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Qualitative Data Analysis: What is it, Methods + Examples

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

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

What is Qualitative Data Analysis?

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

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

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

Importance of Qualitative Data Analysis

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

In-Depth Understanding

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

Contextual Insight

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

Theory Development

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

Participant Perspectives

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

Exploratory Research

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

Types of Qualitative Data

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

Interviews and Focus Groups

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

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

Observations and Field Notes

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

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

Textual and Visual Data

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

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

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

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

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

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

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

Qualitative Data Analysis Methods and Examples

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

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

Method 1: Content Analysis

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

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

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

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

Benefits & Challenges

There are various advantages to using content analysis:

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

However, keep in mind the challenges that arise:

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

Example of Content Analysis

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

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

Method 2: Thematic Analysis

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

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

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

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

Thematic analysis has various benefits:

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

However, challenges may arise, such as:

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

Example of Thematic Analysis

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

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

Method 3: Narrative Analysis

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

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

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

There are various advantages to narrative analysis:

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

However, difficulties may arise, such as:

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

Example of Narrative Analysis

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

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

Method 4: Grounded Theory Analysis

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

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

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

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

Grounded theory analysis has various benefits:

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

However, challenges might arise with:

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

Example of Grounded Theory Analysis

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

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

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

Method 5: Discourse Analysis

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

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

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

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

There are various advantages of using discourse analysis:

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

However, the following challenges may arise:

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

Example of Discourse Analysis

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The 5 steps to doing qualitative data analysis

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

What is Qualitative Data Analysis?

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

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

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

How is qualitative data analysis different from quantitative data analysis?

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

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

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

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

Qualitative Data Analysis methods

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

Content Analysis

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

Narrative Analysis

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

Discourse Analysis

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

Thematic Analysis

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

Grounded Theory

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

Challenges of Qualitative Data Analysis

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

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

Benefits of qualitative data analysis

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

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

How to do Qualitative Data Analysis: 5 steps

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

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

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

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

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

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

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

Classic methods of gathering qualitative data

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

Using your existing qualitative feedback

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

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

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

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

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

Utilize untapped qualitative data channels

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

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

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

Step 2: Connect & organize all your qualitative data

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

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

The manual approach to organizing your data

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

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

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

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

Computer-assisted qualitative data analysis software (CAQDAS)

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

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

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

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

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

Organizing your qualitative data in a feedback repository

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

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

Organizing your qualitative data in a feedback analytics platform

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

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

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

Step 3: Coding your qualitative data

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

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

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

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

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

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

How to manually code your qualitative data

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

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

Using software to speed up manual coding of qualitative data

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

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

Automating the qualitative coding process using thematic analysis software

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

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

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

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

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

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

The key benefits of using an automated coding solution

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

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

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

Step 4: Analyze your data: Find meaningful insights

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

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

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

Manually create sub-codes to improve the quality of insights

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

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

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

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

Correlate the frequency of codes to customer segments

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

Segments can be based on:

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

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

Manually visualizing coded qualitative data

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

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

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

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

You can then visualize this data using a bar chart.

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

Trends over time

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

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

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

The visualization could look like this:

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

Using a text analytics solution to automate analysis

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

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

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

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

Step 5: Report on your data: Tell the story

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

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

Creating graphs and reporting in Powerpoint

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

Using visualization software for reporting

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

Visualizing your insights inside a feedback analytics platform

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

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

Conclusion - Manual or Automated?

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

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

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

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

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

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

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

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

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

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Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

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

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

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

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

What (exactly) is qualitative data analysis?

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

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

So, if it’s not numbers, what is it?

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

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

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

So, qualitative analysis is easier than quantitative, right?

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

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

Need a helping hand?

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

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

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

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

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

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

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

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

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

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

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

QDA Method #2: Narrative Analysis 

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

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

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

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

QDA Method #3: Discourse Analysis 

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

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

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

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

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

QDA Method #4: Thematic Analysis

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

Let’s take a look at an example.

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

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

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

QDA Method #5: Grounded theory (GT) 

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

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

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

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

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

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

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

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

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

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

Let’s just stick with IPA, okay?

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

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

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

How to choose the right analysis method

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

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

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

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

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

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

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

Let’s recap on QDA methods…

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

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

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

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

Psst... there’s more!

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

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84 Comments

This has been very helpful. Thank you.

Thank you madam,

Thank you so much for this information

I wonder it so clear for understand and good for me. can I ask additional query?

Very insightful and useful

Good work done with clear explanations. Thank you.

Thanks so much for the write-up, it’s really good.

Thanks madam . It is very important .

thank you very good

This has been very well explained in simple language . It is useful even for a new researcher.

Great to hear that. Good luck with your qualitative data analysis, Pramod!

This is very useful information. And it was very a clear language structured presentation. Thanks a lot.

Thank you so much.

very informative sequential presentation

Precise explanation of method.

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

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

You explained it in very simple language, everyone can understand it. Thanks so much.

Thank you very much, this is very helpful. It has been explained in a very simple manner that even a layman understands

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

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

This is my first time to come across a well explained data analysis. so helpful.

I have thoroughly enjoyed your explanation of the six qualitative analysis methods. This is very helpful. Thank you!

Thank you very much, this is well explained and useful

i need a citation of your book.

Thanks a lot , remarkable indeed, enlighting to the best

Hi Derek, What other theories/methods would you recommend when the data is a whole speech?

Keep writing useful artikel.

It is important concept about QDA and also the way to express is easily understandable, so thanks for all.

Thank you, this is well explained and very useful.

Very helpful .Thanks.

Hi there! Very well explained. Simple but very useful style of writing. Please provide the citation of the text. warm regards

The session was very helpful and insightful. Thank you

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Its Great and help me the most. A Million Thanks you Dr.

It is a very nice work

Very insightful. Please, which of this approach could be used for a research that one is trying to elicit students’ misconceptions in a particular concept ?

This is Amazing and well explained, thanks

great overview

What do we call a research data analysis method that one use to advise or determining the best accounting tool or techniques that should be adopted in a company.

Informative video, explained in a clear and simple way. Kudos

Waoo! I have chosen method wrong for my data analysis. But I can revise my work according to this guide. Thank you so much for this helpful lecture.

This has been very helpful. It gave me a good view of my research objectives and how to choose the best method. Thematic analysis it is.

Very helpful indeed. Thanku so much for the insight.

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Nicely written especially for novice academic researchers like me! Thank you.

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

that was very helpful for me. because these details are so important to my research. thank you very much

I learnt a lot. Thank you

Relevant and Informative, thanks !

Well-planned and organized, thanks much! 🙂

I have reviewed qualitative data analysis in a simplest way possible. The content will highly be useful for developing my book on qualitative data analysis methods. Cheers!

Clear explanation on qualitative and how about Case study

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Wow, Thanks for making my life easy

This was helpful thanks .

Very helpful…. clear and written in an easily understandable manner. Thank you.

This was so helpful as it was easy to understand. I’m a new to research thank you so much.

so educative…. but Ijust want to know which method is coding of the qualitative or tallying done?

Thank you for the great content, I have learnt a lot. So helpful

precise and clear presentation with simple language and thank you for that.

very informative content, thank you.

You guys are amazing on YouTube on this platform. Your teachings are great, educative, and informative. kudos!

Brilliant Delivery. You made a complex subject seem so easy. Well done.

Beautifully explained.

Thanks a lot

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

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

content analysis can be qualitative research?

THANK YOU VERY MUCH.

Thank you very much for such a wonderful content

do you have any material on Data collection

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

Great explanation both written and Video. i have been using of it on a day to day working of my thesis project in accounting and finance. Thank you very much for your support.

very helpful, thank you so much

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How to Analyze Survey Data: Best Practices

Analyzing survey data can be overwhelming, but with the right strategy, you can turn it into a goldmine of insights.  How to analyze survey data?

Our guide provides a step-by-step method to analyze your survey results, and reveal patterns, trends, and findings that inform smart decisions and strategies. 

Ready? Let’s jump right into the topic.

Got survey data, now what? How to analyze survey data

There are twelve steps, each with an actionable checklist, so you won’t miss anything.

Step 1: Define your objectives

Begin by asking, “What do I need to learn from this survey?” 

Your objectives should be clear, specific, and directly linked to what you want to understand. 

For example, if your survey is about customer satisfaction, your objective might be to identify the top factors that influence customer happiness.

Your objectives will shape the way you interpret the data. That’s why it is important. Survey goals determine the questions you ask and how you interpret the responses. Without clear objectives, you might end up with a lot of data but no real insight.

Instead of a broad objective like “understand customer behavior,” aim for something more targeted like “determine the main reasons customers return products.” 

The specificity guides you in selecting the right questions and analyzing the data accurately.

Your actionable checklist:

• Be clear about your survey’s primary purpose.
• Identify the metrics or feedback you want to get.
• Make your objectives align with broader organizational or project goals.
• Think about how the survey findings might affect decision-making.
• Try to be realistic about the insights you can get from the survey.

Step 2: Clean your data

Here, you transform raw data into clean, usable information. 🙂

Eliminate errors and irrelevant data points from your survey results. So if you find responses that are incomplete or don’t fit with your survey’s purpose, it’s best to remove them.

Cleaning your data is a must for its accuracy. Can you think of analyzing data cluttered with mistakes or irrelevant information? A nightmare, isn’t it? 

Clean data makes you see what is really going on. Then, it’s so much easier to spot valid patterns and identify trends.

On top of that, having clean data simplifies the entire analysis process. The trends you identify are based on accurate and reliable information.

Actionable checklist:

• Remove incomplete or irrelevant responses
• Fix any errors in data entry
• Standardize response formats for consistency
• Check for and handle outliers
• Verify that all data points are correctly categorized

Step 3: Organize data structurally

It’s time to bring some clarity and structure to the collected data!

Arrange your data in a logical order. It might be sequencing responses according to the survey’s flow or grouping similar questions together.

Also, create a clear hierarchy in your data . Does your survey cover multiple topics? You can organize the responses under these topic headings. Navigating through the data is enjoyable, then.

Consider time-based organization if your survey captures data over different periods. Organizing data chronologically can make you spot changes over time, and provide actionable insights in your analysis.

Don’t forget about consistent formatting. It helps analyze data without confusion or errors, particularly when working with large datasets.

• Arrange data in the order of the survey’s questions.
• Group responses by topic for easy access.
• Organize data chronologically if relevant.
• Maintain consistent formatting throughout the dataset.
• Prepare a clean, organized dataset for further analysis steps.

Step 4: Quantitative and qualitative data segregation

You have two main types of data: quantitative and qualitative.

➡️ Quantitative data is numerical, like ratings or ages. 

➡️ Qualitative data includes written responses or opinions.

First, handle quantitative data. Organize this data into categories or groups. If your survey includes age groups, arrange the numerical data accordingly.

Then, focus on qualitative survey data from your survey respondents. Sort the responses into thematic groups. For instance, in a product feedback survey, you might categorize comments under ‘positive feedback,’ ‘negative feedback,’ and ‘suggestions.’

It’s a solid foundation for deeper analysis.

• Categorize numerical data into clear groups.
• Sort text responses into themes.
• Label each category for easy reference.
• Double-check for any misplaced data.
• Organize data in a way that aligns with your analysis goals.

Step 5: Analyze individual responses

The fifth step in your survey data analysis is to analyze open-ended responses and closed ones as well. Here is where the real gold lies in survey data. It’s a window into respondents’ thoughts and experiences.

Read every open-ended response carefully. Answers like these are rich with insights that simple yes/no answers can’t capture. You’re looking for patterns, repeated phrases, or unique points of view.

Next, summarize these insights . You might notice certain words or sentiments cropping up repeatedly. 

• Carefully read each open-ended response.
• Look for common themes or unique insights.
• Summarize key findings from these responses.
• Organize insights into clear categories.
• Use these insights to add depth to your overall survey analysis.

Step 6: Identify patterns and trends

When categorizing the responses and analyzing survey data, you might see trends and patterns among your survey participants. This step turns your data into a story. 

Look across all the responses and start noticing what stands out. Maybe a certain opinion is shared widely among a particular age group, or a specific issue keeps cropping up.

You’re combing through the details to find clues that piece together overarching ideas. These are your findings, the real gems hidden in the data.

It’s where numbers and words start forming clear messages. The trends are what turn your survey data from a collection of responses into valuable insights that can drive decisions and strategies.

• Review data for recurring themes.
• Note patterns that emerge across different groups.
• Analyze responses for common keywords or sentiments.
• Correlate findings with demographic information.
• Document these trends as key insights from your survey.

Step 7: Use cross-tabulation

It might be a powerful tool for understanding the relationships in your survey data. Cross-tabulation is comparing two or more variables to see how they interact with each other.

Select a few key demographic data points, like age, gender, or location. Then, pair them with different dependent variables from your survey responses. You might compare age groups with preferences for a particular service or product.

Cross-tabulation helps uncover tendencies that aren’t immediately obvious. It highlights how different demographic groups respond to specific aspects of your survey. 

Let’s say that you examine your data to see the finer details of how different segments of your audience think and feel.

• Choose key demographic data points for comparison.
• Pair these with various survey responses.
• Analyze the intersections to find unique patterns.
• Use these insights to understand how different groups respond.
• Apply this understanding to tailor strategies or products accordingly.

Step 8: Implement statistical analysis

Things can get a bit more technical when implementing statistical analysis. It makes you realize what’s statistically significant. You will see what findings are strong enough to rely on.

Once you find that many respondents prefer a particular product feature, statistical analysis helps you figure out if this preference is a real trend or just a coincidence.

Using statistical tools, you may test for statistical significance. You’ll see whether the results are likely to be true for a larger population, not just the people who took your survey.

Applying statistical analysis to survey data is a necessity. It gives weight to your findings and shows that the conclusions you draw are backed by solid evidence.

• Choose the right statistical methods for your data.
• Test for statistical significance in your findings.
• Interpret the results to see what’s genuinely significant.
• Use these insights to make data-driven decisions.
• Ensure your survey analysis is robust and reliable.

You can also use SurveyLab to get an intelligent analysis of your surveys. And it’s a super intuitive online software tool with plenty of survey templates.

Step 9: Create visual representations

It’s a great way to present survey data and results. When you’ve got a bunch of survey responses, turning them into visuals like pie charts can make the information way more digestible and interesting.

They can show your findings at a glance. A well-made pie chart may instantly convey how your survey respondents are split on a particular question. You take all numbers and responses and transform them into something that anyone can understand quickly.

• Choose the right type of chart for your data.
• Make sure your visuals are easy to read and understand.
• Use colors and labels to clarify your points.
• Keep your design simple and avoid clutter.
• Use these visuals to highlight the most important findings from your survey.

Step 10: Conduct a comparative analysis

It’s one of the survey data analysis methods where you take your current survey findings and compare them with past data, that’s a comparative analysis. It’s possible to spot changes, trends, or consistencies over time.

You look at the same data points across different periods or surveys. 

If your annual customer satisfaction survey shows a shift in opinions from last year, that’s something you want to look into. 

What changed? Why?  

These are the kinds of questions comparative analysis can help answer.

And it’s less likely to miss some tendencies when looking only at one set of survey results in isolation.

• Gather past survey data that are relevant to your current analysis.
• Identify the same data points or survey questions for comparison.
• Analyze any significant differences or similarities.
• Draw insights from how responses have changed or remained consistent.
• Use these insights to add a richer, more informed perspective to your survey report.

Step 11: Draw meaningful conclusions

When you reach the point of drawing conclusions in your survey analysis, you literally put the final pieces of a puzzle together. 

You’ve looked at all the numbers, seen what’s statistically significant, and now it’s time to step back and ask: “What does all this really mean?”

It’s the stage of interpreting the data collected. Think about how the significant trends you’ve identified tie back to your original goals. What story is behind the trends? How do they shed light on the questions you started with?

Conclusions bring closure to your survey analysis and tie your findings back to the real world, giving context and meaning.

• Look at your data in its entirety, considering the bigger picture.
• Focus on insights that are statistically significant.
• Link these insights back to the purpose of your survey.
• Craft conclusions that add depth and understanding to your findings.
• Ensure these conclusions resonate beyond just the numbers, touching on the broader implications of your research.

Step 12: Report findings and take action

After all your hard work analyzing the data, it’s time to put it to use. Create a survey report.

Highlight the most important pieces of information there: significant trends, notable customer feedback, or any surprising discoveries. 

The goal here is to present these findings in a way that’s clear and compelling. 

Your survey report should not only inform but also inspire your audience to make decisions or changes based on what the survey uncovered.

• Summarize the key findings clearly and concisely.
• Include relevant details from customer feedback.
• Make sure your report is easy to read and understand.
• Suggest actionable steps based on your analysis.
• Use the insights to drive meaningful changes or decisions.

How to design the survey so it gives you data that’s easier to analyze

Don’t work harder, work smarter. These tips will help you in data analysis. Maybe here’s a piece of advice that you always overlook, and it may change the way you handle your data for good.

Keep questions clear and concise 

Concise questions prevent respondent fatigue. Long or complex questions can confuse or frustrate people , and it leads to rushed or careless responses, which in turn can muddy your survey analysis.

The goal of each question is to be as clear as possible about what you’re asking. Avoid jargon, double-barreled questions, and overly technical language that might confuse respondents. Each question should focus on one specific topic or idea to avoid ambiguity.

Use a logical flow 

Logical flow is essential for gathering data that’s easy to analyze. Start with broad, general questions and then gradually narrow down to specifics . Respondents may be more comfortable and willing to provide detailed answers further on.

Grouping similar topics together also helps. Once respondents deal with one subject at a time, their answers tend to be more focused and consistent.

