Framework Analysis – Steps to do analysis

Last Updated on August 29, 2026 by Nurseslab.in Editorial Team

A practical guide to conducting systematic, matrix-based qualitative analysis

Framework analysis is a structured approach to analysing qualitative data. It helps researchers organise large volumes of interviews, focus groups, observations, documents, or open-ended responses while preserving a clear link between interpretations and the original material. Its defining feature is the framework matrix: cases are usually arranged in rows, themes or categories in columns, and each cell contains a concise, traceable summary of what that case contributes to the theme.

The method was developed for applied policy research and is now widely used in health, education, public services, implementation research, and multidisciplinary studies. It is especially valuable when a project has focused questions, a defined sample, practical outputs, multiple analysts, or a need for transparent comparison. Framework analysis is systematic, but not mechanical. The matrix organises evidence; researchers still interpret meaning, context, variation, and relationships.

What Is Framework Analysis?

Framework analysis is a qualitative, comparative method in which researchers develop an analytical framework of codes and categories, apply it across a dataset, summarise relevant material into a case-by-category matrix, and then identify patterns, differences, explanations, and themes. It supports both within-case analysis—understanding one participant or site across several topics—and cross-case analysis—comparing how different cases address the same topic.

The traditional five stages are familiarisation, identifying a thematic framework, indexing, charting, and mapping and interpretation. A commonly used contemporary Framework Method expands the process into stages such as transcription, familiarisation, coding, development and application of the analytical framework, charting, and interpretation. These versions are compatible rather than contradictory; they describe the same analytical logic at different levels of detail.

Purpose of Framework Analysis

• Organise complex datasets: reduce large volumes of qualitative material without losing links to the source.
• Compare cases and themes: examine similarities, differences, ranges, patterns, and exceptions systematically.
• Answer applied questions: produce findings relevant to policy, services, practice, education, or programme evaluation.
• Integrate prior and emerging issues: combine deductive categories from objectives or theory with inductive categories from the data.
• Support team analysis: create shared definitions and a visible process for multidisciplinary researchers.
• Maintain transparency: document how raw data became codes, categories, matrices, themes, and conclusions.
• Preserve case context: retain a view of each participant, organisation, or site as a whole.
• Develop explanations: move beyond description towards typologies, relationships, mechanisms, and recommendations.

Characteristics of Framework Analysis

Matrix-Based Organisation

The matrix is central. Rows normally represent cases, while columns represent categories or subcategories. Cells contain summarised data and references back to the transcript, field note, or document. This visual structure makes comparison manageable.

Case- and Theme-Oriented

The researcher can read across a row to understand one case or down a column to compare cases on one issue. This dual perspective distinguishes framework analysis from approaches that focus mainly on themes detached from individual cases.

Systematic and Transparent

Framework development, coding, charting, and interpretation can be documented clearly. The method is therefore well suited to audit trails, team discussion, and applied research requiring defensible recommendations.

Flexible and Iterative

Researchers move back and forth between data, codes, categories, matrices, and interpretation. The initial framework may change when new concepts appear. Early decisions should guide analysis without preventing revision.

Deductive and Inductive

Categories may come from research questions, an interview guide, theory, policy, implementation framework, or existing evidence. Other categories emerge through close engagement with participants’ accounts. The balance depends on the study.

Data Reduction with Traceability

Charting reduces the volume of material, but each summary should preserve meaning, context, and participant language where useful. Researchers must be able to return from a matrix cell to the original source.

Applied Orientation

The method is frequently used when findings must inform decisions. However, it can support descriptive, explanatory, or theoretically informed research, not only evaluation.

Team Compatibility

Shared code definitions, joint framework development, charting conventions, and regular analytical meetings allow multiple researchers to contribute. Agreement is useful, but productive differences in interpretation should also be examined.

Types and Variations of Framework Analysis

Framework analysis is one method with several orientations rather than a set of universally fixed “types.” Researchers should state how their framework was developed and used.

1. Deductive Framework Analysis

A deductive design begins with categories based on research questions, policy priorities, a topic guide, or theory. It suits studies evaluating a known programme or examining predefined implementation outcomes. Researchers must remain open to material that does not fit the framework.

2. Inductive Framework Analysis

An inductive design develops codes and categories primarily from the data. It is useful when participants’ perspectives are not well understood or when the researcher wants findings to remain closely grounded in their accounts.

3. Combined Deductive–Inductive Analysis

Many studies use a hybrid design. Initial categories reflect the research objectives, while new categories and subcategories are added during coding. This balance is practical for applied health and social research.

4. Theoretical Framework Analysis

An existing conceptual model guides the framework. Examples might include an implementation, behaviour-change, access, or equity model. The analysis can examine where data support, modify, or challenge the theory.

