Framework Analysis

Table of Contents

Definition:

Framework Analysis is a qualitative research method that involves organizing and analyzing data using a predefined analytical framework. The analytical framework is a set of predetermined themes or categories that are derived from the research questions or objectives. The framework provides a structured approach to data analysis and can help to identify patterns, themes, and relationships in the data.

Steps in Framework Analysis

Here are the general steps in Framework Analysis:

Familiarization

Get familiar with the data by reading and re-reading it. This step helps you to become immersed in the data and to get a sense of its content, structure, and scope.

Identify a Coding Framework

Identify a coding framework or set of themes that will be used to analyze the data. These themes can be derived from existing literature or theories or developed based on the data itself.

Coding

Code the data by applying the coding framework to the data. This involves breaking down the data into smaller units and assigning each unit to a particular theme or category.

Charting

Chart or summarize the data by creating tables or matrices that display the distribution and frequency of each theme or category across the data set.

Mapping and interpretation

Analyze the data by examining the relationship between different themes or categories, and by exploring the implications and meanings of the findings in relation to the research question.

Verification

Verify the accuracy and validity of the findings by checking them against the original data, comparing them with other sources of data, and seeking feedback from others.

Reporting

Report the findings by presenting them in a clear, concise, and organized manner. This involves summarizing the key themes, presenting supporting evidence, and providing interpretations and recommendations based on the findings.

Framework Analysis Conducting Guide

Here is a step-by-step guide to conducting framework analysis:

Define the research question: The first step in conducting framework analysis is to clearly define the research question or objective that you want to investigate.
Develop the analytical framework: Develop a coding framework or a set of predetermined themes or categories that are relevant to the research question. These themes or categories can be derived from existing literature or theories, or they can be developed based on the data collected.
Data collection: Collect the data using a suitable method such as interviews, focus groups, surveys or observation.
Familiarization: Transcribe and familiarize yourself with the data. Read through the data several times and take notes to identify any patterns, themes or issues that are emerging.
Coding: Code the data by identifying key themes or categories and assigning each piece of information to a specific theme or category.
Charting: Create charts or tables that display the frequency and distribution of each theme or category. This helps to summarize the data and identify patterns.
Mapping and interpretation: Analyze the data to identify patterns, relationships, and themes. Interpret the findings in light of the research objectives and provide explanations for any significant patterns or themes that have emerged.
Validation: Validate the findings by sharing them with others and seeking feedback. This can help to ensure that the findings are robust and reliable.
Report writing: Write a report that summarizes the findings, includes quotes or examples from the data to support the findings and provides recommendations for future research.

Applications of Framework Analysis

Framework Analysis has a wide range of applications in research, including:

Policy analysis: Framework Analysis can be used to analyze policies and policy documents to identify key themes, patterns, and underlying assumptions.
Social science research: Framework Analysis is commonly used in social science research to analyze qualitative data from interviews, focus groups, and other sources.
Health research: Framework Analysis can be used to analyze qualitative data from health research studies, such as patient and provider perspectives, to identify themes and patterns.
Environmental research: Framework Analysis can be used to analyze qualitative data from environmental research studies to identify themes and patterns related to environmental attitudes, behaviors, and practices.
Education research: Framework Analysis can be used to analyze qualitative data from educational research studies to identify themes and patterns related to teaching practices, student learning, and educational policies.
Market research: Framework Analysis can be used to analyze qualitative data from market research studies to identify themes and patterns related to consumer attitudes, behaviors, and preferences.

Examples of Framework Analysis

Here are some examples of Framework Analysis in various research contexts:

Health Research: A study on the experiences of cancer survivors might use Framework Analysis to identify themes related to the psychological, social, and physical aspects of survivorship. Themes might include coping strategies, social support, and health outcomes.
Education Research: A study on the impact of a new teaching approach might use Framework Analysis to identify themes related to the implementation of the approach, the effectiveness of the approach, and barriers to its implementation. Themes might include teacher attitudes, student engagement, and logistical challenges.
Environmental Research: A study on the factors that influence pro-environmental behaviors might use Framework Analysis to identify themes related to environmental attitudes, behaviors, and practices. Themes might include social norms, personal values, and perceived barriers to behavior change.
Policy Analysis: A study on the implementation of a new policy might use Framework Analysis to identify themes related to policy development, implementation, and outcomes. Themes might include stakeholder perspectives, organizational structures, and policy effectiveness.
Social Science Research: A study on the experiences of immigrant families might use Framework Analysis to identify themes related to the challenges and opportunities faced by immigrant families in their new country. Themes might include language barriers, cultural differences, and social support.

