research topics qualitative data analysis

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Qualitative Data Analysis: What is it, Methods + Examples

Explore qualitative data analysis with diverse methods and real-world examples. Uncover the nuances of human experiences with this guide.

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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From text to context: A complete guide to qualitative data analysis

Think of researchers as data detectives. They're like story readers, exploring detailed accounts and observations that don't fit into charts and numbers. They do so by harnessing the power of qualitative data analysis.

Qualitative analysis focuses on uncovering the depth and complexity of human experience, unlike quantitative data analysis that deals mainly with numbers and statistics. Qualitative analysis means looking beyond the surface and delving into human behaviour to understand the reasons behind the ways things happen.

In this article, we will discover in detail the qualitative data analysis methods, their importance and how you can work around them in the best way possible.

What is qualitative data analysis

research topics qualitative data analysis

In qualitative data analysis, non-numerical data is interpreted by researchers to reveal its underlying meaning. This kind of analysis frequently works with textual data – words, pictures, and occasionally sounds – obtained via observations, interviews, papers, and other narrative sources.

Qualitative analysis looks more into the details of the human experience. It’s a journey into the heart of what people say, do, and create, seeking to understand the complexities of human behaviour and societal phenomena.

Dive deeper into qualitative data analysis with the help of Good Tape's transcription services. Transform your interviews, focus groups, and research discussions into accurate text. Let Good Tape take the burden of transcription off your shoulders, so you can focus on extracting valuable insights - explore the difference Good Tape can make in your research today.

Types of qualitative data analysis methods

There are many different approaches in the field of qualitative data analysis, and each one has its own advantages and insights. Comprehending these different methodologies is vital for efficient analysis of qualitative data. We’ve formed a comparison table for each of the types so that it is easier for you to identify which works best for your research topic.

While there are multiple types of qualitative data analysis methods, it is important to understand which one would work best for you based on your qualitative research methods in order to ensure that your research is comprehensive and effective in the world of academia.

Why qualitative data analysis matters

Analysing qualitative data is essential for comprehending detailed aspects of society dynamics and human behaviour. Its use spans across many academic fields since it goes beyond just gathering data; rather, it digs deeper, attempting to understand and provide context to the complex stories that have been gathered from many sources.

Let's examine the many sectors in which qualitative data analysis has a major influence.

Sociology and psychology: Here it provides insights into human behaviours, interpersonal interactions, and societal phenomena. It also aids in the understanding of attitudes, beliefs, and motives.

Education: Supports the creation of an empathetic and responsive educational system by providing insights into the varied experiences of educators and learners.

Business and consumer research: Here it is essential for creating customer-centric strategies because it offers deep insights into employee experiences, corporate culture, and consumer preferences.

Health sciences: By comprehending patient experiences, healthcare workers can improve patient care and the delivery of healthcare. It also provides guidance for medical practices and policies.

While these are just a few to name, qualitative data analysis offers a wide range of benefits for several other fields as well. It helps enhance our understanding of various layers and dimensions of human life by bridging the gap between mass data collection and significant analysis.

It is an essential tool that turns unprocessed data into meaningful stories, enabling us to understand and value the complexity of human experiences in a deeper and more sophisticated way.

Benefits of qualitative data analysis

Qualitative methods of analysis provide rich data, contextual insights, and in-depth knowledge that enable researchers to explore the intricacies of human experiences in a manner that is not possible with only numerical data.

This approach not only enhances our understanding of diverse phenomena but also guarantees that our research is compassionate, culturally aware, and linked to people's actual experiences. With its many applications in academic research, business analysis, and social policy development, qualitative data analysis is a vital tool for understanding and interpreting the world we live in.

Here are some of the benefits one can expect to reap from qualitative data analysis.

In-depth understanding: Offers a thorough comprehension of individuals' experiences, drives, and actions, surpassing what can be measured.

Contextual insights: Provides subtle explanations for the occurrence of certain patterns and trends by capturing the complexity and context of social occurrences.

Flexibility: Allows for a more responsive and iterative approach to data collection and analysis by adapting to a variety of research settings and issues.

Rich data: Provides descriptive and in-depth information that may lead to surprising discoveries and the development of fresh theories.

Empathy and humanisation: Promotes a greater sense of empathy for participants, giving voice to individual experiences and narratives to humanise the data.

Complex problem solving: Suitable for investigating intricate and multidimensional problems for which numerical data may not be adequate on its own.

Theory development: Facilitates the creation of new theories or models due to detailed conversations and follow-up questions

Cultural sensitivity: Facilitates a deeper comprehension of social and cultural subtleties, which is essential in multicultural and international research settings.

Step-by-step: The qualitative data analysis techniques and process

Analysing qualitative data is a complex, multi-layered process that calls for close attention to detail and a thorough comprehension of the data. The main question that arises here is how to analyse qualitative data. A detailed explanation of the methods and procedures used in qualitative data analysis is provided below:

Data preparation and organisation

Collection: Use a variety of techniques, such as document analysis, focus groups, interviews, and observations, to compile qualitative data.

Transcription: If required, accurately capture information by transcribing audio or video data into text.

Organisation: Data should be consistently organised for simple access and analysis using either manual procedures or software solutions.

Data coding and categorisation

Initial reading: Take a close look at the data and make a note of your initial thoughts and feelings as you read it through.

Open coding: As soon as notions and ideas arise from the data, identify and label them to start the coding process.

Categorisation: Create organised groups of related codes to facilitate structured data analysis.

Data interpretation and finding patterns

Thematic development: Locate patterns and themes in the classified data, paying particular attention to how they connect to the study objectives.

Contextual understanding: Take into account the data's larger context, which includes social, cultural, and environmental aspects.

Refinement: As better knowledge is gained, keep improving topics and categories.

Validity and reliability in qualitative analysis

Triangulation: To verify and cross-check results, use many data sources or techniques.

Member checking: In order to verify accuracy and reliability, involve participants in the assessment of results or interpretations.

Reflective practice: Continue to take a thoughtful stance while recognising the influence of researcher biases on the analysis.

Reporting and presentation of findings

Story development: Create a logical and captivating story that revolves around the main ideas and conclusions.

Visual aids: To effectively display the findings, use charts, diagrams, or other visual aids.

Create relations: Give the results some background by relating them to previous studies and the goals of the study.

Implications and suggestions: Talk about how the results may affect things and make suggestions based on the research.

To protect the integrity and worth of your qualitative data analysis, it is essential to follow a strict, moral, and thoughtful process at every stage.

Using tools and software for qualitative data analysis

Within the field of qualitative data analysis, the application of specialised instruments and software greatly expedites and improves the procedure. These tools include a number of functions that improve efficiency and reduce error while organising, coding, and analysing huge amounts of qualitative data. Looking for the best transcription services for qualitative research can be quite daunting, however this should be a critical selection.

One software that you can rely on is Good Tape. It employs AI technology to transcribe texts from audio which means you no longer need to worry about background noise and slight clutters in your recordings. It is also multilingual, which means you can obtain those important meeting minutes with timestamps in your native language as well. It has a lot to offer to make your qualitative research process easier and more manageable.

Confidentiality and privacy: Good Tape guarantees that all recordings are treated privately and in line with data protection regulations. End-to-end encryption safeguards them. Good Tape fully complies with GDPR, which means that files are never transmitted outside of the EU.

Affordable pricing: Each month, Good Tape offers three free transcriptions, allowing researchers to experience its high-quality services without having to make an immediate commitment. Other than that, its paid premium plans are also quite affordable.

Ease of use: Good Tape is a simple interface that allows you to simply convert audio files to text and provides speedy service. Simply upload your audio file, and Good Tape will produce accurate written transcriptions in minutes, making it ideal for professionals in need of quick transcription services.

Unique to business requirements: Good Tape understands that each firm confronts unique problems and objectives. As a result, it offers transcription services fit for all research types. You get what suits you the best.

Does this seem like something that will make your qualitative research process easier and more effective? B egin using audio to text transcription now and witness firsthand the beneficial change that outsourcing transcription can bring to your research.

Discover Good Tape’s transcription software for academics and researchers

Using Good Tape requires no technical knowledge since the interface is pretty much self-explanatory. There’s no technical details which means you can simply upload and get your transcribed documents in your inbox. This implies you get to spend more time on qualitative analysis and interpretation of the data collected to improve your research by many folds. Here’s how you go about the entire process of transcription with Good Tape.

Upload your file: The first step in the process is to upload the file you need to transcribe. Make sure the file is complete and has all the information you require

Select the language: Good Tape has a number of options when it comes to choosing the language of transcription. Select the one you want, although you can also choose the “auto-detect” option for the system to automatically identify the language in the audio.

Transcribe the text: Once the file is uploaded and the language is chosen, proceed further by clicking the “transcribe” button. Your audio transcription process starts here.

To wait or not to wait: If you’re a casual plan user, you will have to wait for some time for your transcription to be completed due to excessive load by the users. However, if you’re a professional or a team user, you get your results ASAP. The wait time depends on the plan you’re subscribed to .

Get notified: You will receive a notification once your transcribed document is ready. An e-mail will be sent to your inbox containing the link to access and download the document.

With Good Tape, every transcription step you take brings you closer to more insightful and detailed discoveries in your research. Good Tape allows you to give more of your time and focus to analysis. Get started with your qualitative audio-to-text transcription today and make your analysis more meaningful and detailed.

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How to conduct qualitative data analysis

Last updated

21 February 2023

Reviewed by

Tanya Williams

This is where qualitative data analysis comes into play. It helps organizations identify and understand the underlying patterns and meanings of data. As a result, numerous fields, including research, customer experience, user experience design, and product design, use qualitative data analysis.

By understanding the underlying meanings and patterns in qualitative data, you can gain valuable insights that can help your business grow.

Read on to learn more about qualitative data analysis, appropriate methods, and how to do qualitative data analysis.

Analyze all your qualitative data

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What is qualitative data analysis?

Qualitative data analysis is a research method that helps identify relevant themes and patterns in data sets.

It involves organizing, coding, and interpreting data to understand how it connects to its subject. Such subjects may be people, products, or behaviors. Qualitative research approaches are generally used to explore questions that call for an explanation of why or how something happens.

Importance of qualitative data

Qualitative data analysis can yield valuable insights often missed by quantitative approaches. Qualitative research generally provides an in-depth understanding of a person's motivations, beliefs, and behaviors.

It can help you better understand how people perceive their experiences and the environment around them. This is because qualitative research focuses on exploring a person's beliefs, values, and actual behaviors, not just responses to multiple choice questions.

Through qualitative analysis, you can uncover underlying meanings in data sets that are not easily captured by numbers. It focuses on the "why" behind decisions, providing organizations with an understanding of consumer behavior that helps inform decision-making.

Qualitative data examples

You can find qualitative data in a range of sources, including:

Text: Transcripts from interviews, open-ended survey questions , newspaper articles, etc.

Audio recordings : Podcasts, audio diaries, etc.

Video recordings: Instructional videos, film footage, etc.

Images: Photographs, illustrations, etc.

Documents: Memos, reports, legal documents, etc.

Qualitative data analysis methods

There are different methods of performing qualitative data analysis. These include content analysis, narrative analysis, discourse analysis, and thematic analysis. Let's take a look at each of these in more detail:

1. Content analysis

Content analysis is a research method used to identify and categorize information in data sets. It involves examining the text for "themes" or patterns that emerge from the data set.

This method is often used when studying large volumes of textual material, such as newspaper articles, survey responses, and blog posts.

2. Narrative analysis

You can use narrative analysis to identify, analyze, and interpret narrative elements in data sets. This method focuses on the stories or experiences of subjects within the data set.

Narrative analysis is often used when studying communication between people or groups, such as interviews or focus groups.

3. Discourse analysis

Discourse analysis is a research method used to interpret data sets by examining the language (how it's used and what it means) structure, and context in conversations between people. The objective is to understand how different social groups use language and what they mean.

This method is also often used when studying communication between people or groups, such as interviews or focus groups .

4. Thematic analysis

You can use thematic analysis to identify and interpret patterns in data sets. Thematic analysis involves breaking down the data set into smaller "themes" or categories and analyzing the relationship between them.

This method is also often used when studying large volumes of textual material, such as newspaper articles, survey responses, and blog posts.

How to do qualitative data analysis

An organization's in-depth understanding of the internal and external business environment is essential for growth. Qualitative data analysis provides tools to make sense of otherwise random and meaningless data.

But in the age of big data, it's not just about gathering and analyzing data. You must determine the right data to collect and the appropriate collection channels to get maximum value. And more importantly, you must have clarity about what you're researching and why.

For instance, if your objective is to understand how customers perceive your brand, the approach will differ from what you'd do if your objective was to discover customer sentiment about a particular product.

So, before you begin qualitative data analysis, set out the objectives. These objectives will help you determine how to conduct the process and the data to focus on.

Understandably, performing qualitative data analysis may be intimidating, as the process is complex. However, you'll get the insight you need with the right approach.

Here are the steps you should follow:

Step 1: Gather your qualitative data and conduct research

Gathering the data you need for analysis is the first step. Your approach here should be guided by the objectives you set. Make sure to document your data collection process and sources.

Depending on your objectives, you can use different data collection methods .

1. Traditional methods of collecting data

With technology advancing, there are new and faster methods of collecting data, such as text analytics. However, traditional methods like surveys and focus groups are still relevant and very effective for qualitative data analysis.

