Best Practice

Framework Analysis: Methods and Use Cases

Framework analysis is a systematic approach used to analyze qualitative data. Read more about this approach in this article.
Lauren Stewart
Qualitative Data Analysis Expert & ATLAS.ti Professional
  1. Introduction
  2. What is framework analysis?
  3. Implementing the framework analysis methodology


Among qualitative methods in social research, framework analysis stands out as a structured approach to analyzing qualitative data. Originally developed for applied policy analysis and multi-disciplinary health research, this method has found application in various domains due to its emphasis on transparency and systematic data analysis. As with other research methods, the objective remains to extract meaningful themes and patterns, but the framework method provides a specific roadmap for doing so.

Whether you're a seasoned researcher or someone new to the realm of qualitative methodology, understanding the nuances of framework analysis can enhance the depth and rigor of your research efforts. In this article, we will explore the methods and use cases of framework analysis, diving deep into its benefits and the analytical framework it enables researchers to develop.

A framework approach to analysis enables researchers to systematically analyze qualitative data.

What is framework analysis?

Framework analysis is a systematic approach for analyzing qualitative data. Rooted in the traditions of social research relevant to policy making, it was found to be a useful tool for analysis in multi-disciplinary health research where the eventual analysis of qualitative data can identify themes and actionable insights relevant to policy outcomes.

Unlike some other qualitative analysis methods, framework analysis is explicitly focused on addressing specific research questions, making it particularly suitable for applied or policy-related qualitative research.

Purpose of framework analysis

The primary aim of framework analysis is to offer a clear and transparent process for conducting qualitative research by managing, reducing, and analyzing large datasets without losing sight of the original context. Given the vast amounts of data often generated in qualitative studies, having a systematic method to sift through this data is crucial.

By using the framework method, researchers can remain focused on their research questions while ensuring that the data collection and analysis process retains its integrity and depth.

Characteristics of framework analysis

Transparent structure: One of the distinct features of framework analysis is its emphasis on transparency. Every step in the analysis process is documented, allowing for easy scrutiny and replication by multiple researchers.

Thematic framework: Central to framework analysis is the development of a framework identifying key themes, concepts, and relationships in the data. The framework guides the subsequent stages of coding and charting.

Flexibility: While it provides a clear structure, framework analysis is also adaptable. Depending on the objectives of the study, researchers can modify the process to better suit their data and questions.

Iterative process: The process in framework analysis is not linear. As data is collected and data analysis progresses, researchers often revisit earlier stages, refining the framework or revising codes to better capture the nuances in the data.

Benefits of framework analysis

Conducting framework analysis has several advantages:

Rigorous data management: The structured approach means data is managed and analyzed with a high level of rigor, minimizing the potential influence of preconceptions.

Inclusivity: Framework analysis accommodates both a priori issues, driven by the research questions, and emergent issues that arise from the data itself.

Comparability: Given its structured nature, framework analysis allows researchers to compare and contrast data, facilitating the identification of patterns and differences.

Accessibility: By presenting data in a summarized, charted form, findings from framework analysis become more accessible and comprehensible, aiding in reporting and disseminating results.

Relevance for applied research: Given its origins in policy research and its clear focus on addressing specific research questions, framework analysis is particularly relevant for studies aiming to inform policy or practice.

Implementing the framework analysis methodology

Successfully conducting framework analysis involves a series of structured steps. Proper implementation of framework analysis not only ensures the rigor of a qualitative analysis but also that the findings are credible and meaningful.

Familiarization with the data

Before delving into detailed analysis, it's paramount to understand the breadth and depth of the data at hand.

Reading and re-reading: Begin by reading textual data such as transcripts, field notes, and other data sources multiple times. This immersion allows researchers to understand participants' perspectives and grasp the overall context.

Noting preliminary ideas: As researchers familiarize themselves with the data, preliminary themes or ideas may start to emerge. Jotting these down in memos helps in forming an initial understanding and can be instrumental in the subsequent phase of developing a set of themes.

Developing a thematic framework

As is the case across nearly all types of qualitative methodology, central to framework analysis is the construction of a robust analytical framework. This structure aids in organizing and interpreting the data.

Identifying key themes: Based on the initial familiarization, it's important to identify themes that occur in the multimedia or textual data. These themes should be relevant to the research question. Researchers can begin assigning codes to specific chunks of data to capture emerging themes.

Categorizing and coding: Each identified theme can further be broken down into sub-themes or brought together under categories. At this stage, researchers can continue coding (or recoding) their data according to these themes or categories.

Refining the framework: As the analysis progresses, the initial themes represented by your coding framework may need adjustments. It's an iterative process, where the framework can be continually refined to better fit the data.

Indexing and charting the data

Once the framework is established, the next phase involves systematically applying it to the data.

Indexing: Using the resulting coding framework, you can verify that codes have been systematically assigned to relevant portions of the data. This ensures every relevant piece of data is categorized under the appropriate theme or sub-theme.

Charting: This step involves creating charts or matrices for each theme. Data from different sources (like interviews or focus groups) is summarized under the relevant theme. For example, a table can be created with each theme in a column and each data source in a row, and researchers can then populate the cells with relevant data extracts or notes. These charts provide a visual representation, allowing researchers to easily see patterns or discrepancies in the data.

Mapping and Interpretation: With the data systematically charted, researchers can begin to map the relationships between themes and interpret the broader implications. This step is where the true essence of the research emerges, as researchers link the patterns in the data to the broader objectives of the study.

Framework analysis is an involved process, with intentional decision-making at every step of the way. As a result, implementing structured qualitative methodologies such as framework analysis requires patience, meticulous attention to detail, and a clear understanding of the research objectives. When conducted diligently, it offers a transparent and systematic approach to analyzing qualitative data, ensuring the research not only has depth but also clarity.

Whether comparing data across multiple sources or drilling down into the nuances of individual narratives, framework analysis equips researchers with the tools needed to derive meaningful insights from their qualitative data. As more researchers across disciplines recognize its value, it stands to become an even more integral part of the research landscape.