When to use Framework Analysis

Framework Analysis is a useful method for analyzing qualitative data when the research questions require an in-depth exploration of a particular phenomenon, concept, or experience. It is particularly useful when:

The research involves multiple sources of qualitative data, such as interviews, focus groups, or documents, that need to be analyzed and compared.
The research questions require a systematic and structured approach to data analysis that enables the identification of patterns, themes, and relationships in the data.
The research involves a large and complex dataset that requires a method for organizing and synthesizing the data in a meaningful way.
The research aims to generate new insights and understandings from the data, rather than testing pre-existing hypotheses or theories.
The research requires a method that is transparent, replicable, and verifiable, as Framework Analysis provides a clear framework for data analysis and reporting.

Purpose of Framework Analysis

The purpose of Framework Analysis is to systematically organize and analyze qualitative data in a structured and transparent manner. The method is designed to identify patterns, themes, and relationships in the data that are relevant to the research question or objective. By using a rigorous and transparent approach to data analysis, Framework Analysis enables researchers to generate new insights and understandings from the data, and to provide a clear and structured presentation of the findings.

The method is particularly useful for analyzing large and complex qualitative datasets that require a method for organizing and synthesizing the data in a meaningful way. It can be used to explore a wide range of research questions and objectives across various fields, including health research, social science research, education research, policy analysis, and environmental research, among others.

Overall, the purpose of Framework Analysis is to provide a systematic and transparent method for analyzing qualitative data that enables researchers to generate new insights and understandings from the data in a rigorous and structured manner.

Characteristics of Framework Analysis

Some Characteristics of Framework Analysis are:

Systematic and Structured Approach: Framework Analysis provides a systematic and structured approach to data analysis that involves a series of steps that are followed in a predetermined order.
Transparency and Replicability: Framework Analysis emphasizes transparency and replicability, as it involves a clearly defined process for data analysis that can be applied consistently across different datasets and research questions.
Flexibility: Framework Analysis is flexible and adaptable to a wide range of research contexts and objectives, as it can be used to analyze qualitative data from various sources and to explore different research questions.
In-depth Exploration of the Data: Framework Analysis enables an in-depth exploration of the data, as it involves a thorough and detailed analysis of the data to identify patterns, themes, and relationships.
Applicable to Large and Complex Datasets: Framework Analysis is particularly useful for analyzing large and complex qualitative datasets, as it provides a method for organizing and synthesizing the data in a meaningful way.
Data-Driven: Framework Analysis is data-driven, as it focuses on the analysis and interpretation of the data rather than on pre-existing hypotheses or theories.
Emphasis on Contextual Understanding: Framework Analysis emphasizes contextual understanding, as it involves a detailed examination of the data to identify the social, cultural, and environmental factors that may influence the phenomena under investigation.

Advantages of Framework Analysis

Some Advantages of Framework Analysis are as follows:

Transparency: Framework Analysis provides a clear and structured approach to data analysis, which makes the process transparent and easy to follow. This ensures that the findings can be easily replicated or verified by other researchers.
Flexibility: Framework Analysis is flexible and adaptable to a wide range of research contexts and objectives, as it can be used to analyze qualitative data from various sources and to explore different research questions.
Thorough Analysis: Framework Analysis enables a thorough and detailed analysis of the data, which allows for the identification of patterns, themes, and relationships that may not be apparent through other methods.
Contextual Understanding: Framework Analysis emphasizes the importance of understanding the context in which the data was collected, which enables a more nuanced interpretation of the findings.
Collaborative Analysis: Framework Analysis can be used as a collaborative method for data analysis, as it allows multiple researchers to work together to analyze the data and develop a shared understanding of the findings.
Efficient and Time-saving: Framework Analysis can be an efficient and time-saving method for analyzing qualitative data, as it provides a structured and organized approach to data analysis that can help researchers manage and synthesize large datasets.
Comprehensive Reporting: Framework Analysis can help ensure that the research findings are comprehensive and well-reported, as the method provides a clear framework for presenting the results.

Limitations of Framework Analysis

Some Limitations of Framework Analysis are as follows:

Subjectivity: Framework Analysis relies on the interpretation of the researchers, which can introduce subjectivity into the analysis process.
Time-consuming: Framework Analysis can be a time-consuming method for data analysis, particularly when working with large and complex datasets.
Limited ability to generate new theory: Framework Analysis is a deductive approach that relies on pre-existing theories and concepts to guide the analysis, which may limit the ability to generate new theoretical insights.
Risk of oversimplification: The structured approach of Framework Analysis can lead to oversimplification of the data, as complex issues may be reduced to predefined categories or themes.
Limited ability to capture the complexity of the data: The predefined categories or themes used in Framework Analysis may not be able to capture the full complexity of the data, particularly when dealing with nuanced or context-specific phenomena.
Limited use with non-textual data: Framework Analysis is primarily designed for analyzing qualitative textual data and may not be as effective for analyzing non-textual data such as images, videos, or audio recordings.