This is why many organizations still rely on traditional methods to collect data for qualitative analysis. Such methods include:

Surveys: allow you to collect data from large numbers of people and include open-ended questions to gather detailed feedback

Focus groups : great for collecting data from small groups of people in a controlled environment, allowing for discussion in groups which can provide opportunities for people to share opinions and build on ideas and feedback together

Interviews: allow you to collect detailed information from individuals or key informants about topics and/or behaviors being studied

2. Leverage existing qualitative data

Sometimes, you don't need to collect new data. You can leverage existing qualitative data already in your organization's public domain. With numerous contact points with customers, you can access tons of solicited and unsolicited customer feedback .

You can access such data from support ticketing systems, emails, chatbots, and other sources. Analyzing such data can give you insights into customer sentiment, CX gaps, and other information that can help you understand your customers better.

Data from such sources is incredible because not only does it provide a lot of information, but it's easily accessible. Instead of wasting time and resources on creating new research studies or focus groups every time you have a question about your customers, simply review data you already have. It will most likely hold the answers you're seeking.

3. Untapped qualitative data channels

Data that is relevant to your research can be found in unexpected places.

For example, if you're looking for customer sentiment regarding a product, you may want to check out comments on YouTube or Reddit. If you're researching consumer behavior, look at reviews of your product on Amazon or Yelp.

These unexpected channels can offer insights that traditional methods cannot provide. Qualitative data in these places is usually unstructured and difficult to analyze, but they are invaluable, unsolicited sources of intelligence.

Step 2: Connect & organize all your qualitative data

After collecting the data, you need to ensure it's in a suitable format for analysis. Qualitative data is usually unstructured and scattered across different channels, so sorting them into usable chunks can be time-consuming.

To make it easier to summarize, draw insight, and make decisions from collected data, it has to be easily accessible.

Some of the methods you can use to organize and make your data more accessible include:

1. Organize data manually

This method involves the use of spreadsheets to organize quantitative feedback. While organizations and departments used this method to analyze data separately, it's inefficient.

This approach can be very cumbersome, time-consuming, and does not allow you to gain insights at scale. It also requires a significant effort to ensure data accuracy.

2. Organize data using qualitative data analysis software

Technology has made it easier to organize qualitative data. Qualitative analysis software helps you to organize quickly and analyze large volumes of qualitative data visually. Such tools allow you to create different categories for the responses and even generate sentiment scores for each response to draw insights from the data.

Qualitative data analysis software also makes it easier to share insights with the rest of your team by creating visual dashboards and reports. With qualitative data analysis software, you can save time and effort while deriving more accurate insights from your data.

3. Use feedback repositories

Feedback repositories are online databases where you can store customer feedback . They make accessing and analyzing qualitative data easier across different channels, as they provide a platform that consolidates all your data into one place.

These platforms also facilitate collaboration, making it easy for teams to collaborate on research projects and gain insights. With feedback repositories, everyone can access the same data, analyze it, and share insights for further discussion.

Using qualitative data analysis software, feedback repositories, and manual methods to organize your qualitative data can help you make sense of your collected feedback. It also makes it easier to identify trends in customer behavior and draw meaningful insights from the data. This is an important step in the qualitative data analysis process.

Step 3: Coding your qualitative data

The next stage of qualitative data analysis is coding. This involves assigning codes to each response you have collected for easy analysis and categorization.

Codes are short descriptions or labels used to identify common themes and topics in each response. For example, you can assign codes such as "Product Quality" or "Customer Service" to customer feedback to categorize them.

Coding qualitative data helps you categorize and organize the responses into different areas of interest, making them easier to analyze. It also makes it possible to identify patterns and trends in customer behavior and allows you to draw meaningful insights from the data.

In order to code your qualitative data, you need to define a set of codes that represent the different topics discussed in the responses. After that, you can assign these codes to each response. This will help you organize them into categories to do further analysis.

Step 4: Analyze your data and find meaningful insights

Once you have coded your qualitative data, the next step is to analyze it. Qualitative data analysis involves looking for patterns and trends in customer behavior and drawing meaningful insights from the data.

You can use qualitative data analysis tools to help you with this process. These tools use different methods, such as content analysis, narrative analysis, and thematic analysis, to help you identify key themes in the responses.

Qualitative data analysis tools can help you make sense of large amounts of data and gain insights that are not immediately obvious. With qualitative data analysis software, you can save time and effort while deriving more accurate insights from your data.

Step 5: Report on your data and tell the story

Once you have analyzed your qualitative data, the next step is to report on it. Qualitative data analysis reports provide a way to convey the insights you have gained from your data in an easily understandable format.

Which qualitative data analysis method should you choose?

When it comes to qualitative data analysis, there is no one-size-fits-all approach. Different methods are suitable for different kinds of customer feedback and research projects.

Content analysis and thematic analysis are suitable for customer feedback and surveys, while narrative analysis can be used to analyze stories and narratives in customer feedback. Qualitative data analysis software can help you decide which method is right for your project.

Advantages of qualitative data

Qualitative data analysis provides insights into customer behaviors, opinions, and experiences that quantitative analysis cannot obtain. Qualitative data can help you understand customer motivations, identify areas of improvement, and gain a deeper understanding of customer feedback.

Disadvantages of qualitative data

One major limitation of qualitative data analysis is that it does not provide statistically significant results. This is because the samples used to collect data are not representative of the population.

As such, measuring the accuracy of qualitative data analysis and drawing quantitative conclusions from it is difficult. Qualitative data also tends to be more subjective, as it focuses on individual opinions rather than hard facts.

How Dovetail can help you

Qualitative data analysis is a powerful tool for gaining insights into customer experiences and behaviors. It can help identify areas of improvement, uncover customer motivations, and provide a deeper understanding of customer feedback.

Dovetail helps you quickly uncover meaningful insights from customer feedback. Our qualitative data analysis tools make it easy to analyze customer feedback , identify key themes, and create compelling reports to share with your stakeholders.

Try Dovetail and unlock the power of your qualitative data.

What are qualitative analysis tools?

Qualitative analysis tools are software programs that help analyze customer feedback and open-ended survey responses. These tools use different qualitative data analysis methods such as content analysis, narrative analysis, and thematic analysis to help identify key themes in customer responses.

What are the 3 main components of qualitative data analysis?

The three main types of qualitative data analysis are content analysis, narrative analysis, and thematic analysis. Content analysis involves looking for keywords and phrases frequently appearing in customer feedback.

In contrast, narrative analysis is used to analyze stories and narratives, and thematic analysis is used to group responses with common themes and topics. Qualitative data analysis software can help you choose the right method for your project.

What is the difference between qualitative and quantitative data analysis?

The main difference between qualitative and quantitative data analysis is that qualitative data analysis focuses on understanding customer behavior, opinions, and experiences to get at the 'why' and 'how,' whereas quantitative data analysis is concerned with measuring numerical results and statistics.

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Qualitative data analysis.

Paul Mihas Paul Mihas University of North Carolina at Chapel Hill
https://doi.org/10.1093/acrefore/9780190264093.013.1195
Published online: 23 May 2019

Qualitative analysis—the analysis of textual, visual, or audio data—covers a spectrum from confirmation to exploration. Qualitative studies can be directed by a conceptual framework, suggesting, in part, a deductive thrust, or driven more by the data itself, suggesting an inductive process. Generic or basic qualitative research refers to an approach in which researchers are simply interested in solving a problem, effecting a change, or identifying relevant themes rather than attempting to position their work in a particular epistemological or ontological paradigm.

Other qualitative traditions include grounded theory, narrative analysis, and phenomenology. Grounded theory encompasses several approaches, including objectivist and constructivist traditions, and commonly invites researchers to theorize a process and perhaps identify its contexts and consequences. Narrative analysis is an approach that treats stories not only as representations of events but as narrative events in themselves. Researchers using this approach analyze the form and content of narrative data and examine how these elements serve the storyteller and the story. Other elements often considered include plot, genre, character, values, resolutions, and motifs. Phenomenology is an approach designed to “open up” a phenomenon and make sense of its invariant structure, its identifiable essence across all narrative accounts. In this approach, the focus is on the lived experiences of those deeply familiar with the phenomenon and how they experience the phenomenon as they are going through it, before it is categorized and conceptualized. Each tradition has its own investigative emphasis and particular tools for analysis—specific approaches to coding, memo writing, and final products, such as diagrams, matrices, and condensed reports.

qualitative analysis
basic qualitative research
generic qualitative research
grounded theory
phenomenology
narrative analysis
memo writing
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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.

Methods of qualitative data analysis; approaches and techniques to qualitative data analysis

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. 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.

G2.com reviews of the product Airtable. You could pull reviews from G2 for your analysis.

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.

The user interface of CAQDAS software 'NVivo'

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, 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.

Some of qualitative data integrations offered by Thematic

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 .

Example of a hierarchical coding frame in qualitative data analysis

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 .

Visualizing qualitative data: Calculating the impact of a code on your score

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:

Analyzing qualitative data: Calculate which codes are linked to increases or decreases in my score

The visualization could look like this:

Visualizing qualitative data trends over time

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 .

Codes displayed by volume within Thematic. You can 'manage themes' to introduce human input.

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.

Two examples of qualitative data visualizations within Thematic

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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The Ultimate Guide to Qualitative Research - Part 1: The Basics

Introduction

Why qualitative research?

What are the uses for qualitative research, why do qualitative researchers need a guide, how to use this guide, table of contents.

What is qualitative research?
What is qualitative data?
Examples of qualitative data
Qualitative vs. quantitative research
Mixed methods
Qualitative research preparation
Theoretical perspective
Theoretical framework
Literature reviews
Research question
Conceptual framework
Conceptual vs. theoretical framework
Data collection
Qualitative research methods
Focus groups
Observational research
Case studies
Ethnographical research
Ethical considerations
Confidentiality and privacy
Power dynamics
Reflexivity

Navigate to other guide parts:

Part 2: Handling Qualitative Data or Part 3: Presenting Qualitative Data

Introduction to our qualitative research guide

If you're interested in conducting qualitative research, then you've come to the right place! This reference provides all the essential information you need to understand qualitative research and qualitative data analysis.

Qualitative research is a valuable and essential approach within the field of research, as it allows for a deep and nuanced understanding of complex human experiences, social phenomena, and behaviors. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research emphasizes the exploration of subjective experiences, perceptions, and interpretations.

Qualitative approaches enable researchers to uncover the meanings and motivations behind human actions, providing rich and detailed insights that cannot be captured through quantitative methods alone.

Qualitative data analysis

Any qualitative research method will produce textual data or other unstructured data that requires qualitative analysis to organize information and extract key insights. Approaches such as grounded theory, thematic analysis, and content analysis all let researchers analyze datasets for addressing complex research inquiries.

Qualitative research has a wide range of applications across various disciplines, including psychology, sociology, education, health, and business, among others. There are a multitude of applications for qualitative research and qualitative data analysis.

Exploring new or complex phenomena

Qualitative research can help researchers investigate poorly understood or under-researched topics, providing novel insights and a foundation for further inquiry and hypothesis generation. Qualitative research can also facilitate inquiry through quantitative methods by describing a concept or phenomenon in rich detail so that it can be quantitatively measured in future studies.

Understanding the perspectives and experiences of individuals or groups

Through methods such as interviews, focus groups, and ethnography, qualitative research allows researchers to gain insight into the beliefs, values, and motivations of participants, fostering a deeper understanding of their experiences.

Evaluating the effectiveness of programs or interventions

Qualitative research can be used to assess the impact of various initiatives, uncover the factors that contribute to their success or failure, and provide valuable information for improvement and decision-making. For example, researchers can collect qualitative data to conduct a needs analysis for a program or initiative or to get user or customer feedback regarding products or services they provide.

Complementing quantitative research

Qualitative research can be employed alongside quantitative methods to provide a more comprehensive understanding of a research topic, triangulating findings and offering a richer and more robust analysis. In a mixed methods research design, quantitative and qualitative methods provide a synthesized analysis useful for understanding complex topics in rich detail.

What fields use qualitative research?

Qualitative research is a versatile approach that is employed across a wide range of disciplines and fields. Its capacity to provide rich, in-depth insights into human experiences, social phenomena, and complex interactions makes it particularly valuable for understanding the intricacies of various subjects. Some of the key fields that utilize qualitative research include:

1. Psychology: Qualitative research is commonly used in psychology to explore individuals' thoughts, emotions, and behaviors, uncovering the underlying motivations, perceptions, and cognitive processes that influence their actions.

2. Sociology: In sociology, qualitative research is often employed to investigate social structures, relationships, and cultural patterns, offering insights into how social norms, values, and institutions shape individual and group experiences.

3. Anthropology: Anthropologists can use qualitative research methods such as ethnography and participant observation to study diverse cultures, traditions, and communities, enabling them to understand the complexities of human societies and behaviors.

4. Education: Qualitative research in education can examine teaching and learning processes, classroom dynamics, and educational policies, providing valuable information for the development of effective curricula, pedagogies, and interventions.

5. Health and medicine: In health and medicine, qualitative research is often used to explore patient experiences, healthcare provider perspectives, and the social and cultural factors that influence health behaviors and outcomes, informing the design and implementation of health interventions and policies.

6. Business and management: Qualitative research can be employed in business and management to study organizational culture, consumer behavior, customer feedback, and employee experiences, among other topics, providing insights that can guide decision-making, strategy formulation, and performance improvement.

7. Communication studies: In communication studies, qualitative research can help investigate media effects, audience reception, and the role of communication technologies in shaping social interactions and cultural practices.

8. Social work: Social workers often use qualitative research to understand the experiences of vulnerable populations, assess the effectiveness of social welfare programs, and inform the development of policies and interventions that promote social justice and well-being.