You can quickly catch patterns without having to sift through a jumble of unrelated responses.

Limit open-ended questions

They can provide rich qualitative data, but they are harder to analyze in bulk. Use them sparingly and, where possible, change them into closed-ended questions. But be careful, open-ended ones may bring more insights, so think twice before replacing them.

With limited open-ended questions, the tracking data process will be less painful, and the data analysis won’t take long. 

Employ consistent rating scales 

Use uniform scales for rating questions (e.g., 1-5 or 1-10). Consistency in scales across questions makes comparative analysis more straightforward.

On top of that, employing consistent rating scales , like interval scales or ratio scales , makes it easier to track responses and draw conclusions.

Interval scales measure the difference between responses and are ideal for questions with equidistant responses. For instance, a scale from 1 to 5 measuring satisfaction levels, where each step up represents an equal increase in satisfaction.

Ratio scales, on the other hand, not only show the differences between responses but also have a true zero point. Could be useful in questions about frequency or quantity, where ‘0’ indicates ‘none’ or ‘never.’

Include demographic questions

Demographic questions (age, gender, location, etc.) are imperative for segmenting and give you a broader context for your research. Include them at the beginning of the survey, but remember that gender-related questions might be sensitive for some. Make sure there’s an “I don’t want to answer this question” option.

Pre-test the survey 

Conduct a pilot test of your survey with a small audience before full deployment. This helps in identifying and rectifying any confusing or misleading questions.

Avoid leading or biased questions

Ensure that the questions are neutral and do not lead the respondent towards a particular answer. Biased questions can skew your data and compromise the integrity of your analysis.

Opt for multiple-choice where possible 

Multiple-choice questions are easier to analyze than narrative responses. They provide structured data that can be easily quantified and compared.

Key takeaways

• Set clear goals for your survey to understand what data you need to collect.
• Clean your data – remove errors and irrelevant responses for smoother analysis.
• Organize your survey data well to make it easier to analyze and understand.
• Separate your data into numbers (quantitative) and words (qualitative) for a detailed study.
• Use different methods like charts and comparisons to find trends and draw conclusions.

Data analysis: it’s the primary step in turning your survey responses into clear, actionable insights.

If you want to make the process easier, consider using SurveyLab. It’s user-friendly and helps you get the most out of your surveys. 

Check out SurveyLab for your next project and see the difference it makes. Sign in today !

FAQ on How to Analyze Survey Data

Do have any questions? Maybe we have already answered it. 

How do you analyze data from a survey?

Categorize and interpret the responses. First, sort the survey data into qualitative and quantitative types. Use data analysis methods to catch key trends and insights. Employ statistical analysis techniques to find statistically significant patterns. Always align your analysis with the original research questions and objectives.

Which method is used to analyze survey results?

Researchers usually combine data analysis methods. They use statistical analysis to understand trends and significance and qualitative methods for open-ended responses. Cross-tabulation is often applied for comparing different data sets, while regression analysis can help understand relationships between variables.

What is the best tool to analyze survey data?

Surveylab offers intelligent analysis , and you can use it for both analysis and survey creation. 

The tool generates survey reports automatically as soon as the first responses are collected. There are useful filters to find all the info you need in seconds. On top of that, exporting the results takes a few clicks.

The tool also provides plenty of survey templates that are customizable, so you don’t have to build a questionnaire from scratch (but you can if you feel like it).

What is the survey data analysis?

It relies on interpreting responses to structured questions using both qualitative and quantitative approaches. Excellent for extracting customer insights, demographic data and determining statistical significance that provides a more accurate picture of your survey results.

How do you manually analyze questionnaire data?

It’s organizing and interpreting responses. For quantitative data, consider calculating numerical trends. For qualitative data, look for themes in open-ended responses. Summarize these findings to answer your research question with clear, actionable insights.

How do you analyze survey data qualitatively?

Qualitative analysis of survey data uses narrative and open-ended responses. Look for common themes and insights that provide depth beyond numerical data. 

How to conduct a quantitative survey analysis?

Focus on statistical analysis of numerical data. Identify trends, calculate statistical significance, and use tools like regression analysis to understand variable relationships. The quantitative method is suited for structured surveys, where each response contributes to statistically significant findings.

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Qualitative Data Analysis

• First Online: 13 April 2022

Cite this chapter

• Yanmei Li 3 &
• Sumei Zhang 4  

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After introducing basic statistical methods analyzing quantitative data this chapter turns to analyzing qualitative data, such as open-ended survey questions, planning documents, and narrative data collected from storytelling, planning workshops, public meetings, public hearings, planning forums, or focus groups. Practicing planners collect these types of data regularly and they are often the foundation of community needs analysis. Analyzing these data requires specialized methods. This chapter introduces methods to analyze qualitative data and conduct content analysis. Identifying trends and patterns of the data is the key to analyzing qualitative data. Related software, such as Atlas.ti, will be briefly explored to help researchers analyze complex qualitative data with complicated content or a large number of observations.

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Li, Y., Zhang, S. (2022). Qualitative Data Analysis. In: Applied Research Methods in Urban and Regional Planning. Springer, Cham. https://doi.org/10.1007/978-3-030-93574-0_8

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The Ultimate Guide to Qualitative Research - Part 2: Handling Qualitative Data

• Handling qualitative data
• Transcripts
• Field notes
• Introduction

Understanding qualitative research in surveys

The nature of data in surveys, transferring survey data into records, understanding survey responses, managing and storing survey data.

• Visual and audio data
• Data organization
• Data coding
• Coding frame
• Auto and smart coding
• Organizing codes
• Qualitative data analysis
• Content analysis
• Thematic analysis
• Thematic analysis vs. content analysis
• Narrative research
• Phenomenological research
• Discourse analysis
• Grounded theory
• Deductive reasoning
• Inductive reasoning
• Inductive vs. deductive reasoning
• Qualitative data interpretation
• Qualitative analysis software

Survey data and responses

To analyze survey data, it is first important to take into consideration the process of organizing your data into a form that facilitates analysis . The analysis method most appropriate for your research will depend on the research inquiry you are looking to address.

You also need to look at how responses are structured before you can start coding or statistical analysis. Keeping all of these in mind will ensure the success of your survey research project.

When we discuss or collect data for survey research , it's essential to distinguish between two main methodologies: qualitative and quantitative research . Both approaches offer unique strengths and can often complement each other in a mixed methods study .

However, each approach requires distinct strategies for data collection , analysis , and interpretation .

Qualitative vs. quantitative approach in surveys

In a nutshell, quantitative research involves numerical data and statistical analysis. It is typically used when researchers want to count frequency, categorize data, or measure things in a way that allows for generalizable, statistical analysis.

Quantitative survey analysis often finds insights in the statistical significance of numerical values, where differences in values between two items (e.g., the popularity of one genre of music over another) are significant enough to be confident in assertions about the survey population.

Qualitative research , on the other hand, is non-numerical and often exploratory. It aims to delve deeper into complex issues, exploring meaning, experiences, or descriptions. Qualitative survey questions often come in the form of open-ended questions, which allow survey respondents to provide unique and individual responses. This kind of data can provide a richness of context, emotion, and depth that is not typically found in numerical data.

Ideally, survey analysis that adopts both quantitative and qualitative methods can prove useful in getting a more holistic view of the target audience and the research inquiry you are looking to address.

The value of qualitative data in surveys

The value of qualitative responses in surveys is in their depth, detail, and ability to provide a nuanced understanding of complex issues. It offers insights into participants' attitudes, behaviors, and experiences in their own words. These insights can be particularly useful in identifying patterns or themes that might not be evident from purely quantitative data.

For example, a quantitative survey might identify that a large number of employees in a company are not satisfied with their work. Still, it is the qualitative data that provides the reasons behind this dissatisfaction - perhaps there are issues with management, workload, or lack of career progression opportunities.

This is not to say that qualitative research is "better" than quantitative - they each have their own strengths and can be incredibly powerful when used together. The important thing, when it comes time to analyze survey data, is to choose the right approach for the research questions you are seeking to answer.

Definition of key terms

In order to best understand and engage with the content of this chapter and prepare for survey analysis, it's necessary to define a few key terms.

These definitions will provide a conceptual framework for our discussions on survey data collection and analysis.

What is survey data?

Survey data refers to the information or responses collected from individuals through a survey . This data can be both qualitative and quantitative.

Data from qualitative responses typically include open-ended responses, descriptions, and narratives. In contrast, quantitative data consists of numerical responses or information that can be categorized or ranked before analyzing survey data.

Survey data is a valuable resource for researchers, businesses, and policymakers, offering insights into the behaviors, attitudes, preferences, or characteristics of a sample group or population.

What is survey response analysis?

Analysis of survey responses is the process of examining, interpreting, and reporting the data collected from a survey.

This process involves a variety of techniques and approaches depending on the type of data in order to draw meaningful conclusions about how respondents answer.

Survey response analysis of customer feedback, for example, looks for customer insights directly embedded in survey results as well as how answers are framed in order to identify useful data points about market trends and consumer preferences.

For qualitative data, analysis often involves processes such as coding , thematic analysis , and narrative interpretation to understand the themes and patterns within the responses.

For quantitative data, statistical analysis methods are often used to summarize, describe, and compare the data.

What is survey data analysis?

Survey data analysis is a term that is often used interchangeably with survey response analysis. It refers to the survey analysis methods and techniques used to process, interpret, and draw conclusions from the data collected in a survey.

The type of analysis sought depends in part on whether the inquiry is qualitative or quantitative in nature. A qualitative survey analysis looks to uncover themes and patterns among survey results, while a quantitative analysis seeks out statistically significant differences among responses from different groups of survey respondents.

The goal of survey data analysis is to transform raw data into meaningful information that can be used to make informed decisions, develop strategies, or contribute to academic knowledge. Depending on the research questions and the nature of the data, different methods of analysis can be applied.

One of the key elements in survey research is the type of data being collected . The data collected from a survey can greatly vary depending on the survey's purpose, target population, and research questions .

Understanding the different ways to collect survey data is fundamental in designing effective surveys and efficiently analyzing the responses .

What kind of data is collected in a survey?

In general, survey data can be categorized into four main types: demographic, behavioral, attitudinal, and relational.

Demographic data

Demographic data provide information about the respondent's characteristics, such as age, gender, race, income, education level, and employment status. This type of data is often used to analyze and compare responses across different demographic groups.

Behavioral data

Behavioral data involves information about the respondent's actions and behaviors. This could include their purchasing habits, use of services, or lifestyle behaviors. Behavioral data can offer valuable insights into what respondents do, helping researchers understand patterns and tendencies in certain populations.

Attitudinal data

Attitudinal data refers to information about a respondent's attitudes, beliefs, and opinions. This data can provide insights into how respondents think or feel about specific issues, brands, policies, or services.

Attitudinal data is often collected through Likert-scale questions or open-ended questions in a survey.

Relational data

Relational data provides information about the relationships between respondents and other entities or individuals. This might include their relationship with their employer, their engagement with brands, or their interactions with public services.

Each of these types of data contributes a piece to the puzzle, helping researchers gain a more comprehensive understanding of their target population.

Understanding the diversity of survey data

While the four categories mentioned above provide a simplified overview of the types of data collected in a survey , it's important to acknowledge the diversity within this data.

For instance, within attitudinal data, researchers could be exploring a wide range of attitudes, from political opinions to consumer preferences. Similarly, behavioral data could span from online browsing habits to physical exercise routines. Each survey is unique and will collect a specific mix of data depending on its individual objectives and research questions.

Powerful survey analysis with ATLAS.ti

Download a free trial to see what our qualitative data analysis software can do for your project.

Once data has been collected from a survey , the next crucial step is to organize this data into a format that can be easily analyzed . This process involves turning survey data into records, an important process for managing and manipulating the data effectively.

The importance of turning survey data into records

Creating records from survey data allows researchers to systematically organize , categorize, and store responses. This process enables easier access to data and facilitates its analysis. When data is appropriately recorded and organized, researchers can more effectively track patterns, identify trends, and derive meaningful insights.

How to structure survey data for recording

The structure for recording survey data largely depends on the type of data collected. Quantitative data, being numerical, is often recorded into structured formats like spreadsheets or databases, where each respondent's answers are stored in a separate row and each question in a separate column.

For qualitative data , such as responses to open-ended questions, the recording process can be a bit more complex. Responses are typically transcribed verbatim and then organized into a format that allows for text analysis, such as spreadsheets or text documents.

These formats could include coding systems or qualitative data analysis software . It's also important to note any non-verbal cues or observations if the survey was conducted face-to-face.

Categorizing qualitative responses

A significant aspect of recording qualitative data involves categorizing responses. Researchers may begin with broad categories based on the survey questions and then develop more specific categories or themes as they familiarize themselves with the data. This process, known as coding, is a crucial step in preparing data for analysis.

Data transcription and its significance

Transcription refers to the process of converting spoken language into written text or transforming written responses into a digital format. For qualitative surveys conducted in person or over the telephone, this often means typing up responses to open-ended questions, focus group discussions , or interview responses .

For surveys that are conducted in a digital format, there is also the great advantage that participants’ responses are already typed out and thus do not require transcription. Data transcription is an essential part of data preparation as it ensures all information is in a format that can be easily analyzed. Depending on the size and scope of the survey, transcription can be a time-consuming process. However, the benefits of having all data in a consistent, analyzable format make it a crucial step in the survey research process.

Once survey data has been properly recorded, the next step is to understand the responses. This process involves closely examining the responses and identifying meaningful patterns, trends, and insights.

Ultimately, a critical examination of the survey results before fully analyzing data will help inform the findings in the survey report.

What are good responses?

Good responses are those that provide valuable and insightful information in relation to the survey's research objectives. While the exact characteristics of a "good" response can vary depending on the survey's purpose, there are a few common features that typically indicate a high-quality response:

Relevance : The response directly addresses the survey question and stays on topic. Completeness : The respondent provides a full and thorough answer to the question. Clarity : The respondent's answer is clear and easy to understand. Detail : The response provides enough detail to give a nuanced understanding of the respondent's perspective.

Characteristics of useful responses

Beyond the qualities mentioned above, useful responses often contain insights that illuminate the respondent's perspectives, experiences, or behaviors. These might include explanations for their attitudes or behaviors, personal experiences that illustrate their point of view, or suggestions for improvements or changes.

Handling incomplete or vague responses

Inevitably, you'll encounter incomplete or vague responses in your survey data. These responses can be challenging to interpret and analyze , but they're a common part of the data collection process .

When dealing with incomplete responses, it's important to handle these in a way that maintains the integrity of your data. If a response is incomplete, it may be best to exclude it from certain analyses where it could skew the results.

For vague responses, you might have to infer the respondent's intended meaning based on the context of their other responses or categorize these responses separately during your analysis.

Validating survey responses

One of the critical aspects of managing responses is ensuring their validity . This process, known as data validation, checks that the responses are accurate, reliable, and fit for their intended use.

What is data validation?

Data validation is a process of checking the quality and accuracy of data before it's used for analysis or decision-making. In the context of surveys, validation involves ensuring that the responses are consistent, complete, and reliable.

This process may involve checking for any discrepancies or errors in the data, ensuring responses are consistent across similar questions, and verifying that the data adheres to the required format.

Why is validating responses important?

Validating responses is crucial for several reasons. Firstly, it ensures the integrity of your data, providing confidence that your findings and conclusions are based on accurate and reliable information.

Secondly, it helps identify any errors or inconsistencies in the data early in the process, preventing potential issues during analysis. This is particularly important for larger surveys, where errors can significantly impact the results.

Moreover, it facilitates the use of survey data analysis methods. Responses should, as best as possible, be properly formatted and organized into a structure allowing for easy and efficient survey analysis later.

Finally, validation can also provide insights into the quality of your survey design. If many respondents are skipping certain questions or providing inconsistent responses, it may suggest that these questions are confusing or poorly designed.

How to validate responses?

There are several strategies you can employ to validate your responses:

• Consistency checks: Compare responses to similar or related questions to check for consistency. If a respondent provides conflicting answers, it could indicate a misunderstanding or error.
• Range checks: If your survey includes numerical responses, check that these fall within a reasonable or expected range. Any outliers may require further investigation.
• Completeness checks: Review your data for any missing or incomplete responses. Depending on the nature of the missing data, you may decide to exclude these responses from your analysis or use statistical methods to impute the missing values.
• Coding checks: If you've coded your responses (particularly for open-ended questions), review a sample of these to ensure the coding is accurate and consistent.

Remember that while data validation is a crucial step, it's not foolproof. It's always important to interpret your survey results with an understanding of the potential limitations and sources of error in your data.

After data collection and validation, a critical step is the proper management and storage of survey data. Adequate data management ensures that data remains accessible, secure, and reliable throughout the research process.

Importance of data management in survey research

Data management involves a host of activities, including data entry, storage, backup, and security. Good data management practices are essential to maintain the integrity of your research data and ensure its availability for current and future use.

Effective data management can enhance the efficiency of your research process, reduce the risk of data loss, and protect your data from unauthorized access. Additionally, proper data management can also make data sharing and collaboration easier if needed.

How to organize your survey data for easy retrieval?

When managing survey data, organization is key. Good data organization makes it easier to navigate your data, identify specific subsets of data, and streamline the data analysis process.

Here are a few strategies for organizing your survey data:

• File naming conventions: Use consistent and descriptive file names to help you identify what each file contains at a glance.
• Folder structures: Use a logical folder structure to organize your data files. This could be based on the survey round, data type, or any other system that suits your project.
• Metadata: Keep a record of metadata - information about your data. This could include details about when and how the data was collected, who collected it, what each variable represents, and any coding or transformation that has been applied to the data.

