how to write data analysis in qualitative research proposal

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Qualitative Data Analysis: What is it, Methods + Examples

In a world rich with information and narrative, understanding the deeper layers of human experiences requires a unique vision that goes beyond numbers and figures. This is where the power of qualitative data analysis comes to light.

In this blog, we’ll learn about qualitative data analysis, explore its methods, and provide real-life examples showcasing its power in uncovering insights.

What is Qualitative Data Analysis?

Qualitative data analysis is a systematic process of examining non-numerical data to extract meaning, patterns, and insights.

In contrast to quantitative analysis, which focuses on numbers and statistical metrics, the qualitative study focuses on the qualitative aspects of data, such as text, images, audio, and videos. It seeks to understand every aspect of human experiences, perceptions, and behaviors by examining the data’s richness.

Companies frequently conduct this analysis on customer feedback. You can collect qualitative data from reviews, complaints, chat messages, interactions with support centers, customer interviews, case notes, or even social media comments. This kind of data holds the key to understanding customer sentiments and preferences in a way that goes beyond mere numbers.

Importance of Qualitative Data Analysis

Qualitative data analysis plays a crucial role in your research and decision-making process across various disciplines. Let’s explore some key reasons that underline the significance of this analysis:

In-Depth Understanding

It enables you to explore complex and nuanced aspects of a phenomenon, delving into the ‘how’ and ‘why’ questions. This method provides you with a deeper understanding of human behavior, experiences, and contexts that quantitative approaches might not capture fully.

Contextual Insight

You can use this analysis to give context to numerical data. It will help you understand the circumstances and conditions that influence participants’ thoughts, feelings, and actions. This contextual insight becomes essential for generating comprehensive explanations.

Theory Development

You can generate or refine hypotheses via qualitative data analysis. As you analyze the data attentively, you can form hypotheses, concepts, and frameworks that will drive your future research and contribute to theoretical advances.

Participant Perspectives

When performing qualitative research, you can highlight participant voices and opinions. This approach is especially useful for understanding marginalized or underrepresented people, as it allows them to communicate their experiences and points of view.

Exploratory Research

The analysis is frequently used at the exploratory stage of your project. It assists you in identifying important variables, developing research questions, and designing quantitative studies that will follow.

Types of Qualitative Data

When conducting qualitative research, you can use several qualitative data collection methods , and here you will come across many sorts of qualitative data that can provide you with unique insights into your study topic. These data kinds add new views and angles to your understanding and analysis.

Interviews and Focus Groups

Interviews and focus groups will be among your key methods for gathering qualitative data. Interviews are one-on-one talks in which participants can freely share their thoughts, experiences, and opinions.

Focus groups, on the other hand, are discussions in which members interact with one another, resulting in dynamic exchanges of ideas. Both methods provide rich qualitative data and direct access to participant perspectives.

Observations and Field Notes

Observations and field notes are another useful sort of qualitative data. You can immerse yourself in the research environment through direct observation, carefully documenting behaviors, interactions, and contextual factors.

These observations will be recorded in your field notes, providing a complete picture of the environment and the behaviors you’re researching. This data type is especially important for comprehending behavior in their natural setting.

Textual and Visual Data

Textual and visual data include a wide range of resources that can be qualitatively analyzed. Documents, written narratives, and transcripts from various sources, such as interviews or speeches, are examples of textual data.

Photographs, films, and even artwork provide a visual layer to your research. These forms of data allow you to investigate what is spoken and the underlying emotions, details, and symbols expressed by language or pictures.

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

As you begin your research journey, understanding why the analysis of qualitative data is important will guide your approach to understanding complex events. If you analyze qualitative data, it will provide new insights that complement quantitative methodologies, which will give you a broader understanding of your study topic.

It is critical to know when to use qualitative analysis over quantitative procedures. You can prefer qualitative data analysis when:

• Complexity Reigns: When your research questions involve deep human experiences, motivations, or emotions, qualitative research excels at revealing these complexities.
• Exploration is Key: Qualitative analysis is ideal for exploratory research. It will assist you in understanding a new or poorly understood topic before formulating quantitative hypotheses.
• Context Matters: If you want to understand how context affects behaviors or results, qualitative data analysis provides the depth needed to grasp these relationships.
• Unanticipated Findings: When your study provides surprising new viewpoints or ideas, qualitative analysis helps you to delve deeply into these emerging themes.
• Subjective Interpretation is Vital: When it comes to understanding people’s subjective experiences and interpretations, qualitative data analysis is the way to go.

You can make informed decisions regarding the right approach for your research objectives if you understand the importance of qualitative analysis and recognize the situations where it shines.

Qualitative Data Analysis Methods and Examples

Exploring various qualitative data analysis methods will provide you with a wide collection for making sense of your research findings. Once the data has been collected, you can choose from several analysis methods based on your research objectives and the data type you’ve collected.

There are five main methods for analyzing qualitative data. Each method takes a distinct approach to identifying patterns, themes, and insights within your qualitative data. They are:

Method 1: Content Analysis

Content analysis is a methodical technique for analyzing textual or visual data in a structured manner. In this method, you will categorize qualitative data by splitting it into manageable pieces and assigning the manual coding process to these units.

As you go, you’ll notice ongoing codes and designs that will allow you to conclude the content. This method is very beneficial for detecting common ideas, concepts, or themes in your data without losing the context.

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

• Collect and Immerse: Begin by collecting the necessary textual or visual data. Immerse yourself in this data to fully understand its content, context, and complexities.
• Assign Codes and Categories: Assign codes to relevant data sections that systematically represent major ideas or themes. Arrange comparable codes into groups that cover the major themes.
• Analyze and Interpret: Develop a structured framework from the categories and codes. Then, evaluate the data in the context of your research question, investigate relationships between categories, discover patterns, and draw meaning from these connections.

Benefits & Challenges

There are various advantages to using content analysis:

• Structured Approach: It offers a systematic approach to dealing with large data sets and ensures consistency throughout the research.
• Objective Insights: This method promotes objectivity, which helps to reduce potential biases in your study.
• Pattern Discovery: Content analysis can help uncover hidden trends, themes, and patterns that are not always obvious.
• Versatility: You can apply content analysis to various data formats, including text, internet content, images, etc.

However, keep in mind the challenges that arise:

• Subjectivity: Even with the best attempts, a certain bias may remain in coding and interpretation.
• Complexity: Analyzing huge data sets requires time and great attention to detail.
• Contextual Nuances: Content analysis may not capture all of the contextual richness that qualitative data analysis highlights.

Example of Content Analysis

Suppose you’re conducting market research and looking at customer feedback on a product. As you collect relevant data and analyze feedback, you’ll see repeating codes like “price,” “quality,” “customer service,” and “features.” These codes are organized into categories such as “positive reviews,” “negative reviews,” and “suggestions for improvement.”

According to your findings, themes such as “price” and “customer service” stand out and show that pricing and customer service greatly impact customer satisfaction. This example highlights the power of content analysis for obtaining significant insights from large textual data collections.

Method 2: Thematic Analysis

Thematic analysis is a well-structured procedure for identifying and analyzing recurring themes in your data. As you become more engaged in the data, you’ll generate codes or short labels representing key concepts. These codes are then organized into themes, providing a consistent framework for organizing and comprehending the substance of the data.

The analysis allows you to organize complex narratives and perspectives into meaningful categories, which will allow you to identify connections and patterns that may not be visible at first.

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

• Code and Group: Start by thoroughly examining the data and giving initial codes that identify the segments. To create initial themes, combine relevant codes.
• Code and Group: Begin by engaging yourself in the data, assigning first codes to notable segments. To construct basic themes, group comparable codes together.
• Analyze and Report: Analyze the data within each theme to derive relevant insights. Organize the topics into a consistent structure and explain your findings, along with data extracts that represent each theme.

Thematic analysis has various benefits:

• Structured Exploration: It is a method for identifying patterns and themes in complex qualitative data.
• Comprehensive knowledge: Thematic analysis promotes an in-depth understanding of the complications and meanings of the data.
• Application Flexibility: This method can be customized to various research situations and data kinds.

However, challenges may arise, such as:

• Interpretive Nature: Interpreting qualitative data in thematic analysis is vital, and it is critical to manage researcher bias.
• Time-consuming: The study can be time-consuming, especially with large data sets.
• Subjectivity: The selection of codes and topics might be subjective.

Example of Thematic Analysis

Assume you’re conducting a thematic analysis on job satisfaction interviews. Following your immersion in the data, you assign initial codes such as “work-life balance,” “career growth,” and “colleague relationships.” As you organize these codes, you’ll notice themes develop, such as “Factors Influencing Job Satisfaction” and “Impact on Work Engagement.”

Further investigation reveals the tales and experiences included within these themes and provides insights into how various elements influence job satisfaction. This example demonstrates how thematic analysis can reveal meaningful patterns and insights in qualitative data.

Method 3: Narrative Analysis

The narrative analysis involves the narratives that people share. You’ll investigate the histories in your data, looking at how stories are created and the meanings they express. This method is excellent for learning how people make sense of their experiences through narrative.

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

• Gather and Analyze: Start by collecting narratives, such as first-person tales, interviews, or written accounts. Analyze the stories, focusing on the plot, feelings, and characters.
• Find Themes: Look for recurring themes or patterns in various narratives. Think about the similarities and differences between these topics and personal experiences.
• Interpret and Extract Insights: Contextualize the narratives within their larger context. Accept the subjective nature of each narrative and analyze the narrator’s voice and style. Extract insights from the tales by diving into the emotions, motivations, and implications communicated by the stories.

There are various advantages to narrative analysis:

• Deep Exploration: It lets you look deeply into people’s personal experiences and perspectives.
• Human-Centered: This method prioritizes the human perspective, allowing individuals to express themselves.

However, difficulties may arise, such as:

• Interpretive Complexity: Analyzing narratives requires dealing with the complexities of meaning and interpretation.
• Time-consuming: Because of the richness and complexities of tales, working with them can be time-consuming.

Example of Narrative Analysis

Assume you’re conducting narrative analysis on refugee interviews. As you read the stories, you’ll notice common themes of toughness, loss, and hope. The narratives provide insight into the obstacles that refugees face, their strengths, and the dreams that guide them.

The analysis can provide a deeper insight into the refugees’ experiences and the broader social context they navigate by examining the narratives’ emotional subtleties and underlying meanings. This example highlights how narrative analysis can reveal important insights into human stories.

Method 4: Grounded Theory Analysis

Grounded theory analysis is an iterative and systematic approach that allows you to create theories directly from data without being limited by pre-existing hypotheses. With an open mind, you collect data and generate early codes and labels that capture essential ideas or concepts within the data.

As you progress, you refine these codes and increasingly connect them, eventually developing a theory based on the data. Grounded theory analysis is a dynamic process for developing new insights and hypotheses based on details in your data.

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

• Initial Coding: First, immerse yourself in the data, producing initial codes that represent major concepts or patterns.
• Categorize and Connect: Using axial coding, organize the initial codes, which establish relationships and connections between topics.
• Build the Theory: Focus on creating a core category that connects the codes and themes. Regularly refine the theory by comparing and integrating new data, ensuring that it evolves organically from the data.

Grounded theory analysis has various benefits:

• Theory Generation: It provides a one-of-a-kind opportunity to generate hypotheses straight from data and promotes new insights.
• In-depth Understanding: The analysis allows you to deeply analyze the data and reveal complex relationships and patterns.
• Flexible Process: This method is customizable and ongoing, which allows you to enhance your research as you collect additional data.

However, challenges might arise with:

• Time and Resources: Because grounded theory analysis is a continuous process, it requires a large commitment of time and resources.
• Theoretical Development: Creating a grounded theory involves a thorough understanding of qualitative data analysis software and theoretical concepts.
• Interpretation of Complexity: Interpreting and incorporating a newly developed theory into existing literature can be intellectually hard.

Example of Grounded Theory Analysis

Assume you’re performing a grounded theory analysis on workplace collaboration interviews. As you open code the data, you will discover notions such as “communication barriers,” “team dynamics,” and “leadership roles.” Axial coding demonstrates links between these notions, emphasizing the significance of efficient communication in developing collaboration.

You create the core “Integrated Communication Strategies” category through selective coding, which unifies new topics.

This theory-driven category serves as the framework for understanding how numerous aspects contribute to effective team collaboration. This example shows how grounded theory analysis allows you to generate a theory directly from the inherent nature of the data.

Method 5: Discourse Analysis

Discourse analysis focuses on language and communication. You’ll look at how language produces meaning and how it reflects power relations, identities, and cultural influences. This strategy examines what is said and how it is said; the words, phrasing, and larger context of communication.

The analysis is precious when investigating power dynamics, identities, and cultural influences encoded in language. By evaluating the language used in your data, you can identify underlying assumptions, cultural standards, and how individuals negotiate meaning through communication.

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

• Select Discourse: For analysis, choose language-based data such as texts, speeches, or media content.
• Analyze Language: Immerse yourself in the conversation, examining language choices, metaphors, and underlying assumptions.
• Discover Patterns: Recognize the dialogue’s reoccurring themes, ideologies, and power dynamics. To fully understand the effects of these patterns, put them in their larger context.

There are various advantages of using discourse analysis:

• Understanding Language: It provides an extensive understanding of how language builds meaning and influences perceptions.
• Uncovering Power Dynamics: The analysis reveals how power dynamics appear via language.
• Cultural Insights: This method identifies cultural norms, beliefs, and ideologies stored in communication.

However, the following challenges may arise:

• Complexity of Interpretation: Language analysis involves navigating multiple levels of nuance and interpretation.
• Subjectivity: Interpretation can be subjective, so controlling researcher bias is important.
• Time-Intensive: Discourse analysis can take a lot of time because careful linguistic study is required in this analysis.

Example of Discourse Analysis

Consider doing discourse analysis on media coverage of a political event. You notice repeating linguistic patterns in news articles that depict the event as a conflict between opposing parties. Through deconstruction, you can expose how this framing supports particular ideologies and power relations.

You can illustrate how language choices influence public perceptions and contribute to building the narrative around the event by analyzing the speech within the broader political and social context. This example shows how discourse analysis can reveal hidden power dynamics and cultural influences on communication.

How to do Qualitative Data Analysis with the QuestionPro Research suite?

QuestionPro is a popular survey and research platform that offers tools for collecting and analyzing qualitative and quantitative data. Follow these general steps for conducting qualitative data analysis using the QuestionPro Research Suite:

• Collect Qualitative Data: Set up your survey to capture qualitative responses. It might involve open-ended questions, text boxes, or comment sections where participants can provide detailed responses.
• Export Qualitative Responses: Export the responses once you’ve collected qualitative data through your survey. QuestionPro typically allows you to export survey data in various formats, such as Excel or CSV.
• Prepare Data for Analysis: Review the exported data and clean it if necessary. Remove irrelevant or duplicate entries to ensure your data is ready for analysis.
• Code and Categorize Responses: Segment and label data, letting new patterns emerge naturally, then develop categories through axial coding to structure the analysis.
• Identify Themes: Analyze the coded responses to identify recurring themes, patterns, and insights. Look for similarities and differences in participants’ responses.
• Generate Reports and Visualizations: Utilize the reporting features of QuestionPro to create visualizations, charts, and graphs that help communicate the themes and findings from your qualitative research.
• Interpret and Draw Conclusions: Interpret the themes and patterns you’ve identified in the qualitative data. Consider how these findings answer your research questions or provide insights into your study topic.
• Integrate with Quantitative Data (if applicable): If you’re also conducting quantitative research using QuestionPro, consider integrating your qualitative findings with quantitative results to provide a more comprehensive understanding.

Qualitative data analysis is vital in uncovering various human experiences, views, and stories. If you’re ready to transform your research journey and apply the power of qualitative analysis, now is the moment to do it. Book a demo with QuestionPro today and begin your journey of exploration.

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Developing a Research Proposal for Qualitative Research: A Step-by-Step Guide

Creating a research proposal for qualitative studies can seem like a huge task. This guide will help you step by step. From understanding the basics to writing the final proposal, we will cover everything you need to know. By the end, you will have a clear plan to follow.

Key Takeaways

• Understanding the basics of qualitative research is important for a strong proposal.
• A clear research question guides your study and ensures it stays on track.
• Choosing the right methods and being ethical are key parts of your research design.
• Recruiting the right participants and using proper sampling methods are crucial.
• Analyzing data carefully and presenting your findings clearly is essential.

Understanding the Foundations of Qualitative Research

Qualitative research is essential for exploring complex social phenomena. It provides an in-depth understanding and rich data analysis, complementing quantitative research. Choosing the right research methodology for your Ph.D. thesis is crucial for obtaining meaningful results.

Formulating a Research Question

Identifying the research problem.

The first step in formulating qualitative research questions is to have a clear understanding of what you aim to discover or understand through your research. How much do we know about the problem? What are the gaps in our knowledge? How would new insights contribute to society or clinical practice? Why is this research worth doing? And who might have an interest in this topic?

Using the SPIDER Tool

The SPIDER tool is a useful framework for defining the research question. SPIDER stands for Sample, Phenomenon of Interest, Design, Evaluation, and Research type. This tool helps in highlighting the gap in knowledge that your research aims to address. It ensures that your research question is focused and researchable, whether through primary or secondary sources.

Ensuring Feasibility and Relevance

After formulating the question(s), you must consider how you will answer it. Answering the question(s) will depend on the question, the design, and the research type. Your research question should be feasible to answer within a given timeframe and specific enough for you to answer thoroughly.

Designing the Research Methodology

After formulating your research question, you must consider how to answer it. Answering the question will depend on the question itself, the design, and the research type.

Selecting Appropriate Methods

Choosing the right methods is crucial. Each design method has pros and cons, and the selection depends on the question, the participants, and the time scale. For example, if you're looking at the experiences of someone who's had severe trauma or exploring a sensitive topic, a one-to-one interview is probably the most appropriate method to respect privacy.