9. Political science: Qualitative research in political science can be employed to study political systems, institutions, and actors, exploring the dynamics of power, governance, and policy-making in various contexts.

10. Environmental studies: Researchers in environmental studies can use qualitative research to examine the social, cultural, and political dimensions of environmental issues, exploring the human-environment relationship and informing the development of sustainable policies and practices.

Beyond this list, qualitative research methods can be applied to numerous other fields and areas of inquiry. The flexibility and adaptability of qualitative research make it a valuable approach for researchers seeking to understand the complexities and nuances of human experiences and social phenomena across a wide range of disciplines.

ATLAS.ti is more than data analysis software

Start with a free trial of ATLAS.ti, then rely on our instructional resources and live support for the entire process of qualitative research.

Especially in the social sciences, the qualitative research process lives in the minds of researchers. Quantitative data in the natural sciences can be largely processed by technology, but qualitative research often involves human interaction and interpretation to collect and analyze data. As qualitative research is characterized by its exploratory, interpretive, and context-sensitive nature, it is essential for qualitative researchers to have a guide that provides clear and concise information on the key concepts, methodologies, and best practices for conducting research in this field. Let's examine some of the important tasks that qualitative researchers face and that this guide can address.

Navigate the various qualitative research methods and techniques

This guide provides an overview of the different approaches available, helping researchers to select the most appropriate methods for their specific research questions and contexts.

Qualitative research has a multitude of theoretical, methodological, and analytical approaches that a researcher should consider when planning and conducting a research study. When dealing with social phenomena, cultural practices, or personal beliefs, certain choices in study design are more appropriate than others. This guide will outline the key essentials that are commonly found throughout qualitative research so that you can make fully informed decisions.

Ensure rigor and trustworthiness

Research is always about getting to the key insights found through the organization of raw data. However, researchers carry a particular responsibility to transparently outline how those insights are identified. Rigorous research should not only detail the essential findings but persuade the audience that the findings are built on sound research study design and empirical data collection and analysis, which are important concepts that are emphasized throughout this guide.

Develop essential skills

The information in this guide will support researchers in honing their interviewing, observation, and data analysis skills, fostering their growth as competent and confident qualitative researchers.

Knowing qualitative data analysis methods is one thing, but recognizing the discrete roles they play in making contributions to research is essential to good research practices.

Focus on good research practices

In any research project, there are some tasks that are arguably more mundane (such as data organization and data preparation) relative to other tasks (such as data collection and data analysis). Still, these routine aspects of research are just as important as coding data and developing rigorous findings, so we have included a space for them in this guide.

Adhere to ethical considerations

This guide will help researchers navigate the unique ethical challenges associated with qualitative research, ensuring that their studies are conducted responsibly and with respect for the rights and well-being of participants. By providing emerging researchers with essential information and guidance, this qualitative research guide can support them in conducting rigorous, relevant, and impactful studies that contribute to the advancement of knowledge in their respective fields.

Researchers come from all different backgrounds and have different levels of expertise. This guide is here to support them all.

For all the essentials

Think of this resource as a basic step-by-step guide to qualitative research, from study design to data analysis. If you are new to qualitative research, you can read this guide from start to finish to gain an essential knowledge base that can guide you in your research endeavors.

For a quick refresher

Even experienced researchers from time to time need to refresh their understanding of qualitative research. In that case, feel free to browse through our table of contents to find the appropriate page where you need support.

This section is part of an entire guide. Use this table of contents to jump to any page in the guide.

Part 1: The Basics

10 examples of qualitative data
What is mixed methods research?
Research questions
Survey research
What is ethnographic research?
Confidentiality and privacy in research
Bias in research
Power dynamics in research

Part 2: Handling Qualitative Data

Research transcripts
Field notes in research
Research memos
Survey data
Images, audio, and video in qualitative research
Coding qualitative data
Coding frame
Auto-coding and smart coding
Organizing codes
Content analysis
Thematic analysis
Thematic analysis vs. content analysis
Narrative research
Phenomenological research
Discourse analysis
Grounded theory
Deductive reasoning
What is inductive reasoning?
Inductive vs. deductive reasoning
What is data interpretation?
Qualitative analysis software

Part 3: Presenting Qualitative Data

Presenting qualitative data
Data visualization - What is it and why is it important?
Research paper writing
Transparency and rigor in research
How to publish a research paper

Qualitative Data Analysis

Choosing QDA software
Free QDA tools
Transcription tools
Social media research
Mixed and multi-method research
Network Diagrams
Publishing qualitative data
Student specialists

General Information

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NYU Stream Tutorial

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gathering text corpora,
copyright considerations
data cleaning,
an introduction to some computational software tools,
reading the output and analysis of topic modeling and cluster analysis, and
general overview of common questions asked in computational literary studies.

Presented with grateful consultation from Dr. David Hoover, NYU English Department.

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Learn more about Voyant

With Voyant, you can upload or paste in a corpus of text and get some visualizations and analyses right in your browser. After uploading your corpus, you have 5 immediate panes:

Cirrus (word cloud)
Reader (displays your text)
Trends (frequency of terms throughout text)
Summary (summary statistics)
Contexts (keywords with surrounding text)
Learn more about Network Mapping

Network visualization from Gephi, blue pink and yellow nodes on a black field connected by multi-colored lines.

Network diagrams are a great way to visualize the relationships between entities in your source material. It can sometimes help to explore materials from different angles to uncover new patterns and understanding.

If you're looking for more types of visualizations, you can find more information about data visualization generally at the corresponding library guide: https://guides.nyu.edu/viz/ .

BACKING UP BIBLIOGRAPHIC DATA

EndNote is freely available for the NYU community. You can find more information about downloading this software at the NYU EndNote support page . Below you can find a link to Tutorial slides that will walk you through the process of setting up and using EndNote to manage your bibliographic data.

Introduction to EndNote Slides demonstrating how to download, use, and connect EndNote to other research tools

1. Open the library of references you want to export.

2. In the toolbar at the top of the screen, click "Select Another Style" and select BibTex Export. BibTeX should be your archival copy. These files can also be used with LaTeX software to create citations and bibliographies.

a screenshot of the export option for endnote

3. Go to File > Export and save the file.

1. In your desktop Zotero, go to File > Export library

a screenshot of zotero's file menu with export library highlighted

2. Make sure both notes and files are clicked so you export everything!

a screenshot of exporting zotero library as zotero RDF with notes and files checked

3. Pick which format you'd like to export. We recommend exporting as a CSV for your archival copy, and also as BibLaTex, particularly if you want to import your library into other programs. BibTeX files can be used with LaTeX software to create citations and bibliographies also.

a screenshot of export options from zotero, which is a long list

1. Go to File > Export

a screenshot of mendeley the bibliogrpahic manager, with its export options highlighted

1. Go to References > Export or Tools > Export, depending on your version.

screenshot of refworks export file menu selection

2. Select All References or a specific folder of references you want to export.

3. Under "Export Formats" select BibTex and XML. XML will be your archival copy, and BibTex will be the secondary copy, one which you can import into other reference managers or use with LaTex software.

screenshot of refworks export options for format and list of citations to export

4. Click "Export to Text" and save.

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Home » Qualitative Data – Types, Methods and Examples

Qualitative Data – Types, Methods and Examples

Table of Contents

Qualitative Data

Definition:

Qualitative data is a type of data that is collected and analyzed in a non-numerical form, such as words, images, or observations. It is generally used to gain an in-depth understanding of complex phenomena, such as human behavior, attitudes, and beliefs.

Types of Qualitative Data

There are various types of qualitative data that can be collected and analyzed, including:

Interviews : These involve in-depth, face-to-face conversations with individuals or groups to gather their perspectives, experiences, and opinions on a particular topic.
Focus Groups: These are group discussions where a facilitator leads a discussion on a specific topic, allowing participants to share their views and experiences.
Observations : These involve observing and recording the behavior and interactions of individuals or groups in a particular setting.
Case Studies: These involve in-depth analysis of a particular individual, group, or organization, usually over an extended period.
Document Analysis : This involves examining written or recorded materials, such as newspaper articles, diaries, or public records, to gain insight into a particular topic.
Visual Data : This involves analyzing images or videos to understand people’s experiences or perspectives on a particular topic.
Online Data: This involves analyzing data collected from social media platforms, forums, or online communities to understand people’s views and opinions on a particular topic.

Qualitative Data Formats

Qualitative data can be collected and presented in various formats. Some common formats include:

Textual data: This includes written or transcribed data from interviews, focus groups, or observations. It can be analyzed using various techniques such as thematic analysis or content analysis.
Audio data: This includes recordings of interviews or focus groups, which can be transcribed and analyzed using software such as NVivo.
Visual data: This includes photographs, videos, or drawings, which can be analyzed using techniques such as visual analysis or semiotics.
Mixed media data : This includes data collected in different formats, such as audio and text. This can be analyzed using mixed methods research, which combines both qualitative and quantitative research methods.
Field notes: These are notes taken by researchers during observations, which can include descriptions of the setting, behaviors, and interactions of participants.

Qualitative Data Analysis Methods

Qualitative data analysis refers to the process of systematically analyzing and interpreting qualitative data to identify patterns, themes, and relationships. Here are some common methods of analyzing qualitative data:

Thematic analysis: This involves identifying and analyzing patterns or themes within the data. It involves coding the data into themes and subthemes and organizing them into a coherent narrative.
Content analysis: This involves analyzing the content of the data, such as the words, phrases, or images used. It involves identifying patterns and themes in the data and examining the relationships between them.
Discourse analysis: This involves analyzing the language and communication used in the data, such as the meaning behind certain words or phrases. It involves examining how the language constructs and shapes social reality.
Grounded theory: This involves developing a theory or framework based on the data. It involves identifying patterns and themes in the data and using them to develop a theory that explains the phenomenon being studied.
Narrative analysis : This involves analyzing the stories and narratives present in the data. It involves examining how the stories are constructed and how they contribute to the overall understanding of the phenomenon being studied.
Ethnographic analysis : This involves analyzing the culture and social practices present in the data. It involves examining how the cultural and social practices contribute to the phenomenon being studied.

Qualitative Data Collection Guide

Here are some steps to guide the collection of qualitative data:

Define the research question : Start by clearly defining the research question that you want to answer. This will guide the selection of data collection methods and help to ensure that the data collected is relevant to the research question.
Choose data collection methods : Select the most appropriate data collection methods based on the research question, the research design, and the resources available. Common methods include interviews, focus groups, observations, document analysis, and participatory research.
Develop a data collection plan : Develop a plan for data collection that outlines the specific procedures, timelines, and resources needed for each data collection method. This plan should include details such as how to recruit participants, how to conduct interviews or focus groups, and how to record and store data.
Obtain ethical approval : Obtain ethical approval from an institutional review board or ethics committee before beginning data collection. This is particularly important when working with human participants to ensure that their rights and interests are protected.
Recruit participants: Recruit participants based on the research question and the data collection methods chosen. This may involve purposive sampling, snowball sampling, or random sampling.
Collect data: Collect data using the chosen data collection methods. This may involve conducting interviews, facilitating focus groups, observing participants, or analyzing documents.
Transcribe and store data : Transcribe and store the data in a secure location. This may involve transcribing audio or video recordings, organizing field notes, or scanning documents.
Analyze data: Analyze the data using appropriate qualitative data analysis methods, such as thematic analysis or content analysis.
I nterpret findings : Interpret the findings of the data analysis in the context of the research question and the relevant literature. This may involve developing new theories or frameworks, or validating existing ones.
Communicate results: Communicate the results of the research in a clear and concise manner, using appropriate language and visual aids where necessary. This may involve writing a report, presenting at a conference, or publishing in a peer-reviewed journal.

Qualitative Data Examples

Some examples of qualitative data in different fields are as follows:

Sociology : In sociology, qualitative data is used to study social phenomena such as culture, norms, and social relationships. For example, a researcher might conduct interviews with members of a community to understand their beliefs and practices.
Psychology : In psychology, qualitative data is used to study human behavior, emotions, and attitudes. For example, a researcher might conduct a focus group to explore how individuals with anxiety cope with their symptoms.
Education : In education, qualitative data is used to study learning processes and educational outcomes. For example, a researcher might conduct observations in a classroom to understand how students interact with each other and with their teacher.
Marketing : In marketing, qualitative data is used to understand consumer behavior and preferences. For example, a researcher might conduct in-depth interviews with customers to understand their purchasing decisions.
Anthropology : In anthropology, qualitative data is used to study human cultures and societies. For example, a researcher might conduct participant observation in a remote community to understand their customs and traditions.
Health Sciences: In health sciences, qualitative data is used to study patient experiences, beliefs, and preferences. For example, a researcher might conduct interviews with cancer patients to understand how they cope with their illness.

Application of Qualitative Data

Qualitative data is used in a variety of fields and has numerous applications. Here are some common applications of qualitative data:

Exploratory research: Qualitative data is often used in exploratory research to understand a new or unfamiliar topic. Researchers use qualitative data to generate hypotheses and develop a deeper understanding of the research question.
Evaluation: Qualitative data is often used to evaluate programs or interventions. Researchers use qualitative data to understand the impact of a program or intervention on the people who participate in it.
Needs assessment: Qualitative data is often used in needs assessments to understand the needs of a specific population. Researchers use qualitative data to identify the most pressing needs of the population and develop strategies to address those needs.
Case studies: Qualitative data is often used in case studies to understand a particular case in detail. Researchers use qualitative data to understand the context, experiences, and perspectives of the people involved in the case.
Market research: Qualitative data is often used in market research to understand consumer behavior and preferences. Researchers use qualitative data to gain insights into consumer attitudes, opinions, and motivations.
Social and cultural research : Qualitative data is often used in social and cultural research to understand social phenomena such as culture, norms, and social relationships. Researchers use qualitative data to understand the experiences, beliefs, and practices of individuals and communities.