Data security and privacy considerations in survey research

Given the sensitive nature of some survey data, it's crucial to ensure your data is stored securely and that respondent privacy is maintained. This involves protecting your data from both physical and digital threats.

Digital data should be encrypted and protected by strong, unique passwords. Physical data, such as printed surveys or interview transcripts, should be stored securely, and access should be restricted to authorized individuals.

In addition, it's important to adhere to relevant data privacy laws and regulations and to anonymize your data where appropriate to protect respondent confidentiality.

Let ATLAS.ti guide you through every step of your research

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Qualitative study design: Surveys & questionnaires

• Qualitative study design
• Phenomenology
• Grounded theory
• Ethnography
• Narrative inquiry
• Action research
• Case Studies
• Field research
• Focus groups
• Observation
• Surveys & questionnaires
• Study Designs Home

Surveys & questionnaires

Qualitative surveys use open-ended questions to produce long-form written/typed answers. Questions will aim to reveal opinions, experiences, narratives or accounts. Often a useful precursor to interviews or focus groups as they help identify initial themes or issues to then explore further in the research. Surveys can be used iteratively, being changed and modified over the course of the research to elicit new information. 

Structured Interviews may follow a similar form of open questioning.  

Qualitative surveys frequently include quantitative questions to establish elements such as age, nationality etc. 

Qualitative surveys aim to elicit a detailed response to an open-ended topic question in the participant’s own words.  Like quantitative surveys, there are three main methods for using qualitative surveys including face to face surveys, phone surveys, and online surveys. Each method of surveying has strengths and limitations.

Face to face surveys  

• Researcher asks participants one or more open-ended questions about a topic, typically while in view of the participant’s facial expressions and other behaviours while answering. Being able to view the respondent’s reactions enables the researcher to ask follow-up questions to elicit a more detailed response, and to follow up on any facial or behavioural cues that seem at odds with what the participants is explicitly saying.
• Face to face qualitative survey responses are likely to be audio recorded and transcribed into text to ensure all detail is captured; however, some surveys may include both quantitative and qualitative questions using a structured or semi-structured format of questioning, and in this case the researcher may simply write down key points from the participant’s response.

Telephone surveys

• Similar to the face to face method, but without researcher being able to see participant’s facial or behavioural responses to questions asked. This means the researcher may miss key cues that would help them ask further questions to clarify or extend participant responses to their questions, and instead relies on vocal cues.

Online surveys

• Open-ended questions are presented to participants in written format via email or within an online survey tool, often alongside quantitative survey questions on the same topic.
• Researchers may provide some contextualising information or key definitions to help ‘frame’ how participants view the qualitative survey questions, since they can’t directly ask the researcher about it in real time. 
• Participants are requested to responses to questions in text ‘in some detail’ to explain their perspective or experience to researchers; this can result in diversity of responses (brief to detailed).
• Researchers can not always probe or clarify participant responses to online qualitative survey questions which can result in data from these responses being cryptic or vague to the researcher.
• Online surveys can collect a greater number of responses in a set period of time compared to face to face and phone survey approaches, so while data may be less detailed, there is more of it overall to compensate.

Qualitative surveys can help a study early on, in finding out the issues/needs/experiences to be explored further in an interview or focus group. 

Surveys can be amended and re-run based on responses providing an evolving and responsive method of research. 

Online surveys will receive typed responses reducing translation by the researcher 

Online surveys can be delivered broadly across a wide population with asynchronous delivery/response. 

Limitations

Hand-written notes will need to be transcribed (time-consuming) for digital study and kept physically for reference. 

Distance (or online) communication can be open to misinterpretations that cannot be corrected at the time. 

Questions can be leading/misleading, eliciting answers that are not core to the research subject. Researchers must aim to write a neutral question which does not give away the researchers expectations. 

Even with transcribed/digital responses analysis can be long and detailed, though not as much as in an interview. 

Surveys may be left incomplete if performed online or taken by research assistants not well trained in giving the survey/structured interview. 

Narrow sampling may skew the results of the survey. 

Example questions

Here are some example survey questions which are open ended and require a long form written response:

• Tell us why you became a doctor? 
• What do you expect from this health service? 
• How do you explain the low levels of financial investment in mental health services? (WHO, 2007) 

Example studies

• Davey, L. , Clarke, V. and Jenkinson, E. (2019), Living with alopecia areata: an online qualitative survey study. British Journal of Dermatology, 180 1377-1389. Retrieved from https://onlinelibrary-wiley-com.ezproxy-f.deakin.edu.au/doi/10.1111%2Fbjd.17463    
• Richardson, J. (2004). What Patients Expect From Complementary Therapy: A Qualitative Study. American Journal of Public Health, 94(6), 1049–1053. Retrieved from http://ezproxy.deakin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=s3h&AN=13270563&site=eds-live&scope=site  
• Saraceno, B., van Ommeren, M., Batniji, R., Cohen, A., Gureje, O., Mahoney, J., ... & Underhill, C. (2007). Barriers to improvement of mental health services in low-income and middle-income countries. The Lancet, 370(9593), 1164-1174. Retrieved from https://www-sciencedirect-com.ezproxy-f.deakin.edu.au/science/article/pii/S014067360761263X?via%3Dihub  

Below has more detail of the Lancet article including actual survey questions at: 

• World Health Organization. (2007.) Expert opinion on barriers and facilitating factors for the implementation of existing mental health knowledge in mental health services. Geneva: World Health Organization. https://apps.who.int/iris/handle/10665/44808
• Green, J. 1961-author., & Thorogood, N. (2018). Qualitative methods for health research. SAGE. Retrieved from http://ezproxy.deakin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=cat00097a&AN=deakin.b4151167&authtype=sso&custid=deakin&site=eds-live&scope=site   
• JANSEN, H. The Logic of Qualitative Survey Research and its Position in the Field of Social Research Methods. Forum Qualitative Sozialforschung, 11(2), Retrieved from http://www.qualitative-research.net/index.php/fqs/article/view/1450/2946  
• Neilsen Norman Group, (2019). 28 Tips for Creating Great Qualitative Surveys. Retrieved from https://www.nngroup.com/articles/qualitative-surveys/   
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• Last Updated: Apr 8, 2024 11:12 AM
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5 qualitative data analysis methods

Qualitative data uncovers valuable insights that help you improve the user and customer experience. But how exactly do you measure and analyze data that isn't quantifiable?

There are different qualitative data analysis methods to help you make sense of qualitative feedback and customer insights, depending on your business goals and the type of data you've collected.

Before you choose a qualitative data analysis method for your team, you need to consider the available techniques and explore their use cases to understand how each process might help you better understand your users. 

This guide covers five qualitative analysis methods to choose from, and will help you pick the right one(s) based on your goals. 

Content analysis

Thematic analysis

Narrative analysis

Grounded theory analysis

Discourse analysis

5 qualitative data analysis methods explained

Qualitative data analysis ( QDA ) is the process of organizing, analyzing, and interpreting qualitative research data—non-numeric, conceptual information, and user feedback—to capture themes and patterns, answer research questions, and identify actions to improve your product or website.

Step 1 in the research process (after planning ) is qualitative data collection. You can use behavior analytics software—like Hotjar —to capture qualitative data with context, and learn the real motivation behind user behavior, by collecting written customer feedback with Surveys or scheduling an in-depth user interview with Engage .

Use Hotjar’s tools to collect feedback, uncover behavior trends, and understand the ‘why’ behind user actions.

1. Content analysis

Content analysis is a qualitative research method that examines and quantifies the presence of certain words, subjects, and concepts in text, image, video, or audio messages. The method transforms qualitative input into quantitative data to help you make reliable conclusions about what customers think of your brand, and how you can improve their experience and opinion.

Conduct content analysis manually (which can be time-consuming) or use analysis tools like Lexalytics to reveal communication patterns, uncover differences in individual or group communication trends, and make broader connections between concepts.

How content analysis can help your team

Content analysis is often used by marketers and customer service specialists, helping them understand customer behavior and measure brand reputation.

For example, you may run a customer survey with open-ended questions to discover users’ concerns—in their own words—about their experience with your product. Instead of having to process hundreds of answers manually, a content analysis tool helps you analyze and group results based on the emotion expressed in texts.

Some other examples of content analysis include:

Analyzing brand mentions on social media to understand your brand's reputation

Reviewing customer feedback to evaluate (and then improve) the customer and user experience (UX)

Researching competitors’ website pages to identify their competitive advantages and value propositions

Interpreting customer interviews and survey results to determine user preferences, and setting the direction for new product or feature developments

Content analysis was a major part of our growth during my time at Hypercontext.

[It gave us] a better understanding of the [blog] topics that performed best for signing new users up. We were also able to go deeper within those blog posts to better understand the formats [that worked].

2. Thematic analysis

Thematic analysis helps you identify, categorize, analyze, and interpret patterns in qualitative study data , and can be done with tools like Dovetail and Thematic .

While content analysis and thematic analysis seem similar, they're different in concept: 

Content analysis can be applied to both qualitative and quantitative data , and focuses on identifying frequencies and recurring words and subjects

Thematic analysis can only be applied to qualitative data, and focuses on identifying patterns and themes

How thematic analysis can help your team

Thematic analysis can be used by pretty much anyone: from product marketers, to customer relationship managers, to UX researchers.

For example, product teams use thematic analysis to better understand user behaviors and needs and improve UX . Analyzing customer feedback lets you identify themes (e.g. poor navigation or a buggy mobile interface) highlighted by users and get actionable insight into what they really expect from the product. 

💡 Pro tip: looking for a way to expedite the data analysis process for large amounts of data you collected with a survey? Try Hotjar’s AI for Surveys : along with generating a survey based on your goal in seconds, our AI will analyze the raw data and prepare an automated summary report that presents key thematic findings, respondent quotes, and actionable steps to take, making the analysis of qualitative data a breeze.

3. Narrative analysis

Narrative analysis is a method used to interpret research participants’ stories —things like testimonials , case studies, focus groups, interviews, and other text or visual data—with tools like Delve and AI-powered ATLAS.ti .

Some formats don’t work well with narrative analysis, including heavily structured interviews and written surveys, which don’t give participants as much opportunity to tell their stories in their own words.

How narrative analysis can help your team

Narrative analysis provides product teams with valuable insight into the complexity of customers’ lives, feelings, and behaviors.

In a marketing research context, narrative analysis involves capturing and reviewing customer stories—on social media, for example—to get in-depth insight into their lives, priorities, and challenges. 

This might look like analyzing daily content shared by your audiences’ favorite influencers on Instagram, or analyzing customer reviews on sites like G2 or Capterra to gain a deep understanding of individual customer experiences. The results of this analysis also contribute to developing corresponding customer personas .

💡 Pro tip: conducting user interviews is an excellent way to collect data for narrative analysis. Though interviews can be time-intensive, there are tools out there that streamline the workload. 

Hotjar Engage automates the entire process, from recruiting to scheduling to generating the all-important interview transcripts you’ll need for the analysis phase of your research project.

4. Grounded theory analysis

Grounded theory analysis is a method of conducting qualitative research to develop theories by examining real-world data. This technique involves the creation of hypotheses and theories through qualitative data collection and evaluation, and can be performed with qualitative data analysis software tools like MAXQDA and NVivo .

Unlike other qualitative data analysis techniques, this method is inductive rather than deductive: it develops theories from data, not the other way around.

How grounded theory analysis can help your team

Grounded theory analysis is used by software engineers, product marketers, managers, and other specialists who deal with data sets to make informed business decisions. 

For example, product marketing teams may turn to customer surveys to understand the reasons behind high churn rates , then use grounded theory to analyze responses and develop hypotheses about why users churn, and how you can get them to stay. 

Grounded theory can also be helpful in the talent management process. For example, HR representatives may use it to develop theories about low employee engagement, and come up with solutions based on their research findings.

5. Discourse analysis

Discourse analysis is the act of researching the underlying meaning of qualitative data. It involves the observation of texts, audio, and videos to study the relationships between information and its social context.

In contrast to content analysis, this method focuses on the contextual meaning of language: discourse analysis sheds light on what audiences think of a topic, and why they feel the way they do about it.

How discourse analysis can help your team

In a business context, this method is primarily used by marketing teams. Discourse analysis helps marketers understand the norms and ideas in their market , and reveals why they play such a significant role for their customers. 

Once the origins of trends are uncovered, it’s easier to develop a company mission, create a unique tone of voice, and craft effective marketing messages.

Which qualitative data analysis method should you choose?

While the five qualitative data analysis methods we list above are all aimed at processing data and answering research questions, these techniques differ in their intent and the approaches applied.  

Choosing the right analysis method for your team isn't a matter of preference—selecting a method that fits is only possible once you define your research goals and have a clear intention. When you know what you need (and why you need it), you can identify an analysis method that aligns with your research objectives.

Gather qualitative data with Hotjar

Use Hotjar’s product experience insights in your qualitative research. Collect feedback, uncover behavior trends, and understand the ‘why’ behind user actions.

FAQs about qualitative data analysis methods

What is the qualitative data analysis approach.

The qualitative data analysis approach refers to the process of systematizing descriptive data collected through interviews, focus groups, surveys, and observations and then interpreting it. The methodology aims to identify patterns and themes behind textual data, and other unquantifiable data, as opposed to numerical data.

What are qualitative data analysis methods?

Five popular qualitative data analysis methods are:

What is the process of qualitative data analysis?

The process of qualitative data analysis includes six steps:

Define your research question

Prepare the data

Choose the method of qualitative analysis

Code the data

Identify themes, patterns, and relationships

Make hypotheses and act

Qualitative data analysis guide

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QDA challenges

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How to Analysis of Survey Data: Methods & Examples

Varun Saharawat is a seasoned professional in the fields of SEO and content writing. With a profound knowledge of the intricate aspects of these disciplines, Varun has established himself as a valuable asset in the world of digital marketing and online content creation.

Analysis of Survey Data transforms raw data into meaningful insights. By adhering to best practices, you can leverage survey findings to enhance business strategies or inform research outcomes.

Analysis of Survey Data : As a researcher, marketer, or student, have you ever struggled to make sense of all the responses from a survey you administered? You’re not alone – understanding large amounts of survey data can be an overwhelming task. Data, data everywhere – but are you making sense of it all? However, raw survey results don’t always tell the full story – real understanding comes from carefully analyzing your data.

Remember to break down your data, use visual aids, and look for patterns in the responses. These strategies will help you make informed decisions and guide your next steps. But don’t stop here! Keep learning and honing your skills by enrolling in a data analytics course like the one offered by Physics Wallah.

With our experienced instructors and practical approach, you’ll be equipped with the tools and techniques needed to master survey analysis. And as a token of appreciation for being a dedicated reader, use “ READER ” as a coupon code to receive a discounted price for the course.

Table of Contents

Survey Data Analysis Examples

Let’s consider a hypothetical survey about customer satisfaction with a new mobile application. The survey was distributed to 500 users, and we collected both quantitative and qualitative data. Here’s a simplified example of how you might analyze the survey data:

Quantitative Data Analysis:

• Descriptive Statistics : Begin by calculating basic statistics like mean, median, mode, and standard deviation for questions that had numerical responses, such as “On a scale of 1-10, how satisfied are you with the app?”
• Cross-Tabulation : Create cross-tabulation tables to analyze relationships between different variables. For instance, you could cross-tabulate satisfaction levels with the frequency of app usage.
• Regression Analysis : Determine if there’s a correlation between user demographics (like age, location, or occupation) and satisfaction levels. A regression model might help predict satisfaction based on these variables.

Qualitative Data Analysis:

• Thematic Analysis : Manually review open-ended responses to identify recurring themes or sentiments. For instance, common themes might include “ease of use,” “features lacking,” or “customer support.”
• Sentiment Analysis : Use text analytics tools or software to perform sentiment analysis on qualitative responses. This will help categorize feedback as positive, negative, or neutral, providing an overall sentiment score.
• Word Clouds : Generate word clouds to visualize frequently mentioned words or phrases in the qualitative feedback. This gives a quick snapshot of what users are talking about most frequently.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

How do you Analyze Survey Data? (Effective Ways)

Analyzing survey data is a crucial step in extracting meaningful insights that can drive informed decision-making and strategic planning. While gathering data is essential, interpreting it correctly is equally vital. Here’s a comprehensive guide on how to analyze survey data effectively, utilizing various techniques and best practices.

1) Comprehend the Measurement Scales

Understanding the types of measurement scales—nominal, ordinal, interval, and ratio—is foundational. Each scale serves a unique purpose and requires distinct analytical approaches:

• Nominal Scales : Utilized for qualitative data, nominal scales categorize responses without imposing any order.
• Ordinal Scales : These scales rank responses based on preferences or orders, allowing for comparative analysis.
• Interval Scales : Ideal for capturing responses within a predefined range, facilitating more nuanced analysis.
• Ratio Scales : Similar to interval scales but starting at zero, these scales provide a comprehensive quantitative assessment.

2) Prioritize Quantitative Insights

Initiate your analysis by focusing on quantitative questions that yield numerical data. Metrics like the Net Promoter Score (NPS) can offer insights into customer sentiments and brand loyalty, enabling you to identify brand advocates and areas for improvement.

3) Harness Qualitative Feedback

While quantitative data provides numerical insights, qualitative feedback offers depth and context. Analyze open-ended responses by:

• Creating visual representations to identify common themes or keywords.
• Individualizing responses to understand unique customer perspectives and expectations.

4) Implement Cross-Tabulation Analysis

Cross-tabulation facilitates a deeper understanding of the relationship between variables, especially when targeting specific demographics or segments. By segmenting data based on relevant criteria, such as age or location, you can derive more targeted insights relevant to your objectives.