Data Collection Techniques

Data collection is a vital part of your research design . You need to clearly explain your data collection methods so readers understand how you will conduct your study. This section should provide enough detail for readers to evaluate its validity and reliability. Poorly articulated research design can lead to misunderstandings and questions about your study's credibility.

Ethical Considerations

Ethical considerations are paramount in qualitative research. You must ensure that your study respects the rights and dignity of participants. This includes obtaining informed consent, ensuring confidentiality, and being sensitive to the needs and vulnerabilities of your participants. Addressing these ethical issues is not just a formality but a fundamental part of your research design.

Recruiting and Sampling Participants

Defining the target population.

When defining your target population, it's crucial to set clear criteria that align with your research objectives . Quality over quantity is essential; recruiting the right participants ensures the integrity of your study. Sometimes, you might not reach your planned sample size, but it's better to have fewer participants who meet your criteria than to compromise your results.

Sampling Strategies

There is no magic number for how many people you should recruit for qualitative research. The sample sizes are usually smaller than in quantitative research and will depend on many variables. When writing a research proposal, provide justification and rationale for your chosen number of participants. Considerations include the scope of your study and the depth of data you aim to collect.

Recruitment Procedures

Recruitment can be done online via social media or through advertising posters in outpatient clinics. Choose the most convenient method that will link you to the most suitable people. For example, a social media advert might be ideal for a study on e-health, as your cohort should be comfortable using computers. Researchers should avoid directly approaching potential participants to prevent any feeling of obligation to take part. Instead, use a gatekeeper who can act as a go-between to advertise the study to potential participants who meet the criteria.

Data Analysis and Interpretation

Coding and thematic analysis.

When we analyze qualitative data , we need systematic, rigorous, and transparent ways of manipulating our data in order to begin developing answers to our research questions. Coding is a crucial first step in this process. It involves labeling segments of data with codes that represent themes or patterns. Using software tools can make this task more efficient and help maintain consistency.

Ensuring Rigor and Trustworthiness

To ensure the rigor and trustworthiness of your analysis, you should employ strategies such as member checking, triangulation, and maintaining an audit trail. Member checking involves sharing your findings with participants to verify accuracy. Triangulation uses multiple data sources or methods to confirm findings. An audit trail documents the research process in detail, providing transparency.

Presenting Findings

Presenting your findings in a clear and organized manner is essential. Use direct quotes from participants to illustrate key themes and provide evidence for your interpretations. Tables can be helpful for summarizing data and highlighting important points. Remember to discuss the implications of your findings and how they contribute to the existing body of knowledge.

Writing the Research Proposal

When preparing a research proposal, it is essential to follow the specific guidelines provided by your institution or program. Some institutions may have additional requirements, such as excluding references, figures, or timelines from the page limit.

Structuring the Proposal

A research proposal is a document that describes the idea, importance, and method of the research. The format can vary widely among different higher education settings, different funders, and different organizations. When thinking of the research proposal, it's your tool to sell the research to probably an ethics committee or a research funder, so you want to show them why your research is important to be done. Here are some prompting questions to help with writing the background:

• What is the main problem or question your research aims to address?
• Why is this research important?
• What are the key objectives of your study?

Writing the Literature Review

The title of your research proposal can be different from the publishing title. It can be considered a working title that you can revisit after finishing the research proposal and amend if needed. "The title" should contain keywords of what your research encompasses, such as:

• The main topic of your research
• The specific aspect you are focusing on
• Any key terms or concepts

Developing a Timeline

When thinking about how to start thesis , setting clear goals, utilizing online databases, conducting interviews, and collecting relevant data are key steps. The length of your research proposal can vary. Make sure to include a timeline that outlines the major milestones of your research project. This can help you stay on track and ensure that you meet all deadlines.

By following these tips for researching and organizing your thesis , you can create a strong and compelling research proposal.

Addressing Ethical and Practical Issues

Informed consent.

When conducting qualitative research, obtaining informed consent is crucial. Participants must be fully aware of the study's purpose, procedures, and any potential risks. Mastering the interview process includes ensuring that participants understand their rights and can withdraw at any time without penalty.

Confidentiality and Anonymity

Protecting the privacy of participants is a key aspect of ethical research. Researchers must take steps to ensure that data is stored securely and that identifying information is kept confidential. This includes using pseudonyms and removing any details that could reveal a participant's identity.

Dealing with Practical Challenges

Qualitative research often involves addressing sensitive topics, which can present practical challenges. Researchers need to be prepared to handle emotional responses and provide support if needed. Additionally, defining the research scope clearly can help in managing time and resources effectively.

When tackling ethical and practical issues, it's important to have the right tools and guidance. Our step-by-step Thesis Action Plan is designed to help you navigate these challenges with ease. Whether you're struggling with sleepless nights or feeling overwhelmed, our resources are here to support you. Don't let stress hold you back any longer. Visit our website to learn more and take the first step towards a smoother thesis journey.

In conclusion, developing a qualitative research proposal is a detailed and thoughtful process that requires careful planning and consideration. By following the steps outlined in this guide, researchers can ensure that their proposals are comprehensive and well-structured. This not only helps in gaining approval from review boards but also sets a strong foundation for conducting meaningful and impactful research. Remember, the key to a successful research proposal lies in clarity, coherence, and a thorough understanding of the research topic. With dedication and attention to detail, anyone can master the art of crafting a qualitative research proposal.

Frequently Asked Questions

What is a qualitative research proposal.

A qualitative research proposal is a document that outlines the idea, importance, and methods of your research. It helps to plan out how you will collect and analyze non-numerical data.

Why is it important to have a research question?

Having a research question is important because it guides your study. It helps you focus on what you want to find out and keeps your research on track.

What is the SPIDER tool?

The SPIDER tool is a method used to define a research question in qualitative research. It stands for Sample, Phenomenon of Interest, Design, Evaluation, and Research type.

How do you ensure the ethical considerations in qualitative research?

To ensure ethical considerations, you need to get informed consent from participants, protect their confidentiality, and make sure your study does no harm.

What are some common data collection techniques in qualitative research?

Common data collection techniques include interviews, focus groups, and observations. These methods help gather detailed and in-depth information.

How do you present your findings in a qualitative research proposal?

You present your findings by coding the data and identifying themes. Then, you explain these themes and what they mean in relation to your research question.

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Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) | Reviewed By: Eunice Rautenbach (D.Tech) | May 2020 (Updated April 2023)

Qualitative data analysis methods. Wow, that’s a mouthful. 

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much bulky terminology and so many abstract, fluffy concepts. It certainly can be a minefield!

Don’t worry – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project.

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s step back and ask the question, “what exactly is qualitative data?”.

Qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex equipment, nor do you analyse it using complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

So, qualitative analysis is easier than quantitative, right?

Not quite. In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might also have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes. All of this needs to work its way into your analysis.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work! Of course, quantitative analysis is no piece of cake either, but it’s important to recognise that qualitative analysis still requires a significant investment in terms of time and effort.

Need a helping hand?

In this post, we’ll explore qualitative data analysis by looking at some of the most common analysis methods we encounter. We’re not going to cover every possible qualitative method and we’re not going to go into heavy detail – we’re just going to give you the big picture. That said, we will of course includes links to loads of extra resources so that you can learn more about whichever analysis method interests you.

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

There are many different types of qualitative data analysis, all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each.

The 6 most popular methods (or at least the ones we see at Grad Coach) are:

• Content analysis
• Narrative analysis
• Discourse analysis
• Thematic analysis
• Grounded theory (GT)
• Interpretive phenomenological analysis (IPA)

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time-consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its limitations , so don’t be put off by these – just be aware of them ! If you’re interested in learning more about content analysis, the video below provides a good starting point.

QDA Method #2: Narrative Analysis 

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . Simply put, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , too. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Similarly, researcher bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative analysis method – just keep these limitations in mind and be careful not to draw broad conclusions . If you’re keen to learn more about narrative analysis, the video below provides a great introduction to this qualitative analysis method.

QDA Method #3: Discourse Analysis 

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication are important factors to consider. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in terms of how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time-consuming  as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Again, if you’re keen to learn more, the video below presents a good starting point.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 online reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

QDA Method #5: Grounded theory (GT) 

Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions ”. Strictly speaking, GT is more a research design type than an analysis method, but we’ve included it here as it’s often referred to as a method.

What’s most important with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name). 

Let’s look at an example of GT in action.

Assume you’re interested in developing a theory about what factors influence students to watch a YouTube video about qualitative analysis. Using Grounded theory , you’d start with this general overarching question about the given population (i.e., graduate students). First, you’d approach a small sample – for example, five graduate students in a department at a university. Ideally, this sample would be reasonably representative of the broader population. You’d interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory would develop . As we mentioned earlier, what’s important with grounded theory is that the theory develops from the data – not from some preconceived idea.

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

Interpretive. Phenomenological. Analysis. IPA . Try saying that three times fast…

Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” that makes up the “P” in IPA. Such phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

How to choose the right analysis method

In light of all of the qualitative analysis methods we’ve covered so far, you’re probably asking yourself the question, “ How do I choose the right one? ”

Much like all the other methodological decisions you’ll need to make, selecting the right qualitative analysis method largely depends on your research aims, objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

• Perhaps your research aims to analyse the use of words and what they reveal about the intention of the storyteller and the cultural context of the time.
• Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event, or
• Perhaps your research aims to develop insight regarding the influence of a certain culture on its members.

As you can probably see, each of these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. For example, narrative analysis would likely be a good option for the first aim, while grounded theory wouldn’t be as relevant. 

It’s also important to remember that each method has its own set of strengths, weaknesses and general limitations. No single analysis method is perfect . So, depending on the nature of your research, it may make sense to adopt more than one method (this is called triangulation ). Keep in mind though that this will of course be quite time-consuming.

As we’ve seen, all of the qualitative analysis methods we’ve discussed make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s very important to come into your research with a clear intention before you decide which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a qualitative analysis method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

Let’s recap on QDA methods…

In this post, we looked at six popular qualitative data analysis methods:

• First, we looked at content analysis , a straightforward method that blends a little bit of quant into a primarily qualitative analysis.
• Then we looked at narrative analysis , which is about analysing how stories are told.
• Next up was discourse analysis – which is about analysing conversations and interactions.
• Then we moved on to thematic analysis – which is about identifying themes and patterns.
• From there, we went south with grounded theory – which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question.
• And finally, we looked at IPA – which is about understanding people’s unique experiences of a phenomenon.

Of course, these aren’t the only options when it comes to qualitative data analysis, but they’re a great starting point if you’re dipping your toes into qualitative research for the first time.

If you’re still feeling a bit confused, consider our private coaching service , where we hold your hand through the research process to help you develop your best work.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

87 Comments

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Hi, may we use 2 data analysis methods in our qualitative research?

Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

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Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify – https://onlinelibrary.wiley.com/doi/10.1111/nhs.12048

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choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?

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Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

content analysis can be qualitative research?

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do you have any material on Data collection

What a powerful explanation of the QDA methods. Thank you.

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This is an eye opener for me and very informative, I have used some of your guidance notes on my Thesis, I wonder if you can assist with your 1. name of your book, year of publication, topic etc., this is for citing in my Bibliography,

I certainly hope to hear from you

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Qualitative Data Analysis

Qualitative data refers to non-numeric information such as interview transcripts, notes, video and audio recordings, images and text documents. Qualitative data analysis can be divided into the following five categories:

1. Content analysis . This refers to the process of categorizing verbal or behavioural data to classify, summarize and tabulate the data.

2. Narrative analysis . This method involves the reformulation of stories presented by respondents taking into account context of each case and different experiences of each respondent. In other words, narrative analysis is the revision of primary qualitative data by researcher.

3. Discourse analysis . A method of analysis of naturally occurring talk and all types of written text.

4. Framework analysis . This is more advanced method that consists of several stages such as familiarization, identifying a thematic framework, coding, charting, mapping and interpretation.

5. Grounded theory . This method of qualitative data analysis starts with an analysis of a single case to formulate a theory. Then, additional cases are examined to see if they contribute to the theory.

Qualitative data analysis can be conducted through the following three steps:

Step 1: Developing and Applying Codes . Coding can be explained as categorization of data. A ‘code’ can be a word or a short phrase that represents a theme or an idea. All codes need to be assigned meaningful titles. A wide range of non-quantifiable elements such as events, behaviours, activities, meanings etc. can be coded.

There are three types of coding:

• Open coding . The initial organization of raw data to try to make sense of it.
• Axial coding . Interconnecting and linking the categories of codes.
• Selective coding . Formulating the story through connecting the categories.

Coding can be done manually or using qualitative data analysis software such as

 NVivo,  Atlas ti 6.0,  HyperRESEARCH 2.8,  Max QDA and others.

When using manual coding you can use folders, filing cabinets, wallets etc. to gather together materials that are examples of similar themes or analytic ideas. Manual method of coding in qualitative data analysis is rightly considered as labour-intensive, time-consuming and outdated.

In computer-based coding, on the other hand, physical files and cabinets are replaced with computer based directories and files. When choosing software for qualitative data analysis you need to consider a wide range of factors such as the type and amount of data you need to analyse, time required to master the software and cost considerations.

Moreover, it is important to get confirmation from your dissertation supervisor prior to application of any specific qualitative data analysis software.

The following table contains examples of research titles, elements to be coded and identification of relevant codes:

 Qualitative data coding

Step 2: Identifying themes, patterns and relationships . Unlike quantitative methods , in qualitative data analysis there are no universally applicable techniques that can be applied to generate findings. Analytical and critical thinking skills of researcher plays significant role in data analysis in qualitative studies. Therefore, no qualitative study can be repeated to generate the same results.

Nevertheless, there is a set of techniques that you can use to identify common themes, patterns and relationships within responses of sample group members in relation to codes that have been specified in the previous stage.

Specifically, the most popular and effective methods of qualitative data interpretation include the following:

• Word and phrase repetitions – scanning primary data for words and phrases most commonly used by respondents, as well as, words and phrases used with unusual emotions;
• Primary and secondary data comparisons – comparing the findings of interview/focus group/observation/any other qualitative data collection method with the findings of literature review and discussing differences between them;
• Search for missing information – discussions about which aspects of the issue was not mentioned by respondents, although you expected them to be mentioned;
• Metaphors and analogues – comparing primary research findings to phenomena from a different area and discussing similarities and differences.

Step 3: Summarizing the data . At this last stage you need to link research findings to hypotheses or research aim and objectives. When writing data analysis chapter, you can use noteworthy quotations from the transcript in order to highlight major themes within findings and possible contradictions.

It is important to note that the process of qualitative data analysis described above is general and different types of qualitative studies may require slightly different methods of data analysis.

My  e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach  contains a detailed, yet simple explanation of qualitative data analysis methods . The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in simple words. John Dudovskiy

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7 Writing a proposal

• Published: May 2018
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When researchers plan to undertake qualitative research with a pilot or full RCT they write a proposal to apply for funding, seek ethical approval, or as part of their PhD studies. These proposals can be published in journals. Guidance for writing a proposal for the qualitative research undertaken with RCTs has been published, and there is existing guidance for writing proposals in related areas such as mixed methods research. In this chapter, existing guidance is introduced and built upon to offer comprehensive and detailed guidance for writing a proposal for the qualitative research undertaken with an RCT. There are challenges to writing these proposals and these are discussed and potential solutions proposed.

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Designing a Research Proposal in Qualitative Research

• First Online: 27 October 2022

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• Md. Ismail Hossain 4 ,
• Nafiul Mehedi 4 &
• Iftakhar Ahmad 4  

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The chapter discusses designing a research proposal in qualitative research. The main objective is to outline the major components of a qualitative research proposal with example(s) so that the students and novice scholars easily get an understanding of a qualitative proposal. The chapter highlights the major components of a qualitative research proposal and discusses the steps involved in designing a proposal. In each step, an example is given with some essential tips. Following these steps and tips, a novice researcher can easily prepare a qualitative research proposal. Readers, especially undergraduate and master’s students, might use this as a guideline while preparing a thesis proposal. After reading this chapter, they can easily prepare a qualitative proposal.

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What Should Be Avoided During Qualitative Research?

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What is Qualitative in Research

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Hossain, M.I., Mehedi, N., Ahmad, I. (2022). Designing a Research Proposal in Qualitative Research. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_18

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Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

When we conduct qualitative methods of research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through:

• Interview transcripts
• Surveys with open-ended questions
• Contact center transcripts
• Texts and documents
• Audio and video recordings
• Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

It's important to understand the differences between quantitative data & qualitative data . But unfortunately, analyzing qualitative data is difficult. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative data with ease, there are a limited number of mainstream tools for analyzing qualitative data . The majority of qualitative data analysis still happens manually.

That said, there are two new trends that are changing this. First, there are advances in natural language processing (NLP) which is focused on understanding human language. Second, there is an explosion of user-friendly software designed for both researchers and businesses. Both help automate the qualitative data analysis process.

In this post we want to teach you how to conduct a successful qualitative data analysis. There are two primary qualitative data analysis methods; manual & automatic. We will teach you how to conduct the analysis manually, and also, automatically using software solutions powered by NLP. We’ll guide you through the steps to conduct a manual analysis, and look at what is involved and the role technology can play in automating this process.

More businesses are switching to fully-automated analysis of qualitative customer data because it is cheaper, faster, and just as accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they can understand customer pain points and sentiment.

We’ll take you through 5 steps to conduct a successful qualitative data analysis. Within each step we will highlight the key difference between the manual, and automated approach of qualitative researchers. Here's an overview of the steps:

The 5 steps to doing qualitative data analysis

• Gathering and collecting your qualitative data
• Organizing and connecting into your qualitative data
• Coding your qualitative data
• Analyzing the qualitative data for insights
• Reporting on the insights derived from your analysis

What is Qualitative Data Analysis?

Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents.

Qualitative data is non-numerical and unstructured. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

Businesses often perform qualitative data analysis on customer feedback. And within this context, qualitative data generally refers to verbatim text data collected from sources such as reviews, complaints, chat messages, support centre interactions, customer interviews, case notes or social media comments.

How is qualitative data analysis different from quantitative data analysis?

Understanding the differences between quantitative & qualitative data is important. When it comes to analyzing data, Qualitative Data Analysis serves a very different role to Quantitative Data Analysis. But what sets them apart?

Qualitative Data Analysis dives into the stories hidden in non-numerical data such as interviews, open-ended survey answers, or notes from observations. It uncovers the ‘whys’ and ‘hows’ giving a deep understanding of people’s experiences and emotions.

Quantitative Data Analysis on the other hand deals with numerical data, using statistics to measure differences, identify preferred options, and pinpoint root causes of issues.  It steps back to address questions like "how many" or "what percentage" to offer broad insights we can apply to larger groups.

In short, Qualitative Data Analysis is like a microscope,  helping us understand specific detail. Quantitative Data Analysis is like the telescope, giving us a broader perspective. Both are important, working together to decode data for different objectives.

Qualitative Data Analysis methods

Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you’ve gathered.  Common qualitative data analysis methods include:

Content Analysis

This is a popular approach to qualitative data analysis. Other qualitative analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis.  Content analysis is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content analysis is useful to quantify the relationship between all of the grouped content. The Columbia School of Public Health has a detailed breakdown of content analysis .

Narrative Analysis

Narrative analysis focuses on the stories people tell and the language they use to make sense of them.  It is particularly useful in qualitative research methods where customer stories are used to get a deep understanding of customers’ perspectives on a specific issue. A narrative analysis might enable us to summarize the outcomes of a focused case study.

Discourse Analysis

Discourse analysis is used to get a thorough understanding of the political, cultural and power dynamics that exist in specific situations.  The focus of discourse analysis here is on the way people express themselves in different social contexts. Discourse analysis is commonly used by brand strategists who hope to understand why a group of people feel the way they do about a brand or product.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis . Often, the outcome of thematic analysis is a code frame that captures themes in terms of codes, also called categories. So the process of thematic analysis is also referred to as “coding”. A common use-case for thematic analysis in companies is analysis of customer feedback.

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. Grounded theory analysis is based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original grounded theory.

Challenges of Qualitative Data Analysis

While Qualitative Data Analysis offers rich insights, it comes with its challenges. Each unique QDA method has its unique hurdles. Let’s take a look at the challenges researchers and analysts might face, depending on the chosen method.

• Time and Effort (Narrative Analysis): Narrative analysis, which focuses on personal stories, demands patience. Sifting through lengthy narratives to find meaningful insights can be time-consuming, requires dedicated effort.
• Being Objective (Grounded Theory): Grounded theory, building theories from data, faces the challenges of personal biases. Staying objective while interpreting data is crucial, ensuring conclusions are rooted in the data itself.
• Complexity (Thematic Analysis): Thematic analysis involves identifying themes within data, a process that can be intricate. Categorizing and understanding themes can be complex, especially when each piece of data varies in context and structure. Thematic Analysis software can simplify this process.
• Generalizing Findings (Narrative Analysis): Narrative analysis, dealing with individual stories, makes drawing broad challenging. Extending findings from a single narrative to a broader context requires careful consideration.
• Managing Data (Thematic Analysis): Thematic analysis involves organizing and managing vast amounts of unstructured data, like interview transcripts. Managing this can be a hefty task, requiring effective data management strategies.
• Skill Level (Grounded Theory): Grounded theory demands specific skills to build theories from the ground up. Finding or training analysts with these skills poses a challenge, requiring investment in building expertise.

Benefits of qualitative data analysis

Qualitative Data Analysis (QDA) is like a versatile toolkit, offering a tailored approach to understanding your data. The benefits it offers are as diverse as the methods. Let’s explore why choosing the right method matters.

• Tailored Methods for Specific Needs: QDA isn't one-size-fits-all. Depending on your research objectives and the type of data at hand, different methods offer unique benefits. If you want emotive customer stories, narrative analysis paints a strong picture. When you want to explain a score, thematic analysis reveals insightful patterns
• Flexibility with Thematic Analysis: thematic analysis is like a chameleon in the toolkit of QDA. It adapts well to different types of data and research objectives, making it a top choice for any qualitative analysis.
• Deeper Understanding, Better Products: QDA helps you dive into people's thoughts and feelings. This deep understanding helps you build products and services that truly matches what people want, ensuring satisfied customers
• Finding the Unexpected: Qualitative data often reveals surprises that we miss in quantitative data. QDA offers us new ideas and perspectives, for insights we might otherwise miss.
• Building Effective Strategies: Insights from QDA are like strategic guides. They help businesses in crafting plans that match people’s desires.
• Creating Genuine Connections: Understanding people’s experiences lets businesses connect on a real level. This genuine connection helps build trust and loyalty, priceless for any business.

How to do Qualitative Data Analysis: 5 steps

Now we are going to show how you can do your own qualitative data analysis. We will guide you through this process step by step. As mentioned earlier, you will learn how to do qualitative data analysis manually , and also automatically using modern qualitative data and thematic analysis software.

To get best value from the analysis process and research process, it’s important to be super clear about the nature and scope of the question that’s being researched. This will help you select the research collection channels that are most likely to help you answer your question.

Depending on if you are a business looking to understand customer sentiment, or an academic surveying a school, your approach to qualitative data analysis will be unique.

Once you’re clear, there’s a sequence to follow. And, though there are differences in the manual and automatic approaches, the process steps are mostly the same.

The use case for our step-by-step guide is a company looking to collect data (customer feedback data), and analyze the customer feedback - in order to improve customer experience. By analyzing the customer feedback the company derives insights about their business and their customers. You can follow these same steps regardless of the nature of your research. Let’s get started.

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.

Classic methods of gathering qualitative data

Most companies use traditional methods for gathering qualitative data: conducting interviews with research participants, running surveys, and running focus groups. This data is typically stored in documents, CRMs, databases and knowledge bases. It’s important to examine which data is available and needs to be included in your research project, based on its scope.

Using your existing qualitative feedback

As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.

Most organizations have now invested in Voice of Customer programs , support ticketing systems, chatbot and support conversations, emails and even customer Slack chats.

These new channels provide companies with new ways of getting feedback, and also allow the collection of unstructured feedback data at scale.

The great thing about this data is that it contains a wealth of valubale insights and that it’s already there! When you have a new question about user behavior or your customers, you don’t need to create a new research study or set up a focus group. You can find most answers in the data you already have.

Typically, this data is stored in third-party solutions or a central database, but there are ways to export it or connect to a feedback analysis solution through integrations or an API.

Utilize untapped qualitative data channels

There are many online qualitative data sources you may not have considered. For example, you can find useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such as Discourse or Reddit also contain valuable data about your customers, or research questions.

If you are considering performing a qualitative benchmark analysis against competitors - the internet is your best friend, and review analysis is a great place to start. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a great way to perform a competitor benchmark analysis.

Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews.

Step 2: Connect & organize all your qualitative data

Now you all have this qualitative data but there’s a problem, the data is unstructured. Before feedback can be analyzed and assigned any value, it needs to be organized in a single place. Why is this important? Consistency!

If all data is easily accessible in one place and analyzed in a consistent manner, you will have an easier time summarizing and making decisions based on this data.

The manual approach to organizing your data

The classic method of structuring qualitative data is to plot all the raw data you’ve gathered into a spreadsheet.

Typically, research and support teams would share large Excel sheets and different business units would make sense of the qualitative feedback data on their own. Each team collects and organizes the data in a way that best suits them, which means the feedback tends to be kept in separate silos.

An alternative and a more robust solution is to store feedback in a central database, like Snowflake or Amazon Redshift .

Keep in mind that when you organize your data in this way, you are often preparing it to be imported into another software. If you go the route of a database, you would need to use an API to push the feedback into a third-party software.

Computer-assisted qualitative data analysis software (CAQDAS)

Traditionally within the manual analysis approach (but not always), qualitative data is imported into CAQDAS software for coding.

In the early 2000s, CAQDAS software was popularised by developers such as ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.  

The benefits of using computer-assisted qualitative data analysis software:

• Assists in the organizing of your data
• Opens you up to exploring different interpretations of your data analysis
• Allows you to share your dataset easier and allows group collaboration (allows for secondary analysis)

However you still need to code the data, uncover the themes and do the analysis yourself. Therefore it is still a manual approach.

Organizing your qualitative data in a feedback repository

Another solution to organizing your qualitative data is to upload it into a feedback repository where it can be unified with your other data , and easily searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative research data. Here are a couple solutions that you could investigate:  

• Dovetail: Dovetail is a research repository with a focus on video and audio transcriptions. You can tag your transcriptions within the platform for theme analysis. You can also upload your other qualitative data such as research reports, survey responses, support conversations ( conversational analytics ), and customer interviews. Dovetail acts as a single, searchable repository. And makes it easier to collaborate with other people around your qualitative research.
• EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more sophisticated search engine, but it has a higher starting subscription cost.

Organizing your qualitative data in a feedback analytics platform

If you have a lot of qualitative customer or employee feedback, from the likes of customer surveys or employee surveys, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment analysis and thematic analysis . Companies use the integrations offered by these platforms to directly tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data collected is then organized and analyzed consistently within the platform.

If you have data prepared in a spreadsheet, it can also be imported into feedback analytics platforms.

Once all this rich data has been organized within the feedback analytics platform, it is ready to be coded and themed, within the same platform. Thematic is a feedback analytics platform that offers one of the largest libraries of integrations with qualitative data sources.

Step 3: Coding your qualitative data

Your feedback data is now organized in one place. Either within your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The next step is to code your feedback data so we can extract meaningful insights in the next step.

Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the data, and the relationships between these themes.

To simplify the coding process, you will take small samples of your customer feedback data, come up with a set of codes, or categories capturing themes, and label each piece of feedback, systematically, for patterns and meaning. Then you will take a larger sample of data, revising and refining the codes for greater accuracy and consistency as you go.

If you choose to use a feedback analytics platform, much of this process will be automated and accomplished for you.

The terms to describe different categories of meaning (‘theme’, ‘code’, ‘tag’, ‘category’ etc) can be confusing as they are often used interchangeably.  For clarity, this article will use the term ‘code’.

To code means to identify key words or phrases and assign them to a category of meaning. “I really hate the customer service of this computer software company” would be coded as “poor customer service”.

How to manually code your qualitative data

• Decide whether you will use deductive or inductive coding. Deductive coding is when you create a list of predefined codes, and then assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the data itself. Codes arise directly from the data and you label them as you go. You need to weigh up the pros and cons of each coding method and select the most appropriate.
• Read through the feedback data to get a broad sense of what it reveals. Now it’s time to start assigning your first set of codes to statements and sections of text.
• Keep repeating step 2, adding new codes and revising the code description as often as necessary.  Once it has all been coded, go through everything again, to be sure there are no inconsistencies and that nothing has been overlooked.
• Create a code frame to group your codes. The coding frame is the organizational structure of all your codes. And there are two commonly used types of coding frames, flat, or hierarchical. A hierarchical code frame will make it easier for you to derive insights from your analysis.
• Based on the number of times a particular code occurs, you can now see the common themes in your feedback data. This is insightful! If ‘bad customer service’ is a common code, it’s time to take action.

We have a detailed guide dedicated to manually coding your qualitative data .

Using software to speed up manual coding of qualitative data

An Excel spreadsheet is still a popular method for coding. But various software solutions can help speed up this process. Here are some examples.

• CAQDAS / NVivo - CAQDAS software has built-in functionality that allows you to code text within their software. You may find the interface the software offers easier for managing codes than a spreadsheet.
• Dovetail/EnjoyHQ - You can tag transcripts and other textual data within these solutions. As they are also repositories you may find it simpler to keep the coding in one platform.
• IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
• Ascribe - Ascribe’s ‘Coder’ is a coding management system. Its user interface will make it easier for you to manage your codes.

Automating the qualitative coding process using thematic analysis software

In solutions which speed up the manual coding process, you still have to come up with valid codes and often apply codes manually to pieces of feedback. But there are also solutions that automate both the discovery and the application of codes.

Advances in machine learning have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software .

Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural language processing (NLP) into the software, the AI looks across sentences and phrases to identify common themes meaningful statements. Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI by providing examples. You could say that the AI learns the meaning of the feedback on its own.

Thematic automates the coding of qualitative feedback regardless of source. There’s no need to set up themes or categories in advance. Simply upload your data and wait a few minutes. You can also manually edit the codes to further refine their accuracy.  Experiments conducted indicate that Thematic’s automated coding is just as accurate as manual coding .

Paired with sentiment analysis and advanced text analytics - these automated solutions become powerful for deriving quality business or research insights.

You could also build your own , if you have the resources!

The key benefits of using an automated coding solution

Automated analysis can often be set up fast and there’s the potential to uncover things that would never have been revealed if you had given the software a prescribed list of themes to look for.

Because the model applies a consistent rule to the data, it captures phrases or statements that a human eye might have missed.

Complete and consistent analysis of customer feedback enables more meaningful findings. Leading us into step 4.

Step 4: Analyze your data: Find meaningful insights

Now we are going to analyze our data to find insights. This is where we start to answer our research questions. Keep in mind that step 4 and step 5 (tell the story) have some overlap . This is because creating visualizations is both part of analysis process and reporting.

The task of uncovering insights is to scour through the codes that emerge from the data and draw meaningful correlations from them. It is also about making sure each insight is distinct and has enough data to support it.

Part of the analysis is to establish how much each code relates to different demographics and customer profiles, and identify whether there’s any relationship between these data points.

Manually create sub-codes to improve the quality of insights

If your code frame only has one level, you may find that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.

Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.

While time-consuming, this exercise will improve the quality of your analysis. Here is an example of what sub-codes could look like.

You need to carefully read your qualitative data to create quality sub-codes. But as you can see, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you can get insight into which  customer service problems you can immediately address.

Correlate the frequency of codes to customer segments

Many businesses use customer segmentation . And you may have your own respondent segments that you can apply to your qualitative analysis. Segmentation is the practise of dividing customers or research respondents into subgroups.

Segments can be based on:

• Demographic
• And any other data type that you care to segment by

It is particularly useful to see the occurrence of codes within your segments. If one of your customer segments is considered unimportant to your business, but they are the cause of nearly all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!

Manually visualizing coded qualitative data

There are formulas you can use to visualize key insights in your data. The formulas we will suggest are imperative if you are measuring a score alongside your feedback.

If you are collecting a metric alongside your qualitative data this is a key visualization. Impact answers the question: “What’s the impact of a code on my overall score?”. Using Net Promoter Score (NPS) as an example, first you need to:

• Calculate overall NPS
• Calculate NPS in the subset of responses that do not contain that theme
• Subtract B from A

Then you can use this simple formula to calculate code impact on NPS .

You can then visualize this data using a bar chart.

You can download our CX toolkit - it includes a template to recreate this.

Trends over time

This analysis can help you answer questions like: “Which codes are linked to decreases or increases in my score over time?”

We need to compare two sequences of numbers: NPS over time and code frequency over time . Using Excel, calculate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).

Now you need to plot code frequency against the absolute value of code correlation with NPS. Here is the formula:

The visualization could look like this:

These are two examples, but there are more. For a third manual formula, and to learn why word clouds are not an insightful form of analysis, read our visualizations article .

Using a text analytics solution to automate analysis

Automated text analytics solutions enable codes and sub-codes to be pulled out of the data automatically. This makes it far faster and easier to identify what’s driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.

Another benefit of AI-driven text analytics software is its built-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative textual data therein.

Thematic provides text analytics that goes further by allowing users to apply their expertise on business context to edit or augment the AI-generated outputs.

Since the move away from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is mostly to make sure important business nuances in the feedback aren’t missed during coding. The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence .

Step 5: Report on your data: Tell the story

The last step of analyzing your qualitative data is to report on it, to tell the story. At this point, the codes are fully developed and the focus is on communicating the narrative to the audience.

A coherent outline of the qualitative research, the findings and the insights is vital for stakeholders to discuss and debate before they can devise a meaningful course of action.

Creating graphs and reporting in Powerpoint

Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.

Using visualization software for reporting

With data transformation and APIs, the analyzed data can be shared with data visualisation software, such as Power BI or Tableau , Google Studio or Looker. Power BI and Tableau are among the most preferred options.

Visualizing your insights inside a feedback analytics platform

Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs.  This removes the time consuming work of constructing charts to visually identify patterns and creates more time to focus on building a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.

Using a feedback analytics platform with visualization tools means you don’t have to use a separate product for visualizations. You can export graphs into Powerpoints straight from the platforms.

Conclusion - Manual or Automated?

There are those who remain deeply invested in the manual approach - because it’s familiar, because they’re reluctant to spend money and time learning new software, or because they’ve been burned by the overpromises of AI.  

For projects that involve small datasets, manual analysis makes sense. For example, if the objective is simply to quantify a simple question like “Do customers prefer X concepts to Y?”. If the findings are being extracted from a small set of focus groups and interviews, sometimes it’s easier to just read them

However, as new generations come into the workplace, it’s technology-driven solutions that feel more comfortable and practical. And the merits are undeniable.  Especially if the objective is to go deeper and understand the ‘why’ behind customers’ preference for X or Y. And even more especially if time and money are considerations.

The ability to collect a free flow of qualitative feedback data at the same time as the metric means AI can cost-effectively scan, crunch, score and analyze a ton of feedback from one system in one go. And time-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a matter of hours or days.