Purpose of Qualitative Data

The purpose of qualitative data is to gain a deeper understanding of social phenomena that cannot be captured by numerical or quantitative data. Qualitative data is collected through methods such as observation, interviews, and focus groups, and it provides descriptive information that can shed light on people’s experiences, beliefs, attitudes, and behaviors.

Qualitative data serves several purposes, including:

Generating hypotheses: Qualitative data can be used to generate hypotheses about social phenomena that can be further tested with quantitative data.
Providing context : Qualitative data provides a rich and detailed context for understanding social phenomena that cannot be captured by numerical data alone.
Exploring complex phenomena : Qualitative data can be used to explore complex phenomena such as culture, social relationships, and the experiences of marginalized groups.
Evaluating programs and intervention s: Qualitative data can be used to evaluate the impact of programs and interventions on the people who participate in them.
Enhancing understanding: Qualitative data can be used to enhance understanding of the experiences, beliefs, and attitudes of individuals and communities, which can inform policy and practice.

When to use Qualitative Data

Qualitative data is appropriate when the research question requires an in-depth understanding of complex social phenomena that cannot be captured by numerical or quantitative data.

Here are some situations when qualitative data is appropriate:

Exploratory research : Qualitative data is often used in exploratory research to generate hypotheses and develop a deeper understanding of a research question.
Understanding social phenomena : Qualitative data is appropriate when the research question requires an in-depth understanding of social phenomena such as culture, social relationships, and experiences of marginalized groups.
Program evaluation: Qualitative data is often used in program evaluation to understand the impact of a program on the people who participate in it.
Needs assessment: Qualitative data is often used in needs assessments to understand the needs of a specific population.
Market research: Qualitative data is often used in market research to understand consumer behavior and preferences.
Case studies: Qualitative data is often used in case studies to understand a particular case in detail.

Characteristics of Qualitative Data

Here are some characteristics of qualitative data:

Descriptive : Qualitative data provides a rich and detailed description of the social phenomena under investigation.
Contextual : Qualitative data is collected in the context in which the social phenomena occur, which allows for a deeper understanding of the phenomena.
Subjective : Qualitative data reflects the subjective experiences, beliefs, attitudes, and behaviors of the individuals and communities under investigation.
Flexible : Qualitative data collection methods are flexible and can be adapted to the specific needs of the research question.
Emergent : Qualitative data analysis is often an iterative process, where new themes and patterns emerge as the data is analyzed.
Interpretive : Qualitative data analysis involves interpretation of the data, which requires the researcher to be reflexive and aware of their own biases and assumptions.
Non-standardized: Qualitative data collection methods are often non-standardized, which means that the data is not collected in a standardized or uniform way.

Advantages of Qualitative Data

Some advantages of qualitative data are as follows:

Richness : Qualitative data provides a rich and detailed description of the social phenomena under investigation, allowing for a deeper understanding of the phenomena.
Flexibility : Qualitative data collection methods are flexible and can be adapted to the specific needs of the research question, allowing for a more nuanced exploration of social phenomena.
Contextualization : Qualitative data is collected in the context in which the social phenomena occur, which allows for a deeper understanding of the phenomena and their cultural and social context.
Subjectivity : Qualitative data reflects the subjective experiences, beliefs, attitudes, and behaviors of the individuals and communities under investigation, allowing for a more holistic understanding of the phenomena.
New insights : Qualitative data can generate new insights and hypotheses that can be further tested with quantitative data.
Participant voice : Qualitative data collection methods often involve direct participation by the individuals and communities under investigation, allowing for their voices to be heard.
Ethical considerations: Qualitative data collection methods often prioritize ethical considerations such as informed consent, confidentiality, and respect for the autonomy of the participants.

Limitations of Qualitative Data

Here are some limitations of qualitative data:

Subjectivity : Qualitative data is subjective, and the interpretation of the data depends on the researcher’s own biases, assumptions, and perspectives.
Small sample size: Qualitative data collection methods often involve a small sample size, which limits the generalizability of the findings.
Time-consuming: Qualitative data collection and analysis can be time-consuming, as it requires in-depth engagement with the data and often involves iterative processes.
Limited statistical analysis: Qualitative data is often not suitable for statistical analysis, which limits the ability to draw quantitative conclusions from the data.
Limited comparability: Qualitative data collection methods are often non-standardized, which makes it difficult to compare findings across different studies or contexts.
Social desirability bias : Qualitative data collection methods often rely on self-reporting by the participants, which can be influenced by social desirability bias.
Researcher bias: The researcher’s own biases, assumptions, and perspectives can influence the data collection and analysis, which can limit the objectivity of the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

Primary Data – Types, Methods and Examples

Research Data – Types Methods and Examples

Quantitative Data – Types, Methods and Examples

Secondary Data – Types, Methods and Examples

Information in Research – Types and Examples

Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

Research topics and ideas about data science and big data analytics

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

Developing machine learning models for real-time fraud detection in online transactions.
The use of big data analytics in predicting and managing urban traffic flow.
Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
The application of predictive analytics in personalizing cancer treatment plans.
Analyzing consumer behavior through big data to enhance retail marketing strategies.
The role of data science in optimizing renewable energy generation from wind farms.
Developing natural language processing algorithms for real-time news aggregation and summarization.
The application of big data in monitoring and predicting epidemic outbreaks.
Investigating the use of machine learning in automating credit scoring for microfinance.
The role of data analytics in improving patient care in telemedicine.
Developing AI-driven models for predictive maintenance in the manufacturing industry.
The use of big data analytics in enhancing cybersecurity threat intelligence.
Investigating the impact of sentiment analysis on brand reputation management.
The application of data science in optimizing logistics and supply chain operations.
Developing deep learning techniques for image recognition in medical diagnostics.
The role of big data in analyzing climate change impacts on agricultural productivity.
Investigating the use of data analytics in optimizing energy consumption in smart buildings.
The application of machine learning in detecting plagiarism in academic works.
Analyzing social media data for trends in political opinion and electoral predictions.
The role of big data in enhancing sports performance analytics.
Developing data-driven strategies for effective water resource management.
The use of big data in improving customer experience in the banking sector.
Investigating the application of data science in fraud detection in insurance claims.
The role of predictive analytics in financial market risk assessment.
Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

The application of big data in public transportation systems for route optimization.
Investigating the impact of big data analytics on e-commerce recommendation systems.
The use of data mining techniques in understanding consumer preferences in the entertainment industry.
Developing predictive models for real estate pricing and market trends.
The role of big data in tracking and managing environmental pollution.
Investigating the use of data analytics in improving airline operational efficiency.
The application of machine learning in optimizing pharmaceutical drug discovery.
Analyzing online customer reviews to inform product development in the tech industry.
The role of data science in crime prediction and prevention strategies.
Developing models for analyzing financial time series data for investment strategies.
The use of big data in assessing the impact of educational policies on student performance.
Investigating the effectiveness of data visualization techniques in business reporting.
The application of data analytics in human resource management and talent acquisition.
Developing algorithms for anomaly detection in network traffic data.
The role of machine learning in enhancing personalized online learning experiences.
Investigating the use of big data in urban planning and smart city development.
The application of predictive analytics in weather forecasting and disaster management.
Analyzing consumer data to drive innovations in the automotive industry.
The role of data science in optimizing content delivery networks for streaming services.
Developing machine learning models for automated text classification in legal documents.
The use of big data in tracking global supply chain disruptions.
Investigating the application of data analytics in personalized nutrition and fitness.
The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
Developing predictive models for customer churn in the telecommunications industry.
The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies, so they can provide some useful insight as to what a research topic looks like in practice.

Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
An Essay on How Data Science Can Strengthen Business (Santos, 2023)
A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest. In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

Research Topic Kickstarter - Need Help Finding A Research Topic?

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Open access
Published: 08 July 2023

Understanding geriatric models of care for older adults living with HIV: a scoping review and qualitative analysis

Kristina Marie Kokorelias 1 , 2 , 3 ,
Anna Grosse 1 , 4 ,
Alice Zhabokritsky 5 , 6 , 7 &
Luxey Sirisegaram 1 , 4

BMC Geriatrics volume 23 , Article number: 417 ( 2023 ) Cite this article

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Advances in Human Immunodeficiency Virus (HIV) treatment have reduced mortality rates and consequently increased the number of individuals with HIV living into older age. Despite this, people aged 50 years and older have been left behind in recent HIV treatment and prevention campaigns, and a gold-standard model of care for this population has not yet been defined. Developing evidence-based geriatric HIV models of care can support an accessible, equitable, and sustainable HIV health care system that ensures older adults have access to care that meets their needs now and in the future.

Guided by Arksey & O’Malley (2005)’s methodological framework, a scoping review was conducted to determine the key components of, identify gaps in the literature about, and provide recommendations for future research into geriatric models of care for individuals with HIV. Five databases and the grey literature were systematically searched. The titles, abstracts and full texts of the search results were screened independently in duplicate. Data were analyzed using a qualitative case study and key component analysis approach to identify necessary model components.

5702 studies underwent title and abstract screening, with 154 entering full-text review. 13 peer-reviewed and 0 grey literature sources were included. Most articles were from North America. We identified three primary model of care components that may improve the successful delivery of geriatric care to people living with HIV: Collaboration and Integration; Organization of Geriatric Care; and Support for Holistic Care. Most articles included some aspects of all three components.

To provide effective geriatric care to older persons living with HIV, health services and systems are encouraged to use an evidence-based framework and should consider incorporating the distinct model of care characteristics that we have identified in the literature. However, there is limited data about models in developing countries and long-term care settings, and limited knowledge of the role of family, friends and peers in supporting the geriatric care of individuals living with HIV. Future evaluative research is encouraged to determine the impact of optimal components of geriatric models of care on patient outcomes.

Peer Review reports

Human immunodeficiency virus (HIV) continues to be characterized as one of the most prominent public health threats [ 1 ], although advances in antiretroviral therapy (ART) have reduced mortality rates and transformed HIV into a manageable, chronic disease [ 2 ]. The life expectancy for people living with HIV who have had early and sustained access to ART is now similar to that of HIV-negative populations [ 3 , 4 , 5 ]. Thus, there is now an increase in the number of individuals living with HIV into older age [ 6 ] and the number of older adults (aged ≥ 50 years [ 7 ]) living with HIV is expected to increase even further in the coming years [ 8 ]. The proportion of older adults living with HIV has nearly tripled since 2000 [ 9 ].

Older adults with HIV have an increased risk of dementia, diabetes, frailty, depression, osteoporosis, and some cancers, compared to those who are HIV negative [ 10 , 11 , 12 ]. Comorbidities commonly associated with ageing (e.g., diabetes) have been found to increase the risk of opportunistic infections (e.g., HIV-related concerns) in older adults with HIV [ 13 , 14 , 15 , 16 ]. Moreover, stigma is associated with higher rates of loneliness, social isolation and depression in the HIV population [ 17 ]. Despite their increased risk of poor health and social outcomes, older adults living with HIV face many challenges accessing appropriate health and social care, further exacerbating their poor health outcomes [ 18 ]. The stigma associated with HIV may result in a fear of disclosure that delays treatment [ 19 ], and individuals with HIV can feel discriminated against by healthcare providers, resulting in hesitation about or refusal to seek medical care [ 20 , 21 ]. Older adults also tend to not access social services designed for the HIV-infected population because of their own assumption that these programs are created only for younger individuals [ 22 ]. Consequently, HIV scholars have urged for a health and social care system where knowledge and communication about geriatric HIV care are encouraged amongst advocates who work directly with this population, such as geriatric healthcare workers [ 23 ].

Geriatric specialists have expertise in managing many comorbidities that share associations with both ageing and HIV, despite geriatricians being hesitant to take a prominent role in the care of HIV in older adults [ 24 ] due to a lack of experience and training [ 25 ]. While health policy reports a preference for general practice-based HIV care over specialist care [ 26 , 27 ], general practitioners may have a less nuanced understanding about the holistic care of an older adult with complex comorbidities, geriatric syndromes, and metabolic complications when compared with geriatricians [ 28 ]. The use of the Comprehensive Geriatric Assessment (CGA) has been explored, and may lead to improved health and social outcomes in the older adult-HIV population [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], and may be used to measure outcomes in clinical trials that aim to improve the delivery of HIV care for the older adult-HIV population [ 36 ]. However, in the absence of specialized geriatric models of HIV care, many older adults with HIV fail to receive a CGA [ 37 , 38 ] and the recommendations from CGAs are rarely implemented due to a lack of feasibility following a geriatric consult for older adults with HIV [ 39 ].

Numerous models of care, defined as “the way health services are delivered” [ 40 ] (pg., 3), have been developed for older adults with HIV. Many involve geriatric specialists in HIV care, with geriatricians taking on various responsibilities ranging from consultation to leadership roles [ 36 , 41 ]. However, the gold-standard model of care for older adults living with HIV have not yet been defined [ 34 , 35 ], and geriatric care is often delivered by non-geriatric specialists [ 16 ]. Instead of examining models of care, recent literature reviews have tended to focus on the prevalence and experiences of older adults in HIV care [7, NaN], or the experiences of geriatricians [ 24 ]. As implementing geriatric models of HIV care into healthcare settings requires unique considerations [ 28 ], an improved understanding of existing models of care may inform best-practices. This approach has been done to inform the design and delivery of other models of healthcare [ 42 , 43 , 44 , 45 ]. Therefore, we conducted a scoping review of the existing evidence about geriatric models of care for older adults within the context of HIV. To our knowledge, this is the first review to systematically identify the core operational components of existing models of care specific to older adults living with HIV.