5) Distinguish Between Correlation and Causation

Avoid conflating correlation with causation, as it can lead to misleading interpretations. Scrutinize data meticulously, considering external factors and variables, to draw accurate conclusions and avoid erroneous assumptions.

6) Benchmark Against Historical Data

Comparing current survey results with past data sets enables you to assess progress, identify trends, and evaluate the effectiveness of implemented strategies. By tracking key metrics over time, you can measure improvements and refine your approach continually.

7) Utilize Industry Benchmarks

Benchmarking against industry standards provides context and perspective, allowing you to gauge your performance relative to competitors and market leaders. Aligning your survey results with industry benchmarks ensures realistic goals and actionable insights.

8) Mitigate Inaccurate or Incomplete Responses

Addressing incomplete or inaccurate survey responses is crucial for maintaining data integrity. Identify and categorize inattentive respondents, such as speeders, straight-liners, slackers, and imposters, to filter out unreliable data and enhance the validity of your analysis.

Analyzing survey data is a multifaceted process that necessitates a structured approach, incorporating both quantitative and qualitative methods. By understanding measurement scales, prioritizing actionable insights, leveraging analytical techniques like cross-tabulation, and benchmarking against relevant benchmarks, organizations can derive meaningful insights to inform decision-making, optimize strategies, and drive continuous improvement.

Statistical Analysis of Survey Data

Statistical analysis of survey data involves employing various statistical techniques to analyze and interpret the collected survey responses. This analytical process aims to uncover patterns, trends, relationships, and insights from the data, enabling organizations to make informed decisions, optimize strategies, and address specific research objectives. Here’s an overview of the statistical analysis techniques commonly used in survey data analysis:

1) Descriptive Statistics:

Descriptive statistics provide a summary of the main aspects of the survey data, including measures of central tendency (mean, median, mode), variability (standard deviation, variance, range), and distribution (skewness, kurtosis). These statistics offer an initial understanding of the data’s characteristics, such as the average response, variability among responses, and distribution patterns.

2) Inferential Statistics:

Inferential statistics enable researchers to generalize findings from a sample to a larger population, assess relationships between variables, and test hypotheses. Common inferential statistical tests include:

• T-tests: Used to compare the means of two groups or assess differences between two sets of data.
• ANOVA (Analysis of Variance): Employed to compare means across multiple groups simultaneously.
• Chi-Square Test: Applied to examine the association between categorical variables and determine if observed frequencies differ significantly from expected frequencies.
• Regression Analysis: Used to identify and quantify relationships between a dependent variable and one or more independent variables, predicting the outcome based on predictor variables.

3) Correlation Analysis:

Correlation analysis assesses the strength and direction of the relationship between two continuous variables. The Pearson correlation coefficient measures the linear relationship between variables, ranging from -1 (negative correlation) to 1 (positive correlation), with 0 indicating no correlation.

4) Factor Analysis:

Factor analysis is a multivariate statistical technique used to identify underlying relationships between observed variables, uncover latent variables or factors, and reduce data dimensionality. By grouping related variables into distinct factors, researchers can simplify complex data structures and identify underlying patterns or constructs.

5) Cluster Analysis:

Cluster analysis categorizes survey respondents or variables into distinct groups or clusters based on similarities within groups and differences between groups. This technique helps segment the target population, identify distinct respondent profiles, or group similar survey items, facilitating more targeted and personalized strategies.

6) Regression Modeling:

Regression modeling encompasses various regression techniques, including linear regression, logistic regression, and multiple regression, to predict or explain the relationship between dependent and independent variables. By evaluating the impact of predictor variables on the outcome variable, organizations can identify key drivers, assess relationships, and develop predictive models.

Also Read: Analysis vs. Analytics: How Are They Different?

Analysis of Survey Data in Research

Analysis of survey data in research is a critical component that involves examining, interpreting, and making sense of the collected survey responses. It enables researchers to derive meaningful insights, identify patterns, trends, and relationships, and draw valid conclusions to address research objectives or hypotheses effectively. Here’s a comprehensive overview of the analysis of survey data in research:

1) Data Preparation:

Before conducting any analysis, researchers must prepare the survey data by cleaning, organizing, and coding the responses. This involves:

• Data Cleaning: Identifying and addressing missing, incomplete, or erroneous responses to ensure data accuracy and reliability.
• Data Transformation: Converting raw survey data into a format suitable for analysis, such as numerical coding, categorization, or scaling.
• Variable Identification: Defining variables, distinguishing between independent and dependent variables, and categorizing variables based on their type (e.g., nominal, ordinal, interval, ratio).

2) Descriptive Analysis:

Descriptive analysis involves summarizing and describing the main features of the survey data using:

• Measures of Central Tendency: Calculating mean, median, and mode to determine the average or typical response.
• Measures of Dispersion: Assessing variability using standard deviation, variance, and range to understand the spread or dispersion of responses.
• Frequency Distributions: Creating frequency tables, histograms, or bar charts to display the distribution of categorical or continuous variables.

3) Inferential Analysis:

Inferential analysis focuses on making predictions, generalizing findings, or testing hypotheses based on the survey sample data. Common inferential techniques include:

• Hypothesis Testing: Using statistical tests such as t-tests, ANOVA, chi-square tests, or regression analysis to test research hypotheses, assess differences between groups, or determine associations between variables.
• Confidence Intervals: Estimating the range within which population parameters (e.g., means, proportions) are likely to fall based on sample data.

4) Correlation and Regression Analysis:

Correlation and regression analysis help researchers understand relationships between variables, predict outcomes, and identify key predictors:

• Correlation Analysis: Using correlation coefficients to assess the strength and direction of relationships between two or more continuous variables.
• Regression Analysis: Developing predictive models to explain the relationship between dependent and independent variables, identify significant predictors, and predict outcomes based on predictor variables.

5) Factor and Cluster Analysis:

Factor and cluster analysis are advanced techniques used to identify underlying patterns, group variables or respondents, and reduce data complexity:

• Factor Analysis: Identifying latent variables or underlying constructs, reducing data dimensionality, and uncovering patterns or relationships between observed variables.
• Cluster Analysis: Segmenting respondents or variables into distinct groups based on similarities, facilitating targeted analysis, and understanding respondent segments or patterns.

Survey Data Analysis Methods

Survey data analysis serves as a critical step in understanding the collected information, drawing meaningful insights, and making informed decisions. By employing specific methods tailored to the type and structure of the survey data, researchers and analysts can effectively interpret and leverage the information gathered. Here’s a detailed exploration of various survey data analysis methods:

1) Statistical Analysis:

Statistical analysis stands as a cornerstone in survey data analysis, offering rigorous methods to examine relationships, differences, and patterns within the data. Key statistical techniques include:

• Regression Analysis: Assessing the relationship between dependent and independent variables to predict outcomes or understand associations.
• T-Test: Comparing means between two groups to determine if there are significant differences.
• Analysis of Variance (ANOVA): Evaluating differences in means across multiple groups or categories.
• Cluster Analysis: Identifying distinct groups or clusters within the data based on similarities.
• Factor Analysis: Uncovering underlying relationships between observed variables by identifying latent factors or constructs.
• Conjoint Analysis: Analyzing respondent preferences and trade-offs among different attributes or features.

2) Measurement Scales Understanding:

Recognizing the measurement scales of survey questions forms a foundational aspect of data analysis . Different scales, including nominal, ordinal, interval, and ratio scales, dictate the type of statistical tests and analyses appropriate for the data, ensuring accurate and meaningful interpretation.

3) Quantitative Questions Analysis:

Initiating the analysis with quantitative questions facilitates establishing numerical trends, patterns, and relationships within the data. By prioritizing quantitative analysis, researchers can quantify responses, calculate descriptive statistics, and derive statistical inferences to address research objectives effectively.

4) Visualization Tools:

Visual representation of survey data plays a pivotal role in conveying insights, identifying trends, and communicating findings to stakeholders. Utilizing visualization tools such as pie charts, Venn diagrams, line graphs, scatter plots, histograms, and pictograms enhances data interpretation, fosters comprehension, and facilitates decision-making processes.

5) Popular Methods Utilization:

Embracing popular methods specific to survey data analysis ensures comprehensive insights extraction. By leveraging the nine most recognized methods for survey data analysis, researchers can navigate the complexities of data interpretation, uncover hidden patterns, validate research hypotheses, and inform strategic decisions effectively.

Also Read: Learning Path to Become a Data Analyst in 2024

How to Present Survey Data

Presenting survey data in a coherent, compelling, and easily digestible manner is crucial for conveying insights, fostering understanding, and driving informed decision-making among stakeholders. By employing various methods tailored to the nature and complexity of the survey data, you can effectively communicate findings and facilitate meaningful discussions. Here’s an in-depth exploration of how to present survey data:

1) Graphical Representation:

Graphs stand as a cornerstone in presenting survey data due to their ability to simplify complex information and facilitate visual interpretation. Depending on the nature of your data, consider utilizing the following graphical representations:

• Pie Charts: Ideal for illustrating proportions and percentages, pie charts offer a clear visualization of categorical data distribution.
• Venn Diagrams: Useful for showcasing overlaps or intersections between different data sets or categories.
• Scatter Plots: Effective for displaying relationships and correlations between two variables, facilitating trend identification.
• Histograms: Perfect for representing frequency distributions and identifying data distribution patterns.
• Pictograms: Employ visuals or icons to represent data quantities, making data more relatable and engaging.

Ensure selecting the most appropriate graph type that aligns with your data characteristics and resonates with your target audience’s preferences and comprehension levels.

2) Data Tables:

Data tables serve as a structured and systematic approach to presenting numerical survey data. By leveraging tools like Excel, you can organize, categorize, and display quantitative data in a tabular format, enhancing clarity, and facilitating comparative analysis. Ensure incorporating relevant headers, footnotes, and annotations to provide context and facilitate interpretation.

3) Interactive Presentations:

Crafting interactive presentations enables you to amalgamate textual and graphical data, fostering engagement and facilitating comprehensive understanding. Begin by outlining the research objectives, methodology, and hypothesis, followed by systematically presenting survey findings, insights, and implications. Utilize visuals, animations, and infographics to enhance engagement, convey key messages, and facilitate interactive discussions.

4) Infographics:

Infographics emerge as a potent tool for presenting survey data in a visually appealing, concise, and easily consumable format. By transforming survey results into compelling visuals, statistics, and narratives, infographics enhance information retention, facilitate comprehension, and augment the aesthetic appeal of your presentations. Consider incorporating color coding, icons, and concise text to convey key findings, trends, and insights succinctly.

5) Comprehensive Reports:

For investor meetings, shareholder discussions, or detailed presentations, comprehensive reports serve as an invaluable tool for presenting survey data. While incorporating graphs, tables, and infographics, reports provide an in-depth analysis, interpretation, and contextualization of survey findings.

Ensure structuring your report systematically, including an executive summary, methodology, findings, discussions, conclusions, and recommendations. Facilitate accessibility by incorporating a table of contents, appendices, and references, ensuring stakeholders can delve deeper into specific sections or data points as required.

Common Mistakes in Analysis of Survey Data and How to Avoid Them

Analyzing survey data is a pivotal step in extracting valuable insights that can drive informed decisions, shape strategies, and inform future research endeavors. However, several common pitfalls can compromise the accuracy, reliability, and validity of your findings. Recognizing these challenges and implementing strategies to mitigate them is crucial for ensuring robust and actionable survey data analysis. Here’s a comprehensive exploration of these common mistakes and how to navigate them effectively:

1) Premature Interpretation of Results:

Common Mistake: Succumbing to confirmation bias by hastily interpreting survey results that align with preconceived notions or expectations without ensuring statistical significance.

Mitigation Strategy: Prioritize a rigorous statistical analysis approach to ascertain the validity, reliability, and significance of your findings. Emphasize the importance of a sufficiently large sample size to minimize the likelihood of skewed or coincidental results. Adopt a systematic and unbiased approach to data interpretation, emphasizing objectivity, and evidence-based conclusions.

2) Misinterpreting Correlation as Causation:

Common Mistake: Conflating correlation with causation, attributing causative relationships between variables solely based on observed correlations without considering potential confounding variables or underlying mechanisms.

Mitigation Strategy: Exercise caution and critical thinking when interpreting relationships between variables. Emphasize the importance of exploring underlying factors, mechanisms, and variables that may influence observed correlations. Encourage a comprehensive and nuanced analysis that considers potential confounders, alternative explanations, and causal pathways, ensuring accurate and informed interpretations.

3) Overlooking Nuances in Qualitative Natural Language Data:

Common Mistake: Oversimplifying the analysis of qualitative survey data, such as speech or text responses, by relying solely on superficial categorizations or failing to capture the richness, context, and intricacies of human language.

Mitigation Strategy: Leverage advanced AI solutions and machine learning algorithms capable of sophisticated sentiment analysis, contextual understanding, and nuanced interpretation of qualitative data. Prioritize tools that emulate human-like comprehension, considering context, emotion, intent, and conversational dynamics. Foster a multidimensional approach to qualitative data analysis, emphasizing depth, richness, and comprehensive understanding to extract meaningful insights effectively.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Tools for Analysis of Survey Data

Analyzing survey data requires specialized tools that can efficiently process, visualize, and interpret the collected information. Here are some commonly used tools for the analysis of survey data:

If you still feel overwhelmed or want to enhance your skills further, we highly recommend enrolling in the Data Analytics course offered by Physics Wallah . Their comprehensive syllabus covers all aspects of survey data analysis and is taught by experienced professionals who are passionate about imparting their knowledge. And as a token of appreciation for being a reader of this blog post, use the “READER” coupon code to avail yourself of a special discount on the course fee.

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Analysis of Survey Data FAQs

What is the survey method of data analysis.

The survey method of data analysis involves collecting structured information from respondents through questionnaires or interviews. Once gathered, this data undergoes systematic examination to extract insights, trends, or patterns that can inform decision-making or research objectives.

What is the best tool to analyze survey data?

Several tools can effectively analyze survey data based on specific needs, such as SPSS, Qualtrics, SurveyMonkey, and Microsoft Excel. The "best" tool often depends on the complexity of the survey, required analytical techniques, user expertise, and desired output formats.

What is the purpose of survey analysis?

The purpose of survey analysis is to interpret collected data to understand respondent opinions, behaviors, preferences, or attitudes. By analyzing survey results, organizations or researchers can derive insights, make informed decisions, assess trends, identify patterns, and address research objectives or business challenges effectively.

What is the primary objective of analyzing survey data?

The primary objective of analyzing survey data is to extract valuable insights, patterns, and trends from the collected responses. This analysis aids in understanding respondent behaviors, preferences, opinions, and perceptions, enabling organizations to make informed decisions, shape strategies, and inform future initiatives effectively.

What are the key steps involved in analyzing survey data?

The key steps involved in analyzing survey data encompass data cleaning and preparation, defining objectives and research questions, selecting appropriate analytical techniques, conducting statistical analyses (e.g., regression analysis, t-tests, ANOVA), interpreting findings, and communicating results effectively.

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Writing Survey Questions

Perhaps the most important part of the survey process is the creation of questions that accurately measure the opinions, experiences and behaviors of the public. Accurate random sampling will be wasted if the information gathered is built on a shaky foundation of ambiguous or biased questions. Creating good measures involves both writing good questions and organizing them to form the questionnaire.

Questionnaire design is a multistage process that requires attention to many details at once. Designing the questionnaire is complicated because surveys can ask about topics in varying degrees of detail, questions can be asked in different ways, and questions asked earlier in a survey may influence how people respond to later questions. Researchers are also often interested in measuring change over time and therefore must be attentive to how opinions or behaviors have been measured in prior surveys.

Surveyors may conduct pilot tests or focus groups in the early stages of questionnaire development in order to better understand how people think about an issue or comprehend a question. Pretesting a survey is an essential step in the questionnaire design process to evaluate how people respond to the overall questionnaire and specific questions, especially when questions are being introduced for the first time.

For many years, surveyors approached questionnaire design as an art, but substantial research over the past forty years has demonstrated that there is a lot of science involved in crafting a good survey questionnaire. Here, we discuss the pitfalls and best practices of designing questionnaires.

Question development

There are several steps involved in developing a survey questionnaire. The first is identifying what topics will be covered in the survey. For Pew Research Center surveys, this involves thinking about what is happening in our nation and the world and what will be relevant to the public, policymakers and the media. We also track opinion on a variety of issues over time so we often ensure that we update these trends on a regular basis to better understand whether people’s opinions are changing.

At Pew Research Center, questionnaire development is a collaborative and iterative process where staff meet to discuss drafts of the questionnaire several times over the course of its development. We frequently test new survey questions ahead of time through qualitative research methods such as  focus groups , cognitive interviews, pretesting (often using an  online, opt-in sample ), or a combination of these approaches. Researchers use insights from this testing to refine questions before they are asked in a production survey, such as on the ATP.

Measuring change over time

Many surveyors want to track changes over time in people’s attitudes, opinions and behaviors. To measure change, questions are asked at two or more points in time. A cross-sectional design surveys different people in the same population at multiple points in time. A panel, such as the ATP, surveys the same people over time. However, it is common for the set of people in survey panels to change over time as new panelists are added and some prior panelists drop out. Many of the questions in Pew Research Center surveys have been asked in prior polls. Asking the same questions at different points in time allows us to report on changes in the overall views of the general public (or a subset of the public, such as registered voters, men or Black Americans), or what we call “trending the data”.