But aside from the ever-present business case to speed things up and keep costs down, there are also powerful research imperatives for automated analysis of qualitative data: namely, accuracy and consistency.

Finding insights hidden in feedback requires consistency, especially in coding.  Not to mention catching all the ‘unknown unknowns’ that can skew research findings and steering clear of cognitive bias.

Some say without manual data analysis researchers won’t get an accurate “feel” for the insights. However, the larger data sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places.  And, the more difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.

Though the process steps for qualitative data analysis have remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years ago, the impact digital technology has had on types of qualitative feedback data and the approach to the analysis are profound.  

If you want to try an automated feedback analysis solution on your own qualitative data, you can get started with Thematic .

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Designing a Research Proposal in Qualitative Research

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Writing Your Qualitative Methods in a Proposal

Hello Qualitative Mind,

We continue talking about writing qualitative research proposals, and hopefully setting you up for success with your own proposals. One of the areas you need to detail in a qualitative research proposal is methods. Writing your qualitative methods commonly feels like walking the line: you need to provide enough details yet demonstrate you’ll be flexible and responsive to your qualitative data. Moreover, it’s hard not to wonder how much qualitative expertise a reviewer committee will have and, as such, how much you need to explain your sample size and sampling strategy, for example.

While we cannot control who the reviewers will be, we will strive to do the best we can on our side of things. Over the years, I’ve learned a few key things about writing qualitative methods for proposals that helped me to be more comfortable with the task without losing the flexibility and creativity I strive to have as a qualitative researcher. Here are the key elements of a methods section and what they mean to me:

Methodological coherence: I describe qualitative methods, approaches, data collection, and data analysis strategically. Although we are often limited by the number of words we can use and/or available space, we need to offer the reviewer enough details about the research setting, sampling and recruitment strategies, data collection, and data analysis. This is when we need to think about methodological cohesion and assume a savvy qualitative reviewer might adjudicate your project. What does this mean? If you are claiming you will be conducting phenomenological research to explore individuals’ lived experiences, and recruiting approximately 25 individuals for focus groups, you might have just raised your reviewers’ eyebrows (and lost a few points). Your expected sample size is too big and the method of data collection is not congruent with phenomenology. A qualitative reviewer would quickly notice that, and red flag your methods.

Qualitative research can change once a project starts and the researcher needs to be responsive.

However, qualitative research can change once a project starts and the researcher needs to be responsive. So what to do?

Rigor Description: What strategies are planned regardless of unplanned changes? How will you strive for concurrent data collection and analysis? What records will you keep, e.g., a journal with field notes, audio-recorded debriefings? How will you practice reflexivity? What external supports and expertise will you have as you move along with your project? Thinking of these questions, and describing them in a paragraph, can demonstrate to your reviewer that even though you are  penciling in certain methods, you will be using strategies that may cause you to revisit your methods, and make changes when needed.

Responsiveness in qualitative research: In my opinion, rigor and responsiveness go together so if you thoughtfully demonstrate the former, you are also thinking about the latter. This tells a reviewer that you know qualitative inquiry well enough the be covering important topics in your methods.

In addition, many funding agencies want researchers to outline what they will do with the results/findings. So at the end of your methods or under an “expected outcomes” subheading, try to discuss what you envision for knowledge translation/mobilization. I think qualitative researchers have almost a natural advantage when it comes to knowledge translation because our work is relational and full of possibilities for creative, meaningful, community-led mobilization. Aim high when thinking where your research results will go, and the impact they might have.

Now you have two posts outlining both the key aspects of writing the literature review and methods for qualitative research proposals. The next one will be about supports for writing and reviewing qualitative research proposals before you click the submit button in whatever platform your university or funding agency uses!

Maira Quintanilha

How to Write Analysis of Qualitative Data

Analysing and presenting data properly is one of the most important parts of any research project. Remember that if your dissertation includes weak analysis, your overall grade will be negatively affected.

That said, it’s important to analyse qualitative data carefully and accurately. But how do you write an analysis for qualitative data? Well, qualitative data comes from a range of sources (words, observations, images and even symbols), so there is no ‘one-size-fits-all’ approach.

In this article, we will take you through everything you need to know about qualitative data analysis. So, let’s get started!

Methods of Qualitative data

Whichever method of data gathering used, the preparation and analysis follow the same stages:

Good Practice for Qualitative Data Analysis

• In the initial stages of reading the information and identifying basic observations, you can try writing out lists so you can then add in the sub-themes as the analysis progresses. This helps to understand the data and key outcomes better.
• Keep your research questions to hand so you can refer back to them constantly and keep that all-important focus.
• Make sure your data is trustworthy and meets the following criteria:
• Credibility: the validity of conclusions achieved through extended engagement, checking with peers, and reviewing with interviewees as well as multiple data sources
• Transferability: how well the results can be applied in similar situations / settings
• Dependability: whether similar outcomes would occur if the study were repeated
• Confirmability: how objective the researcher (and survey instruments) was in gathering the data.

Once the data has been successfully interpreted and you are confident that the results achieved are trustworthy, you are good to go on writing up your findings!

Writing up your qualitative data

Introduction.

Your introduction should start with an overview of your respondent profile. Narratives can be one good way, but a table is often an effective way to provide your readers with key information such as gender, age, socio-economic status, or other areas relevant to your work. Your introduction should also include an overview of key themes.

To make sure your work is clear and of the highest quality, the body text for qualitative data, irrespective of the analysis process followed, should be broken up into sub-sections for each theme. We suggest having a main heading of a key theme, with sub-headings for each of the sub themes identified in the analysis.

The content of each paragraph or topic theme should identify the codes used for the analysis, followed by the conclusions you have drawn. Note: it is a good idea to include quotations from the raw data to illustrate the points you have made.

But be careful not to use overly long quotes and only use the parts which reinforce your findings. It is also, subject to confidentiality, sensible to identify the source of the quotation (e.g., “respondent 1, female, age 25) as this provides the reader with some context for the views expressed. Hint: Code different respondents with a number so that it is clear when using quotations that they come from a range of sources.

Plus, instead of indicating a number of respondents, it is better to give in fractions rather than percentages e.g., 7/10 respondents indicated, rather than 70%. We also recommend, where possible, to avoid the use of the word “significant” as this can suggest statistical significance which would be inappropriate in qualitative data.

As part of the presentation of the results it is also good practice to refer back to research questions and previous research. Whether the results back up or contradict previous research, including previous works shows that you have undertaken a wider level of reading and understanding of the topic being researched and gives a greater depth to your own work.

Using graphs or figures of key words and themes and how frequently they occurred during the data collection makes your work stand out as this provides illustrative evidence of your analysis process and findings.

Summary of results

Rather than a conclusion, when presenting qualitative results, remember that you at this stage you are giving an overall summation of the key findings, ideally with a conceptual framework. This could be an illustration, diagram, or existing framework, for example a strengths, weaknesses, opportunities, and threats (SWOT) analysis, or a conceptual framework that is original and emerged from your results. This shows that you have understood your data, and that your interpretation has led to some firm outcomes.

Key Phrases for use in writing up qualitative research.

“ A strong theme that emerged was…. with the term “x” being used by (%) of respondents.

“5/20 felt that the issue under discussion was…”

“ A high number of respondents (give fraction) felt that…”

“Underlying this main theme, a number of sub-themes emerged, suggesting some variation”.

“Indications from the core themes are that… but through examination of the sub-themes it was found at…”

“From these quotes, it can be inferred that…” which is in line with work by …”

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How to write a research proposal?

Department of Anaesthesiology, Bangalore Medical College and Research Institute, Bengaluru, Karnataka, India

Devika Rani Duggappa

Writing the proposal of a research work in the present era is a challenging task due to the constantly evolving trends in the qualitative research design and the need to incorporate medical advances into the methodology. The proposal is a detailed plan or ‘blueprint’ for the intended study, and once it is completed, the research project should flow smoothly. Even today, many of the proposals at post-graduate evaluation committees and application proposals for funding are substandard. A search was conducted with keywords such as research proposal, writing proposal and qualitative using search engines, namely, PubMed and Google Scholar, and an attempt has been made to provide broad guidelines for writing a scientifically appropriate research proposal.

INTRODUCTION

A clean, well-thought-out proposal forms the backbone for the research itself and hence becomes the most important step in the process of conduct of research.[ 1 ] The objective of preparing a research proposal would be to obtain approvals from various committees including ethics committee [details under ‘Research methodology II’ section [ Table 1 ] in this issue of IJA) and to request for grants. However, there are very few universally accepted guidelines for preparation of a good quality research proposal. A search was performed with keywords such as research proposal, funding, qualitative and writing proposals using search engines, namely, PubMed, Google Scholar and Scopus.

Five ‘C’s while writing a literature review

BASIC REQUIREMENTS OF A RESEARCH PROPOSAL

A proposal needs to show how your work fits into what is already known about the topic and what new paradigm will it add to the literature, while specifying the question that the research will answer, establishing its significance, and the implications of the answer.[ 2 ] The proposal must be capable of convincing the evaluation committee about the credibility, achievability, practicality and reproducibility (repeatability) of the research design.[ 3 ] Four categories of audience with different expectations may be present in the evaluation committees, namely academic colleagues, policy-makers, practitioners and lay audiences who evaluate the research proposal. Tips for preparation of a good research proposal include; ‘be practical, be persuasive, make broader links, aim for crystal clarity and plan before you write’. A researcher must be balanced, with a realistic understanding of what can be achieved. Being persuasive implies that researcher must be able to convince other researchers, research funding agencies, educational institutions and supervisors that the research is worth getting approval. The aim of the researcher should be clearly stated in simple language that describes the research in a way that non-specialists can comprehend, without use of jargons. The proposal must not only demonstrate that it is based on an intelligent understanding of the existing literature but also show that the writer has thought about the time needed to conduct each stage of the research.[ 4 , 5 ]

CONTENTS OF A RESEARCH PROPOSAL

The contents or formats of a research proposal vary depending on the requirements of evaluation committee and are generally provided by the evaluation committee or the institution.

In general, a cover page should contain the (i) title of the proposal, (ii) name and affiliation of the researcher (principal investigator) and co-investigators, (iii) institutional affiliation (degree of the investigator and the name of institution where the study will be performed), details of contact such as phone numbers, E-mail id's and lines for signatures of investigators.

The main contents of the proposal may be presented under the following headings: (i) introduction, (ii) review of literature, (iii) aims and objectives, (iv) research design and methods, (v) ethical considerations, (vi) budget, (vii) appendices and (viii) citations.[ 4 ]

Introduction

It is also sometimes termed as ‘need for study’ or ‘abstract’. Introduction is an initial pitch of an idea; it sets the scene and puts the research in context.[ 6 ] The introduction should be designed to create interest in the reader about the topic and proposal. It should convey to the reader, what you want to do, what necessitates the study and your passion for the topic.[ 7 ] Some questions that can be used to assess the significance of the study are: (i) Who has an interest in the domain of inquiry? (ii) What do we already know about the topic? (iii) What has not been answered adequately in previous research and practice? (iv) How will this research add to knowledge, practice and policy in this area? Some of the evaluation committees, expect the last two questions, elaborated under a separate heading of ‘background and significance’.[ 8 ] Introduction should also contain the hypothesis behind the research design. If hypothesis cannot be constructed, the line of inquiry to be used in the research must be indicated.

Review of literature

It refers to all sources of scientific evidence pertaining to the topic in interest. In the present era of digitalisation and easy accessibility, there is an enormous amount of relevant data available, making it a challenge for the researcher to include all of it in his/her review.[ 9 ] It is crucial to structure this section intelligently so that the reader can grasp the argument related to your study in relation to that of other researchers, while still demonstrating to your readers that your work is original and innovative. It is preferable to summarise each article in a paragraph, highlighting the details pertinent to the topic of interest. The progression of review can move from the more general to the more focused studies, or a historical progression can be used to develop the story, without making it exhaustive.[ 1 ] Literature should include supporting data, disagreements and controversies. Five ‘C's may be kept in mind while writing a literature review[ 10 ] [ Table 1 ].

Aims and objectives

The research purpose (or goal or aim) gives a broad indication of what the researcher wishes to achieve in the research. The hypothesis to be tested can be the aim of the study. The objectives related to parameters or tools used to achieve the aim are generally categorised as primary and secondary objectives.

Research design and method

The objective here is to convince the reader that the overall research design and methods of analysis will correctly address the research problem and to impress upon the reader that the methodology/sources chosen are appropriate for the specific topic. It should be unmistakably tied to the specific aims of your study.

In this section, the methods and sources used to conduct the research must be discussed, including specific references to sites, databases, key texts or authors that will be indispensable to the project. There should be specific mention about the methodological approaches to be undertaken to gather information, about the techniques to be used to analyse it and about the tests of external validity to which researcher is committed.[ 10 , 11 ]

The components of this section include the following:[ 4 ]

Population and sample

Population refers to all the elements (individuals, objects or substances) that meet certain criteria for inclusion in a given universe,[ 12 ] and sample refers to subset of population which meets the inclusion criteria for enrolment into the study. The inclusion and exclusion criteria should be clearly defined. The details pertaining to sample size are discussed in the article “Sample size calculation: Basic priniciples” published in this issue of IJA.

Data collection

The researcher is expected to give a detailed account of the methodology adopted for collection of data, which include the time frame required for the research. The methodology should be tested for its validity and ensure that, in pursuit of achieving the results, the participant's life is not jeopardised. The author should anticipate and acknowledge any potential barrier and pitfall in carrying out the research design and explain plans to address them, thereby avoiding lacunae due to incomplete data collection. If the researcher is planning to acquire data through interviews or questionnaires, copy of the questions used for the same should be attached as an annexure with the proposal.

Rigor (soundness of the research)

This addresses the strength of the research with respect to its neutrality, consistency and applicability. Rigor must be reflected throughout the proposal.

It refers to the robustness of a research method against bias. The author should convey the measures taken to avoid bias, viz. blinding and randomisation, in an elaborate way, thus ensuring that the result obtained from the adopted method is purely as chance and not influenced by other confounding variables.

Consistency

Consistency considers whether the findings will be consistent if the inquiry was replicated with the same participants and in a similar context. This can be achieved by adopting standard and universally accepted methods and scales.

Applicability

Applicability refers to the degree to which the findings can be applied to different contexts and groups.[ 13 ]

Data analysis

This section deals with the reduction and reconstruction of data and its analysis including sample size calculation. The researcher is expected to explain the steps adopted for coding and sorting the data obtained. Various tests to be used to analyse the data for its robustness, significance should be clearly stated. Author should also mention the names of statistician and suitable software which will be used in due course of data analysis and their contribution to data analysis and sample calculation.[ 9 ]

Ethical considerations

Medical research introduces special moral and ethical problems that are not usually encountered by other researchers during data collection, and hence, the researcher should take special care in ensuring that ethical standards are met. Ethical considerations refer to the protection of the participants' rights (right to self-determination, right to privacy, right to autonomy and confidentiality, right to fair treatment and right to protection from discomfort and harm), obtaining informed consent and the institutional review process (ethical approval). The researcher needs to provide adequate information on each of these aspects.

Informed consent needs to be obtained from the participants (details discussed in further chapters), as well as the research site and the relevant authorities.

When the researcher prepares a research budget, he/she should predict and cost all aspects of the research and then add an additional allowance for unpredictable disasters, delays and rising costs. All items in the budget should be justified.

Appendices are documents that support the proposal and application. The appendices will be specific for each proposal but documents that are usually required include informed consent form, supporting documents, questionnaires, measurement tools and patient information of the study in layman's language.

As with any scholarly research paper, you must cite the sources you used in composing your proposal. Although the words ‘references and bibliography’ are different, they are used interchangeably. It refers to all references cited in the research proposal.

Successful, qualitative research proposals should communicate the researcher's knowledge of the field and method and convey the emergent nature of the qualitative design. The proposal should follow a discernible logic from the introduction to presentation of the appendices.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

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Writing a Rsearch Proposal

A  research proposal  describes what you will investigate, why it’s important, and how you will conduct your research.  Your paper should include the topic, research question and hypothesis, methods, predictions, and results (if not actual, then projected).