A scoping review was selected to map the available literature on geriatric models of care for older adults within the context HIV [ 46 ]. The protocol for our scoping review followed the well-established framework outlined by Arksey and O’Malley [ 46 ] and later refined by Levac et al. [ 47 ] and Colquhoun et al. [ 48 ]. The framework was selected as it provides guidance to ensure a rigorous scoping review approach utilizing a comprehensive search strategy [ 46 ]. Our protocol has been published elsewhere (blinded for review #1) but is briefly described within this section of the manuscript. There were no deviations from our protocol. The framework includes five steps: 1) identifying the research questions; 2) identifying relevant literature; 3) study selection; 4) charting the data; 5) collating, summarizing and reporting the results [ 46 ]. The optional sixth step of consulting with key stakeholders was not followed due to financial resource constraints. We briefly summarize each step and report our findings in accordance with The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews (PRISMA-Scr) [ 49 ] (see Supplemental Material A).

Step 1: Identifying the research questions

Our questions were developed to support a knowledge synthesis that could mobilize the current evidence into practice. Our study aimed to answer: What are the key components of the existing models of HIV care for older adults (aged ≥ 50 years [ 7 , 29 ])?

Step 2: Searching for relevant studies

To identify studies, we developed a comprehensive search strategy with an experienced medical information specialist (CDC) who first conducted the search in MEDLINE(R) ALL (in Ovid, including Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Ovid MEDLINE(R) Daily) and then translated it into NLM’s PubMed OVID Embase + Embase Classic, EBSCO’s CINAHL Complete, Clarivate’s Web of Science Core Collection, and Elsevier’s Scopus from the earliest record to 2022 (see Supplemental Material B for the full strategies ) . The search strategy was peer-reviewed according to the peer-review of electronic search strategy guidelines (the PRESS strategy) [ 50 ]. MeSH terms were used. All searches were limited to English language. The final searches were completed on Friday, October 21, 2022. Duplicates were removed using the Bramer method in EndNote [ 51 ]. Covidence was used to manage the review process, including the deduplication of database results [ 52 ].

Gray literature and non-indexed articles were searched for using Google Scholar, Open Grey, open Google searches and relevant websites, including the World Health Organization, UK National Research Register, CADTH’s “Grey Matters”, New York Academy of Medicine's Grey Literature Report, the Canadian Medical Association InfoBase and the National Institute for Heath and Care Excellence – Guidance. Similar search terms used in the scientific search were used. We also consulted with stakeholders of our research (i.e. geriatricians, infectious disease specialists) for any gray literature missed.

Step 3: Selecting studies

Three reviewers (LS, KMK and AG) independently screened article titles and abstracts (level 1-screening) and then full articles (level 2-screening) were screened in duplicate to identify potentially relevant studies. In both levels of screening, any disagreements were resolved through team-based discussion. Articles were included if they described an implemented model or models of care to treat older adults living with HIV exclusively (i.e., not as part of the treatment for multi-morbidity including HIV) and included a registered healthcare provider that specialized in geriatric care (e.g., gerontology social worker, geriatric clinical nurse specialist, geriatrician). Perspective (viewpoint) papers that describe implemented models of HIV care were also included. Book sections, theses, film broadcasts, abstracts without adequate data, and literature reviews were excluded. Articles were also excluded if they: (1) did not propose an original model of HIV care specifically for older adults (i.e., models of care for all adults or models that may include older adults), (2) focused on ethical issues or the theoretical understandings of HIV care or geriatric care, (3) focused on training healthcare providers on how to deliver HIV and/or geriatric care; and (4) described social support, rather than care in a clinical, health-care context. Forward and backward searching were conducted on the final full-text articles to ensure a broad search using EndNote and Citationchaser [ 53 , 54 ].

Step 4: Charting the data

The same three reviewers independently extracted data from the included studies using a data abstraction form that was developed and pilot tested by two researchers (LS and KMK). The data form was tested on five articles for consistency in understanding and ensuring that all relevant data was captured. No changes were made after comparing the pilot test results. The fields for abstraction included author last name, year, study type, setting, geographic location (country), methodology, characteristics of intervention (model of care) and delivery method, participant and provider characteristics, patient inclusion and exclusion criteria, desired outcomes (primary and secondary), results and key conclusions.

Step 5: Collating, summarizing and reporting the results

Data were analyzed using a systematic qualitative case study analytic approach [ 55 ]. First, each author reviewed the abstracted data and independently noted the core operational components (i.e., model structure and process for delivery) described in the models of care. Then the authors came together to list all the identified model components across the included articles, by exploring the similar and different terms to describe the same model components. Each model component was given a label and a definition. These components became the basis of codes that were then appropriately applied by one author (KMK) to each article using NVivo 12 software [ 56 ]. Next the coded data was reviewed by all authors to determine how each model of care described in the articles adhered or did not adhere to each of the particular model components (codes). The authors met weekly to discuss the process of adherence. This discussion process was informed by adherence analyses [ 57 ]. During this process, authors were encouraged to identify any components that were potentially originally overlooked. No additional suggestions were made on key model components. The model components adhered to across the articles and models of care formed the basis of the results.

After a comprehensive list of the identified model components had been determined, two authors (KMK and AG) went through each article and identified them as either adhering or not adhering to each particular characteristic component, as determined by written evidence within the articles. This was done by having the two authors each providing their vote (i.e., adhering or not) and then comparing the two scoring. Any uncertainty in adherence assignment or discrepancies in voting was resolved through discussion amongst all the investigators as done in other reviews with similar methodologies [ 42 ].

Step 6: Consultation

To further contribute to our component adherence, we shared our model components with the senior investigators of our peer-reviewed articles for feedback. We also asked the investigators to assess their level of agreement with our interpretations of their study's component adherence. Lastly, we asked authors to send along any studies that they believed would be relevant to our review. This was done via email by the first (KMK) and senior author (LS) in December 2022. After two months, we only received five replies from 13 potential authors (n = 5/13, 38%) and all five authors agreed with the adherence we provided their article with, suggesting an accurate adherence analysis. No investigators provided us with additional materials or feedback on the model components, rather just commenting on their article specifically.

The databases search yielded a total of 5699 unique citations, from which 151 articles were selected for full text review. Of these 151 articles, 12 peer-reviewed articles were included. An additional peer-reviewed article was obtained from hand searching. No grey literature was included. Thirteen articles were included in the final analysis (see Fig. 1 PRISMA flow chart).

PRISMA flow chat diagram

Most ( n = 10/13, 77%) of the publication activity occurred in the United States (USA) [ 28 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 ]. The remaining three articles ( n = 3/13,23%) were from the United Kingdom (UK)[ 66 , 67 , 68 ]. Over half ( n = 9/13,69%) of the articles were published in the last 5 years (2018–2023) [ 28 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ]. In published papers, the most common research methods were qualitative. The key description from these studies were abstracted and are summarized in Table 1 .

Patient population

Patients in the included models of care ranged from 48 [ 60 ]–87 years of age [ 67 ]. The number of patients served ranged from 76 [ 39 ] over 4 years to a maximum of 4000 at the time of data collection (period unspecified) [ 66 ]. Of those articles that reported sex ( n = 9/13,69%), the majority described primarily male samples [ 39 , 60 , 61 , 62 , 63 , 64 , 65 , 68 ]. Articles that reported race/ethnicity ( n = 7/13, 54%), described including participants who were mostly White [ 60 , 61 , 67 ] or African American [ 39 , 62 , 63 , 65 , 68 ]. These articles all included White individuals. Of the two ( n = 2/13, 15%) studies that reported the median time since HIV diagnosis [ 39 ], the average was 12.5 [ 63 ]- 21.5 [ 39 ] years. Medicaid was used as the patients’ primary health insurance in the USA [ 39 , 61 , 62 ].

Key operational components of geriatric models of HIV care

The qualitative analysis identified three distinct model of care components, each with one or more sub-components. These components are listed and described in Table 2 . Table 3 also lists the articles adherent to each component. These model components entail: Collaboration and Integration; Organization of Geriatric Care; and Support for Holistic Care. These three components are described and are illustrated in Fig. 2 .

Main Model Components

Model Component 1: Collaboration and integration

Eleven ( n = 11/13, 85%) [ 28 , 39 , 41 , 59 , 60 , 61 , 64 , 65 , 66 , 67 , 68 ] articles described the importance of collaboration and integration for providers caring for older adults with HIV. Models of care frequently incorporated a team of multidisciplinary professionals from the health and social care sectors that were linked in with community supports to improve healthcare delivery for older adults with HIV.

i) Multidisciplinary care roles

Multidisciplinary teams supported the care of older adults living with HIV in all eleven articles that adhered to the Collaboration and Integration model component ( n = 11/13, 85%). These articles described several provider roles, including designated HIV specialists (infectious diseases or internal medicine physicians) [ 39 , 41 , 60 , 61 , 65 , 66 , 67 , 68 ], geriatricians [ 39 , 41 , 60 , 61 , 64 , 65 , 67 , 68 ] and/or dual-trained HIV and geriatric physicians. Other physician roles included psychiatrists [ 39 ], endocrinologists [ 65 ], cardiologists [ 41 , 60 , 61 , 68 ] and medicine fellows [ 64 ]. Numerous nursing roles [ 41 , 59 , 60 , 61 , 64 , 65 ] were involved, such as HIV clinical nurse specialists [ 41 , 66 , 67 ] and nurse practioners [ 41 , 64 , 65 ]. Allied health professionals included dieticians [ 39 , 65 , 66 ]/ nutritionists[ 41 ], social workers[ 39 , 41 , 59 , 61 , 65 , 66 , 68 ], phsysiotherapists [ 41 , 59 , 66 ], occupational therapists [ 41 , 59 , 66 ], speech-language pathologists[ 59 ], counselors/therapists [ 59 ], homecare aides [ 59 ], clinical psychologists [ 65 , 66 ] and specialist pharmacists [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 ].

In addition to healthcare providers, several models of care also included research team members (i.e. research coordinators [ 39 ], research assistants [ 39 ], graduate students in gerontology and epidemiology [ 41 ]), medical directors and administrative staff [ 59 , 61 ] (e.g., program coordinator[ 60 ], a gerontologist [i.e., non-clinician] [ 41 ]), chaplains [ 59 ] and volunteers [ 59 ]. Peer navigator roles were also described [ 28 , 41 , 65 , 68 ].

The key responsibilities of these providers differed between models of care and many had overlapping functions. Physicians [ 39 , 41 , 60 , 61 , 64 , 65 , 66 , 67 , 68 ] and nurses [ 41 , 59 , 60 , 61 , 64 , 65 ] were often responsible for overseeing and ensuring appropriate medical care, such as disease and symptom management. Other healthcare professional roles and designated navigation-specific roles [ 28 , 65 , 68 ], provided medication, rehabilitation [ 41 , 59 , 66 ], dietary [ 39 , 59 , 65 , 66 ], or emotional counseling to patients and caregivers [ 59 ]. Geriatricians, in particular, provided evidence-based, best-practice advice that was shared with patients’ primary care providers [ 39 , 41 , 60 , 61 , 64 , 65 , 67 , 68 ]. HIV specialists generally oversaw HIV-related treatments and community services [ 39 , 41 , 60 , 61 , 65 , 66 , 67 , 68 ]. Pharmacists often provided medication instructions and explained care protocols [ 41 , 60 , 65 , 66 , 67 ]. All care providers were described as providing informational and tangible (i.e., hands-on care) support. Administrative and research staff were responsible for documenting relevant information accurately [ 39 , 41 , 59 , 61 ]. Only one article mentioned the role of non-professional caregivers (i.e., spouse, partner, or friend) as part of the care team [ 59 ], in which they were described as providing much of the personal care involved in the home management of HIV [ 59 ].