When measuring change over time, it is important to use the same question wording and to be sensitive to where the question is asked in the questionnaire to maintain a similar context as when the question was asked previously (see  question wording  and  question order  for further information). All of our survey reports include a topline questionnaire that provides the exact question wording and sequencing, along with results from the current survey and previous surveys in which we asked the question.

The Center’s transition from conducting U.S. surveys by live telephone interviewing to an online panel (around 2014 to 2020) complicated some opinion trends, but not others. Opinion trends that ask about sensitive topics (e.g., personal finances or attending religious services ) or that elicited volunteered answers (e.g., “neither” or “don’t know”) over the phone tended to show larger differences than other trends when shifting from phone polls to the online ATP. The Center adopted several strategies for coping with changes to data trends that may be related to this change in methodology. If there is evidence suggesting that a change in a trend stems from switching from phone to online measurement, Center reports flag that possibility for readers to try to head off confusion or erroneous conclusions.

Open- and closed-ended questions

One of the most significant decisions that can affect how people answer questions is whether the question is posed as an open-ended question, where respondents provide a response in their own words, or a closed-ended question, where they are asked to choose from a list of answer choices.

For example, in a poll conducted after the 2008 presidential election, people responded very differently to two versions of the question: “What one issue mattered most to you in deciding how you voted for president?” One was closed-ended and the other open-ended. In the closed-ended version, respondents were provided five options and could volunteer an option not on the list.

When explicitly offered the economy as a response, more than half of respondents (58%) chose this answer; only 35% of those who responded to the open-ended version volunteered the economy. Moreover, among those asked the closed-ended version, fewer than one-in-ten (8%) provided a response other than the five they were read. By contrast, fully 43% of those asked the open-ended version provided a response not listed in the closed-ended version of the question. All of the other issues were chosen at least slightly more often when explicitly offered in the closed-ended version than in the open-ended version. (Also see  “High Marks for the Campaign, a High Bar for Obama”  for more information.)

Researchers will sometimes conduct a pilot study using open-ended questions to discover which answers are most common. They will then develop closed-ended questions based off that pilot study that include the most common responses as answer choices. In this way, the questions may better reflect what the public is thinking, how they view a particular issue, or bring certain issues to light that the researchers may not have been aware of.

When asking closed-ended questions, the choice of options provided, how each option is described, the number of response options offered, and the order in which options are read can all influence how people respond. One example of the impact of how categories are defined can be found in a Pew Research Center poll conducted in January 2002. When half of the sample was asked whether it was “more important for President Bush to focus on domestic policy or foreign policy,” 52% chose domestic policy while only 34% said foreign policy. When the category “foreign policy” was narrowed to a specific aspect – “the war on terrorism” – far more people chose it; only 33% chose domestic policy while 52% chose the war on terrorism.

In most circumstances, the number of answer choices should be kept to a relatively small number – just four or perhaps five at most – especially in telephone surveys. Psychological research indicates that people have a hard time keeping more than this number of choices in mind at one time. When the question is asking about an objective fact and/or demographics, such as the religious affiliation of the respondent, more categories can be used. In fact, they are encouraged to ensure inclusivity. For example, Pew Research Center’s standard religion questions include more than 12 different categories, beginning with the most common affiliations (Protestant and Catholic). Most respondents have no trouble with this question because they can expect to see their religious group within that list in a self-administered survey.

In addition to the number and choice of response options offered, the order of answer categories can influence how people respond to closed-ended questions. Research suggests that in telephone surveys respondents more frequently choose items heard later in a list (a “recency effect”), and in self-administered surveys, they tend to choose items at the top of the list (a “primacy” effect).

Because of concerns about the effects of category order on responses to closed-ended questions, many sets of response options in Pew Research Center’s surveys are programmed to be randomized to ensure that the options are not asked in the same order for each respondent. Rotating or randomizing means that questions or items in a list are not asked in the same order to each respondent. Answers to questions are sometimes affected by questions that precede them. By presenting questions in a different order to each respondent, we ensure that each question gets asked in the same context as every other question the same number of times (e.g., first, last or any position in between). This does not eliminate the potential impact of previous questions on the current question, but it does ensure that this bias is spread randomly across all of the questions or items in the list. For instance, in the example discussed above about what issue mattered most in people’s vote, the order of the five issues in the closed-ended version of the question was randomized so that no one issue appeared early or late in the list for all respondents. Randomization of response items does not eliminate order effects, but it does ensure that this type of bias is spread randomly.

Questions with ordinal response categories – those with an underlying order (e.g., excellent, good, only fair, poor OR very favorable, mostly favorable, mostly unfavorable, very unfavorable) – are generally not randomized because the order of the categories conveys important information to help respondents answer the question. Generally, these types of scales should be presented in order so respondents can easily place their responses along the continuum, but the order can be reversed for some respondents. For example, in one of Pew Research Center’s questions about abortion, half of the sample is asked whether abortion should be “legal in all cases, legal in most cases, illegal in most cases, illegal in all cases,” while the other half of the sample is asked the same question with the response categories read in reverse order, starting with “illegal in all cases.” Again, reversing the order does not eliminate the recency effect but distributes it randomly across the population.

Question wording

The choice of words and phrases in a question is critical in expressing the meaning and intent of the question to the respondent and ensuring that all respondents interpret the question the same way. Even small wording differences can substantially affect the answers people provide.

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An example of a wording difference that had a significant impact on responses comes from a January 2003 Pew Research Center survey. When people were asked whether they would “favor or oppose taking military action in Iraq to end Saddam Hussein’s rule,” 68% said they favored military action while 25% said they opposed military action. However, when asked whether they would “favor or oppose taking military action in Iraq to end Saddam Hussein’s rule  even if it meant that U.S. forces might suffer thousands of casualties, ” responses were dramatically different; only 43% said they favored military action, while 48% said they opposed it. The introduction of U.S. casualties altered the context of the question and influenced whether people favored or opposed military action in Iraq.

There has been a substantial amount of research to gauge the impact of different ways of asking questions and how to minimize differences in the way respondents interpret what is being asked. The issues related to question wording are more numerous than can be treated adequately in this short space, but below are a few of the important things to consider:

First, it is important to ask questions that are clear and specific and that each respondent will be able to answer. If a question is open-ended, it should be evident to respondents that they can answer in their own words and what type of response they should provide (an issue or problem, a month, number of days, etc.). Closed-ended questions should include all reasonable responses (i.e., the list of options is exhaustive) and the response categories should not overlap (i.e., response options should be mutually exclusive). Further, it is important to discern when it is best to use forced-choice close-ended questions (often denoted with a radio button in online surveys) versus “select-all-that-apply” lists (or check-all boxes). A 2019 Center study found that forced-choice questions tend to yield more accurate responses, especially for sensitive questions.  Based on that research, the Center generally avoids using select-all-that-apply questions.

It is also important to ask only one question at a time. Questions that ask respondents to evaluate more than one concept (known as double-barreled questions) – such as “How much confidence do you have in President Obama to handle domestic and foreign policy?” – are difficult for respondents to answer and often lead to responses that are difficult to interpret. In this example, it would be more effective to ask two separate questions, one about domestic policy and another about foreign policy.

In general, questions that use simple and concrete language are more easily understood by respondents. It is especially important to consider the education level of the survey population when thinking about how easy it will be for respondents to interpret and answer a question. Double negatives (e.g., do you favor or oppose  not  allowing gays and lesbians to legally marry) or unfamiliar abbreviations or jargon (e.g., ANWR instead of Arctic National Wildlife Refuge) can result in respondent confusion and should be avoided.

Similarly, it is important to consider whether certain words may be viewed as biased or potentially offensive to some respondents, as well as the emotional reaction that some words may provoke. For example, in a 2005 Pew Research Center survey, 51% of respondents said they favored “making it legal for doctors to give terminally ill patients the means to end their lives,” but only 44% said they favored “making it legal for doctors to assist terminally ill patients in committing suicide.” Although both versions of the question are asking about the same thing, the reaction of respondents was different. In another example, respondents have reacted differently to questions using the word “welfare” as opposed to the more generic “assistance to the poor.” Several experiments have shown that there is much greater public support for expanding “assistance to the poor” than for expanding “welfare.”

We often write two versions of a question and ask half of the survey sample one version of the question and the other half the second version. Thus, we say we have two  forms  of the questionnaire. Respondents are assigned randomly to receive either form, so we can assume that the two groups of respondents are essentially identical. On questions where two versions are used, significant differences in the answers between the two forms tell us that the difference is a result of the way we worded the two versions.

One of the most common formats used in survey questions is the “agree-disagree” format. In this type of question, respondents are asked whether they agree or disagree with a particular statement. Research has shown that, compared with the better educated and better informed, less educated and less informed respondents have a greater tendency to agree with such statements. This is sometimes called an “acquiescence bias” (since some kinds of respondents are more likely to acquiesce to the assertion than are others). This behavior is even more pronounced when there’s an interviewer present, rather than when the survey is self-administered. A better practice is to offer respondents a choice between alternative statements. A Pew Research Center experiment with one of its routinely asked values questions illustrates the difference that question format can make. Not only does the forced choice format yield a very different result overall from the agree-disagree format, but the pattern of answers between respondents with more or less formal education also tends to be very different.

One other challenge in developing questionnaires is what is called “social desirability bias.” People have a natural tendency to want to be accepted and liked, and this may lead people to provide inaccurate answers to questions that deal with sensitive subjects. Research has shown that respondents understate alcohol and drug use, tax evasion and racial bias. They also may overstate church attendance, charitable contributions and the likelihood that they will vote in an election. Researchers attempt to account for this potential bias in crafting questions about these topics. For instance, when Pew Research Center surveys ask about past voting behavior, it is important to note that circumstances may have prevented the respondent from voting: “In the 2012 presidential election between Barack Obama and Mitt Romney, did things come up that kept you from voting, or did you happen to vote?” The choice of response options can also make it easier for people to be honest. For example, a question about church attendance might include three of six response options that indicate infrequent attendance. Research has also shown that social desirability bias can be greater when an interviewer is present (e.g., telephone and face-to-face surveys) than when respondents complete the survey themselves (e.g., paper and web surveys).

Lastly, because slight modifications in question wording can affect responses, identical question wording should be used when the intention is to compare results to those from earlier surveys. Similarly, because question wording and responses can vary based on the mode used to survey respondents, researchers should carefully evaluate the likely effects on trend measurements if a different survey mode will be used to assess change in opinion over time.

Question order

Once the survey questions are developed, particular attention should be paid to how they are ordered in the questionnaire. Surveyors must be attentive to how questions early in a questionnaire may have unintended effects on how respondents answer subsequent questions. Researchers have demonstrated that the order in which questions are asked can influence how people respond; earlier questions can unintentionally provide context for the questions that follow (these effects are called “order effects”).

One kind of order effect can be seen in responses to open-ended questions. Pew Research Center surveys generally ask open-ended questions about national problems, opinions about leaders and similar topics near the beginning of the questionnaire. If closed-ended questions that relate to the topic are placed before the open-ended question, respondents are much more likely to mention concepts or considerations raised in those earlier questions when responding to the open-ended question.

For closed-ended opinion questions, there are two main types of order effects: contrast effects ( where the order results in greater differences in responses), and assimilation effects (where responses are more similar as a result of their order).

An example of a contrast effect can be seen in a Pew Research Center poll conducted in October 2003, a dozen years before same-sex marriage was legalized in the U.S. That poll found that people were more likely to favor allowing gays and lesbians to enter into legal agreements that give them the same rights as married couples when this question was asked after one about whether they favored or opposed allowing gays and lesbians to marry (45% favored legal agreements when asked after the marriage question, but 37% favored legal agreements without the immediate preceding context of a question about same-sex marriage). Responses to the question about same-sex marriage, meanwhile, were not significantly affected by its placement before or after the legal agreements question.

Another experiment embedded in a December 2008 Pew Research Center poll also resulted in a contrast effect. When people were asked “All in all, are you satisfied or dissatisfied with the way things are going in this country today?” immediately after having been asked “Do you approve or disapprove of the way George W. Bush is handling his job as president?”; 88% said they were dissatisfied, compared with only 78% without the context of the prior question.

Responses to presidential approval remained relatively unchanged whether national satisfaction was asked before or after it. A similar finding occurred in December 2004 when both satisfaction and presidential approval were much higher (57% were dissatisfied when Bush approval was asked first vs. 51% when general satisfaction was asked first).

Several studies also have shown that asking a more specific question before a more general question (e.g., asking about happiness with one’s marriage before asking about one’s overall happiness) can result in a contrast effect. Although some exceptions have been found, people tend to avoid redundancy by excluding the more specific question from the general rating.

Assimilation effects occur when responses to two questions are more consistent or closer together because of their placement in the questionnaire. We found an example of an assimilation effect in a Pew Research Center poll conducted in November 2008 when we asked whether Republican leaders should work with Obama or stand up to him on important issues and whether Democratic leaders should work with Republican leaders or stand up to them on important issues. People were more likely to say that Republican leaders should work with Obama when the question was preceded by the one asking what Democratic leaders should do in working with Republican leaders (81% vs. 66%). However, when people were first asked about Republican leaders working with Obama, fewer said that Democratic leaders should work with Republican leaders (71% vs. 82%).

The order questions are asked is of particular importance when tracking trends over time. As a result, care should be taken to ensure that the context is similar each time a question is asked. Modifying the context of the question could call into question any observed changes over time (see  measuring change over time  for more information).

A questionnaire, like a conversation, should be grouped by topic and unfold in a logical order. It is often helpful to begin the survey with simple questions that respondents will find interesting and engaging. Throughout the survey, an effort should be made to keep the survey interesting and not overburden respondents with several difficult questions right after one another. Demographic questions such as income, education or age should not be asked near the beginning of a survey unless they are needed to determine eligibility for the survey or for routing respondents through particular sections of the questionnaire. Even then, it is best to precede such items with more interesting and engaging questions. One virtue of survey panels like the ATP is that demographic questions usually only need to be asked once a year, not in each survey.

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Survey Question Types: Choosing the Right Format for Effective Data Collection

• April 26, 2024

Choosing the right question types

Multiple-choice questions, yes/no questions, rating scales, rank order survey questions, demographic questions, likert scale questions, semantic differential scale, matrix questions, ranking questions, open-text questions, multiple-choice survey questions, binary scale questions, the role of ordinal questions in surveys, explore different types of questions in questionnaire with surveyplanet.

In the realm of data collection, surveys stand out as one of the most versatile tools for gathering insights and opinions. However, crafting effective surveys requires careful consideration of various factors, one of the most crucial being the types of questions employed. In this guide, we’ll delve into the world of survey question types, exploring their intricacies and discussing how to choose the correct format for optimal data collection.

Understanding survey question types

Before diving into the specifics, let’s establish a foundational understanding of survey question types. Generally, survey questions can be categorized into three main types of questions: open-ended, closed-ended, and scaled:

• Open-ended questions : These allow respondents to provide free-form responses, offering flexibility and depth in their answers. Open-ended questions are valuable for capturing qualitative data and uncovering insights that may have yet to be anticipated by the survey creator. However, they can be more challenging to analyze due to the diverse nature of responses.
• Closed-ended Questions : In contrast, closed-ended questions provide respondents with predefined answer options. This format includes multiple-choice questions, yes/no questions, and rating scales. Closed-ended questions are advantageous for quantitative analysis, as responses can be easily quantified and compared across respondents.
• Scaled Questions : These ask respondents to rate their agreement or disagreement with a statement or to indicate the frequency of a behavior on a predefined scale. Such questions often use Likert scales or semantic differential scales and are commonly employed in measuring attitudes, perceptions, and satisfaction levels.

When designing a survey, it’s essential to consider the specific objectives and target audience to determine the most suitable question types. Here are some considerations for selecting the appropriate format:

• Nature of Data Needed : If seeking in-depth insights and allowing respondents to express themselves freely, open-ended questions are preferable. Conversely, for quantitative analysis and ease of data processing, closed-ended questions or scaled questions may be more appropriate.
• Audience Characteristics : Consider the demographics, literacy levels, and familiarity with the topic of the target audience. Complex or ambiguous questions may lead to confusion among respondents, impacting the quality of data collected.
• Survey Length and Complexity : Balance the depth of information required with the respondents’ willingness to engage with lengthy surveys. Open-ended questions tend to increase survey completion time, while closed-ended questions offer a more streamlined experience. Read about optimal survey length in our blog post How Long Should Your Survey Be?
• Data Analysis Requirements : Think ahead to the analysis phase and consider how you will process and interpret the collected data. Closed-ended questions facilitate quantitative analysis and statistical comparisons, whereas open-ended questions require qualitative coding and interpretation.

Exploring different question types

Let’s take a closer look at some popular question types and their applications.

These present respondents with a set of predetermined choices, enabling them to choose the most suitable option. Multiple-choice questions are versatile and suitable for a wide range of topics, from demographic information to preferences and opinions.

• Example Question : “Which of the following factors influenced your decision to purchase our product? (Select all that apply).”
• Product quality
• Brand reputation
• Customer reviews
• Features and specifications

Simple yet effective, yes/no questions require respondents to choose between two binary options. They are helpful for straightforward inquiries and can help filter respondents based on specific criteria.

• Example Question : “Have you used our mobile app in the past month?”

Rating-scale questions require respondents to rate their agreement, satisfaction, or frequency of behavior on a numerical or ordinal scale. Likert scales, ranging from “strongly disagree” to “strongly agree,” are commonly used in measuring attitudes and opinions.

• Example Question : “Please rate your satisfaction with our customer service.”
• Very Dissatisfied
• Dissatisfied
• Very Satisfied

In rank order survey questions, respondents are asked to prioritize items on a list according to their preference or importance. These questions are valuable for understanding preferences, priorities, and decision-making processes.