Research Proposal Aims

Show your reader why your project is interesting, original, and important. The format of a research proposal varies between fields, but most proposals will contain at least these elements: Introduction Literature review Research design Reference list While the sections may vary, the overall objective is always the same. A research proposal serves as a blueprint and guide for your research plan, helping you get organized and feel confident in the path forward you choose to take. Proposal Format The proposal will usually have a  title page  that includes: The proposed title of your project Your supervisor’s name Your institution and department Introduction The first part of your proposal is the initial pitch for your project. Make sure it succinctly explains what you want to do and why.. Your introduction should: Introduce your  topic Give necessary background and context Outline your  problem statement  and  research questions To guide your  introduction , include information about:   Who could have an interest in the topic (e.g., scientists, policymakers) How much is already known about the topic What is missing from this current knowledge What new insights will your research contribute Why you believe this research is worth doing As you get started, it’s important to demonstrate that you’re familiar with the most important research on your topic. A strong  literature review  shows your reader that your project has a solid foundation in existing knowledge or theory. It also shows that you’re not simply repeating what other people have done or said, but rather using existing research as a jumping-off point for your own. In this section, share exactly how your project will contribute to ongoing conversations in the field by: Comparing and contrasting the main theories, methods, and debates Examining the strengths and weaknesses of different approaches Explaining how will you build on, challenge, or  synthesize  prior scholarship Research design and methods Following the literature review, restate your main  objectives . This brings the focus back to your project. Next, your  research design  or  methodology  section will describe your overall approach, and the practical steps you will take to answer your research questions. Write up your projected, if not actual, results. Contribution to knowledge To finish your proposal on a strong note, explore the potential implications of your research for your field. Emphasize again what you aim to contribute and why it matters. For example, your results might have implications for: Improving best practices Informing policymaking decisions Strengthening a theory or model Challenging popular or scientific beliefs Creating a basis for future research Lastly, your research proposal must include correct  citations  for every source you have used, compiled in a  reference list . To create citations quickly and easily, you can use free APA citation generators like BibGuru. Databases have a citation button you can click on to see your citation. Sometimes you have to re-format it as the citations may have mistakes.  << Previous: ProQuest Research Companion Next: DIR >> Edit this Guide Log into Dashboard Use of RIT resources is reserved for current RIT students, faculty and staff for academic and teaching purposes only. Please contact your librarian with any questions. Help is Available Email a Librarian A librarian is available by e-mail at [email protected] Meet with a Librarian Call reference desk voicemail. A librarian is available by phone at (585) 475-2563 or on Skype at llll Or, call (585) 475-2563 to leave a voicemail with the reference desk during normal business hours . Chat with a Librarian Data analytics resources infoguide url. https://infoguides.rit.edu/DA Use the box below to email yourself a link to this guide Have a language expert improve your writing Run a free plagiarism check in 10 minutes, generate accurate citations for free. Knowledge Base Dissertation How to Write a Results Section | Tips & Examples How to Write a Results Section | Tips & Examples Published on August 30, 2022 by Tegan George . Revised on July 18, 2023. A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean—any evaluation should be saved for the discussion section . Instantly correct all language mistakes in your text Upload your document to correct all your mistakes in minutes Table of contents How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs. discussion vs. conclusion, checklist: research results, other interesting articles, frequently asked questions about results sections. When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis. Here are a few best practices: Your results should always be written in the past tense. While the length of this section depends on how much data you collected and analyzed, it should be written as concisely as possible. Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like “appears” or “implies.” If you have other results you’d like to include, consider adding them to an appendix or footnotes. Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe store: first discuss the shoes as a whole, then the sneakers, boots, sandals, etc. Don't submit your assignments before you do this The academic proofreading tool has been trained on 1000s of academic texts. Making it the most accurate and reliable proofreading tool for students. Free citation check included. Try for free If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis . Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported. The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share: A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section. A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses. A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion. A note on tables and figures In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list . As a rule of thumb: Tables are used to communicate exact values, giving a concise overview of various results Graphs and charts are used to visualize trends and relationships, giving an at-a-glance illustration of key findings Don’t forget to also mention any tables and figures you used within the text of your results section. Summarize or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown. A two-sample t test was used to test the hypothesis that higher social distance from environmental problems would reduce the intent to donate to environmental organizations, with donation intention (recorded as a score from 1 to 10) as the outcome variable and social distance (categorized as either a low or high level of social distance) as the predictor variable.Social distance was found to be positively correlated with donation intention, t (98) = 12.19, p < .001, with the donation intention of the high social distance group 0.28 points higher, on average, than the low social distance group (see figure 1). This contradicts the initial hypothesis that social distance would decrease donation intention, and in fact suggests a small effect in the opposite direction. Figure 1: Intention to donate to environmental organizations based on social distance from impact of environmental damage. In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data. For each theme, start with general observations about what the data showed. You can mention: Recurring points of agreement or disagreement Patterns and trends Particularly significant snippets from individual responses Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix . When asked about video games as a form of art, the respondents tended to believe that video games themselves are not an art form, but agreed that creativity is involved in their production. The criteria used to identify artistic video games included design, story, music, and creative teams.One respondent (male, 24) noted a difference in creativity between popular video game genres: “I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.” Responses suggest that video game consumers consider some types of games to have more artistic potential than others. Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme. It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion . Here's why students love Scribbr's proofreading services Discover proofreading & editing I have completed my data collection and analyzed the results. I have included all results that are relevant to my research questions. I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics . I have stated whether each hypothesis was supported or refuted. I have used tables and figures to illustrate my results where appropriate. All tables and figures are correctly labelled and referred to in the text. There is no subjective interpretation or speculation on the meaning of the results. You've finished writing up your results! Use the other checklists to further improve your thesis. If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools! Research bias Survivorship bias Self-serving bias Availability heuristic Halo effect Hindsight bias Deep learning Generative AI Machine learning Reinforcement learning Supervised vs. unsupervised learning  (AI) Tools Grammar Checker Paraphrasing Tool Text Summarizer AI Detector Plagiarism Checker Citation Generator The results chapter of a thesis or dissertation presents your research results concisely and objectively. In quantitative research , for each question or hypothesis , state: The type of analysis used Relevant results in the form of descriptive and inferential statistics Whether or not the alternative hypothesis was supported In qualitative research , for each question or theme, describe: Recurring patterns Significant or representative individual responses Relevant quotations from the data Don’t interpret or speculate in the results chapter. Results are usually written in the past tense , because they are describing the outcome of completed actions. The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter. In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them. Cite this Scribbr article If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator. George, T. (2023, July 18). How to Write a Results Section | Tips & Examples. Scribbr. Retrieved August 26, 2024, from https://www.scribbr.com/dissertation/results/ Is this article helpful? Tegan George Other students also liked, what is a research methodology | steps & tips, how to write a discussion section | tips & examples, how to write a thesis or dissertation conclusion, what is your plagiarism score. How to Write a Research Proposal: A Complete Guide A research proposal is a piece of writing that basically serves as your plan for a research project. It spells out what you’ll study, how you’ll go about it, and why it matters. Think of it as your pitch to show professors or funding bodies that your project is worth their attention and support. This task is standard for grad students, especially those in research-intensive fields. It’s your chance to showcase your ability to think critically, design a solid study, and articulate why your research could make a difference. In this article, we'll talk about how to craft a good research proposal, covering everything from the standard format of a research proposal to the specific details you'll need to include.  Feeling overwhelmed by the idea of putting one together? That’s where DoMyEssay comes in handy.  Whether you need a little push or more extensive guidance, we’ll help you nail your proposal and move your project forward.  Research Proposal Format When you're putting together a research proposal, think of it as setting up a roadmap for your project. You want it to be clear and easy to follow so everyone knows what you’re planning to do, how you’re going to do it, and why it matters.  Whether you’re following APA or Chicago style, the key is to keep your formatting clean so that it’s easy for committees or funding bodies to read through and understand. Here’s a breakdown of each section, with a special focus on formatting a research proposal: Title Page : This is your first impression. Make sure it includes the title of your research proposal, your name, and your affiliations. Your title should grab attention and make it clear what your research is about. Abstract : This is your elevator pitch. In about 250 words, you need to sum up what you plan to research, how you plan to do it, and what impact you think it will have. Introduction : Here’s where you draw them in. Lay out your research question or problem, highlight its importance, and clearly outline what you aim to achieve with your study. Literature Review : Show that you’ve done your homework. In this section, demonstrate that you know the field and how your research fits into it. It’s your chance to connect your ideas to what’s already out there and show off a bit about what makes your approach unique or necessary. Methodology : Dive into the details of how you’ll get your research done. Explain your methods for gathering data and how you’ll analyze it. This is where you reassure them that your project is doable and you’ve thought through all the steps. Timeline : Keep it realistic. Provide an estimated schedule for your research, breaking down the process into manageable stages and assigning a timeline for each phase. Budget : If you need funding, lay out a budget that spells out what you need money for. Be clear and precise so there’s no guesswork involved about what you’re asking for. References/Bibliography : List out all the works you cited in your proposal. Stick to one citation style to keep things consistent. Get Your Research Proposal Right  Let our experts guide you through crafting a research proposal that stands out. From idea to submission, we've got you covered. Research Proposal Structure When you're writing a research proposal, you're laying out your questions and explaining the path you're planning to take to tackle them. Here’s how to structure your proposal so that it speaks to why your research matters and should get some attention. Introduction An introduction is where you grab attention and make everyone see why what you're doing matters. Here, you’ll pose the big question of your research proposal topic and show off the potential of your research right from the get-go: Grab attention : Start with something that makes the reader sit up — maybe a surprising fact, a challenging question, or a brief anecdote that highlights the urgency of your topic. Set the scene : What’s the broader context of your work? Give a snapshot of the landscape and zoom in on where your research fits. This helps readers see the big picture and the niche you’re filling. Lay out your plan : Briefly mention the main goals or questions of your research. If you have a hypothesis, state it clearly here. Make it matter : Show why your research needs to happen now. What gaps are you filling? What changes could your findings inspire? Make sure the reader understands the impact and significance of your work. Literature Review In your research proposal, the literature review does more than just recap what’s already out there. It's where you get to show off how your research connects with the big ideas and ongoing debates in your field. Here’s how to make this section work hard for you: Connect the dots : First up, highlight how your study fits into the current landscape by listing what others have done and positioning your research within it. You want to make it clear that you’re not just following the crowd but actually engaging with and contributing to real conversations.  Critique what’s out there : Explore what others have done well and where they’ve fallen short. Pointing out the gaps or where others might have missed the mark helps set up why your research is needed and how it offers something different. Build on what’s known : Explain how your research will use, challenge, or advance the existing knowledge. Are you closing a key gap? Applying old ideas in new ways? Make it clear how your work is going to add something new or push existing boundaries. Aims and Objectives Let's talk about the aims and objectives of your research. This is where you set out what you want to achieve and how you plan to get there: Main Goal : Start by stating your primary aim. What big question are you trying to answer, or what hypothesis are you testing? This is your research's main driving force. Detailed Objectives : Now, break down your main goal into smaller, actionable objectives. These should be clear and specific steps that will help you reach your overall aim. Think of these as the building blocks of your research, each one designed to contribute to the larger goal. Research Design and Method This part of your proposal outlines the practical steps you’ll take to answer your research questions: Type of Research : First off, what kind of research are you conducting? Will it be qualitative or quantitative research , or perhaps a mix of both? Clearly define whether you'll be gathering numerical data for statistical analysis or exploring patterns and theories in depth. Research Approach : Specify whether your approach is experimental, correlational, or descriptive. Each of these frameworks has its own way of uncovering insights, so choose the one that best fits the questions you’re trying to answer. Data Collection : Discuss the specifics of your data. If you’re in the social sciences, for instance, describe who or what you’ll be studying. How will you select your subjects or sources? What criteria will you use, and how will you gather your data? Be clear about the methods you’ll use, whether that’s surveys, interviews, observations, or experiments. Tools and Techniques : Detail the tools and techniques you'll use to collect your data. Explain why these tools are the best fit for your research goals. Timeline and Budget : Sketch out a timeline for your research activities. How long will each phase take? This helps everyone see that your project is organized and feasible. Potential Challenges : What might go wrong? Think about potential obstacles and how you plan to handle them. This shows you’re thinking ahead and preparing for all possibilities. Ethical Considerations When you're conducting research, especially involving people, you've got to think about ethics. This is all about ensuring everyone's rights are respected throughout your study. Here’s a quick rundown: Participant Rights : You need to protect your participants' rights to privacy, autonomy, and confidentiality. This means they should know what the study involves and agree to participate willingly—this is what we call informed consent. Informed Consent : You've got to be clear with participants about what they’re signing up for, what you’ll do with the data, and how you'll keep it confidential. Plus, they need the freedom to drop out any time they want. Ethical Approval : Before you even start collecting data, your research plan needs a green light from an ethics committee. This group checks that you’re set up to keep your participants safe and treated fairly. You need to carefully calculate the costs for every aspect of your project. Make sure to include a bit extra for those just-in-case scenarios like unexpected delays or price hikes. Every dollar should have a clear purpose, so justify each part of your budget to ensure it’s all above board. This approach keeps your project on track financially and avoids any surprises down the line. The appendices in your research proposal are where you stash all the extra documents that back up your main points. Depending on your project, this could include things like consent forms, questionnaires, measurement tools, or even a simple explanation of your study for participants.  Just like any academic paper, your research proposal needs to include citations for all the sources you’ve referenced. Whether you call it a references list or a bibliography, the idea is the same — crediting the work that has informed your research. Make sure every source you’ve cited is listed properly, keeping everything consistent and easy to follow. Research Proposal Got You Stuck?  Get expert help with your literature review, ensuring your research is grounded in solid scholarship.  How to Write a Research Proposal? Whether you're new to this process or looking to refine your skills, here are some practical tips to help you create a strong and compelling proposal.  Tip What to Do Stay on Target 🎯 Stick to the main points and avoid getting sidetracked. A focused proposal is easier to follow and more compelling. Use Visuals 🖼️ Consider adding charts, graphs, or tables if they help explain your ideas better. Visuals can make complex info clearer. Embrace Feedback 🔄 Be open to revising your proposal based on feedback. The best proposals often go through several drafts. Prepare Your Pitch 🎤 If you’re going to present your proposal, practice explaining it clearly and confidently. Being able to pitch it well can make a big difference. Anticipate Questions ❓ Think about the questions or challenges reviewers might have and prepare clear responses. Think Bigger 🌍 Consider how your research could impact your field or even broader society. This can make your proposal more persuasive. Use Strong Sources 📚 Always use credible and up-to-date sources. This strengthens your arguments and builds trust with your readers. Keep It Professional ✏️ While clarity is key, make sure your tone stays professional throughout your proposal. Highlight What’s New 💡 Emphasize what’s innovative or unique about your research. This can be a big selling point for your proposal. Research Proposal Template Here’s a simple and handy research proposal example in PDF format to help you get started and keep your work organized: Writing a research proposal can be straightforward if you break it down into manageable steps: Pick a strong research proposal topic that interests you and has enough material to explore. Craft an engaging introduction that clearly states your research question and objectives. Do a thorough literature review to see how your work fits into the existing research landscape. Plan out your research design and method , deciding whether you’ll use qualitative or quantitative research. Consider the ethical aspects to ensure your research is conducted responsibly. Set up a budget and gather any necessary appendices to support your proposal. Make sure all your sources are cited properly to add credibility to your work. If you need some extra support, DoMyEssay is ready to help with any type of paper, including crafting a strong research proposal.  