5. Policy or Evaluation Framework Analysis

This version is organised around policy questions, programme components, outcomes, barriers, facilitators, and stakeholder groups. It aims to produce clear findings for decision-makers while retaining qualitative depth.

6. Comparative Framework Analysis

The matrix is designed to compare sites, professional groups, service models, time points, or demographic groups. Comparisons should be justified by the research question and interpreted in context rather than reduced to counts.

7. Longitudinal Framework Analysis

Repeated interviews or observations are charted to examine change over time. The matrix may include waves, trajectories, turning points, or time-specific columns while preserving the identity of each case.

8. Multidisciplinary Team Framework Analysis

Researchers from different disciplines jointly develop and apply the framework. Diverse perspectives can enrich interpretation but require clear definitions, training, reflexivity, decision rules, and regular review.

Suitable Data Sources

• Semi-structured or in-depth interviews
• Focus groups
• Observation and field notes
• Open-ended survey responses
• Policy, clinical, educational, or organisational documents
• Diaries, journals, and reflective accounts
• Meeting transcripts and implementation records
• Mixed qualitative datasets within evaluations

Data usually need to be available in textual form. Audio, video, or visual material can be included, but the analytical framework and matrix must capture the features relevant to the question.

The Framework Matrix

Imagine a study of nurses’ experiences with a new electronic documentation system. Rows could represent individual nurses. Columns might include training, usability, patient interaction, workload, safety, workarounds, and recommendations. A cell at the intersection of Nurse 04 and Workload would contain a concise summary of that nurse’s account, together with a transcript location or other source reference.

Good charting is not copying long quotations into a spreadsheet. It is analytical summarisation. The researcher decides what matters, preserves nuance and contradiction, and records enough context to interpret the entry. Short quotations may be retained when wording is especially significant.

Steps to Conduct Framework Analysis

Step 1: Define the Research Questions

Develop focused questions suitable for qualitative enquiry. Clarify whether the study seeks description, comparison, evaluation, explanation, or theory development. The questions influence sampling, data collection, framework structure, and interpretation.

Step 2: Establish the Unit of Analysis

Decide what a “case” means. It may be a person, household, team, organisation, site, event, document, or time point. Also define the units to be coded, such as a phrase, sentence, paragraph, or meaningful passage.

Step 3: Select and Generate the Data

Use a sampling strategy aligned with the study aim. Develop interview or observation guides that cover relevant issues while allowing unexpected ideas to emerge. Record contextual information and maintain secure data-management procedures.

Step 4: Transcribe and Prepare the Material

Transcribe interviews or focus groups to the level of detail required. Anonymise carefully, check transcripts against recordings, catalogue documents, and record contextual attributes. Note translation decisions where more than one language is involved.

Step 5: Familiarise Yourself with the Dataset

Read and re-read transcripts, listen to recordings where useful, and review field notes. Write initial memos about recurrent issues, contrasts, context, striking cases, and possible categories. Team members should familiarise themselves with a varied subset rather than only one type of participant.

Step 6: Conduct Initial Coding

Apply short descriptive or conceptual labels to meaningful passages. Coding may begin openly or include provisional deductive codes. Researchers should code enough varied material to capture the range of views before fixing the framework.

Step 7: Develop the Analytical Framework

Group related codes into categories and subcategories. Define each clearly with inclusion and exclusion criteria and examples. Arrange the framework hierarchically where helpful. Ensure that categories answer the research questions but do not force distinct ideas together.

Step 8: Pilot and Refine the Framework

Apply the draft framework to several diverse transcripts. Check whether categories overlap, remain too broad, omit significant data, or are interpreted inconsistently. Revise definitions and document decisions. If working in a team, discuss differences rather than treating agreement as a purely numerical exercise.

Step 9: Index the Full Dataset

Apply the agreed analytical framework systematically to all relevant data. Multiple codes may be appropriate for one passage. Record new issues and agree explicitly whether the framework should change. If changed, review previously coded material as necessary.

Step 10: Create the Matrix

Construct rows for cases and columns for categories or subcategories. Choose a level of detail that is analytically useful. Separate matrices may be needed for major topic areas, participant groups, or time points.

Step 11: Chart the Data

Summarise coded material into the correct cells. Use concise language, preserve the participant’s meaning, record contradictions, and avoid interpretation that cannot be supported. Include references back to the source and retain a distinctive phrase where valuable.

Step 12: Check Charting Quality

Compare a sample of matrix entries with the original data. Check accuracy, context, consistency, and whether important variation has been lost. Review sparse or overloaded cells and refine charting conventions.