• Example Question : “Please rank the following factors in order of importance when choosing a restaurant.”
• Food quality

These gather basic information about respondents, such as age, gender, income, education level, and location. Demographic data is valuable for segmenting survey results and understanding the traits of the target audience. Read more about demographic survey questions in our blog post .

• Example Question : “What is your age group?”

These measure respondents’ agreement or disagreement with a statement on a scale typically ranging from “strongly disagree” to “strongly agree.” Likert scale questions allow for nuanced responses and are commonly used in measuring attitudes and perceptions.

• Example Question : “Please indicate your level of agreement with the following statement: ‘The customer service representatives were helpful and knowledgeable.’”
• Strongly Disagree
• Strongly Agree

Similar to Likert scales, semantic differential scales measure respondents’ attitudes or perceptions by asking them to rate concepts on opposite poles of a scale.

• Example Question : “Please rate the product’s quality on the following scale”
• Poor [ ] Excellent

These present multiple related questions or statements in a grid format, allowing respondents to provide one answer for each row or column. This format is efficient for collecting data on multiple aspects of a single topic.

• Example Question : “Please rate the following attributes of our product.”
• Quality [ ] [ ] [ ] [ ]
• Price [ ] [ ] [ ] [ ]
• Design [ ] [ ] [ ] [ ]
• Customer Service [ ] [ ] [ ] [ ]

These ask respondents to prioritize items or options in order of preference or importance. This format helps understand relative preferences and determine the most preferred options.

• Example Question : “Please rank the following features in order of importance to you.”
• Price competitiveness
• Customer service

These allow respondents to provide free-form responses, expressing their thoughts, opinions, or suggestions in their own words. This format captures qualitative data and can uncover insights not captured by predefined answer options.

• Example Question : “Please share any additional feedback or suggestions you have regarding our product or service.”

These allow respondents to select more than one answer option from a list of choices. Multiple-choice survey questions are a good choice when respondents may have multiple relevant answers or preferences.

• Example Question : “Which of the following social media platforms do you use regularly? (Select all that apply)”

These offer two response options, typically “yes” or “no.” They are straightforward and suitable for inquiries with dichotomous choices.

• Example Question : “Have you purchased our product in the last six months?”

These are crucial in survey design, particularly in capturing nuanced differences in respondents’ perceptions and preferences. Unlike nominal questions, which categorize responses without any inherent order, ordinal questions introduce a level of hierarchy or ranking to the responses.

For instance, consider a customer satisfaction survey that asks respondents to rate their experience on a scale from “very dissatisfied” to “very satisfied.” By structuring the responses in an ordinal manner, the survey can capture varying degrees of satisfaction and identify improvement areas based on the responses’ distribution.

Types of customer satisfaction surveys

Customer satisfaction surveys are instrumental in gauging the experiences of consumers and identifying areas for enhancement. Different types of survey questions can be employed in customer satisfaction surveys to gather comprehensive feedback:

• Overall Satisfaction : Utilize rating scale questions to assess overall satisfaction levels with products, services, or experiences.
• Specific Feedback : Incorporate open-ended questions to allow customers to provide detailed feedback on particular aspects of their experience, such as customer service interactions or product features.
• Net Promoter Score (NPS) : NPS surveys typically include a single question asking respondents how likely they are to recommend the product or service to others, followed by an open-ended question soliciting reasons for their rating.

In conclusion, survey question types are pivotal in shaping the effectiveness and reliability of data collection efforts. By understanding the nuances of different question formats and aligning them with survey objectives, researchers can maximize the utility of survey data to inform decision-making and drive improvements.

Whether employing open-ended questions to uncover qualitative insights or leveraging closed-ended questions for quantitative analysis, thoughtful consideration of question types is essential for crafting surveys that yield meaningful results.

By selecting the right question formats and tailoring them to the audience’s needs, survey creators can unlock their full potential to gather insights and propel organizational success.

Ready to start crafting questionnaires? Explore our different survey templates, examples, and questions . Sign up today and create your online survey with SurveyPlanet, harnessing the power of diverse question types to gather actionable insights!

• Open access
• Published: 20 April 2024

“I am in favour of organ donation, but I feel you should opt-in”—qualitative analysis of the #options 2020 survey free-text responses from NHS staff toward opt-out organ donation legislation in England

• Natalie L. Clark 1 ,
• Dorothy Coe 2 ,
• Natasha Newell 3 ,
• Mark N. A. Jones 4 ,
• Matthew Robb 4 ,
• David Reaich 1 &
• Caroline Wroe 2  

BMC Medical Ethics volume  25 , Article number:  47 ( 2024 ) Cite this article

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In May 2020, England moved to an opt-out organ donation system, meaning adults are presumed to be an organ donor unless within an excluded group or have opted-out. This change aims to improve organ donation rates following brain or circulatory death. Healthcare staff in the UK are supportive of organ donation, however, both healthcare staff and the public have raised concerns and ethical issues regarding the change. The #options survey was completed by NHS organisations with the aim of understanding awareness and support of the change. This paper analyses the free-text responses from the survey.

The #options survey was registered as a National Institute of Health Research (NIHR) portfolio trial [IRAS 275992] 14 February 2020, and was completed between July and December 2020 across NHS organisations in the North-East and North Cumbria, and North Thames. The survey contained 16 questions of which three were free-text, covering reasons against, additional information required and family discussions. The responses to these questions were thematically analysed.

The #options survey received 5789 responses from NHS staff with 1404 individuals leaving 1657 free-text responses for analysis. The family discussion question elicited the largest number of responses (66%), followed by those against the legislation (19%), and those requiring more information (15%). Analysis revealed six main themes with 22 sub-themes.

Conclusions

The overall #options survey indicated NHS staff are supportive of the legislative change. Analysis of the free-text responses indicates that the views of the NHS staff who are against the change reflect the reasons, misconceptions, and misunderstandings of the public. Additional concerns included the rationale for the change, informed decision making, easy access to information and information regarding organ donation processes. Educational materials and interventions need to be developed for NHS staff to address the concepts of autonomy and consent, organ donation processes, and promote family conversations. Wider public awareness campaigns should continue to promote the positives and refute the negatives thus reducing misconceptions and misunderstandings.

Trial registration

National Institute of Health Research (NIHR) [IRAS 275992].

Peer Review reports

In England May 2020, Max and Kiera’s Law, also known as the Organ Donation (Deemed Consent) Bill, came into effect [ 1 , 2 ]. This means adults in England are now presumed to have agreed to deceased organ donation unless they are within an excluded group, have actively recorded their decision to opt-out of organ donation on the organ donor register (ODR), or nominated an individual to make the decision on their behalf [ 1 , 2 ]. The rationale for the legislative change is to improve the organ donation rates and reduce the shortage of organs available to donate following brain or circulatory death within the UK [ 2 , 3 , 4 ]. This is particularly important considering the growing number of patients awaiting a transplant. Almost 7000 patients were waiting in the UK at the end of March 2023 [ 5 ]. Wales was the first to make the legislative change in December 2015, followed by Scotland in March 2021 and lastly Northern Ireland in June 2023 [ 2 ]. Following the change in Wales, consent rates had increased from 58% in 2015/16 to 77% in 2018/19 [ 6 ], suggesting the opt-out system can significantly increase consent, though it further suggests that it might take a few years to fully appreciate the impact [ 7 , 8 ]. Spain, for example, has had an opt-out legislation since 1979 with increases in organ donation seen 10 years later [ 9 ].

Research, however, has raised concerns from both the public and healthcare staff regarding the move to an opt-out system. These concerns predominantly relate to a loss of freedom and individual choice [ 9 , 10 ], as well as an increased perception of state ownership of organs [ 10 , 11 , 12 ] after death. Healthcare staff additionally fear of a loss of trust and a damaged relationship with their patients [ 9 , 11 ]. These concerns are frequently linked to emotional and attitudinal barriers towards organ donation, understanding and acceptance [ 9 ]. Four often referenced barriers include (1) jinx factor: superstitious beliefs [ 13 , 14 , 15 ]; (2) ick factor: feelings of disgust related to donating [ 13 , 14 , 15 ]; (3) bodily integrity: body must remain intact [ 13 , 14 , 15 ]; (4) medical mistrust: believing doctors will not save the life of someone on the ODR [ 13 , 14 , 15 ]. The latter barrier is mostly reported by the general public in countries with opt-out systems [ 13 , 14 , 16 ] although medical mistrust does feature as a barrier across all organ donation systems. In addition, it is a reported barrier healthcare staff believe will occur in the UK under an opt-out system [ 9 , 16 ].

Deceased donation from ethnic minority groups is low in the UK, with family consent being a predominant barrier in these groups. Consent rates are 35% for ethnic minority eligible donors compared to 65% for white eligible donors [ 5 ]. The reasons for declining commonly relate to being uncertain of the person’s wishes and believing it was against their religious/cultural beliefs. Healthcare staff, particularly in the intensive care setting, have expressed a lack of confidence in communication and supporting ethnic minority groups because of language barriers and differing religious/cultural beliefs to their own [ 17 ]. However, one study has highlighted that generally all religious groups are in favour of organ donation with respect to certain rules and processes. Therefore, increasing knowledge amongst healthcare staff of differing religious beliefs would improve communication and help to sensitively support families during this difficult time [ 18 , 19 ]. However, individually and combined, the attitudinal barriers, concerns towards an opt-out system, and lack of understanding about ethnic minority groups, can have a significant impact within a soft opt-out system whereby the family are still approached about donation and can veto if they wish [ 11 , 12 , 20 ].

The #options survey [ 21 ] was completed online by healthcare staff from National Health Service (NHS) organisations in North-East and North Cumbria (NENC) and North Thames. The aim was to gain an understanding of the awareness and support to the change in legislation. The findings of the survey suggested that NHS staff are more aware, supportive, and proactive about organ donation than the general public, including NHS staff from religious and ethnic minority groups. However, there were still a number who express direct opposition to the change in legislation due to personal choice, views surrounding autonomy, misconceptions or lack of information. This paper will focus on the qualitative analysis of free-text responses to three questions included in the #options survey. It aims to explore the reasons for being against the legislation, what additional information they require to make a decision, and why had they not discussed their organ donation decision with their family. It will further explore a subset analysis of place of work, ethnicity, and misconceptions. The findings will aid suggestions for future educational and engagement work.

Design, sample and setting

The #options survey was approved as a clinical research study through the integrated research application system (IRAS) and registered as a National Institute of Health Research (NIHR) portfolio trial [IRAS 275992]. The survey was based on a previously used public survey [ 22 ] and peer reviewed by NHS Blood and Transplant (NHSBT). The free-text responses used in #options were an addition to the closed questions used in both the #options and the public survey. Due to the COVID-19 pandemic, the start of the survey was delayed by 4 months, opening for responses between July to December 2020. All NHS organisations in the NENC and North Thames were invited to take part. Those that accepted invitations were supplied with a communication package to distribute to their staff. All respondents voluntarily confirmed their agreement to participate in the survey at the beginning. The COnsolidated criteria for REporting Qualitative research (COREQ) checklist was used to guide analysis and reporting of findings [ 23 ], see Supplementary material 1 .

Data collection and analysis

The survey contained 16 questions, including a brief description of the change in legislation. The questions consisted of demographic details (age, sex, ethnicity, religion), place of work, and if the respondent had contact with or worked in an area offering support to donors and recipients. Three of the questions filtered to a free-text response, see Supplementary material 2 . These responses were transferred to Microsoft Excel to be cleaned and thematically analysed by DC. Thematic analysis was chosen to facilitate identification of groups and patterns within large datasets [ 24 ]. Each response was read multiple times to promote familiarity and initially coded. Following coding, they were reviewed to allow areas of interest to form and derive themes and sub-themes. Additional subsets were identified and analysed to better reflect and contrast views. This included, at the request of NHSBT, the theme of ‘misconceptions’. The themes were reviewed within the team (DC, CW, NK, NC, MJ) and shared with NHSBT. Any disagreements were discussed and agreed within the team.

Overall, the #options survey received 5789 responses from NHS staff. The COVID-19 pandemic further impacted on NHS organisations from North Thames to participate, resulting in respondents predominantly being from NENC (86%). Of the respondents, 1404 individuals (24%) left 1657 free-text responses for analysis. The family discussion question elicited the largest number of responses, accounting for 66% of the responses ( n  = 1088), followed by against the legislation at 19% ( n  = 316) and more information needed at 15% ( n  = 253). The responses to the against legislation question provided the richest data as they contained the most information. Across the three questions, there were six main themes and 22 sub-themes, see Table  1 . The large number of free-text responses illustrate the multifaceted nature of individuals views with many quotes containing overlap between themes and sub-themes.

Respondent characteristics

In comparison to the whole #options survey respondents, the free-text response group contained proportionally more males (21% vs 27%), less females (78% vs 72%), and marginally more 18–24year-olds (7% vs 8%), respectively. There were 5% more 55 + year olds in the free-text group, however all other age groups were between 2–3% lower when compared to the whole group. Additionally, the free-text group were more ethnically diverse than the whole group (6.9% vs 15.4%), with all named religions also having a higher representation (3.9% vs 7.3%), respectively.

Question one: I am against the legislation – Can you help us understand why you are against this legislation?

Of the three questions, this elicited the largest number of responses from males ( n  = 94, 30%), those aged over 55 years ( n  = 103, 33%), and ethnic minority responders ( n  = 79, 25%). Subset analysis of place of employment indicates 27% were from the transplant centre ( n  = 84), 8% were from the mental health trust ( n  = 26), and 4% from the ambulance trust ( n  = 14). Thematic analysis uncovered four main themes and 12 sub-themes from the responses, with the predominant theme being a perceived loss of autonomy.

Theme one: loss of autonomy

Respondents’ reasons for a loss of autonomy were categorised into four sub-themes. Firstly, calling into question the nature of informed consent and secondly, peoples’ awareness of the legislative change. One respondent stated individuals need to be “fully aware and informed” [R2943] in order to have consented to organ donation. However, one respondent stated that they believe individuals have “not [been] informed well” [R930] and thus “if people are not aware of it, how are they making a choice on what happens to their organs” [R1166] . It was suggested that awareness of the change may have “been overshadowed by COVID” [R4119] .

Furthermore, there was concerns regarding the means to record an opt-out decision, specifically to those that are “not tech savvy” [R167] , “homeless” [R5721] , “vulnerable” [R4553] , and “elderly” [R2155] . Therefore, removing that individual’s right to record their decision due to being at a disadvantage.

Finally, respondents expressed concerns of a move to an authoritarian model of State ownership of organs. This elicited strong, negative reactions from individuals under the belief the State would own and “harvest” a person’s organs under a deemed consent approach, with some removing themselves as a donor consequently, “I am furious that the Government has decided that my organs are theirs to assign. It is MY gift to give, not theirs. I have now removed myself as a long-standing organ donor.” [R593] .

Theme two: consequences

Following respondents stating their reason for being against the legislative change, they discussed further what they believed to be the consequences of an opt-out legislation, with a focus on trust. Respondents cited a lack of trust towards the system, “I have no Trust in the UK government” [R5374] , with some surprisingly citing a lack of trust towards healthcare professionals, “Don’t trust doctors in regard to organ donation” [R3010], as well as a fear of eroding trust with the general public, “This brings the NHS Organ Donation directly into dispute with the public.” [R1237]. Respondents additionally believed the legislative change would lead to an increase in mistakes i.e., organ’s being removed against a person’s wishes by presuming, “not convinced that errors won't be made in my notifying my objection and that this won't be dealt with or handed over correctly” [R3018]. Finally, it is believed this change would also lead to, “additional upset” [R587], for already grieving families.

Theme three: legislation

Respondents were additionally against the legislation itself as they believed it lacked an evidence-base to prove it is successful at increasing the numbers of organs donated. As well as this, respondents perceived the legislation as one that removed the donor’s choice as to which organs they want to donate, some with a religious attribute “I don't mind donating but would like choice of what I like to i.e., not my cornea as for after life I want to see where I am going.” [R5274].

Theme four: religion and culture

Religion and culture was another common theme with sub-themes relating to maintaining bodily integrity following death and the lack of clarity around the definition of brain death. Many others stated that organ donation is against their religion or, were “unsure whether organ donation is permissible” [R1067].

Question two: I need more information to decide—What information would you like to help you decide?

This question elicited the most responses from females ( n  = 188, 74%), those aged over 55 years ( n  = 80, 32%), with 19% being from ethnic minority groups ( n  = 49). Subset analysis of place of employment indicates 18% were from the transplant centre ( n  = 46), 8% were from the mental health trust ( n  = 18), and 9% from the ambulance trust ( n  = 23). Thematic analysis uncovered a main theme of “everything” . There were many responses that did not specify what information was required, but indicated that more general information on organ donation was required, within this there were five sub-themes.

Sub-themes:

The first sub-theme identified a request for information around the influence a family has on the decision to donate and the information that will be provided to families. This included providing “emotional wellbeing” [R162] support, and information on whether families can “appeal against the decision” [R539] or “be consulted” [R923] following their loved one’s death. This was mainly requested by those employed by transplant centres.

The second request was for information on the “process involved after death for organ retrieval” [R171] , predominantly by ethnic minority groups and those employed by the mental health trusts, with specific requests on confirming eligibility. Other examples of requested information on the process and pathway included “how the organs will be used” [R1086] , “what will be donated” [R1629] , and “who benefits from them” [R3730] .

The third request was information regarding the publicity strategy to raise awareness of the legislative change. Many of the respondents stated they did not think there was enough “coverage in the media” [R3668]. Additional considerations of public dissemination were to ensure it was an “ easy read update” [R137 3 ] , specifically for “the elderly or those with poor understanding of English who may struggled with the process” [R1676] .