What Is a Research Proposal? How long should a research proposal be, how do you start writing a research proposal. Examples of Research proposals | York St John University. (n.d.). York St John University. https://www.yorksj.ac.uk/study/postgraduate/research-degrees/apply/examples-of-research-proposals/ Open access Published: 26 August 2024 Candidacy 2.0 (CC) – an enhanced theory of access to healthcare for chronic conditions: lessons from a critical interpretive synthesis on access to rheumatoid arthritis care Sharon Koehn 1 , C Allyson Jones 2 , Claire Barber 3 , Lisa Jasper 2 , Anh Pham 1 , Cliff Lindeman 4 & Neil Drummond 5   BMC Health Services Research volume  24 , Article number:  986 ( 2024 ) Cite this article 37 Accesses Metrics details The Dixon-Woods et al. Candidacy Framework, a valuable tool since its 2006 introduction, has been widely utilized to analyze access to various services in diverse contexts, including healthcare. This social constructionist approach examines micro, meso, and macro influences on access, offering concrete explanations for access challenges rooted in socially patterned influences. This study employed the Candidacy Framework to explore the experiences of individuals living with rheumatoid arthritis (RA) and their formal care providers. The investigation extended to assessing supports and innovations in RA diagnosis and management, particularly in primary care. This systematic review is a Critical Interpretive Synthesis (CIS) of qualitative and mixed methods literature. The CIS aimed to generate theory from identified constructs across the reviewed literature. The study found alignment between the seven dimensions of the Candidacy Framework and key themes emerging from the data. Notably absent from the framework was an eighth dimension, identified as the “embodied relational self.” This dimension, central to the model, prompted the proposal of a revised framework specific to healthcare for chronic conditions. The CIS revealed that the eight dimensions, including the embodied relational self, provided a comprehensive understanding of the experiences and perspectives of individuals with RA and their care providers. The proposed Candidacy 2.0 (Chronic Condition (CC)) model demonstrated how integrating approaches like Intersectionality, concordance, and recursivity enhanced the framework when the embodied self was central. Conclusions The study concludes that while the original Candidacy Framework serves as a robust foundation, a revised version, Candidacy 2.0 (CC), is warranted for chronic conditions. The addition of the embodied relational self dimension enriches the model, accommodating the complexities of accessing healthcare for chronic conditions. Trial Registration This study did not involve a health care intervention on human participants, and as such, trial registration is not applicable. However, our review is registered with the Open Science Framework at https://doi.org/10.17605/OSF.IO/ASX5C . Peer Review reports Introduction Access barriers for people with chronic conditions can result in delays in diagnosis, treatment, and management. These barriers can directly impact a patient’s ability to receive timely and appropriate care and increase healthcare costs [ 1 , 2 ]. Although theoretical frameworks exist to guide health and community care for chronic conditions (e.g., [ 3 ]), the process of attaining a diagnosis and ongoing care for chronic conditions across the micro-to macro continuum is less well understood. The Candidacy Framework is a theoretical model that has the potential to deepen our understanding of the process of gaining access to diverse health and social services needed by people living with chronic conditions. Our study applied the Candidacy Framework to systematically examine the experiences of people living with Rheumatoid Arthritis (RA), a common chronic autoimmune disease, to comprehensively analyze barriers and facilitators in accessing a diagnosis and healthcare services [ 4 ]. Dixon-Woods et al. [ 4 ] developed the framework from an extensive scoping review and a Critical Interpretive Synthesis (CIS) of the literature on access to care by marginalized populations. O’Brien et al. [ 5 ] have since demonstrated that the framework is applicable to a wider population base of people with chronic conditions. We argue, however, that while the Candidacy Framework is an excellent starting point, a more comprehensive theory of access must also incorporate an intersectional lens and a phenomenological understanding of identity and its influences on access. The Candidacy Framework conceptualizes healthcare access as a dynamic process wherein candidacy for care is constructed between those needing and providing services [ 4 , 6 ]. This multi-level approach includes individual (patient, practitioner), interpersonal, institutional, and infrastructural factors [ 7 ], operationalized as seven distinct yet overlapping dimensions with which individuals are likely to engage in an iterative manner [ 6 , 8 ]. According to Dixon-Woods et al. [ 4 ], and depicted in Fig.  1 , establishing access is a fluid process that entails [ 1 ] identification of the need for care, [ 2 ] finding a way to it, (3&4) presenting a claim for it to service providers who judge its credibility, and [ 5 ] accepting or rejecting resultant offers. The ‘openness’ and compatibility of the system [ 6 ] and local operating conditions [ 7 ] are also salient, reflecting the broader organizational and socio-political or environmental conditions that influence claims to candidacy throughout a process of negotiation [ 7 ]. Application of Candidacy dimensions generates concrete and testable explanations of access challenges, which can reveal socially patterned influences underlying seemingly individual behaviours [ 7 , 9 , 10 ]. Seven dimensions of Candidacy to achieve access to healthcare The Candidacy Framework has been used to analyze access to a broad range of medical and non-medical services, including those for chronic conditions such as dementia [ 11 ], fibromyalgia [ 12 ], comorbid obesity [ 7 ], diabetes [ 13 , 14 ], coronary heart disease [ 13 ], multiple sclerosis [ 15 ], mental health problems [ 16 , 17 ], osteoarthritis [ 5 ], and asthma and other ‘long-term conditions’ [ 14 ]. Collectively, these works have demonstrated that gaining access to a diagnosis and receiving appropriate ongoing care for chronic conditions, comorbidities and adverse effects of treatment is key to achieving positive health and social outcomes. These studies also underscore the explanatory power of the original Candidacy Framework but have inevitably suggested refinements that are not always acknowledged in subsequent applications of the framework, although some trends are apparent. Using RA as an exemplar for an augmented Candidacy framework, we discuss the implications of our findings for an interdisciplinary understanding of access to chronic conditions, particularly those that are complex and difficult to diagnose. Drawing upon prior applications of the Candidacy Framework to analyses of primary data and systematic reviews, we propose an expanded version that seeks to capture these additional dimensions: Candidacy 2.0 (Chronic Conditions [CC]). RA affects approximately 0.5% of the world’s population, and its prevalence is increasing globally [ 18 ]. Prevalence rates are higher in industrialized countries (e.g., Canada: 0.65–0.78%) and among women [ 18 ]. RA affects multiple joints, causing pain, swelling, stiffness, warmth, redness, fatigue, weakness, and loss of range of motion. Rheumatoid nodules may develop under the skin near the affected joints and may cause functional and/or cosmetic concerns. Systematic inflammation associated with RA may affect other organs, including nerves, eyes, skin, lungs, or heart. Symptoms vary from person to person and can come and go, with periods of more active disease commonly referred to as flare-ups. The target of treatment is disease remission; however, treatment is often complex and needs frequent reassessment to achieve this goal [ 19 ]. RA can thus have a significant impact on patients’ quality of life due to pain, stiffness, and swelling, as well as fatigue, reduced mobility, and functional disability, reduced ability to work and premature mortality, yet many gaps in care remain due in part to inconsistencies with current treatment guidelines [ 20 ]. Methodology: critical interpretive synthesis (CIS) Our study aimed to explore healthcare access experiences of people living with Rheumatoid Arthritis (PlwRA) through the Candidacy Framework [ 4 ]. Despite qualitative studies offering valuable insights into complex issues, they comprise only 1% of research in top-tier rheumatology journals [ 21 ]. To address this gap, we conducted a systematic review using Critical Interpretive Synthesis methodology [ 4 ], focusing on qualitative and mixed-methods literature on RA care access. This approach allowed us to capture in-depth perspectives of both RA patients and their care providers, offering insights that quantitative studies alone might miss. Qualitative methods, such as interviews and focus groups, are uniquely suited to explore the nuanced, contextual aspects of patient experiences [ 22 ]. Our review also examined supports and innovations in RA diagnosis and management, particularly in primary care settings. A CIS differs from a conventional systematic review by emphasizing the inclusion of diverse study types, beyond randomized controlled trials, to capture a broader range of evidence [ 4 ]. CIS incorporates a more interpretive and reflexive approach, encouraging researchers to critically engage with the context and complexities of the included studies. Unlike traditional systematic reviews that focus on aggregating quantitative data, CIS prioritizes the synthesis of qualitative and quantitative evidence, allowing for a more nuanced understanding of the research topic. Additionally, CIS places a greater emphasis on exploring underlying mechanisms and contextual factors, promoting a deeper and more holistic analysis of the subject matter. A CIS improves on the meta-ethnographic approach [ 23 , 24 ] typically employed in reviews of qualitative literature (1) by utilizing systematic review search strategies, and (2) by seeking to produce more generalizable theoretical conclusions, rather than a simple synopsis of the literature reviewed [ 4 , 25 ]. This is achieved by an inductive approach to analysis that integrates different theoretical categories to achieve deeper understanding of the topic of interest [ 25 ]. Thus, while the review focuses on the literature on RA access, this informs a more general theory of access to care for multiple chronic conditions. Search and sampling process Within a CIS, question formulation, source search and selection, and analysis are dynamic and iterative processes [ 25 ]. The search and sampling process involves selecting a set of guided topics, iteratively identifying ‘probably relevant articles’ through a range of searching strategies that are ‘fit for purpose’, and sampling purposively relative to an emerging theory [ 4 ]. Articles reporting qualitative and mixed method studies were identified from multiple database searches in Medline (OVID) and CINAHL by a health sciences librarian using the terms detailed in Appendix A: Search Terms (see supplementary file 1) and based on our inclusion criteria (Table 1 ). The data bases were searched from their date of inception to search dates ranging from April 12, 2020, to June 13, 2022. Dixon-Woods et al. [ 4 ] advocate for refining database searches in critical interpretive synthesis to ensure relevance to guiding questions. This iterative approach aligns with CIS’s flexible nature, allowing researchers to adapt their strategy as understanding evolves and focus on obtaining the most pertinent literature for theoretical development. Thus, searches were added and refined to better reflect the breadth of articles needed to comprehensively understand all dimensions of access, e.g., to address a paucity of references to allied health professionals. A total of 1244 articles were identified through database searches (see Fig.  2 ). An additional five articles were identified by experts on our team, after their review of our list of articles to be screened in full. Team members include PlwRA, primary care physicians, and rheumatologists as well as researchers with backgrounds in physiotherapy, social epidemiology, health services research, and medical anthropology. Database-identified references were imported into Covidence [ 26 ] and reviewed by two investigators (AP and SK) until all conflicts in classification (i.e., include or exclude for full-text screening) were resolved. Following Dixon-Woods and colleagues [ 4 ], we prioritized papers based on relevance rather than specific study types or strict methodological standards, aiming to include a wide variety of papers at the conceptual level. While our priority was to review the qualitative literature that focuses on patient experience we chose to include three highly relevant review articles [ 27 , 28 , 29 ] and four quantitative studies [ 30 , 31 , 32 , 33 ] that addressed, to a degree, gaps in understanding in the qualitative record. We set a low inclusion threshold, excluding only fatally flawed papers. The concept of “fatally flawed” as a criterion for excluding articles in a CIS refers to studies with severe methodological deficiencies. These include lacking clear aims, having an inappropriate research design, failing to explain its process clearly, providing insufficient data to support conclusions, or using an inadequate or poorly explained analysis method [ 4 ]. Such flaws significantly undermine a study’s credibility and contribution to the synthesis. In our study, this judgement occurred primarily at the abstract screening stage, although 20 articles were excluded for this reason at the full article screening phase (denoted as ‘wrong study design’ in Fig.  2 ). Of the 1093 abstracts reviewed, 307 articles were deemed eligible for full-text screening (by SK) of which 10 could not be retrieved. 111 articles were subsequently excluded, as detailed in Fig.  2 . The exclusion process was in fact iterative since nine of the articles excluded were withdrawn during the analysis phase within NVivo, when their lack of fit with our guiding questions and inclusion criteria became apparent. Ultimately, any study deemed unlikely to contribute to our development of a theory that answers the guiding questions was excluded. In accordance with PRISMA guidelines, these articles were subcategorized in the flow chart as wrong population, language, setting, outcomes, etc.). ‘Wrong setting’ refers to contexts outside of the scope of enquiry relevant to a North American context (e.g., a discussion of patient experiences of Traditional Chinese Medicine care for RA in China). ‘Wrong outcomes’ indicates that the study did not specifically report on either the health and health care experiences of people with RA. One article was a protected PDF that did not allow for text selection and coding in NVivo. Since it was a marginally relevant study, we chose to exclude it. Studies with mixed populations were included if patients with RA were part of the study sample. Another 76 articles were demoted to a secondary list because their focus was not on experience. Ultimately, our sample included 110 multidisciplinary articles employing diverse theoretical approaches (see Appendix C in supplementary file 2, a framework summation of methodological features of sampled literature). Selection flow chart Through the process of familiarization with the materials and consultation with our advisory panel (five people living with RA across Canada) Footnote 1 and RA experts on our team, we refined our overarching question into the five guiding topics that ultimately determined the selection of articles included in the review (Table 2 ). Our topics were further informed by the Candidacy Framework for understanding access to healthcare services. Data analysis The inductive approach of a CIS consists of several phases. The first phase of the analysis is the development of a synthesizing argument through reciprocal translational analysis , which involves the translation of different concepts into each other [ 4 ]. Two studies often discuss the same construct using different terms or use a common term to which each study ascribes a different meaning. Thematically coding all sources of evidence using qualitative data management software, QSR NVivo 12 ® , facilitated this process. SK led the analytic phase but met regularly with the research team’s CIS subcommittee comprised of the remaining authors. Emergent codes and interpretations were discussed with and contextualized by this group and were refined accordingly. All categories and the nodes within them are listed and described in a codebook (Appendix B, supplementary file 1). The analysis employed both deductive and inductive coding methods. Deductive coding was guided by the Candidacy Framework, which provided a structured lens for examining access to healthcare services. Inductive coding emerged organically from the interview data, allowing new themes and insights to surface. The inductive nodes were ultimately grouped as subnodes of each of the Guiding Topics. Two additional node families—‘healthcare provider roles’ and ‘treatment effects’—were created to accommodate relevant topics that did not fit comfortably within any of the Guiding Topics. Most extracts of pertinent text were coded at multiple nodes, which allows for explorations of associations among them using the Matrix feature of NVivo 12 ® . For example, the Guiding Questions nodes are often coded as well with Candidacy Framework nodes which allows us to discern which dimensions of Candidacy are most salient for each of the Guiding Questions. Our reciprocal translational analysis generated an aggregative synthesis of the reviewed literature, although it is distinct from most aggregative reviews in that each article is viewed more as a “repository of concepts” than a source of data per se [ 34 ]. We provide both a condensed review of the dominant themes in tabular form as well as a detailed and cited account of these themes in supplementary file 1. The next phase of CIS analysis— refutational synthesis —involves characterizing and explaining the contradictions between constructs in different studies (e.g., in terms of different study approaches, conceptual assumptions). To facilitate ready access to study details that could explain such discrepancies we created a ‘Methodology’ code category that included the subnodes data collection method , limitations , location of study , sample size , study question or purpose , target population of study , and theory or framework . Using the “framework” feature in NVivo 12 ® , we generated a tabular summary of these details for each study that could be readily checked to potentially explain anomalies in our findings or as a resource for others (supplementary file 2). The final phase is the lines-of-argument synthesis , which aims to create a comprehensive explanatory proposition suggested by the data [ 4 ]. This final interpretation is grounded in the constructs included in the reviewed materials, and seeks to identify and reconcile the most influential themes that represent the combined data [ 35 ]. This process ensures a robust and well-supported understanding of the phenomenon under study. Coding text at both Candidacy nodes and Guiding Questions subnodes systematically connected the ideas more specific to the RA experience represented by the Guiding Questions to the dimensions of access described by the Candidacy Framework and identified what was missing in this framework. Results: making sense of access to RA care through a candidacy lens and beyond The Candidacy Framework offered a relatively inclusive ecological approach that served as a useful starting point for the development of lines of argument that broke down the process of access to RA care into seven dimensions of access. All seven dimensions could be identified in the literature, but some were more thoroughly explored than others, pointing to opportunities for additional inquiry into these areas. Our interpretations were derived from text coded in NVivo 12 ® at different dimensions of Candidacy, many of which were co-coded with the themes emerging as important in association with each of the Guiding Questions (presented as Findings in supplementary document 1). However, the explanations associated with the different dimensions of Candidacy for limitations to access did not suffice to explain findings that linked the bilateral relationship between illness and identity to access challenges. We recognized the need to augment the Candidacy Framework dimensions with insights from phenomenological and intersectional theories. The following interpretation of the CIS analysis will include a consideration of the intersectional, relational and embodied self to enhance the explanatory power of Candidacy as a theory of access. Identification of RA The first step in securing access to care is recognition of the need to seek out medical attention. A common pattern emerged in all studies that focused on initial help seeking whereby symptoms were rationalized in relation to preceding events or current conditions, such as an accident, pregnancy, too much or too little exercise, exposure to heat or cold, consuming certain foods, comorbidities, changes in medication, and so on [ 29 , 36 , 37 , 38 , 39 , 40 ]. At some point, however, these explanations no longer accounted for the persistence or evolution of symptoms, and medical advice was sought [ 40 ]. Alternatively, some explanations of symptoms, such as a curse, were culture-specific and could prompt consultation of non-medical (e.g., religious) practitioners, or remedial actions such as prayer or a change in diet that further delayed consultation [ 29 , 39 , 41 ]. Importantly, family members could be instrumental in either advising recourse to alternative explanations and remedies or persuading the afflicted person to seek medical advice [ 42 , 43 , 44 ]. Regardless, delays in consultation due to failure to recognize the symptoms of RA were attributed in retrospect to a lack of knowledge: “had they known when they developed their symptoms what they knew once they had been diagnosed with RA, they would have consulted much earlier” (43, see also 29). Numerous studies reported that laypeople have little if any knowledge of RA and are unaware that it is a serious degenerative disorder requiring aggressive treatment to prevent irreparable damage [ 29 , 40 , 45 , 46 , 47 , 48 ]. Similarly, failure to recognize the symptoms of foot problems associated with RA, as well as confusion around who to consult or how to access podiatry services resulted in delayed access and potential damage [ 49 , 50 , 51 ]. Ultimately, the evidence pointed to the inadequacy of information available to the public about RA, its expression, and treatment [ 43 , 46 , 48 , 50 , 52 ]. Primary care practitioners who were asked to comment on the viability of campaigns to promote rapid help-seeking behaviour at the onset of RA were nonetheless wary of the impact of a poorly designed campaign on their workload and the possibility that false presentations would “‘clog up’ the referral pathway for genuine cases of RA” [ 53 ]. Navigation to services Navigation through a Candidacy lens refers to the process of finding appropriate services and physically navigating to them. Navigation challenges were rarely and only superficially addressed in the reviewed literature. Once they had identified the need for medical attention, participants in most studies consulted their primary care physicians, with variable success in securing a referral to a rheumatologist [ 29 , 36 , 54 , 55 ]. Of note were reports of “proactive” patients who had been able to find information on the internet and self-advocate for referrals or shorter wait-times, sometimes through recourse to private care [ 36 , 39 , 55 , 56 , 57 ]. While not explicitly explored in these studies, the ability of some to more effectively navigate the system invokes Bourdieu’s notion of social capital whereby symbolic power is gained by some actors due to their access to higher levels of economic, cultural and symbolic capital (e.g., income, education, familiarity with the healthcare system) in different social spaces [ 58 ]. Navigating to other specialists identified by PlwRA as important to their care, such as podiatrists and psychologists, was just as challenging [ 59 ]. Barriers