Step 13: Describe Each Case

Read across rows to build case summaries. Identify each case’s overall position, internal contradictions, context, sequence, and distinctive features. This prevents cross-case comparison from erasing the person or setting.

Step 14: Compare Across Cases and Categories

Read down columns to identify the range of views, similarities, differences, clusters, exceptions, and missing information. Compare relevant groups only where justified. Counts may help describe prevalence within the sample, but should not replace qualitative interpretation.

Step 15: Map Relationships and Develop Explanations

Explore links between categories. Ask what conditions appear to shape an experience, how a process unfolds, why sites differ, or which mechanisms might explain an outcome. Develop conceptual maps, typologies, trajectories, or explanatory propositions.

Step 16: Test Interpretations

Return to the raw data. Search for negative cases and rival explanations. Ask whether the interpretation fits all relevant evidence or whether it should be qualified. Consider how sampling, questions, researcher position, and missing voices shaped the result.

Step 17: Practise Reflexivity

Document assumptions, values, disciplinary perspectives, relationships with participants, emotional responses, and analytical decisions. Team reflexivity should examine how differences in professional background affect coding and interpretation.

Step 18: Report Findings Transparently

Describe the study context, sample, data generation, framework development, coding, charting, team roles, software, reflexivity, and quality procedures. Present findings through themes, case comparisons, matrices or models, and selected quotations. Explain how recommendations follow from the evidence.

Traditional Five Stages at a Glance

1. Familiarisation: immerse in the data and record initial ideas.
2. Identifying a thematic framework: develop categories from objectives, theory, and emerging data.
3. Indexing: apply the framework systematically across the dataset.
4. Charting: summarise indexed data into matrices by case and category.
5. Mapping and interpretation: identify patterns, concepts, typologies, relationships, and explanations.

Methods Used During Framework Analysis

• Coding: assigning labels to meaningful segments.
• Categorisation: grouping codes into related concepts.
• Charting: entering synthesised summaries into matrix cells.
• Constant comparison: comparing data within and across cases and categories.
• Memo writing: recording analytical ideas, questions, and decisions.
• Case summaries: preserving each case’s context and internal pattern.
• Typology building: identifying meaningful configurations or groups.
• Concept mapping: representing relationships between categories.
• Negative-case analysis: examining evidence that challenges an emerging explanation.
• Descriptive counting: cautiously indicating distribution within the sample where useful.

Rigour and Trustworthiness

• Credibility: interpretations are supported by rich, relevant data.
• Dependability: procedures and changes are carefully documented.
• Confirmability: readers can trace findings through the matrix to source data.
• Transferability: context is described so readers can judge relevance elsewhere.
• Reflexivity: the researcher’s influence is examined openly.
• Comprehensiveness: all relevant cases and categories are considered.
• Analytical depth: findings move beyond summary to explanation.
• Attention to deviance: exceptions and contradictions are retained.

Useful procedures include an audit trail, framework definitions, coding meetings, charting checks, peer discussion, triangulation where appropriate, negative-case analysis, and returning to the original data throughout interpretation. Participant feedback may be useful when compatible with the study, but it is not a universal test of truth.

Ethical Considerations

Framework analysis may involve sensitive interviews, service data, or documents containing identifiable information. Obtain appropriate approval and consent, anonymise carefully, restrict access, and store files securely. Matrix summaries can still identify participants when distinctive details are combined, so consider whether attributes or quotations increase disclosure risk.

Represent participants fairly. Do not remove a statement from context simply because it fits a category. Pay attention to power, distress, safeguarding, third parties, and the potential consequences of reporting criticism about organisations or services.

Using Software

Framework analysis can be conducted with spreadsheets, tables, word-processing software, or qualitative data-analysis programs. Specialist software can support coding, retrieval, framework matrices, links to source data, memos, queries, and team working.

Software does not determine sound categories, produce trustworthy summaries, or interpret relationships automatically. Researchers remain responsible for data quality, framework logic, contextual understanding, reflexivity, and claims. If artificial intelligence assists transcription, coding, or summarisation, its role should be disclosed, outputs verified, confidential data protected, and human analytical responsibility retained.

Strengths of Framework Analysis

• Provides a systematic and transparent analytical process.
• Supports comparison across cases while preserving case context.
• Combines deductive and inductive analysis.
• Handles large, applied qualitative datasets effectively.
• Works well for multidisciplinary research teams.
• Produces outputs useful to practice, policy, and evaluation.
• Maintains traceability from findings to source data.
• Can accommodate multiple participant groups and time points.