The fourth request was information around the systems in place to record a decision. There were additional requests for the opt-out processes if someone was within the excluded group and “what safeguards are in place” [R3777], as well as what if individuals change their mind and the ease of recording this new decision.

Finally, and similarly to the first question, the fifth request was information for an evidence-base. Respondents stated that they, “would like to know the reasons behind this change” [R3965] , believing that if they had a greater understanding then this might increase their support towards the legislative change.

Question three: Have you discussed your decision with a family member? If no, can you help us understand what has stopped you discussing this with your family?

The free-text responses to analyse were from those who responded “No” to, “Have you discussed your decision with a family member?”. This received 1430 responses with females ( n  = 1025, 27%) predominantly answering “No”. However, not everyone left a free-text response, leaving 1088 comments for analysis. These were predominantly made by those aged over 55 years ( n  = 268, 24%), with 5% being from ethnic minority groups ( n  = 49). Subset analysis of the 1088 responses regarding place of employment indicated 14% were from the transplant centre ( n  = 147), 7% were from the mental health trust ( n  = 78), and 9% from the ambulance trust ( n  = 96). The analysis uncovered a main theme of priority and relevance made up of five sub-themes.

The first sub-theme identified one reason to be that it was their “individual decision” [R3] and there would be “nothing to be gained” [R248] from a discussion. Some respondents stated that despite a lack of discussion, their family members would assume their wishes in relation to organ donation and support these, “I imagine they are all of the same mindset” [R4470]. However, some stated their reasons to be because they “don’t have a family” [R1127] to discuss this with or have “young ones whose understanding is limited about organ donation” [R356] . Positively, there were several respondents who suggested the question had acted as a prompt to speak to their family.

Another reason stated by respondents was that they found the topic to be too difficult to discuss due to “recent bereavements” [R444], “current environment” [R441] , and “a reluctance to address death” [R4486] . As evident in the latter quote, many respondents viewed discussions around death and dying as a “taboo subject” [R3285] , increasing the avoidance of having such conversations.

Finally, the fifth reason was that several respondents “had not made any decision yet” [R2478] . One respondent wanted to ensure they had reviewed all available information before deciding and having a well-informed discussion with them.

Misconceptions

A further subset analysis of responses coded as misconceptions was reviewed at the request of NHSBT, with interest in whether these occurred from healthcare staff working with donors and recipients. Misconceptions were identified across the three questions, with misconceptions accounting for 24% of the responses to the against the legislation question. Responses used emotive, powerful words with suggestions of State ownership of organs, abuse of the system to procure organs, change in treatment of donors to hasten death, religious and cultural objections, and recipient worthiness.

I worked in organ retrieval theatre during my career and I was uncomfortable with the way the operations were performed during this period. Although the 'brain dead' tests had been completed prior to the operation the vital signs of the patient often reflected that the patient was responding to painful stimuli. Sometimes the patient was not given the usual analgesia that is often given during routine operations. This made me rethink organ donation therefore I am uncomfortable with this. I always carried a donation card prior to my experience but subsequently would not wish to donate. This may be a personal feeling but that is my experience. [R660].

I think that this is a choice that should be left to individuals and families to make. After many years in nursing lots of it spent with transplant patients not all recipients embrace a 'healthy lifestyle' post-transplant with many going back to old lifestyle choices which made a transplant necessary in the first place. [R867].

Additional comments suggested certain medical conditions and advancing age precludes donation and that the ability to choose which organs to donate had been removed.

Most of them will be of no use as I have had a heart attack, I smoke and have Type 2 diabetes. [R595]

Further analysis indicated that 27% ( n  = 24) of these comments were made by individuals who worked with or in an area that supported donors and recipients.

In summary, this qualitative paper has evidenced that the ability to make an autonomous informed decision is foremost in the respondent’s thoughts regarding an opt-out system. This has been commonly cited as a reason throughout the literature by those against an opt-out system [ 9 , 10 , 25 , 26 ]. The loss of that ability was the primary reason for respondents being against the change in legislation with the notion that the decision is a personal choice cited as a reason for lack of discussion with family members. Respondents stated that the ability to make autonomous decisions needs to be adequately supported by evidence-based information that is accessible to all. If the latter is unavailable, they expressed concern for negative consequences. This includes an increase in the perceived belief of the potential for mistakes and abuse of the system, as well as family distress and loss of trust in the donation system and the staff who work in it, as supported by previous literature [ 9 , 11 ].

Our findings further coincide with that of previous literature, highlighting views suggesting that the opt-out system is a move towards an authoritarian system, illustrating the commercialisation of organs, and a system that is open to abuse and mistakes [ 10 , 11 , 12 , 27 , 28 , 29 ]. Healthcare staff require reassurance that the population, specifically the hard-to-reach groups like the elderly and homeless, have access to information and systems in order to be able to make an informed decision [ 30 , 31 ]. Whilst the findings from the overall #options survey demonstrated awareness is higher in NHS staff, there was a significant narrative in the free-text response regarding a lack of awareness and a concern the general public must also lack the same awareness of the system change. Some responses also reflected medical mistrust concerns of the general public [ 13 , 14 , 16 ] as well as expressing a fear of losing trust with the public [ 9 , 11 , 16 ], as found within previous work. Additional research articles raising awareness of the opt-out system in England suggest that despite publicising the change with carefully crafted positive messaging, negative views and attitudes are likely to influence interpretation leading to an increase in misinformation [ 28 ]. Targeted, evidence-based interventions and campaigns that address misinformation, particularly in sub-groups like ethnic minorities, is likely to provide reassurance to NHS staff and the general public, as well as providing reliable resources of information [ 28 ].

Respondents also requested more detailed information about the process of organ donation. The disparity of information and the knowledge of the processes of donation includes eligibility criteria, perceived religious and cultural exclusions, practical processes of brain and circulatory death, and subsequent organ retrieval. As well as, most importantly, more information on the care provided to the donor before and after the donation procedure. The gap of available factual knowledge is instead filled by misconceptions and misunderstandings which is perpetuated until new information and knowledge is acquired. It may also be attributed to the increased awareness of ethical and regulatory processes. These attitudes and views illustrate the complexity of opinions associated with religion, culture, medical mistrust, and ignorance of the donation processes [ 11 , 15 , 32 ]. There is evidently a need for healthcare staff to display openness and transparency about the processes of organ donation and how this is completed, particularly with the donor’s family. It further reinforces the need to increase the knowledge of differing religious and cultural beliefs to support conversations with families [ 18 , 19 ].

Both healthcare staff and the public would benefit from educational materials and interventions to address attitudes towards organ donation [ 19 , 28 , 33 ]. This would assist in correcting misconceptions and misunderstandings held by NHS staff, specifically those who support and work with organ donors and recipients. Previous work illustrates support for donation being higher in intensivists, recommending educational programmes to increase awareness across all healthcare staff [ 34 ]. The quantitative and qualitative findings of the #options survey would support this recommendation, adding that interventions need to be delivered by those working within organ donation and transplantation. This would build on the community work being conducted by NHSBT, hopefully leading NHS staff to become transplant ambassadors within their local communities.

A further finding was that of confusion and misunderstanding surrounding the role of the family, a finding also supported by the literature [ 11 ]. It was suggested that family distress would be heightened, and families would override the premise of opt-out. Literature also supports this could be further impacted if the family holds negative attitudes towards organ donation [ 20 ]. The uncertainty of the donors’ wishes was the most common reason for refusing from ethnic minority groups [ 35 ], further highlighting the need for family discussions. Without this, families feel they are left with no prior indication so they opt-out as a precaution. Making an opt-in decision known can aid the grieving process as the family takes comfort in knowing they are fulfilling the donors wishes [ 26 ] and reduces the likelihood of refusal due to uncertainty about their wishes [ 36 ]. The ambiguity around the role of the family, coupled with not explicitly stating a choice via the organ donor register or discussions with family can make it problematic for next of kin and NHS staff.

Limitations

It is acknowledged that the findings of this study could have been influenced by the COVID-19 pandemic beyond the changes to the research delivery plan including a shift in critical care priorities, initial increase of false information circulating social media, delayed specialist nurse training, and removal of planned public campaigns [ 37 , 38 ]. The degree of the impact is unknown and supports the view that ongoing research into healthcare staff attitudes is required. Additionally, the survey did not collect job titles and is therefore limited to combining all healthcare staff responses. It is understood not all staff, such as those working in mental health, would know in depth details of organ donation and legislation, but it is expected that their level of knowledge would be greater than that of the general public.

The quantitative analysis [ 21 ] of the #options survey showed that overall NHS staff are well informed and more supportive of the change in legislation when compared to the general public. This qualitative analysis of the free-text responses provides a greater insight into the views of the healthcare staff who against the change. The reasons given reflect the known misconceptions and misunderstandings held by the general public and evidenced within the literature [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. There are further concerns about the rationale for the change, the nature of the informed decision making, ease of access to information including information regarding organ donation processes. We therefore propose that educational materials and interventions for NHS staff are developed to address the concepts of autonomy and consent, are transparent about organ donation processes, and address the need for conversations with family. Regarding the wider public awareness campaigns, there is a continued need to promote the positives and refute the negatives to fill the knowledge gap with evidence-based information [ 39 ] and reduce misconceptions and misunderstandings.

Availability of data and materials

The datasets analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Coronavirus Disease 2019

Integrated research application system

North-East and North Cumbria

• National Health Service

National Health Service Blood and Transplant

National Institute of Health Research

Organ donor register

United Kingdom

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Acknowledgements

With thanks to the NHSBT legislation implementation team for peer review of the questionnaire and the Kantar population survey data.

Funding for the project was gained from the Northern Counties Kidney Research Fund. Grant number 16.01.

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NC, DC, and CW were responsible for the drafting and revising of the manuscript. NN, MJ, MR, DR, and CW were responsible for the design of the study. DC completed the qualitative analysis. NC, DC, NN, MJ, MR, DR, and CW read and approved the final manuscript.

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The research was carried out in accordance with the Declaration of Helsinki. The study was reviewed and approved by a Health Research Authority (HRA) and Health and Care Research Wales (HCRW) [REC reference: 20/HRA/0150] via the integrated research application system (IRAS) and registered as a National Institute of Health Research (NIHR) portfolio trial [IRAS 275992]. Informed Consent was obtained from all the participants and/or their legal guardians.

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Clark, N.L., Coe, D., Newell, N. et al. “I am in favour of organ donation, but I feel you should opt-in”—qualitative analysis of the #options 2020 survey free-text responses from NHS staff toward opt-out organ donation legislation in England. BMC Med Ethics 25 , 47 (2024). https://doi.org/10.1186/s12910-024-01048-6

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• Volume 33, Issue 5
• Quality framework for remote antenatal care: qualitative study with women, healthcare professionals and system-level stakeholders
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• http://orcid.org/0000-0002-6082-3151 Lisa Hinton 1 ,
• Francesca H Dakin 2 ,
• Karolina Kuberska 1 ,
• Nicola Boydell 3 ,
• Janet Willars 4 ,
• Tim Draycott 5 ,
• Cathy Winter 6 ,
• Richard J McManus 2 ,
• Lucy C Chappell 7 ,
• Sanhita Chakrabarti 8 ,
• Elizabeth Howland 9 ,
• Jenny George 10 ,
• Brandi Leach 10 ,
• Mary Dixon-Woods 1
• 1 THIS Institute (The Healthcare Improvement Studies), Department of Public Health and Primary Care , University of Cambridge , Cambridge , UK
• 2 Nuffield Department of Primary Health Care Sciences , Oxford University , Oxford , UK
• 3 Usher Institute , University of Edinburgh , Edinburgh , UK
• 4 Department of Health Sciences , University of Leicester , Leicester , UK
• 5 Royal College of Obstetricians and Gynaecologists , London , UK
• 6 PROMPT Maternity Foundation , Bristol , UK
• 7 Maternal and Fetal Research Unit Division of Women's Health , St Thomas’ Hospital , London , UK
• 8 NHS Bedfordshire Clinical Commissioning Group , Bedford , Bedfordshire , UK
• 9 University Hospitals Birmingham NHS Foundation Trust , Birmingham , UK
• 10 RAND Europe , Cambridge , Cambridgeshire , UK
• Correspondence to Dr Lisa Hinton, THIS Institute (The Healthcare Improvement Studies), Department of Public Health and Primary Care, University of Cambridge, Cambridge, Cambridgeshire, UK; lisa.hinton{at}thisinstitute.cam.ac.uk

Background High-quality antenatal care is important for ensuring optimal birth outcomes and reducing risks of maternal and fetal mortality and morbidity. The COVID-19 pandemic disrupted the usual provision of antenatal care, with much care shifting to remote forms of provision. We aimed to characterise what quality would look like for remote antenatal care from the perspectives of those who use, provide and organise it.

Methods This UK-wide study involved interviews and an online survey inviting free-text responses with: those who were or had been pregnant since March 2020; maternity professionals and managers of maternity services and system-level stakeholders. Recruitment used network-based approaches, professional and community networks and purposively selected hospitals. Analysis of interview transcripts was based on the constant comparative method. Free-text survey responses were analysed using a coding framework developed by researchers.

Findings Participants included 106 pregnant women and 105 healthcare professionals and managers/stakeholders. Analysis enabled generation of a framework of the domains of quality that appear to be most relevant to stakeholders in remote antenatal care: efficiency and timeliness; effectiveness; safety; accessibility; equity and inclusion; person-centredness and choice and continuity. Participants reported that remote care was not straightforwardly positive or negative across these domains. Care that was more transactional in nature was identified as more suitable for remote modalities, but remote care was also seen as having potential to undermine important aspects of trusting relationships and continuity, to amplify or create new forms of structural inequality and to create possible risks to safety.

Conclusions This study offers a provisional framework that can help in structuring thinking, policy and practice. By outlining the range of domains relevant to remote antenatal care, this framework is likely to be of value in guiding policy, practice and research.

• healthcare quality improvement
• health services research
• obstetrics and gynecology
• qualitative research
• womens health

Data availability statement

Data are available on reasonable request. The data that support the findings of this study are available from the corresponding author on reasonable request.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjqs-2021-014329

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WHAT IS ALREADY KNOWN ON THIS TOPIC

The COVID-19 pandemic disrupted the usual provision of antenatal care, with much care shifting to remote forms of provision. Yet, research on remote antenatal care undertaken prior to 2020 is surprisingly limited.

WHAT THIS STUDY ADDS

This large UK qualitative study enabled the generation of a framework of the domains of quality that appear to be most relevant to stakeholders who use, provide and organise antenatal care remotely: efficiency and timeliness; effectiveness; safety; accessibility; equity and inclusion; person-centredness and choice and continuity.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE AND/OR POLICY

By offering a systematic way of structuring thinking about quality in remote antenatal care, this new maternity-specific framework can guide policy and practice. Our findings suggest that a hybrid model should be on offer, but one that has sufficient flexibility to accommodate the needs and priorities of different groups and that is highly sensitised to equity and inclusion.

Introduction

Accessed by around 750 000 women in the UK in 2019 alone, 1–3 antenatal care is crucial in improving birth outcomes and in reducing risks of maternal and fetal mortality and morbidity. 4–7 Traditionally delivered face-to-face, antenatal care monitors the well-being of pregnant women, promotes healthy pregnancies, discusses options for care during pregnancy, labour and birth and offers a safe space to answer questions and provide reassurance. Those under the care of England’s National Health Service are, in normal circumstances, offered 10 antenatal appointments in their first pregnancy or 7 if they have previously given birth, with care provided according to defined best practice.

Though antenatal care normally involves a defined schedule of professional consultations, the COVID-19 pandemic created powerful imperatives to reduce in-person contact as a means of infection control. From March 2020, remote antenatal consultations (receiving care via telephone or video platforms) were recommended where possible, steered by guidance that was revised and updated over time. 8 A survey of over 80 UK obstetric units conducted between May and July 2020 reported that almost 90% of the available antenatal appointments were being conducted remotely, indicating a major shift in the organisation and delivery of care. 9 Some evidence of reduced attendance at antenatal care appointments has since emerged. 10

Recent policy developments have shown an appetite to ‘lock in’ what appear to be promising solutions that were deployed during the pandemic. 11–14 Given emerging indications of increases in some adverse outcomes of pregnancy linked to the pandemic, 15 it is important that the future role, design and organisation of remote antenatal care is based on good evidence. Yet, research on the remote provision of antenatal care undertaken prior to 2020 is surprisingly limited. 16 Pre-pandemic studies reported promising findings in terms of safety and patient experience for remote antenatal care. However, these studies are typically small-scale and concerned only with low-risk women. They also tend to focus narrowly on just one component of care (e.g., gestational diabetes monitoring or blood pressure monitoring) 17 18 or to address only one aspect of quality, such as satisfaction. 19 Attention to issues of equity and inclusion has been notably weak. 20 Ethnic and socioeconomic diversity of participants has been mostly lacking in studies, 20–24 even though minority ethnicity and deprivation are strongly associated with poor pregnancy outcomes. 5 25 26

The danger is that well-intentioned enthusiasm for realising a post-pandemic vision of remote antenatal care may, as in other areas, risk unintended consequences for quality and safety including the perpetuation and amplification of inequalities. 20 27 28 Recent work has drawn attention to the need for clear principles to guide and evaluate the quality of remote care. 29 While there is no universally agreed definition of quality in health systems, it is generally recognised as a multidimensional construct. 30–32 What those dimensions look like for specific areas of care needs to be grounded in understanding of what matters to the stakeholders—those who use, provide and organise care. 15 16 33–40

In this article, we propose that this understanding is key to the design, operationalisation, delivery and evaluation of remote antenatal care. 41 We report a study that sought to use the real-world experiment of the shift to remote antenatal care during the COVID-19 pandemic in 2020–2021 to generate evidence about what quality would look like for remote antenatal care, based on the experiences and perspectives of pregnant women, the healthcare professionals who care for them and system leaders.

