What Is Framework Analysis? A Practical Guide

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Stevens, G (2026) What Is Framework Analysis? A Practical Guide, Academic Writing and Research. Available at: https://academic-writing.uk/what-is-framework-analysis-a-practical-guide/ (Accessed on: January 13, 2026)

Framework Analysis is a structured approach to qualitative data analysis that is especially well suited to applied research with predefined research questions. It is widely used in disciplines such as health, education, public policy, evaluation, and mixed-methods research because it supports transparent, systematic comparison across cases and produces outputs (notably matrices) that map cleanly onto research questions and results sections.

This article explains what Framework Analysis is, where it comes from, how it is conducted step by step, how it differs from thematic analysis and content analysis, and how it is typically implemented using qualitative software such as NVivo. An FAQ and a recommended reading list are included at the end.

Where does Framework Analysis come from?

Framework Analysis is most strongly associated with Ritchie & Spencer (1994), who developed it for applied policy research. The approach was designed to handle large volumes of qualitative data in a way that remained rigorous, transparent, and useful for decision-making, particularly when research aims and questions are defined in advance. Since then, the method has been widely adopted and clarified for contemporary research contexts, including multidisciplinary health research (e.g., Gale et al., 2013).

Core principles of Framework Analysis

Question-led structure: analysis is organised around predefined research questions or objectives.
Systematic indexing: data are consistently classified under a clearly defined framework.
Matrix-based organisation: charting into matrices supports comparison across cases and groups.
Transparency and auditability: decisions are documented so findings can be traced back to the data.
Applied orientation: outputs are designed to be interpretable and useful in real-world contexts.

The stages of Framework Analysis (with practical explanations)

Terminology varies slightly across authors, but Framework Analysis is commonly described in five stages. These stages are iterative in practice, but the sequence below captures the typical workflow.

1) Familiarisation

The analyst reads and re-reads the dataset to gain an overview of content, tone, and variation. In Framework Analysis, familiarisation is typically oriented toward the research questions from the outset (rather than open-ended exploration). Analysts often record early notes about how participants respond to each question and where views diverge across cases.

2) Identifying the analytical framework

An initial framework is developed, usually based on the study’s research questions, topic guide, or objectives. These become the top-level analytic categories. The framework should be clearly defined, bounded (what is included/excluded), and suitable for systematic indexing across the dataset.

3) Indexing (systematic classification)

Indexing involves assigning data segments to framework categories. The emphasis is on consistent classification (so similar content is indexed in the same way across cases). In Framework Analysis, analysts often minimise overlap across top-level categories because the framework is designed to separate data by research question/domain.

4) Charting into matrices

Charting is the defining feature of Framework Analysis. Indexed data are summarised and entered into a matrix, typically structured as cases × categories. The goal is to condense the dataset while keeping summaries traceable to the original data. Charting enables rapid cross-case comparison (e.g., how different participants discuss the same category) and supports clear results writing.

5) Mapping and interpretation

Once matrices are populated, the analyst identifies patterns across cases and categories. This includes similarities, differences, and configurations (e.g., how particular views cluster within certain groups). Interpretation in Framework Analysis typically aims to provide applied, explanatory answers to the research questions rather than generating high-level theory.

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Hybrid Framework Analysis (deductive + inductive)

Although Framework Analysis is often described as deductive because it begins with predefined research questions, many contemporary studies use a hybrid approach. In hybrid Framework Analysis, the top-level framework categories are fixed (aligned to the research questions), while inductive subthemes are allowed to emerge within each category during indexing and charting. This approach retains structure and comparability without forcing data into overly rigid categories.

Framework Analysis compared to thematic analysis and content analysis

Framework Analysis is often confused with other popular approaches because it shares some procedures (e.g., coding, categories). The key difference is how strongly the method is anchored to predefined research questions and how central the matrix is to producing results.

