology for doctoral dissertations when the goal is to develop a new theory based on real-world data. It is especially valuable for understanding processes in education, psychology, and social sciences.

A strong grounded theory study does not just describe data—it builds a conceptual model that explains how and why a process occurs.

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