identified were a lack of information about their roles, the lack of clear pathways to care, and the limited availability of RA-informed specialists [ 51 , 60 ]. Barber et al. [ 61 ] recommended the development of a peer navigation system to expedite information gathering about the disease or the healthcare system. Appearances and adjudications (the patient-care provider interaction) Once a PlwRA has successfully navigated to a care provider they need to present their symptoms in a manner that precipitates some kind of offer, such as a referral, screening, or treatment. These ‘appearances’ are often difficult to identify without reference to the adjudications or decisions made by the care provider positioned to make such offers. Adjudications are based in part on the clarity of the claim to care made by the PlwRA, but the provider’s own training and biases also play a role. Appearances or presentation of RA symptoms Evidence on appearances of people seeking diagnosis or care from gatekeepers such as primary care physicians underscored the relevance of considerations of social capital and reflected the importance of considering sampling bias. In some studies, participants saw themselves as team members engaged in a bilateral relationship with their physicians in which the PlwRA’s expertise of their own bodies was on a par with the physician’s professional expertise [ 47 , 62 ]. Confident ‘expert’ patients were more likely to access information on the internet and request blood tests for RA and hence secured a diagnosis or referral to a rheumatologist more expediently [ 36 , 55 ]. The confidence needed to assume the role of expert patient sometimes derived from prior familiarity with RA [ 36 ]. Firth et al. [ 51 ] maintained, however, that patient education could also build self-efficacy to develop PlwRAs’ confidence to proactively seek solutions. Given the common expectation that the ‘Expert Patient’ can self-manage their disease, Townsend et al. [ 52 ] suggested that the failure to adequately inform patients about RA is ethically problematic; without adequate knowledge or support, the potential for benefits is limited, whereas the likelihood of harm is increased. Subsequent to diagnosis, Laires et al. [ 46 ] found that many PlwRA had limited awareness of treatment options and typically had a passive relationship with their physicians characterized by unilateral decision making. PlwRA, healthcare professionals, and decision-makers participating in a focus group identified those with lower SES as being less assertive in their relationships with physicians and therefore less likely to gain a prompt referral [ 45 ]. More passive patients hesitated to raise their concerns about sources of pain or discomfort needing treatment for fear of being ‘a nuisance’ or because they viewed themselves as a ‘coper’ or ‘stoic’ and preferred not to ‘complain’ [ 42 , 50 , 63 , 64 ]. Flurey et al’s [ 63 ] study of British men with RA highlighted the gendered nature of stoicism among them. However, the culturally-mediated and cohort-specific nature of this orientation is also salient [ 65 , 66 ]. Some studies found that past experiences of sexist or racist treatment by healthcare providers influenced the presentation and healthcare preferences of PlwRA [ 64 , 67 ]. To address such constraints, studies recommended that healthcare providers afford their patients sufficient time to express themselves, pay attention to patient context and increase patients’ health literacy [ 68 , 69 ]. Adjudications Access to RA care may be influenced by the biases of primary care providers, or patients’ anticipation of such biases based on prior experience. Some biases were widely shared by providers and patients alike. For example, several studies found that primary care providers were less likely to suspect RA, an inflammatory arthritis, as a possible diagnosis among younger patients because ‘arthritis’ is perceived as a normal part of aging and is not associated with the young; this view also prevented them from taking a proactive approach to the symptoms of RA in older patients [ 45 , 54 , 55 ]. The scarcity of or distant location from specialists and other resources also inhibited some primary care practitioners from referring patients to them [ 45 , 70 , 71 ]. Patients who were overweight or consumed excessive alcohol delayed medical consultations in anticipation of the physician’s attribution of their symptoms to their behaviours, which triggered feelings of guilt [ 29 , 40 ]. Thurston et al. [ 67 ] reported that healthcare providers who did not regularly work with Indigenous patients attributed their perceived lack of buy-in to medical treatment of their RA to a lack of education about the value of specialists and their services. However, the evidence pointed instead to the historical treatment of Indigenous peoples that has undermined their trust in Western institutions, and cultural constraints such as family obligations that prevented Indigenous PlwRA from attending appointments or establishing concordance with treatment plans [ 67 , 69 , 72 ]. The most frequently reported cause of treatment delays, according to PlwRA, was a gatekeeper—usually a primary care provider—who lacked the expertise, time or consideration to recognize their signs and symptoms of RA [ 36 , 39 , 46 , 50 , 64 , 70 , 73 , 74 , 75 , 76 , 77 ]. Lopatina et al. [ 78 ] have advocated for improved access to information and resources on RA for primary care practitioners to this end. Some studies emphasized that delays occurred because primary care physicians viewed themselves as gatekeepers to scarce secondary services and, hence, to integrated care [ 71 , 73 ]. Some authors advocated for the greater availability or awareness of diagnostic tools [ 46 , 73 , 76 ]. More commonly, though, patients identified the importance of physicians who took a person-centred approach that resulted in better health outcomes. This entailed the affordance of mutual respect through bilateral communication and inclusion in treatment decisions; such physicians saw them in the context of their whole lives, not just in terms of their disease [ 42 , 52 , 61 , 62 , 64 , 67 , 69 , 71 , 75 , 79 , 80 , 81 ]. Acceptance and resistance of offers (diagnosis, screening, treatment) So far, we have seen ample evidence of the Candidacy Framework premise that “accomplishing access to healthcare requires considerable work on the part of users, and the amount, difficulty, and complexity of that work may operate as barriers to receipt of care” [ 4 ]. Ultimately, people seeking care aim to secure an offer of some kind. This may be a prescription, a treatment plan, a referral to a specialist or for screening. Yet receipt of an offer does not guarantee access because the ability or willingness to accept the offer may depend on a great many factors such as proximity, affordability, and cultural congruence. This dimension of Candidacy was by far the most densely coded. Of the nodes cross-coded with this dimension, ‘medications’ and ‘exercise and physiotherapy programs’ were most populated. Medications The pain and disability experienced by PlwRA, especially during flares, was reported as extreme to the extent that some had contemplated suicide [ 59 , 60 , 76 ], and the relief that the appropriate prescription of DMARDS and/or biologics can bring was described as ‘dramatic’ and life-changing’ across multiple studies [ 80 , 82 , 83 , 84 , 85 , 86 ]. Thus, the offer of therapeutic drugs for RA is typically accepted, albeit with some reservations: “Most felt or had been told that they had no choice other than to take potentially toxic drugs to alleviate their symptoms or to slow down the deterioration of their chronic condition” [ 64 ]. Yet arriving at the point of relief was often a long and painful process of trial and error to find the right combination of medications ([ 75 ],e.g., [ 87 ])—there was no ‘one size fits all’—which, in turn, took a toll on the PlwRA’s mental health [ 60 ]. PlwRA for whom effective medication had all but eradicated their symptoms hesitated to taper or temporarily discontinue it to resolve an infection, for fear that the intensity of the flares could increase and the drug would no longer be as effective upon resumption [ 49 , 88 , 89 , 90 ], although in principle, they were willing to try. While some temporarily opted out of taking DMARDS and biologics due to their incompatibility with their reproductive goals [ 91 ], others had relinquished the possibility of conceiving because they felt unable to cope without these medications [ 60 ]. Adherence to pharmaceutical treatment for RA was highly variable, ranging from 30 to 80% [ 33 , 92 ]. Multiple studies explained non-adherence in terms of the burden of administration and monitoring DMARD/biologic treatment, which required a considerable commitment from PlwRA [ 44 , 71 , 91 , 93 , 94 , 95 ]. Side effects of the drugs were also found to negatively the PlwRA’s wellbeing and add another level of healthcare complication [ 44 , 68 , 84 ], (e.g., [ 91 , 96 , 97 ]). Ultimately, the extent to which PlwRA perceived their medications to be helpful or harmful exerted the greatest influence on their acceptance or resistance to treatment [ 92 , 93 ]. Some had tried multiple medications with limited or no relief or recovery of their prior capacity and quality of life [ 54 , 84 , 86 , 94 , 96 , 97 ], or the effectiveness of any treatment was short-lived [ 44 ]. It should be noted, however, that the introduction of new more efficacious treatments has decreased the risk of negative outcomes such as joint arthroplasty, excess mortality or adverse pregnancy outcomes following parental exposure to DMARDs [ 98 , 99 , 100 ]. Considerable evidence points to resistance as partial rather than absolute. Most commonly, PlwRA decided to ‘take control’ of their disease by varying the dose according to their perceived need, periodically abstaining from certain medications [ 38 , 63 , 64 , 92 ], or utilizing complementary and alternative medicine or over-the-counter drugs [ 29 , 38 , 43 , 44 , 54 , 59 , 64 , 67 , 92 , 93 , 97 , 101 , 102 , 103 ]. Studies found that the understanding of the mechanism of action of DMARDs and biologics, was often poorly understood by PlwRA; in particular, they did not understand the preventive value of the medications [ 38 , 93 ]. Moreover, they did not understand the harms of long-term use of glucocorticoids like Prednisone [ 38 , 91 ]. A patient-initiated self‐monitoring service for PlwRA on methotrexate was well received in the UK, in large part because the training they received to prepare them to monitor and initiate the drug themselves “increased their knowledge of arthritis, their treatment, the reasons for regular testing and the meaning of test results” and thus “enabled them to gain a sense of control and ownership over their arthritis” [ 95 ]. This speaks to the importance of the provision of person-centred information and education to address harmful misconceptions. Exercise and physiotherapy programs Engagement in exercise programs benefitted PlwRA physically and psychologically and contributed to participants’ empowerment and their ability to manage RA [ 104 , 105 ]. However, exercise programs tailored to the needs of PlwRA were reportedly scarce [ 61 ] and many home exercise programs were viewed as boring and difficult to prioritize [ 106 ]. Studies of RA-tailored exercise programs have identified some key components that increase uptake and maintenance of an exercise routine. These include expert person-centred guidance, flexibility, and sensitivity to and accommodation of RA-specific limitations. Successful programs were typically moderated by a physiotherapist or other exercise professional with the expertise to guide participants through the challenges of pain or fatigue, even when the program was largely participant-led [ 104 , 107 ]. This guidance was especially important for those unfamiliar with exercise [ 108 ], but was also valued for broad-based feedback and back-up, particularly when participants needed to adjust their routines or increase their exercise load [ 105 , 109 , 110 ]. Professionals who took a person-centred approach, recognized the need for PlwRA to feel heard, and adopted a holistic approach to symptom management were especially valued [ 108 , 109 , 110 , 111 ]. Flexibility in programming was appreciated by PlwRA and took many forms. Some programs were customized so PlwRA could exercise at home and fit the exercises into their normal routines [ 104 , 108 ]; being able to modify exercises to their own pace, limitations, and goals (which may vary on a daily basis contingent on disease activity), was also important (104, 108, 110, 111). PlwRA were often afraid to participate in exercise if they believed it would cause pain or they may not be able to get in and out of exercise positions, and adherence to exercise programs often waned as participants experienced flares, fluctuating symptoms or medication changes [ 104 , 105 , 111 ]. Programming must therefore be sensitive to these fears and adapted accordingly. Permeability of the healthcare system Permeability invokes the metaphor of the passage of fluid through material, with slower transmission representing the more stringent qualifications of candidacy (e.g., the need for a referral, lack of cultural alignment) needed to gain access to the healthcare system, and rapid transmission representing easier processes of access [ 4 ]. In our sample, evidence of healthcare system impermeability was primarily found in accounts of untimely delivery of care that disrupted continuity. New models of care were proposed to increase system permeability. The benefits and challenges of different approaches aimed at increasing system permeability according to the reviewed literature are summarized in Table  3 . These characteristics were often reported in combination. Local operating conditions The final dimension of Candidacy considers local influences on the ability of the person needing access to services that may extend beyond the healthcare system [ 4 ]. There is ample evidence in the reviewed literature that access to care for RA is especially compromised for those living in rural and remote areas, in which people who are low-income and Indigenous populations in Canada are disproportionately represented [ 61 , 72 ]. Primarily, access in remote areas was found to be limited because of the absence or low representation of all types of healthcare providers from primary care providers to rheumatologists as well as other specialists and allied health professionals to whom access is needed for optimal care of RA [ 32 , 45 , 46 , 61 ]. As a result, PlwRA faced long drives to access these services. For example, more than half the PlwRA in rural and northern Saskatchewan (a Canadian prairie province) had to drive an hour or more to access primary care providers, Physical Therapy (PT) and Occupational Therapy (OT), pharmacies, labs and medical imaging facilities, and > 25% travelled 4 + hours to see a rheumatologist [ 32 ]. In addition to distance, factors such as weather, road conditions and maintenance, a lack of transportation and the need to arrange childcare compromised PlwRAs’ ability to access care [ 32 ]. PlwRA also had to absorb multiple out-of-pocket costs for fuel or other transportation, childcare, overnight accommodation, etc. Some PT and OT services external to hospitals or outpatient services were also not covered by medical insurance for many, hence affordability of care was a barrier despite Canada’s universal healthcare coverage [ 32 , 75 ]. Also missing in these communities are resources such as community pools or rehabilitation centres needed for optimal RA care [ 45 ]. The availability of potential solutions to address access limitations in these communities, such as traveling clinics, telehealth, and patient travel grants was found to vary by province. The viability of each was hampered by lack of available staff, lack of patient buy-in, and low cost-effectiveness, respectively [ 61 ]. Even outside of rural and remote areas, local influences can affect access to care. Hand et al. [ 27 ] noted that access to services could be compromised for low-income seniors when cost-saving policies resulted in housing that was poorly situated (e.g., on a hill, near a highway, far from services). These authors also reported that despite protective policies, employers did not always cooperate with accommodations for modified working arrangements to facilitate a PlwRA’s ability to return to work. Beyond candidacy – the embodied self Candidacy focuses on the negotiation of access at micro, meso and macro levels, but none of its dimensions fully capture how the identities of PlwRA are challenged by illness experiences and the ways in which access is consequently compromised. This understanding emerges from our sample, in part, because of the phenomenological/phenomenographic approach adopted by 18 of the reviewed studies, for which the focus was on meaning-making. Yet evidence of the same is found across studies with diverse methodological approaches. These findings speak to Kleinman’s [ 117 ] emphasis on the importance of understanding illness as an embodied experience, rather than just a set of symptoms or diagnostic criteria. Kleinman argued that illness is not just a matter of biological dysfunction but is also shaped by a person’s cultural and social context, as well as their own subjective experiences and meanings. Similarly, Bury [ 118 ], who conducted his research with PlwRA, viewed chronic illness as a biological disruption, in the face of which people were forced to reappraise their selfhood as intimately bound to the body. To this, Goodacre [ 119 ] added the importance of the social interpretation of self. Both Charmaz [ 120 ] and Ratcliffe [ 121 ], influenced by Merleau-Ponty, spoke to the loss of identity and self that could arise from bodily disruptions. A profound sense of loss is apparent in PlwRA’s accounts of flares or the effects of pain, deformity and immobilization caused by RA in which not only the body but the mind and sense of self are profoundly affected by the illness (e.g., [ 28 , 31 , 37 , 49 , 60 , 68 , 83 , 86 , 91 , 94 , 107 , 109 , 122 ]). Consistent with Martin’s [ 123 ] work on how women’s culturally-informed and gendered identities are affected by illness, and sources of women’s power in the body discussed by Chrisler et al. [ 124 ], women with RA in several studies detailed identity compromises. Illness affected their appearance and sexuality (e.g., [ 49 , 109 , 125 ]). It also hindered gendered roles like housework, food preparation, and childcare [ 76 , 85 , 126 , 127 , 128 , 129 ]. Yet, some studies reported women exhibiting stoicism [ 55 ] and facing peer exclusion [ 87 ], akin to patterns more commonly found among men [ 63 ]. This highlights the importance of adopting an intersectional approach, wherein identities (e.g., age, gender, SES, rural/urban residence) and concomitant experiences of social domination are seen as intertwined with a compounding effect [ 130 , 131 ]. Additionally, the identities of PlwRA could be supported or undermined by members of their social networks. People who understood their challenges and offered necessary support, without undermining the PlwRA’s self-determination, promoted their identity and facilitated coping [ 27 , 37 , 83 , 128 , 129 ]. Others undermined their sense of self and personal value by dismissing their suffering or assuming control over them [ 60 , 83 , 102 , 108 , 128 , 129 , 132 ]. A PlwRA’s selfhood must therefore be understood in relation to their embodiment of illness (RA and comorbidities), their intersections of identity (e.g., gender, SES, age) and the nature of their relationships with close others. In the studies reviewed, the PlwRA’s selfhood primarily undermined access through the effect of identity loss on their mental health, expressed variously as embarrassment, shame, and loss of self-esteem, power and control [ 31 , 60 , 133 ], which in turn led to self-isolation and societal withdrawal [ 37 , 57 , 59 , 68 , 91 , 134 ]. PlwRA were thus impeded from seeking the help they needed, which could further undermine their mental and physical health [ 28 , 63 , 85 , 94 , 109 , 127 ]. Kristiansen and Antoft [ 135 ] reported on a program for PlwRA that overlooked the debilitating effect of RA on self-esteem and thus further undermined it. “Narratives of chaos” created by illness in relation to identity, as theorized by Frank [ 136 ], were also evident in the efforts of some PlwRA to ‘gain control’ of their illness and hence their sense of self by resisting or modifying treatment [ 38 , 63 , 64 , 91 , 132 ]. Some supplemented or replaced biomedical treatments with complementary and alternative medicines (CAM) in ways that were detrimental to their health [ 59 , 97 , 103 ]. While CAM use can indicate patient engagement and a proactive stance to treatment, it sometimes reflected an uninformed approach to medication resistance. Other such examples were suspending medication while on vacation to permit greater alcohol intake [ 63 ] or substituting medication with unconventional remedies (e.g., horse liniment) [ 44 ] that delayed access and compromised health outcomes. To summarize, we illustrate in Table  4 the seven dimensions of Candidacy plus the embodied self with brief descriptions, based on our CIS, of factors that promote (✔) and limit (✘) candidacy in each. Discussion: extending a theory of access to care for chronic conditions: candidacy 2.0 (CC) Previous applications of the Candidacy Framework have suggested the addition of supplementary constructs to enhance the theory. Most notable among them are recursivity [ 137 ] and concordance [ 138 ], the importance of intersectionality theory [ 139 ] and the relational nature of negotiations of candidacy [ 10 ]. Our findings not only support inclusion of these concepts, the centrality of the embodied self in the model facilitates their integration as components rather than adjuncts of candidacy and suggests the need for an enhanced Candidacy 2.0 model. Selfhood can be challenged by chronic disease, comorbidities and treatments that may have severe side-effects. A person’s identity facilitates their creation of meaning, which is a central tenet of mental health [ 140 ]. The relationship between illness and selfhood can be cyclical when challenges to selfhood due to pain, disability or extreme fatigue, for example, result in deteriorating mental health. This, in turn, has a bidirectional relationship with social isolation and withdrawal [ 141 , 142 ]. Yet the self is not a blank slate: rather it is composed of multiple intersecting identities that are more or less privileged in the societal context in which the person living with a chronic condition is embedded [ 141 , 143 ]. The compounding effect of these identities and societal expectations of and responses to them shapes the extent to which illness-related changes to physical features, sexuality, or the roles that people perform are important to them and the meanings that they ascribe to their illness and its effect on their selfhood [ 144 , 145 ]. In parallel with Mackenzie et al. [ 139 ], we identified the positive or negative influence of intersecting identities on the ability of people to establish candidacy in the articles reviewed for the CIS, especially in the dimensions of navigation and appearances. The addition of an intersectional lens facilitates a more nuanced understanding of help-seeking behaviours and acceptance or rejection of treatment: gender matters in its own