Compared to thematic analysis: Framework Analysis is typically more structured and question-led. Thematic analysis is often used for exploratory work where the analytic structure is developed primarily from the data.
Compared to qualitative content analysis: Content analysis often emphasises systematic categorisation and (in some variants) quantification. Framework Analysis prioritises structured comparison and synthesis through charting and matrices, with counting used mainly to support descriptive comparison where helpful.

Using Framework Analysis with NVivo

Framework Analysis aligns well with qualitative data analysis software because software can support consistent indexing, retrieval, and comparison. NVivo (and similar tools) can support framework work by enabling structured categorisation and matrix-based review. The central idea remains the same regardless of software: data are indexed to framework categories and then charted into matrices for cross-case comparison.

It can also be helpful to consult software guidance on framework matrices to understand the purpose and logic of charting (e.g., matrix-based condensation for comparison and synthesis).

Strengths and limitations

Strengths

Transparent and auditable: decisions are explicit and traceable to data.
Excellent for comparison: matrices enable systematic cross-case analysis.
Well suited to applied research: findings map directly to research questions and stakeholder needs.
Team-friendly: structure supports consistency across multiple analysts.

Limitations

Less suitable for theory generation: it is designed primarily for applied answers.
Requires good research questions: poorly defined questions can weaken the framework.
Can feel restrictive for highly exploratory studies: although hybrid approaches can mitigate this.

Framework Analysis FAQ

What is Framework Analysis?

Framework Analysis is a structured approach to qualitative data analysis designed for applied research with predefined research questions. It uses systematic indexing and charting into matrices (cases × categories) to support transparent comparison across participants or documents.

Is Framework Analysis the same as the Framework Method?

In practice, the terms are often used interchangeably. “Framework Analysis” is common in policy/applied social research, while “Framework Method” is often used in health research. Both refer to the same core matrix-based analytic logic.

When should I choose Framework Analysis instead of thematic analysis?

Framework Analysis is best when your research questions are defined in advance and you need structured cross-case comparison. Thematic analysis is often better when your study is exploratory and you want themes to emerge more freely from the data.

Can Framework Analysis be inductive?

Framework Analysis is usually deductive at the top level (framework categories align to research questions), but many studies use a hybrid approach where inductive subthemes are developed within each category during indexing and charting.

Does Framework Analysis require NVivo?

No. Framework Analysis can be done manually using tables or spreadsheets. Software can make indexing, charting, and comparison more efficient, especially for larger datasets or team projects.

Can I use Framework Analysis with open-ended survey responses?

Yes. It works well with open-ended survey data, particularly when responses are organised by question. Each respondent can be treated as a case and charted into matrices aligned to the research questions.

Is Framework Analysis suitable for dissertations and theses?

Yes. It is widely accepted because it is transparent, systematic, and produces clear outputs (e.g., matrices and comparison tables) that map directly onto research questions and results chapter structure.

How many participants do I need for Framework Analysis?

There is no fixed rule. It can be used with small or large samples; appropriate size depends on the aims of the study, the complexity of the questions, and the depth required for cross-case comparison.

What are “indexing” and “charting”?

Indexing is the systematic classification of data segments under framework categories (aligned to research questions). Charting is the summarisation of indexed data into a matrix so you can compare cases and categories side-by-side.

Do I need to include quotes if I use Framework Analysis?

Not necessarily. Framework Analysis often uses summarised data in matrices, but short illustrative quotes can still be included where appropriate. The key requirement is that summaries and claims remain traceable to the original data.

What are the main strengths of Framework Analysis?

Its main strengths are transparency, structure, and comparability. It is especially useful for applied research where you need clear answers to research questions and a defensible audit trail of how findings were produced.

What are the limitations of Framework Analysis?

Because it is question-led, it can be less suitable for highly exploratory research where aims evolve during analysis. It also requires well-formulated research questions and careful discipline to avoid forcing data into categories prematurely.

What Is Framework Analysis? A Practical Guide

Where does Framework Analysis come from?

Core principles of Framework Analysis