Administrative team members and researchers support the collection of client information to systematically standardize clinical and research operations [ 39 , 41 , 59 , 60 , 61 ].

ii) Team-Based care

Ten articles ( n = 10/13, 77%) described the team-based delivery of multidisciplinary care, which was facilitated by several different mechanisms. Informational continuity was identified as being vital in ensuring a consistent and coherent approach to the management of older adults’ evolving needs [ 67 ]. A shared electronic health record was found to enable team-based care, including the ability for multiple providers to chat in real-time [ 28 , 41 , 60 , 61 , 68 ]. Moreover, the multidisciplinary team would often meet to discuss each patient’s background, their outcome measures, current clinical problems, and anticipated needs [ 28 ]. Consequently, the team would facilitate the appropriate screenings through access to different providers, services, and resources [ 28 , 39 , 41 , 60 , 61 , 65 , 68 ]. Following a referral and initial clinical visit, the HIV-geriatric specialists would maintain communication with the primary care team [ 28 ], make recommendations based on the identified age-related needs for care [ 28 ], initiate referrals to other specialist care providers and communicate with community stakeholders to meet other needs [ 59 ]. Team-based care allowed for all members of the circle of care to have a comprehensive knowledge of patients’ health and social care needs (e.g., functional, cognitive) [ 28 ]. Results from retrospective medical and pharmacy chart reviews helped inform all team decisions [ 65 ]. When deemed necessary, the team would be able to create a new action plan [ 39 ] and determine follow-up [ 64 ]. Nurses who worked in case manager roles helped to facilitate this care by coordinating a comprehensive, holistic care plan in collaboration with the patient, caregiver(s), physician(s), and other members of the care team [ 59 ]. Team-based models of care were felt to improve the coordination of care [ 41 ].

iii) Community linkages

Nine articles ( n = 9/13, 69%) described how the management of HIV in older adults involved active, collaborative partnerships between multidisciplinary healthcare providers and the various community resources available to individuals living with HIV. Models of care were often delivered in linkage with community resources (e.g., social groups) [ 41 ] and through community partners (e.g., volunteer organizations) [ 41 ]. Social workers often helped to facilitate community linkages [ 59 ], and grant-funding helped to pay for community services [ 65 ]. By working with community partners [ 41 ], models of care were able to deliver both nonclinical care [ 39 ] (e.g., peer support to decrease isolation and depression [ 41 ]), as well as clinical care [ 28 ] (e.g., care facilitated by a community nurse [ 39 ]). Community outreach also helped to foster friendships amongst older adults living with HIV through social and community-building activities including dinners, speeches, dances, and trips [ 59 ]. Local partner agencies assisted with meeting the housing needs for patients with marginal housing [ 61 ], and with the provision of legal services [ 61 ]. Partnering medical HIV-geriatric services with community services was thought to result in improved access to services [ 28 ], reduced social isolation [ 60 ], improved home safety management [ 59 ] and the provision of spiritual care such as priests, rabbis, or pastoral personnel [ 59 ].

Model Component 2: Organization of geriatric care

The specific organizational structure of each model of care varied, particularly as it related to staffing models, processes for access and referrals, and the implementation of evidence-based, best-practice care and follow-up. All articles adhered and contributed to this model component. Models of care were often delivered through clinics that were predominantly hospital-based (i.e., operating within a hospital) [ 39 , 60 , 61 , 65 , 66 , 67 ]. Additionally, geriatric clinics were outpatient clinics housed within existing HIV clinics [ 41 ] or community-based services providing home care [ 59 ]. Some models of care were able to be delivered virtually, either solely via phone [ 62 ] or in addition to in-person delivery [ 65 , 66 ]. Some clinics ran weekly [ 66 ], bi-weekly [ 65 ] or monthly [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 ], whereas others were full-time [ 39 , 65 ].

i) Staffing models

Within the identified models of care, various staffing models were described. All articles contributed to this sub-component. The Geriatrician-Referral model included a geriatrician who consulted on patients [ 39 , 41 , 60 , 61 , 64 , 65 ] based on a referral from the primary care team (often an HIV provider [ 41 ]), according to the perceived need (e.g., cognitive concerns). Six articles ( n = 6/13, 46%) adhered to this. The Joint-Clinic model involved a geriatrician and HIV physician who were present in a single, combined clinic [ 41 , 66 , 67 , 68 ]. Four articles ( n = 4/13, 31%) adhered to this model. The HIV-Physician-led model involved staffing clinics with a HIV physician and clinical nurse specialist trained in geriatrics, without geriatrician involvement [ 65 , 66 ]. Two articles ( n = 2/13, 15%) adhered to this model. A further staffing model, the Dual-Trained Provider model, involved a dually-trained HIV and geriatrics provider, as either a physician [ 41 , 68 ] or psychotherapist [ 62 , 63 ]. Four articles ( n = 4/13, 31%) adhered to this model. The Nurse-led model, involved nurse-lead teams of allied health professionals [ 59 ]. Only one article ( n = 1/13, 8%) adhered to this model [ 59 ].

i) Access and referrals

All articles described processes to ensure appropriate access to care, and thus contributed to this sub-component. Referrals and on-call services [ 59 ] were used to facilitate access to care [ 59 ]. In some models of care, older adults were only able to access geriatric services via a referral from their HIV primary care team [ 39 , 41 , 60 , 61 , 67 ], while in other models, referrals were triggered by a combination of age (i.e., 50 years of age or older) and need (e.g., complexity) [ 28 , 66 , 67 , 68 ]. The process of receiving geriatric care often began with an assessment of patients’ needs and functional status (e.g., cognition) [ 39 ] and the collection of demographic information (e.g., age, sex, race/ethnicity, HIV risk factors, marital status, insurance status [ 39 ])[ 28 , 61 , 65 ]. Provider referrals were often documented through tracking scheduled appointments [ 60 , 61 , 68 ], however, limitations of this method included HIV providers not remembering to refer [ 41 ] and patient barriers such as confusion over the need for the referral which may result in skipping geriatric appointments [ 41 ]. One model of care implemented patient reminders to help ensure appointments were attended [ 64 ]. Two articles ( n = 2/13, 15%) relied on referrals through an AIDS service organization [ 62 , 63 ]Moreover, across the models, patients could choose to be referred to one service (e.g. cardiology clinic) or multiple (e.g., geriatrics clinic) [ 60 , 68 ]. Patients could choose to have follow up with the geriatrician[ 28 ] and/or be connected with a primary care provider [ 41 ]. Clinics have developed guidelines and policies to guide the operation of services [ 28 ].

ii) Implementation of evidence-based screening

All articles described the incorporation of gold-standard, evidence-based screening practices into their geriatric care. Mood symptoms were assessed using the Hospital Anxiety and Depression Scale [ 60 , 62 , 63 , 67 ], the Geriatric Depression Scale [ 62 , 63 ], the Older Peoples’ Quality of Life Questionnaire [ 67 ] and/or the Patient Health Questionnaire [ 39 ], while cognition was assessed using tools such as the Montreal Cognitive Assessment [ 60 ]. CGAs were followed up with direct actions such as counseling (e.g., about ageing) [ 28 , 39 , 60 ], assessments of comorbidities, age-appropriate preventative health screening[ 41 , 60 , 61 ], and pharmacist reviews targeting polypharmacy and drug safety [4, NaN]. In addition to the CGA, clinics offered British HIV Association (BHIVA)-recommended screening (i.e., guidelines for the management of HIV), an antiretroviral review, a functional review and full medication review [ 28 , 66 ]. Emotional support was monitored using the ‘Therapy Content Checklist’ [ 62 , 63 ]. The goal of using valid measurements was to promote best practice [ 59 ].

Model Component 3: Support for holistic care

As older persons are more likely to experience cumulative health challenges that affect their quality of life, models of care for people ageing with HIV have incorporated a comprehensive holistic management approach. All included articles adhered and contributed to this model component. Clinics provided care for patients with multimorbidity [ 60 , 61 , 66 , 67 ] and helped them to overcome socioeconomic challenges [ 41 ], substance use disorders [ 60 , 65 ] and social isolation [ 60 , 62 , 63 ] by understanding their backgrounds[ 41 ]. Physical health consultations considered cardiovascular disease, dental health, eye health and bone health[ 28 , 41 , 60 , 61 , 64 , 68 ] to address HIV and metabolic-related complications [ 41 ]. Care plans incorporated medication prescriptions [ 28 , 39 , 60 , 61 , 66 , 67 , 68 ], preventative screening [ 28 , 39 , 60 , 61 , 64 , 65 , 66 , 67 , 68 ], age-related disease processes (e.g., cognitive-testing) [ 28 , 39 , 41 , 59 , 60 , 61 , 64 , 65 , 66 , 67 , 68 ], psychosocial interventions to improve social networks and mental health [ 28 , 39 , 59 , 60 , 62 , 63 , 64 , 65 ], exercise and nutrition regimens [ 39 ] and behavioural health supports (e.g., smoking cessation, therapy) [ 28 , 39 , 59 , 60 , 61 , 62 , 63 , 64 , 67 ] to meet the holistic needs of each patient. Spiritual support delivered through religious leaders, mental health counselors/therapists, and emotional support volunteers was also offered [ 59 , 64 ].

i)Comprehensive geriatric assessment

Most models of care ( n = 8/13,61.5%) involved a CGA [ 28 , 39 , 41 , 60 , 61 , 66 , 68 ] or utilized geriatric screening tools [ 65 ] to guide holistic care plans. Most CGAs were delivered by geriatricians who would write full consultation notes [ 39 , 60 , 61 ], although non-geriatrician health care providers were often trained to administer geriatric screening tests [ 41 , 64 ]. The CGA provided an overview of physical and mental health, as well as social support systems [ 39 ], using validated scales [ 39 ].

ii)Supporting self-management

The models of care in six articles ( n = 6/13, 46%) aimed to support the self-management of older adults living with HIV. The goal of self-management was to enable patients to better manage their health outside of the clinic setting by involving older adults in medical decision-making [ 60 , 68 ] and managing their chronic illnesses [ 59 , 60 , 61 ]. Self-management involved education [ 39 , 59 , 60 , 65 ] and coaching [ 28 ] about health behaviours, guidance for choosing appropriate interventions [ 39 , 59 , 65 ] to improve a patient’s health status [ 28 , 65 ], and increased health care utilization to improve patient involvement in care [ 60 , 65 ]. Some models involved classes where older adults could learn about various health conditions [ 60 , 61 , 62 , 63 ]. Where self-management was not possible due to cognitive or functional impairments, healthcare professionals provided education to individuals’ social support networks such as to encourage their inclusion in care [ 39 , 59 ]. To evaluate self-management, some studies included surveys about knowledge in the evaluations of the clinic models [ 60 , 61 ].

Our scoping review of the literature identified thirteen articles describing geriatric models of care for older adults living with HIV. The identified models came from two countries, the USA and the United Kingdom, and incorporated screening for geriatric syndromes [ 28 , 39 , 41 , 60 , 61 , 65 , 66 , 68 ]. From these articles, we identified three overarching key model components: Collaboration and Integration; Organization of Geriatric Care; and Support for Holistic Care. The models of care were largely delivered by a consulting geriatrician [ 39 , 41 , 60 , 61 , 64 , 65 ] via a referral from an HIV provider [ 41 ], from a joint clinic model involving a geriatrician and HIV physician[ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 ], or through a dually-trained HIV-geriatrics provider [ 41 , 62 , 63 , 68 ]. However, some models did not involve a geriatrician [59, NaN]. Table 4 summarizes the future recommendations from the included articles.

Our review identified that most models of geriatric-HIV care are delivered by multidisciplinary teams that facilitate integrated health and social care. Multidisciplinary providers who work in team-based care models have been shown to improve clinical outcomes among HIV patients [ 70 , 71 , 72 , 73 ]. This study provided examples of collaborations in which practitioners worked together to meet the diverse needs of patients. Our data expand this finding by suggesting that multidisciplinary care providers help to facilitate referrals to even more providers, particularly those working in community settings, to ensure care continuity and care coordination to meet holistic needs for support. However, it is important for future research to further understand what staffing model of multidisciplinary team care contributes best to the quadruple aim of optimizing health system performance (i.e., improving the individual experience of care; improving the health of populations; reducing the per capita cost of healthcare and creating better provider experiences [ 74 ]) and the limitations of the existing approaches. Moreover, given the shortage of geriatricians [ 45 ] to meet patient needs, it is important to consider the transferability of models that involve a geriatrician [ 39 , 41 , 60 , 61 , 64 , 65 ][ 66 , 67 , 68 ], or dually-trained HIV-geriatrics provider [ 41 , 62 , 63 , 68 ].

The increasing proportion of older adults living with multimorbidity, including HIV, has evoked calls for tailored geriatric services that respond to their evolving needs. Our results suggest that care delivery should address multiple complex and multidimensional aspects of health and wellness, including psychosocial needs such as strategies to reduce social isolation. However, none of the articles discussed the provision of palliative or hospice care. Palliative care has been posited to augment HIV patients’ health and social care outcomes [ 75 ]. Implementation science may help researchers identify how to implement novel palliative care interventions into exiting practices and support uptake and sustainability by considering why, how and in what circumstances barriers and facilitators may be present [ 76 ]. In addition, older adults were described as being decision makers in their care such as being able to choose the follow up services they receive [ 60 , 68 ]. While some programs sought the input of older adults (e.g., through focus groups, none explicitly mentioned partnering with older adults to co-design their models of HIV care. Other HIV interventions have included individuals living with HIV on their steering committees and in development teams, such that care meaningfully reflects their wishes and preferences [ 77 , 78 , 79 ]. These interventions do not include older adults. Future models of care may wish to engage older adults in co-design to conceptualize and brainstorm program delivery [ 80 , 81 ].

Our review identified several areas of research with limited information. Most literature was published in the USA. Only one article mentioned the role of family caregivers in the care of HIV [ 59 ]. However, individuals living with HIV may receive support from non-kin family caregivers, such as friends [ 82 ]. Research is needed to better understand how broader conceptualizations of family can be embedded into the multidisciplinary care teams to help facilitate family-centered care [ 43 , 83 ]. Moreover, none of the articles mentioned care being delivered in the context of nursing or long-term care homes, nor did they mention offered referrals to long-term care facilities or services. Research is needed to determine the optimal approach for delivering geriatric services in long-term care settings to older adults living with HIV. Strategies are also needed to effectively embed HIV care into the already overburdened and under-resourced long-term care sector. While telehealth has proven to be an effective strategy for delivering HIV care [ 84 , 85 ], particularly in rural and remote communities where specialists may not be readily available [ 86 ], additional research is needed to identify the best practices and limitations for delivering geriatric-focused models of care virtually. Lastly, no studies have evaluated how to best incorporate culturally-sensitive geriatric care across racial and ethnic groups [ 87 , 88 ]. Thus, more data are needed to develop culturally-informed models of care to better engage and care for diverse populations of older adults living with HIV, particularly for adults with certain racial and ethnic backgrounds who may face pervasive stigma for accessing HIV care [ 89 , 90 ].