Limitations and Challenges

• Charting is time-intensive and requires analytical skill.
• Premature frameworks can force data into predefined categories.
• Over-summarisation may strip away emotion, language, and context.
• Matrices can create a false impression of completeness or objectivity.
• The method may be less suitable for highly unstructured, language-focused, or deeply narrative questions.
• Large frameworks become unwieldy and difficult to interpret.
• Team consistency requires training and ongoing communication.
• Comparative displays can tempt researchers to overuse counts.

Framework Analysis Compared with Other Methods

Versus thematic analysis: both identify patterns, but framework analysis places stronger emphasis on matrices, case comparison, predefined questions, and traceability. Some forms of thematic analysis are more flexible and less case-centred.

Versus grounded theory: grounded theory aims primarily to generate theory through iterative sampling and analysis. Framework analysis may develop explanations, but often begins with a defined sample and applied questions.

Versus qualitative content analysis: both use systematic categories. Framework analysis is distinguished by charting summaries into a case-by-category matrix and explicitly comparing within and across cases.

Versus narrative analysis: narrative analysis usually preserves story sequence, performance, and plot as central. Framework analysis may break an account into categories, making it less suitable when the story as a whole is the unit of interest.

Worked Example

Suppose researchers ask: “What helps or hinders nurses from adopting a new digital triage tool?” They interview 24 nurses across four emergency departments. Deductive categories include training, usability, workflow, safety, leadership, and resources. During familiarisation, new categories emerge: trust in the algorithm, effects on professional identity, and workarounds.

After coding and refining definitions, researchers create a matrix with nurses in rows and categories in columns. Charting shows that training was generally rated positively, but confidence differed by access to supervised practice. Reading across cases reveals that experienced triage nurses were concerned about losing professional discretion. Reading down the leadership column shows that rapid local problem-solving mattered more than generic executive messages.

The team develops an explanation: adoption improves when formal training is combined with supervised use, visible technical support, permission to question recommendations, and local leaders who act on feedback. One site challenges this pattern because staff have support but poor connectivity. The final recommendations therefore address both relational and technical conditions.

Common Mistakes to Avoid

• Fixing the framework before becoming familiar with the data.
• Using topic-guide headings as themes without analysis.
• Creating too many overlapping categories.
• Copying long extracts into cells instead of summarising.
• Charting without a route back to the source.
• Ignoring case context while comparing themes.
• Equating frequency with importance.
• Removing contradictory or minority perspectives.
• Allowing one team member’s assumptions to dominate.
• Reporting themes without explaining interpretation.
• Treating the matrix as the finished analysis.
• Using automated summaries without verification.

Practical Checklist

• Are the questions suitable for applied qualitative comparison?
• Is the case and unit of analysis defined clearly?
• Is the sampling strategy justified?
• Has the team familiarised itself with varied data?
• Does the framework include deductive and emergent issues appropriately?
• Are categories clearly defined, distinct, and piloted?
• Has the full dataset been indexed systematically?
• Do matrix summaries preserve meaning and link to sources?
• Have within-case and cross-case patterns both been examined?
• Were negative cases and alternative explanations considered?
• Is reflexivity documented?
• Are recommendations supported by evidence?

Conclusion

Framework analysis provides a clear and flexible way to manage qualitative data while preserving comparison, context, and traceability. Its strength lies in the movement from familiarisation and coding to a carefully constructed analytical framework, systematic indexing, matrix charting, and interpretation.

The method works particularly well for applied studies with focused questions, multiple cases, practical outputs, or team-based analysis. However, the matrix is a tool rather than an answer. Rigorous framework analysis depends on thoughtful category development, accurate summarisation, repeated return to the source data, reflexivity, attention to exceptions, and transparent reporting. Used well, it transforms complex qualitative material into findings that are both analytically credible and useful for decision-making.

REFERENCES

1. Gale, N.K., Heath, G., Cameron, E. et al. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol 13, 117 (2013). https://doi.org/10.1186/1471-2288-13-117
2. Klingberg, S., Stalmeijer, R. E., & Varpio, L. (2024). Using framework analysis methods for qualitative research: AMEE Guide No. 164. Medical Teacher, 46(5), 603–610. https://doi.org/10.1080/0142159X.2023.2259073
3. Ritchie, J., Spencer, L., & O’Connor, W. (2003). Carrying Out Qualitative Analysis. Sage Publications.
4. Srivastava, A., & Thomson, S. B. (2009). Framework Analysis: A Qualitative Methodology for Applied Policy Research. Journal of Administration and Governance.
5. Braun, V., & Clarke, V. (2019). Thematic Analysis: A Practical Guide. Sage Publications.
6. Pope, C., Ziebland, S., & Mays, N. (2000). Analysing Qualitative Data. BMJ.
7. Flick, U. (2018). An Introduction to Qualitative Research. Sage Publications.

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