right, for example, but it also interacts with other identities and social determinants of health to influence these behaviours [ 143 ]. Intersectional awareness achieved through the integration of candidacy and intersectionality also offers policy makers and practitioners “a means of enhancing knowledge of how the political becomes enacted in the personal” [ 139 ]. Intersecting identities also influence the extent to which a person is likely to reach ‘concordance’ (i.e., agreement with care providers about the problem and best solutions) and engage in shared decision-making with them [ 138 ]. The importance of concordance in the establishment of candidacy has been recognized in multiple studies [ 16 , 17 ] and corresponds well with the original construct of permeability whereby “the service provider’s alignment with service users, including personality, gender, and ethno-linguistic characteristics” facilitates access at the organizational level [ 11 ]. However it is only when intersectionality and candidacy are united that we can fully appreciate how “discordant healthcare encounters are not simply a manifestation of essential cultural differences between the two parties but are shaped by factors that emanate from a complex interplay of historical and contemporary discourses, inequitable structures, multiple intersecting identities and past experiences” [ 146 ]. In the CIS sample, this is illustrated most clearly with reference to experiences of access to RA care by Indigenous people, immigrants and refugees, the frail elderly and people who are low-income or live in rural and remote areas; importantly, people are often marginalized by the intersection of two or more of these identities [ 67 , 69 , 72 ]. Intersections of identity also influence the degree of agency that people can exercise. As Chase et al. [ 6 ] point out, Dixon-Woods et al. [ 4 ] originally conceived of the establishment of candidacy as a ‘negotiation’ between patients and healthcare providers, but this underestimates the power differentials between them that can undermine the agency of people at marginalized intersections of identity, effectively foreclosing any true negotiation and reducing candidacy. For example, asylum seekers who encountered discriminatory and unjust treatment when applying for services were subsequently more likely to pay out of pocket and showed greater reluctance to seek further help or even information [ 6 ]. Similarly, in the articles reviewed for our CIS, we saw multiple examples of curtailed interaction with health professionals due to diminished trust arising from what were seen as biased adjudications rooted in power differentials between healthcare providers and PlwRA at marginalized intersections of identity (e.g., low SES, Indigenous, etc.). This phenomenon is identified as ‘recursivity’ [ 137 ]: “the interdependency between a user’s experiences of health services and her/his future actions in regards [sic.] to health and help seeking” [ 16 ]. Thus “the key determinants of patient choice of healthcare are social and diachronic” [ 14 ]. The outcome of reduced candidacy due to negative recursivity in the CIS data and in Koehn et al’s dementia study [ 11 ] was most apparent in people’s resistance to offers but was also seen in their interactions with healthcare providers (appearances and adjudications). For example, Machin et al. [ 81 ] suggested that PlwRA’s perception of their primary care practitioner as too busy and primarily focused on physical over mental health provoked anxiety and recursively prevented them from discussing mood or seeking assistance to locate mental health resources in subsequent consultations. The connection of recursivity to the embodied self was illustrated by Flurey et al. [ 63 ] who reported that the selfhood of men living with RA, already diminished by their inability to work and perform other roles central to their masculinity, was further degraded when physicians did not take their medical complaints seriously. In response they sought to recover some sense of control by reducing medications, and engaging in excessive exercise or alcohol consumption, and only consulted the physician as a last resort. The permeability of services is also implicated in recursivity as illustrated by Hunter et al.’s [ 14 ] finding that people with ‘long-term conditions’ are frequent users of emergency care services for illness exacerbations because they provide the most expedient access to needed care and technology. Dixon-Woods et al. [ 4 ] have described emergency care as the most permeable healthcare service which, as a result, tends to be utilized more frequently by the most vulnerable members of society, thus underscoring the importance of the inclusion of an intersectional lens. These examples further emphasize Kovandžić et al.’s [ 17 ] point that recursivity is important because it unites the concepts of access to and utilization of healthcare services. Rather than viewing recursivity and candidacy as separate processes [ 14 , 16 , 17 ], we propose that recursivity be understood as an integral component of the process of establishing candidacy that reflects the relative agency of people living with chronic conditions at different intersections of identity as they attempt to negotiate access across its different dimensions. Another key element to arise from the articles included in this CIS is the potential for family, friends, and the broader social network of the person with a chronic condition to either promote or undermine their self-determination and personal value. The notion of an interdependent or sociocentric self, whereby the self is viewed “not in terms of one’s independence from others, but rather by one’s connection to them” [ 147 ] is more commonly attributed to non-Eurocentric cultures, yet examples of this interdependence between PlwRA and their social networks abound throughout our CIS sample. The influence of the social network on candidacy is also apparent in other analyses utilizing the framework. For example, family members who may have more social capital than the person with a chronic condition, particularly dementia, are more likely to identify the need to seek medical attention, facilitate navigation to care, and convey observed symptoms to family physicians and other gatekeepers [ 11 , 14 ]. Accordingly, we propose that the embodied self or personhood conceived as central to the Candidacy 2.0 model is necessarily understood as relational. The centrality of the intersectional relational self in the enhanced model also makes sense in relation to the abundance of evidence that person-centred interactions, information, and service configuration are key to addressing many of the access challenges faced by PlwRA and, we would argue, people living with chronic conditions in general. Candidacy 2.0 provides a strong explanatory framework that maps out why this is the case. Focusing attention on selfhood in the Candidacy model further reinforces the importance of understanding movement through the dimensions of access as iterative, not only because the need for care among people with chronic conditions is ongoing, but because, as Saari [ 140 ] reminds us, “meaning systems must be constantly maintained and amended so that the content will fit with the context and experience of the present. The processes of the self must therefore be active in creating and altering meaning throughout life.” This in turn serves as a reminder to those designing and delivering person-centred systems and care that the ‘person’ is not a static entity, and their values, needs and goals of care may well shift over time. Figure  3 depicts the movement of the intersectional relational self through each dimension of candidacy. Appearances and adjudications are inextricably linked, while permeability is characterized as a barrier with variable holes through which some of the offers made by gatekeepers (adjudicators) will pass. Offers such as referrals, medications or screening are depicted by the small balls that need to be accepted by the self and be compatible with the organizational framework represented by permeability and wider environment or local conditions. Concordance and recursivity are integral to the framework and are most often salient to the dimensions they touch in the diagram. The outcome is ideally access, but different resources may require an iterative process to obtain and for some, the obstacles represented by the accumulated dimensions may defeat access. Candidacy 2.0: An enhanced Candidacy Framework to understand access to healthcare for chronic conditions Limitations The CIS is a very flexible approach to systematic synthesis, which can be both a strength and a weakness. This critical approach draws on the reviewer’s expertise in the field on the one hand and responds to the knowledge needs of the research team, on the other. In this sense, it is not entirely reproducible [ 35 ]. SK, the first author, was primarily responsible for data analysis for which task she drew on her considerable expertise in access to health care and theoretical approaches to illness experience. She consulted frequently with the remaining authors whose expertise in RA and primary care provision strengthened the validity of her interpretations. To remain accountable, the authors of a CIS must demonstrate both systematicity (soundness of execution) and transparency (explicitness of reporting), for which Depraetere and colleagues (25) have developed seven evaluative criteria that distinguish a CIS. Most important among them are (1) Data-extraction method for identifying themes/concepts, (2) Formulation of a synthesizing argument, (3) Inclusion of qualitative and quantitative research results, and (4) Flexible inclusion criteria. We believe that all criteria in this list have been met. While most sources were qualitative to address the deficit in comprehensive studies of RA access experience, eleven were mixed methods with a quantitative component, four were quantitative, and three were review articles. Adoption of the CIS methodology using the lens of the Candidacy Framework to review literature focused on the experiences of people living with a chronic condition (specifically RA) has generated a rich analysis of the challenges and complexity of access to care for RA. Perhaps more importantly, this analysis has identified the key phenomenological dimension of embodied selfhood that is missing in the original formulation of Candidacy. The importance of this central concept is reinforced when considered in relation to other applications of Candidacy, particularly those concerned with access to care for chronic conditions. Modifications suggested by these studies underscore the importance of considering the intersectional and relational self as integral to an enhanced version of the framework. Availability of data and materials All data generated or analysed during this study are included in this published article and its supplementary information files. Data availability Data is provided within the manuscript or supplementary information files. Advisory committee members continue to be involved with the overall programme of RA research but did not feel equipped to contribute to the CIS given their lack of familiarity with academic literature. Barber CE, Lacaille D, Croxford R, Barnabe C, Marshall DA, Abrahamowicz M, et al. 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Toward an interdependent conception of the self: implications for Canadian policy reform. The Canadian Society for Study of Practical Ethics / Société Canadienne Pour L’étude De L’éthique Appliquée. — SCEEA. 2019;15–40. Download references Acknowledgements The authors would like to express a sincere thank you to Nikoletta Wood for the creation of the graphics for the Candidacy 2.0 framework model, and to all of the members of the advisory committee, composed of people living with RA, who provided support and insight in the development of the research questions. The authors would also like to convey their gratitude to The Arthritis Society Canada for the support through the Strategic Operating Grant 20 − 0000000018 SOG, and to Mitacs for the. support through an Accelerate Postdoctoral Fellowship. Author information Authors and affiliations. Faculty of Rehabilitation Medicine, University of Alberta, 8205 114 Street, 2-50 Corbett Hall, Edmonton, AB, T6G 2G4, Canada Sharon Koehn & Anh Pham Department of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, 8205 114 Street, 2-50 Corbett Hall, Edmonton, AB, T6G 2G4, Canada C Allyson Jones & Lisa Jasper Division of Rheumatology, Cumming School of Medicine, Health Sciences Center, University of Calgary, Room #B130Z 3300, Hospital Drive NW, Calgary, AB, T2N 4N1, Canada Claire Barber Prescribing, Analytics & Tracked Prescription Program Alberta, College of Physicians & Surgeons, 2700 – 10020 100 Street NW, Edmonton, AB, T5J 0N3, Canada Cliff Lindeman Faculty of Medicine and Dentistry – Family Medicine Department, University of Alberta, 6- 10L4 University Terrace, 8303 – 112 Street NW, Edmonton, AB, T6G 2T4, Canada Neil Drummond You can also search for this author in PubMed   Google Scholar Contributions SK and AP reviewed articles for inclusion.  SK analyzed the included articles.  All authors (SK, CAJ, CB, LJ, AP, CL, ND) were involved in writing, reviewing, and approving the final manuscript. 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BMC Health Serv Res 24 , 986 (2024). https://doi.org/10.1186/s12913-024-11438-6 Download citation Received : 01 December 2023 Accepted : 14 August 2024 Published : 26 August 2024 DOI : https://doi.org/10.1186/s12913-024-11438-6 Share this article Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Critical interpretive synthesis Candidacy Framework Access to care Rheumatoid arthritis Intersectionality Social support Primary care BMC Health Services Research ISSN: 1472-6963 General enquiries: [email protected] Working together, we can reimagine medicine to improve and extend people’s lives. Senior Analyst Forecasting About the role. Major accountabilities: Create and deliver Forecasting solutions to Novartis Internal Customers as per agreed SLAs. 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Qualitative Data Analysis: What is it, Methods + Examples Qualitative data analysis is a systematic process of examining non-numerical data to extract meaning, patterns, and insights. In contrast to quantitative analysis, which focuses on numbers and statistical metrics, the qualitative study focuses on the qualitative aspects of data, such as text, images, audio, and videos. Developing a Research Proposal for Qualitative Research: A Step-by-Ste Understanding the basics of qualitative research is important for a strong proposal. A clear research question guides your study and ensures it stays on track. Choosing the right methods and being ethical are key parts of your research design. Recruiting the right participants and using proper sampling methods are crucial. Qualitative Data Analysis Methods: Top 6 + Examples QDA Method #3: Discourse Analysis. Discourse is simply a fancy word for written or spoken language or debate. So, discourse analysis is all about analysing language within its social context. In other words, analysing language - such as a conversation, a speech, etc - within the culture and society it takes place. Data Analysis in Qualitative Research: A Brief Guide to Using Nvivo Abstract. Qualitative data is often subjective, rich, and consists of in-depth information normally presented in the form of words. Analysing qualitative data entails reading a large amount of transcripts looking for similarities or differences, and subsequently finding themes and developing categories. Traditionally, researchers 'cut and ... PDF Qualitative Research Proposal Sample Time Between: The Full-Time Adult Undergraduate. Sample Qualitative Research Proposal Written in the APA 6th Style. [Note: This sample proposal is based on a composite of past proposals, simulated information and references, and material I've included for illustration purposes - it is based roughly on fairly standard research proposal; I ... Qualitative Data Analysis 5. Grounded theory. This method of qualitative data analysis starts with an analysis of a single case to formulate a theory. Then, additional cases are examined to see if they contribute to the theory. Qualitative data analysis can be conducted through the following three steps: Step 1: Developing and Applying Codes. PDF The qualitative research proposal The process of writing a qualitative research proposal is discussed with regards to ... studied; yet it is expected to write how data analysis will be done when the data is not known. However, it is imperative that the researcher must convince the proposal evaluation committee or Writing a proposal Guidance for writing a research proposal for the qualitative component of a mixed methods evaluation is offered in Table 7.1. The foundation of this guidance is Drabble et al. (2014), expanded using the range of wider guidance detailed in Section 7.2. The strength of Drabble et al's guidance is that it is short and can be used when space is ... Designing a Research Proposal in Qualitative Research The chapter discusses designing a research proposal in qualitative research. The main objective is to outline the major components of a qualitative research proposal with example (s) so that the students and novice scholars easily get an understanding of a qualitative proposal. The chapter highlights the major components of a qualitative ... Writing Qualitative Research Proposals Using the Pathway Project To address this gap, the Pathway Project Mapping Tool (PPMT) was developed in 2020 and has since been used to assist students and trainees with planning quantitative research proposals (Matthews et al., 2020). The original PPMT has been used in grant writing courses, seminars, and consultations. Qualitative Data Analysis: Step-by-Step Guide (Manual vs ... Step 1: Gather your qualitative data and conduct research (Conduct qualitative research) The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources. (PDF) Designing a Research Proposal in Qualitative Research meaning of the linguistic aspects. They are one-of-a-kind and are conducted in various. ways to provide quite diverse sorts of information. When a researcher considers a. natural investigation of ... Writing Your Qualitative Methods in a Proposal Methodological coherence: I describe qualitative methods, approaches, data collection, and data analysis strategically. Although we are often limited by the number of words we can use and/or available space, we need to offer the reviewer enough details about the research setting, sampling and recruitment strategies, data collection, and data analysis. How to Write Analysis of Qualitative Data Good Practice for Qualitative Data Analysis. In the initial stages of reading the information and identifying basic observations, you can try writing out lists so you can then add in the sub-themes as the analysis progresses. This helps to understand the data and key outcomes better. Keep your research questions to hand so you can refer back to ... Creating a Data Analysis Plan: What to Consider When Choosing The first step in a data analysis plan is to describe the data collected in the study. This can be done using figures to give a visual presentation of the data and statistics to generate numeric descriptions of the data. Selection of an appropriate figure to represent a particular set of data depends on the measurement level of the variable. PDF The qualitative research proposal The process of writing a qualitative research proposal is discussed with regards to ... data analysis will be done when the data is not hiown. However, it is imperative that the researcher must convince the proposal evaluadon committee or funding agency reviewers in order to How to Write a Research Proposal Research proposal examples. Writing a research proposal can be quite challenging, but a good starting point could be to look at some examples. We've included a few for you below. Example research proposal #1: "A Conceptual Framework for Scheduling Constraint Management". PDF A Sample Qualitative Dissertation Proposal word guidelines to highlight the flexibility of this qualitative analytic method. These guidelines. are (1) familiarizing yourself with your data, (2) generating initial codes, (3) The researcher read. throughout each transcript to immerse in the data, (4) reviewing themes, (5) defining and naming. Writing Qualitative Research Proposals Using the Pathway Project Lobe et al. (2022) systematically compared in-person and video-based online interviewing and found that each modality has strengths, weaknesses, and ethical issues to consider in a qualitative research proposal. The second data collection type is observation, ranging from non-participant to participant observers. How to write a research proposal? A proposal needs to show how your work fits into what is already known about the topic and what new paradigm will it add to the literature, while specifying the question that the research will answer, establishing its significance, and the implications of the answer. [ 2] The proposal must be capable of convincing the evaluation committee about ... Data Analytics Resources: Writing a Research Proposal Literature review. As you get started, it's important to demonstrate that you're familiar with the most important research on your topic. A strong literature review shows your reader that your project has a solid foundation in existing knowledge or theory. It also shows that you're not simply repeating what other people have done or said, but rather using existing research as a jumping ... How to Write a Results Section The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share: A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression). A more detailed description of your analysis should go in your methodology section. Facilitators and barriers of midwife-led model of care at public health Data analysis. Atlas.ti7, a qualitative data analysis program, was used for analyzing the data thematically. An inductive approach to thematic analysis involves six steps: familiarization, coding, generation of themes, review of themes, defining and naming of themes, and writing up. By listening to the taped interview again, the data was ... How to Write a Research Proposal To Sum Up. Writing a research proposal can be straightforward if you break it down into manageable steps: Pick a strong research proposal topic that interests you and has enough material to explore.; Craft an engaging introduction that clearly states your research question and objectives.; Do a thorough literature review to see how your work fits into the existing research landscape. Candidacy 2.0 (CC) Data analysis. The inductive approach of a CIS consists of several phases. The first phase of the analysis is the development of a synthesizing argument through reciprocal translational analysis, which involves the translation of different concepts into each other . Two studies often discuss the same construct using different terms or use a ... Senior Analyst Forecasting Expertise in handling datasets - IQVIA, Evaluate, IPD, Kantar, DRG et al.Desirable requirements:• Effective forecast story boarding, capturing key insights backed by relevant. Data and Quantitative Analytics, Business Analysis and Analog Analysis• Supporting team in proposal writing and managing complex business problems. assignment essay homework paper phd resume speech thesis