Limitations

As with any review, our findings must be considered within the context of the limitations. Despite our best efforts (i.e., multiple databases, peer-reviewed strategy, screening in duplicate, bibliographic searches, contacting authors of the reviewed articles), we may have inadvertently missed potentially relevant articles. Moreover, we may have missed papers of programs not yet described in the literature, such as those recently funded or piloted. Similarly, we limited the inclusion criteria to studies available in English due to resource constraints (i.e., lack of funding to support translation) and, consequently, may have biased our included studies to those published in English-speaking countries [ 91 ]. However, the intention of scoping reviews is to provide an overview or “map” of the breadth of existing literature, and thus, future exploration is warranted that builds upon our search strategy. Studies focused on individuals with HIV, but did not include description of older adults living with co-morbidities that impair healthcare decision-making, such as dementia, making it difficult to comment about models of care for individuals who require decision-making support. Lastly, stakeholders in implementing, delivering and receiving models of care (e.g., individuals with HIV, policy-makers, healthcare professionals) were not involved in the study design nor analysis.

Conclusions

Our review suggests that novel models of geriatric care for older adults living with HIV should include collaboration and integration, an organization of care that considers appropriate and timely referrals, communication of medical information and the implementation of evidence-based recommendations, as well as a holistic understanding of the dimensions of care, such that they support self-management. This proposed geriatric-based model can provide the framework to inform future implementation science and evaluative research to support further refining and developing this model. However, further research is needed to inform models of geriatric-HIV care in long-term care settings. Given the increasing number of older adults living with HIV, the development of best-practice models of integrated care can hopefully guide healthcare professionals to provide optimal care in the context of the complexities of care for older adults with HIV.

Availability of data and materials

The analysis files and data used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Comprehensive Geriatric Assessment

Human Immunodeficiency Virus

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Acknowledgements

We would like to thank and acknowledge the contributions of Charmaine De Castro, Information Specialist at the Mount Sinai Hospital– Sinai Health System, for providing guidance on the search strategy development, and conducting the literature search. We would like to thank and acknowledge the contributions of the authors who replied to our emails for contributing to our analysis.

This work was supported by Sinai Health’s Healthy Ageing and Geriatrics Program Research Fund.

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Alice Zhabokritsky

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CIHR Canadian HIV Trails Network, 570-1081 Burrard Street, Vancouver, BC, V6Z 1Y6, Canada

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All authors contributed to the project idea and initiated the project. KMK and LS conceptualized the study design. KMK wrote the first draft of this manuscript and revised the article during the review process. KMK and LS provided guidance to the Information Specialist with respect to the design of the search strategy. All authors finalized the literature search strategy. KMK piloted the search strategy. AG and LS were involved in editing and revising the manuscript. All authors approved the final version of the protocol and are accountable for all aspects of the work.

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Kokorelias, K.M., Grosse, A., Zhabokritsky, A. et al. Understanding geriatric models of care for older adults living with HIV: a scoping review and qualitative analysis. BMC Geriatr 23 , 417 (2023). https://doi.org/10.1186/s12877-023-04114-7

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v.3(1); 2008

Data Analysis in Qualitative Research: A Brief Guide to Using Nvivo

MSc, PhD, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

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 paste’ and use coloured pens to categorise data. Recently, the use of software specifically designed for qualitative data management greatly reduces technical sophistication and eases the laborious task, thus making the process relatively easier. A number of computer software packages has been developed to mechanise this ‘coding’ process as well as to search and retrieve data. This paper illustrates the ways in which NVivo can be used in the qualitative data analysis process. The basic features and primary tools of NVivo which assist qualitative researchers in managing and analysing their data are described.

QUALITATIVE RESEARCH IN MEDICINE

Qualitative research has seen an increased popularity in the last two decades and is becoming widely accepted across a wide range of medical and health disciplines, including health services research, health technology assessment, nursing, and allied health. 1 There has also been a corresponding rise in the reporting of qualitative research studies in medical and health related journals. 2

The increasing popularity of qualitative methods is a result of failure of quantitative methods to provide insight into in-depth information about the attitudes, beliefs, motives, or behaviours of people, for example in understanding the emotions, perceptions and actions of people who suffer from a medical condition. Qualitative methods explore the perspective and meaning of experiences, seek insight and identify the social structures or processes that explain people”s behavioural meaning. 1 , 3 Most importantly, qualitative research relies on extensive interaction with the people being studied, and often allows researchers to uncover unexpected or unanticipated information, which is not possible in the quantitative methods. In medical research, it is particularly useful, for example, in a health behaviour study whereby health or education policies can be effectively developed if reasons for behaviours are clearly understood when observed or investigated using qualitative methods. 4

ANALYSING QUALITATIVE DATA

Qualitative research yields mainly unstructured text-based data. These textual data could be interview transcripts, observation notes, diary entries, or medical and nursing records. In some cases, qualitative data can also include pictorial display, audio or video clips (e.g. audio and visual recordings of patients, radiology film, and surgery videos), or other multimedia materials. Data analysis is the part of qualitative research that most distinctively differentiates from quantitative research methods. It is not a technical exercise as in quantitative methods, but more of a dynamic, intuitive and creative process of inductive reasoning, thinking and theorising. 5 In contrast to quantitative research, which uses statistical methods, qualitative research focuses on the exploration of values, meanings, beliefs, thoughts, experiences, and feelings characteristic of the phenomenon under investigation. 6

Data analysis in qualitative research is defined as the process of systematically searching and arranging the interview transcripts, observation notes, or other non-textual materials that the researcher accumulates to increase the understanding of the phenomenon. 7 The process of analysing qualitative data predominantly involves coding or categorising the data. Basically it involves making sense of huge amounts of data by reducing the volume of raw information, followed by identifying significant patterns, and finally drawing meaning from data and subsequently building a logical chain of evidence. 8

Coding or categorising the data is the most important stage in the qualitative data analysis process. Coding and data analysis are not synonymous, though coding is a crucial aspect of the qualitative data analysis process. Coding merely involves subdividing the huge amount of raw information or data, and subsequently assigning them into categories. 9 In simple terms, codes are tags or labels for allocating identified themes or topics from the data compiled in the study. Traditionally, coding was done manually, with the use of coloured pens to categorise data, and subsequently cutting and sorting the data. Given the advancement of software technology, electronic methods of coding data are increasingly used by qualitative researchers.

Nevertheless, the computer does not do the analysis for the researchers. Users still have to create the categories, code, decide what to collate, identify the patterns and draw meaning from the data. The use of computer software in qualitative data analysis is limited due to the nature of qualitative research itself in terms of the complexity of its unstructured data, the richness of the data and the way in which findings and theories emerge from the data. 10 The programme merely takes over the marking, cutting, and sorting tasks that qualitative researchers used to do with a pair of scissors, paper and note cards. It helps to maximise efficiency and speed up the process of grouping data according to categories and retrieving coded themes. Ultimately, the researcher still has to synthesise the data and interpret the meanings that were extracted from the data. Therefore, the use of computers in qualitative analysis merely made organisation, reduction and storage of data more efficient and manageable. The qualitative data analysis process is illustrated in Figure 1 .

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Qualitative data analysis flowchart

USING NVIVO IN QUALITATIVE DATA ANALYSIS

NVivo is one of the computer-assisted qualitative data analysis softwares (CAQDAS) developed by QSR International (Melbourne, Australia), the world’s largest qualitative research software developer. This software allows for qualitative inquiry beyond coding, sorting and retrieval of data. It was also designed to integrate coding with qualitative linking, shaping and modelling. The following sections discuss the fundamentals of the NVivo software (version 2.0) and illustrates the primary tools in NVivo which assist qualitative researchers in managing their data.

Key features of NVivo

To work with NVivo, first and foremost, the researcher has to create a Project to hold the data or study information. Once a project is created, the Project pad appears ( Figure 2 ). The project pad of NVivo has two main menus: Document browser and Node browser . In any project in NVivo, the researcher can create and explore documents and nodes, when the data is browsed, linked and coded. Both document and node browsers have an Attribute feature, which helps researchers to refer the characteristics of the data such as age, gender, marital status, ethnicity, etc.

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Project pad with documents tab selected

The document browser is the main work space for coding documents ( Figure 3 ). Documents in NVivo can be created inside the NVivo project or imported from MS Word or WordPad in a rich text (.rtf) format into the project. It can also be imported as a plain text file (.txt) from any word processor. Transcripts of interview data and observation notes are examples of documents that can be saved as individual documents in NVivo. In the document browser all the documents can be viewed in a database with short descriptions of each document.

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Document browser with coder and coding stripe activated

NVivo is also designed to allow the researcher to place a Hyperlink to other files (for example audio, video and image files, web pages, etc.) in the documents to capture conceptual links which are observed during the analysis. The readers can click on it and be taken to another part of the same document, or a separate file. A hyperlink is very much like a footnote.

The second menu is Node explorer ( Figure 4 ), which represents categories throughout the data. The codes are saved within the NVivo database as nodes. Nodes created in NVivo are equivalent to sticky notes that the researcher places on the document to indicate that a particular passage belongs to a certain theme or topic. Unlike sticky notes, the nodes in NVivo are retrievable, easily organised, and give flexibility to the researcher to either create, delete, alter or merge at any stage. There are two most common types of node: tree nodes (codes that are organised in a hierarchical structure) and free nodes (free standing and not associated with a structured framework of themes or concepts). Once the coding process is complete, the researcher can browse the nodes. To view all the quotes on a particular Node, select the particular node on the Node Explorer and click the Browse button ( Figure 5 ).

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Node explorer with a tree node highlighted

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Browsing a node

Coding in NVivo using Coder

Coding is done in the document browser. Coding involves the desegregation of textual data into segments, examining the data similarities and differences, and grouping together conceptually similar data in the respective nodes. 11 The organised list of nodes will appear with a click on the Coder button at the bottom of document browser window.

To code a segment of the text in a project document under a particular node, highlight the particular segment and drag the highlighted text to the desired node in the coder window ( Figure 3 ). The segments that have been coded to a particular node are highlighted in colours and nodes that have attached to a document turns bold. Multiple codes can be assigned to the same segment of text using the same process. Coding Stripes can be activated to view the quotes that are associated with the particular nodes. With the guide of highlighted text and coding stripes, the researcher can return to the data to do further coding or refine the coding.

Coding can be done with pre-constructed coding schemes where the nodes are first created using the Node explorer followed by coding using the coder. Alternatively, a bottom-up approach can be used where the researcher reads the documents and creates nodes when themes arise from the data as he or she codes.

Making and using memos

In analysing qualitative data, pieces of reflective thinking, ideas, theories, and concepts often emerge as the researcher reads through the data. NVivo allows the user the flexibility to record ideas about the research as they emerge in the Memos . Memos can be seen as add-on documents, treated as full status data and coded like any other documents. 12 Memos can be placed in a document or at a node. A memo itself can have memos (e.g. documents or nodes) linked to it, using DocLinks and NodeLinks .

Creating attributes

Attributes are characteristics (e.g. age, marital status, ethnicity, educational level, etc.) that the researcher associates with a document or node. Attributes have different values (for example, the values of the attribute for ethnicity are ‘Malay’, ‘Chinese’ and ‘Indian’). NVivo makes it possible to assign attributes to either document or node. Items in attributes can be added, removed or rearranged to help the researcher in making comparisons. Attributes are also integrated with the searching process; for example, linking the attributes to documents will enable the researcher to conduct searches pertaining to documents with specified characteristics ( Figure 6 ).

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Document attribute explorer

Search operation

The three most useful types of searches in NVivo are Single item (text, node, or attribute value), Boolean and Proximity searches. Single item search is particularly important, for example, if researchers want to ensure that every mention of the word ‘cure’ has been coded under the ‘Curability of cervical cancer’ tree node. Every paragraph in which this word is used can be viewed. The results of the search can also be compiled into a single document in the node browser and by viewing the coding stripe. The researcher can check whether each of the resulting passages has been coded under a particular node. This is particularly useful for the researcher to further determine whether conducting further coding is necessary.

Boolean searches combine codes using the logical terms like ‘and’, ‘or’ and ‘not’. Common Boolean searches are ‘or’ (also referred to as ‘combination’ or ‘union’) and ‘and’ (also called ‘intersection’). For example, the researcher may wish to search for a node and an attributed value, such as ‘ever screened for cervical cancer’ and ‘primary educated’. Search results can be displayed in matrix form and it is possible for the researcher to perform quantitative interpretations or simple counts to provide useful summaries of some aspects of the analysis. 13 Proximity searches are used to find places where two items (e.g. text patterns, attribute values, nodes) appear near each other in the text.

Using models to show relationships

Models or visualisations are an essential way to describe and explore relationships in qualitative research. NVivo provides a Modeler designated for visual exploration and explanation of relationships between various nodes and documents. In Model Explorer, the researcher can create, label and connect ideas or concepts. NVivo allows the user to create a model over time and have any number of layers to track the progress of theory development to enable the researcher to examine the stages in the model-building over time ( Figure 7 ). Any documents, nodes or attributes can be placed in a model and clicking on the item will enable the researcher to inspect its properties.

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Model explorer showing the perceived risk factors of cervical cancer

NVivo has clear advantages and can greatly enhance research quality as outlined above. It can ease the laborious task of data analysis which would otherwise be performed manually. The software certainly removes the tremendous amount of manual tasks and allows more time for the researcher to explore trends, identify themes, and make conclusions. Ultimately, analysis of qualitative data is now more systematic and much easier. In addition, NVivo is ideal for researchers working in a team as the software has a Merge tool that enables researchers that work in separate teams to bring their work together into one project.

The NVivo software has been revolutionised and enhanced recently. The newly released NVivo 7 (released March 2006) and NVivo 8 (released March 2008) are even more sophisticated, flexible, and enable more fluid analysis. These new softwares come with a more user-friendly interface that resembles the Microsoft Windows XP applications. Furthermore, they have new data handling capacities such as to enable tables or images embedded in rich text files to be imported and coded as well. In addition, the user can also import and work on rich text files in character based languages such as Chinese or Arabic.

To sum up, qualitative research undoubtedly has been advanced greatly by the development of CAQDAS. The use of qualitative methods in medical and health care research is postulated to grow exponentially in years to come with the further development of CAQDAS.

More information about the NVivo software

Detailed information about NVivo’s functionality is available at http://www.qsrinternational.com . The website also carries information about the latest versions of NVivo. Free demonstrations and tutorials are available for download.

ACKNOWLEDGEMENT

The examples in this paper were adapted from the data of the study funded by the Ministry of Science, Technology and Environment, Malaysia under the Intensification of Research in Priority Areas (IRPA) 06-02-1032 PR0024/09-06.

TERMINOLOGY

Attributes : An attribute is a property of a node, case or document. It is equivalent to a variable in quantitative analysis. An attribute (e.g. ethnicity) may have several values (e.g. Malay, Chinese, Indian, etc.). Any particular node, case or document may be assigned one value for each attribute. Similarities within or differences between groups can be identified using attributes. Attribute Explorer displays a table of all attributes assigned to a document, node or set.

CAQDAS : Computer Aided Qualitative Data Analysis. The CAQDAS programme assists data management and supports coding processes. The software does not really analyse data, but rather supports the qualitative analysis process. NVivo is one of the CAQDAS programmes; others include NUDIST, ATLAS-ti, AQUAD, ETHNOGRAPH and MAXQDA.

Code : A term that represents an idea, theme, theory, dimension, characteristic, etc., of the data.

Coder : A tool used to code a passage of text in a document under a particular node. The coder can be accessed from the Document or Node Browser .

Coding : The action of identifying a passage of text in a document that exemplifies ideas or concepts and connecting it to a node that represents that idea or concept. Multiple codes can be assigned to the same segment of text in a document.

Coding stripes : Coloured vertical lines displayed at the right-hand pane of a Document ; each is named with title of the node at which the text is coded.

DataLinks : A tool for linking the information in a document or node to the information outside the project, or between project documents. DocLinks , NodeLinks and DataBite Links are all forms of DataLink .

Document : A document in an NVivo project is an editable rich text or plain text file. It may be a transcription of project data or it may be a summary of such data or memos, notes or passages written by the researcher. The text in a document can be coded, may be given values of document attributes and may be linked (via DataLinks ) to other related documents, annotations, or external computer files. The Document Explorer shows the list of all project documents.

Memo : A document containing the researcher”s commentary flagged (linked) on any text in a Document or Node. Any files (text, audio or video, or picture data) can be linked via MemoLink .

Model : NVivo models are made up of symbols, usually representing items in the project, which are joined by lines or arrows, designed to represent the relationship between key elements in a field of study. Models are constructed in the Modeller .

Node : Relevant passages in the project”s documents are coded at nodes. A Node represents a code, theme, or idea about the data in a project. Nodes can be kept as Free Nodes (without organisation) or may be organised hierarchically in Trees (of categories and subcategories). Free nodes are free-standing and are not associated to themes or concepts. Early on in the project, tentative ideas may be stored in the Free Nodes area. Free nodes can be kept in a simple list and can be moved to a logical place in the Tree Node when higher levels of categories are discovered. Nodes can be given values of attributes according to the features of what they represent, and can be grouped in sets. Nodes can be organised (created, edited) in Node Explorer (a window listing all the project nodes and node sets). The Node Browser displays the node”s coding and allow the researcher to change the coding.

Project : Collection of all the files, documents, codes, nodes, attributes, etc. associated with a research project. The Project pad is a window in NVivo when a project is open which gives access to all the main functions of the programme.

Sets : Sets in NVivo hold shortcuts to any nodes or documents, as a way of holding those items together without actually combining them. Sets are used primarily as a way of indicating items that in some way are related conceptually or theoretically. It provides different ways of sorting and managing data.

Tree Node : Nodes organised hierarchically into trees to catalogue categories and subcategories.

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U.S. centenarian population is projected to quadruple over the next 30 years

A WWII Coast Guard veteran celebrates her 100th birthday in Boston, Massachusetts, on Aug. 19, 2023. (John Tlumacki/The Boston Globe via Getty Images)

The number of Americans ages 100 and older is projected to more than quadruple over the next three decades, from an estimated 101,000 in 2024 to about 422,000 in 2054, according to projections from the U.S. Census Bureau. Centenarians currently make up just 0.03% of the overall U.S. population, and they are expected to reach 0.1% in 2054.

A line chart showing that the U.S. centenarians projected to quadruple in number by 2054.

The number of centenarians in the United States has steadily ticked up since 1950, when the Census Bureau estimates there were just 2,300 Americans ages 100 and older. (The Census Bureau uses calculated estimates for years prior to the 1990 census because it has identified large errors in the census counts of centenarians for those years.)

In the last three decades alone, the U.S. centenarian population has nearly tripled. The 1990 census counted around 37,000 centenarians in the country.

Pew Research Center conducted this analysis to understand how the population of Americans ages 100 and older looks today, and how it is expected to change in the next 30 years. U.S. population estimates come from the U.S. Census Bureau , and global projections are drawn from the United Nations’ population projections under its medium variant scenario .

All racial groups are single-race and non-Hispanic. Hispanics are of any race.

Today, women and White adults make up the vast majority of Americans in their 100s. This trend is largely projected to continue, though their shares will decrease:

A bar chart showing that the vast majority of Americans in their 100s are women, White.

In 2024, 78% of centenarians are women, and 22% are men. In 30 years, women are expected to make up 68% of those ages 100 and older, while 32% will be men.
77% of today’s centenarians are White. Far fewer are Black (8%), Asian (7%) or Hispanic (6%). And 1% or fewer are multiracial; American Indian or Alaska Native; or Native Hawaiian or other Pacific Islander. By 2054, White and Asian adults are projected to make up smaller shares of centenarians (72% and 5%, respectively), while the shares who are Hispanic (11%) or Black (10%) will be larger. (All racial categories here are single-race and non-Hispanic. Hispanics are of any race.)

The U.S. population overall is expected to trend older in the coming decades as life expectancies increase and the birth rate declines. There are currently roughly 62 million adults ages 65 and older living in the U.S., accounting for 18% of the population. By 2054, 84 million adults ages 65 and older will make up an estimated 23% of the population.

Even as the 65-and-older population continues to grow over the next 30 years, those in their 100s are projected to roughly double as a percentage of that age group, increasing from 0.2% of all older Americans in 2024 to 0.5% in 2054.

Centenarians around the world

A chart showing the five countries with the largest centenarian populations.

The world is home to an estimated 722,000 centenarians, according to the United Nations’ population projections for 2024. The U.S. centenarian population is the world’s second largest – the UN estimates it at 108,000, slightly larger than the Census Bureau’s estimate.

Japan is the country with the greatest number of people in their 100s, at 146,000. China (60,000), India (48,000) and Thailand (38,000) round out the top five.

In each of these countries, centenarians make up less than 1% of the overall population, but combined, they account for more than half (55%) of the world’s population ages 100 and older.

Looked at another way, centenarians make up a bigger proportion of the total population in Japan, Thailand and the U.S., and smaller shares in China and India, which have large but relatively young populations. There are about 12 centenarians for every 10,000 people in Japan, five for every 10,000 in Thailand and three for every 10,000 in the U.S. That compares with fewer than one centenarian for every 10,000 people in China and India.

By 2054, the global centenarian population is projected to grow to nearly 4 million. China is expected to have the largest number of centenarians, with 767,000, followed by the U.S., India, Japan and Thailand. As a proportion, centenarians are projected to account for about 49 out of every 10,000 people in Thailand, 40 of every 10,000 in Japan and 14 of every 10,000 in the U.S. Six out of every 10,000 people in China will be centenarians, as will about two of every 10,000 in India.

A map showing that publics in North America, Europe and Asia are projected to see large growth in centenarian populations by 2054.

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Older workers are growing in number and earning higher wages, teens, social media and technology 2023, dating at 50 and up: older americans’ experiences with online dating, about half of americans say the best age for a u.s. president is in their 50s, most popular.

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There are a multitude of applications for qualitative research and qualitative data analysis. Exploring new or complex phenomena. Qualitative research can help researchers investigate poorly understood or under-researched topics, providing novel insights and a foundation for further inquiry and hypothesis generation. Qualitative research can ...
Qualitative Research
Qualitative Research. Qualitative research is a type of research methodology that focuses on exploring and understanding people's beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.
Research Guides: Qualitative Data Analysis: Special topics
Introduction to EndNote. Slides demonstrating how to download, use, and connect EndNote to other research tools. 1. Open the library of references you want to export. 2. In the toolbar at the top of the screen, click "Select Another Style" and select BibTex Export. BibTeX should be your archival copy.
Qualitative Data
There are various types of qualitative data that can be collected and analyzed, including: Interviews: These involve in-depth, face-to-face conversations with individuals or groups to gather their perspectives, experiences, and opinions on a particular topic. Focus Groups: These are group discussions where a facilitator leads a discussion on a ...
Qualitative vs. Quantitative Research
When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge. Quantitative research. Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions.
Qualitative Methods in Health Care Research
Constant comparative analysis, theoretical sampling, theoretical coding, and theoretical saturation are unique features of grounded theory research.[26,27,28] Data analysis includes analyzing data through 'open coding,' 'axial coding,' and 'selective coding.'[1,7] Open coding is the first level of abstraction, and it refers to the creation of a ...
Research Topics & Ideas: Data Science
Analysis on the Application of Data Science in Business Analytics (Wang, 2022) As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you'll need to get specific and laser-focused on a specific context with specific variables of ...
PDF QUALITATIVE DATA ANALYSIS ONLINE WORKSHOP Qualitative Data Analysis
Workshop Modules. Workshop modules are designed to provide participants with an overview of approaches to analyzing qualitative data and the skills to begin analyzing textual data. Each module balances theoretical principles and practical skills. The following topics are covered: Approaches to Data Analysis.
Understanding geriatric models of care for older adults living with HIV
Data were analyzed using a qualitative case study and key component analysis approach to identify necessary model components. 5702 studies underwent title and abstract screening, with 154 entering full-text review. 13 peer-reviewed and 0 grey literature sources were included. Most articles were from North America.
Religious Composition by Country, 2010-2050
ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions.
Data Analysis in Qualitative Research: A Brief Guide to Using Nvivo
Data analysis in qualitative research is defined as the process of systematically searching and arranging the interview transcripts, ... codes are tags or labels for allocating identified themes or topics from the data compiled in the study. Traditionally, coding was done manually, with the use of coloured pens to categorise data, and ...
Striking findings from 2023
Here's a look back at 2023 through some of our most striking research findings. These findings only scratch the surface of the Center's research from this past year. A record-high share of 40-year-olds in the U.S. have never been married, according to a Center analysis of the most recent U.S. Census Bureau data. As of 2021, a quarter of 40 ...
Number of people 100 and older is growing in US ...
The number of Americans ages 100 and older is projected to more than quadruple over the next three decades, from an estimated 101,000 in 2024 to about 422,000 in 2054, according to projections from the U.S. Census Bureau. Centenarians currently make up just 0.03% of the overall U.S. population, and they are expected to reach 0.1% in 2054.

Qualitative Data Analysis: What is it, Methods + Examples

What is Qualitative Data Analysis?

Importance of Qualitative Data Analysis

In-Depth Understanding

Contextual Insight

Theory Development

Participant Perspectives

Exploratory Research

Types of Qualitative Data

Interviews and Focus Groups

Observations and Field Notes

Textual and Visual Data

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

Qualitative Data Analysis Methods and Examples

Method 1: Content Analysis

Steps to Do Content Analysis

Benefits & Challenges

Example of Content Analysis

Method 2: Thematic Analysis

Steps to Do Thematic Analysis

Example of Thematic Analysis

Method 3: Narrative Analysis

Steps to Do Narrative Analysis

Example of Narrative Analysis

Method 4: Grounded Theory Analysis

Steps to Do Grounded Theory Analysis

Example of Grounded Theory Analysis

Method 5: Discourse Analysis

Steps to Do Discourse Analysis

Example of Discourse Analysis

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

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How to conduct qualitative data analysis

Analyze all your qualitative data

1. Content analysis

2. Narrative analysis

3. Discourse analysis

4. Thematic analysis

Step 1: Gather your qualitative data and conduct research

1. Traditional methods of collecting data

2. Leverage existing qualitative data

3. Untapped qualitative data channels

Step 2: Connect & organize all your qualitative data

1. Organize data manually

2. Organize data using qualitative data analysis software

3. Use feedback repositories

Step 3: Coding your qualitative data

Step 4: Analyze your data and find meaningful insights

Step 5: Report on your data and tell the story

What are qualitative analysis tools?

What are the 3 main components of qualitative data analysis?

What is the difference between qualitative and quantitative data analysis?

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