Participants

Between September and December 2020, we undertook a UK-based qualitative study involving three groups:

People aged 16 or over who were or had been pregnant since March 2020. All participants self-identified as women, and so for simplicity of language, we use ‘pregnant women’ and ‘women’ to describe them. 42

Healthcare professionals involved in delivering maternity care services, including community, unit-based and specialist midwives; maternity service support workers; consultant and trainee obstetricians and physicians with an interest in maternal medicine.

Managers of maternity services and system-level stakeholders, including individuals from local, regional and national maternity systems, royal colleges, charities and advocacy groups.

Recruitment

We intentionally sought diversity in terms of ethnic and socioeconomic backgrounds and geographical location and, for health professionals, a range of specialities, job roles and seniorities. Using purposive sampling, 43 we invited a subset of survey respondents to take part in the interview, aiming to identify a broad range of experiences. We also recruited using online network-based approaches, professional and organisational networks and snowball sampling. 44 45 Nine English NHS maternity units were purposively selected to increase diversity of participants and assisted in identifying participants in all three groups. Further, we recruited via organisations that support women underrepresented in research with the help of our Expert Collaborative Group as well as via professional organisations. 46 Vouchers were offered to service user participants (women) on completion of an in-depth interview. As data collection and analysis progressed in parallel, the size of the sample was adapted to ensure a variety of experiences were captured, in line with the principle of information power. 47

Ethical approval

All participants were provided with information about the study and gave consent ( online supplemental file S3 ). We followed the Standards for Reporting Qualitative Research recommendations ( online supplemental file ). 48

Supplemental material

Data collection: free-text surveys and interviews.

To comply with UK lockdown regulations, all data were collected remotely. The study comprised two elements: first, a survey inviting free-text responses and, second, remote semi-structured interviews either conducted live by an interviewer (over telephone or video platform) or asynchronously (using online prompts without an interviewer present). To enable broader participation from those without digital devices or internet availability, the survey could be completed either online or via telephone.

The survey was designed to generate qualitative data in response to free text questions and to support sampling for the interviews, to ensure we could recruit a diverse sample for in-depth interviews. Survey questions ( online supplemental file S1 ) were developed and piloted by the research team with input from the study’s Expert Collaborative Group. A Qualtrics survey was embedded in THIS Institute’s online research platform Thiscovery 49 and was open for 6 weeks between September and October 2020.

Interview topic guides ( online supplemental file S2 ) were developed following review of existing literature. They were discussed with the study's Expert Collaborative Group and clinical co-investigators and were piloted internally by members of the research team and tested on the Thiscovery platform. Interviews were completed between October and December 2020. The live interviews were conducted by one of four experienced qualitative researchers (LH, KK, FD, JW) and audio-recorded for transcription and analysis. Interviews were transcribed by a professional transcription service. Two health professionals were interviewed together in one interview. Two interviews with women involved a partner. Four interviews involved an interpreter from a community group supporting women through pregnancy in order to facilitate inclusion. Three-way telephone interviews, with the women, interpreter and researcher, were conducted with the interpreter simultaneously translating. The interviews were then full transcribed into English by a professional translator.

Data analysis

The two datasets (free text responses from the survey and interview transcripts) were analysed sequentially. Researchers at RAND Europe undertook an initial analysis of the free-text responses from the survey using a coding framework developed by researchers, with additional analysis by LH and FD to establish reliability and validity. 50 Interview analysis, supported by NVIVO, 51 was undertaken by LH, FD, KK and NB based on the constant comparative method, with a coding framework developed by LH in discussion with KK, NB and FD. 52 Analysis was adaptive, integrating thematic areas that researchers had generated with quality domains that we had identified from the literature on quality in healthcare 30–32 53 as sensitising concepts. 54 Consensus was reached through regular analysis discussions. The deductive codes were based on a literature review conducted by KK and included, for example, language and communication, practical barriers and practical benefits.

Service user, stakeholder and public involvement

A 13-member Expert Collaborative Group provided advice and guidance throughout the study. It included ‘lay’ people who were (or had recently been) pregnant, representatives from charities and healthcare professionals. Members helped frame the research question, design the study, provided feedback on study instruments, supported inclusive recruitment, provided guidance on analytic strategy and increased sensitisation at every stage to the needs and priorities of the groups under study.

In total, 211 people took part across the two elements (survey and interviews) of the study. Survey respondents (143) included 75 women who were or had recently been pregnant, 54 healthcare professionals and 14 managerial/systems-level staff ( table 1 ).

• View inline

Total study sample (created by the authors)

We conducted 90 live interviews and two asynchronous interviews with 45 women, 34 healthcare care professionals and 14 managers and system leaders. One interview involved two healthcare professionals. Six managers held dual roles as clinicians and spoke from both perspectives. Twenty-five participants took part in both survey and interview. The duration of the interviews ranged from 24 to 164 min. Both survey and interview responses reflected a broad geographical spread across the United Kingdom (UK). We collected data on ethnicity for women only. Those who responded to the survey largely reflected makeup of the population of theUK by ethnic group. The interview sample for women ( table 1 ) had greater representation from ethnic minority groups compared with the distribution in the UK population. 55

In both survey responses and interviews, a widespread shift to remote antenatal care was described. Women reported receiving much of their antenatal care remotely via telephone or video platforms, and healthcare professionals reported providing care remotely from their clinical base or working from home, suggesting that their views were directly informed by experience. Our analysis enabled generation of a framework ( table 2 ) of the domains of quality that appear to be most relevant to the key stakeholders in antenatal care: efficiency and timeliness; effectiveness; safety; accessibility; equity and inclusion; person-centredness and choice and continuity. Table 3 reports this framework with supporting quotations for the analysis we present below.

Framework for quality in remote antenatal care (created by the authors)

Study findings on quality domains for remote antenatal care

Impacts of remote care on equity

Efficiency and timeliness

Efficient and timely care—avoiding wasted effort, waits and delays—is an important domain of quality for remote antenatal care that was identified across all participant groups. Potential efficiency advantages of remote care reported by women included saving time, stress, travel expenses and needing to take time off work or arranging childcare. Healthcare professionals suggested that remote consultations had the potential to be more time-efficient and allow increased flexibility, under optimal conditions. Among the aspects of care that were considered by participants to have potential for the efficiency gains associated with remote care were the form-filling components of the initial antenatal ‘booking’ appointment, discussions about birth after a previous caesarean section and conversations about induction of labour.

Participants also reported, however, that achieving the potential for efficiency through remote care was not straightforward. Digital infrastructure (compatible hardware, software and connectivity) was critical, but varied across different organisations. Some healthcare professionals had well-integrated electronic records that they were comfortable navigating, but others operated heterogeneous systems, where information was easily lost or hard to access. Remote consultations were often frustrated by inconsistently or incompletely digitised records/notes and incompatibilities between different care providers’ record systems. Women and healthcare professionals reported technical issues affecting remote consultations including difficulties getting through, dropped calls and inability to use video.

Professionals and managers emphasised that remote care often generated hidden work that increased workload, describing many challenges in organising remote appointment lists and keeping to schedule. Women also found that the organisation of remote care was inefficient and inconvenient. They reported often being offered an extended timeslot during which they might expect a call rather than a specific appointment time, but this led to missed or late appointments because they could not be reliably available throughout. Rescheduling appointments resulted in invisible work or compensatory labour for healthcare professionals, 56 including rework, extra steps or additional complexity, and for women it added to the burden of treatment. 57 Further, though remote care enabled faster throughput of appointments and thus apparent efficiency gains, women often described their appointments as feeling rushed. Crucially, healthcare professionals emphasised that providing care remotely resulted in the loss of shared professional spaces that are fundamental to teamwork, communication, cooperation and positive working relationships, resulting in potential efficiency and safety challenges.

Effectiveness

Effectiveness describes care that is based on high-quality evidence. 31 Participants expressed concern about whether remote care was as effective in achieving the same outcomes of antenatal care as in-person care. Some participants suggested that remote provision might improve effectiveness of some forms of care, for example, by enabling women to participate more actively in their own care through self-monitoring of blood pressure or blood glucose. However, there was consensus across all participant groups, from system level stakeholders and healthcare professionals through to women themselves, that there is not yet enough evidence available to demonstrate whether remote care has similar, better or worse effectiveness in achieving good outcomes of pregnancy compared with in-person models. Also clear is that effectiveness might vary according to outcome, including clinical outcomes such as live births and normal birth weights, or participant-reported outcomes such as user experience.

Ensuring safety—which can be defined as preventing or reducing risks of avoidable harm 58 —was consistently identified by participants as a key goal of antenatal care. An immediate safety benefit of remote care was that it reduced risks of COVID-19 transmission. However, participants were not always confident that remote antenatal care was reliably safe. In particular, concerns were raised as to whether remote care was as safe as in-person care, given the risk of missing physical and other signs.

Further concerns arose when harm was broadly defined beyond narrow clinical parameters. For example, healthcare professionals reported concerns that remote care suppressed opportunities for women to raise concerns, including those relating to domestic violence or abuse, previous trauma or to flag up complex social issues. When providing care remotely, even with video, professionals’ view of the room, and who was in it, was restricted. They felt that remote care was likely to have adverse impacts on women’s trust of professionals, particularly if continuity of care was low.

Healthcare professionals were concerned about what was missed through remote care, including touch, and picking up on vital visual and non-verbal cues and clues as to the pregnant woman’s physical and mental well-being. Women reported they felt brushed over and found it harder to raise concerns. For those experiencing or at risk of domestic abuse, telephone appointments removed the safe space of face-to-face consultations and obscured many cues that midwives or other healthcare professionals would be able to spot in person. Other vulnerable groups identified included those with previous trauma or learning difficulties or those for whom remote care could increase social isolation.

Accessibility

Accessibility describes the ease with which care can be reached without barriers to service use. 59 60 Remote antenatal care was seen by participants as offering some advantages in increasing accessibility, for example, by expanding the ways care could be provided, reducing challenges to access associated with location and travel and offering opportunities for additional contacts between appointments. Examples of better accessibility cited by participants included provision of perinatal mental support and facilitating consultations requiring multidisciplinary teams or specialist obstetricians working at different hospitals. Some participants were also very positive about new modes of communication (e.g., mobile telephone, email, social media and apps) and digital resources (e.g., hospital trust webpages, videos and podcasts) that they saw as improving accessibility to information and support.

Again, however, remote care was not straightforward in its impacts on accessibility. Care that was more transactional in nature, such as information exchange during the initial antenatal ‘booking’ appointment, was identified by participants as increasing in accessibility when offered remotely. But action that relied on relational care or continuity, such as raising concerns or safeguarding, became less accessible.

Importantly, the resource requirements for delivering and engaging with remote care were cited by participants as a major influence on accessibility. All forms of remote contact assumed access to a quiet, private space. This was often difficult or impossible for women and not always straightforward for healthcare professionals either. Telephone calls required women to have a device, a phone signal and enough credit and charge on the phone. Video calls required clinicians and women to have access to a video-consulting platform, a stable internet connection and internet-enabled device and to be able to use them reliably. Remote care also relied on individuals having the skills and language competence to participate in remote consultations or information provision and to share in the sociocultural expectations of NHS-provided maternity care.

Equity and inclusion

Equitable care is care that does not vary in quality or accessibility because of personal characteristics such as sex/gender, ethnicity, geography or socioeconomic status. 31 We identified major concerns in relation to equity of remote care, detailed in table 4 . Participants reported that remote care worked very well for digitally enabled and health-literate women who were confident in what to expect from their care, and for women who had pre-existing relationships with health professionals. However, all participant groups raised concerns about the potential for remote care to further disadvantage some groups and to risk amplifying existing structural inequalities.

Groups identified as especially vulnerable included those who were digitally excluded through lack of internet access or the hardware to connect and/or had low levels of digital literacy, a low base level of oracy and literacy in English language and challenges in reading instructions, inputting data and communicating effectively. Participants reported concerns that particular social, cultural and economic risk factors, often associated with communities at risk of marginalisation, could lead to inequality of access and other forms of exclusion.

Person-centredness

Person-centred care can be understood as care that is respectful of and responsive to individual needs, preferences and values, taking into account the preferences and aspirations of individuals and the culture of their community. 53 All participant groups reported that establishing and maintaining the relationships and trust necessary to achieve person-centred care was much harder to do remotely. The remote appointments that worked best were those that were largely transactional and protocol-driven in character. Such consultations were typically those that did not rely too much on non-verbal cues, for example, providing uncomplicated information or results without negative implications, or routine recording of blood pressure/glucose levels. However, women and health professionals emphasised that these ‘content-focused’ consultations were only one small part of antenatal care, or one part of a wider antenatal appointment.

Women often described a lack of rapport and reassurance associated with remote care. Because appointments were experienced as shorter and more transactional than therapeutic, women reported that they felt like a ‘tick-box exercise’ focused on the clinical aspects of care at the expense of the relational. They found it harder to raise concerns about symptoms or mental health issues. Healthcare professionals similarly worried that women found it harder to open up about what mattered to them. They reported that it was particularly challenging for women who did not speak sufficient English to follow rapid exchanges.

Choice and continuity

Responsiveness to individual choices and preferences is an important feature of quality of care. 31 A particularly important preference for healthcare professionals and women alike was for relational continuity, which they saw as underpinning trusting relationships in antenatal care. 61 62 Both choice and continuity were reported to have been adversely impacted by the turbulence of the pandemic. Some women reported that they felt lost in the system and unable to make choices about their care. Participants agreed that one size does not fit all, and that ideally women would be offered a blend and choice of care mode (for example between remote and in-person care), through shared decision-making. They proposed that choice should be supported by information about different pathways. They also emphasised the importance of clear guidance for healthcare professionals for risk assessments to consider the woman’s medical, social and cultural histories, some of which would only revealed through trusting relationships over time.

Main findings

The COVID-19 pandemic has led to a new era of remote care, but the principles that should inform its development remain underdeveloped. 29 Given the enthusiasm for retaining aspects of remote antenatal care postpandemic, it is important that policy and practice are guided by clarity about ‘what good looks like’. 11 12 Evidence in other clinical fields has mostly focused on consultations and on aspects of experience of care. 19–23 63–65 Our study suggests that remote care needs to be understood as a whole system—of which consultations are just one part—and that a much broader conceptualisation of the relevant dimensions of care along entire pathways is needed. This large qualitative study of the views and experiences of women, healthcare professionals and system-level stakeholders has generated a framework ( table 2 ) that identifies relevant dimensions of quality and standards for remote antenatal care. The dimensions identified in our analysis map closely onto existing frameworks for quality in health systems, including the Institute of Medicine framework, 41 with the additional dimension of Choice and Continuity. The similarity between the two offers some confidence in the validity of the findings. By offering a systematic way of structuring thinking about quality in remote antenatal care, this new maternity-specific framework can guide policy and practice.

Our findings suggest that there are both advantages and disadvantages of remote care across each of the domains. Although participants valued the potential convenience and flexibility offered by remote care, what may appear to be efficiency gains may also involve hidden burdens leading to invisible work and compensatory labour. 56 66 Permeating women’s accounts were concerns about safety, effectiveness and person-centredness, linked to the risk that absence of in-person contact might undermine the quality of interactions and hinder safeguarding and recognition of other safety issues. The risks facing women vary and some may need antenatal care that is wholly face-to-face. There was also much concern about the potential for negative impacts of remote care on equality and inclusion, especially given disparities in digital access and variation in maternity outcomes linked to structural inequalities. 67–69 Our findings also highlight differences between modes of remote care. While telephones are often cheaper and more ubiquitous, video consultations provide visual as well as audio information. However, both telephone and video platforms are vulnerable to poor connections, and people do not always have access to the necessary hardware or subscriptions to data services. A high-quality evidence-base will need to be built to address these concerns.

In identifying that remote care should be regarded neither as a utopia nor a dystopia, our findings are suggestive of a number of recommendations for policy and practice if the potential of remote antenatal care is to be realised while the risks are mitigated. Optimising remote care for the future will require investment in high quality technology infrastructure, human resources and digital literacy skills and in codesigning pathways, work systems, workflows and processes to support efficiency and convenience for both service users and healthcare professionals. These are not solely practical considerations—they also have profound implications for structural equity. Given evidence of widespread digital poverty—a significant proportion of the UK public lacks adequate access to data infrastructures, such as broadband, connectivity and smartphones 69 —the design of remote care models will need to mitigate the risks that disproportionately affect some groups.

A particularly striking finding of our study was the emphasis across all participants on safety as a concern for remote antenatal care, including potential barriers to the role of trusting relationships and continuity 70 71 in achieving both safety and person-centred care. In foregrounding the central importance of relationships, our study emphasises that any lasting shift to remote provision will need to be highly attentive to designing care pathways so that they facilitate successful relationships between people who are pregnant and those who are caring for them. 72 73 Opportunities and mechanisms for reporting safety concerns will need to be built into these pathways 74 and should be broadly conceived. For instance, the loss of ‘communicative spaces’ for healthcare professionals to engage in debriefs, handovers and corridor conversations is likely to generate safety issues as well as impairing their experience of work. 75

Strengths and limitations

A strength of this study is its large and diverse sample that brings together of the voices of pregnant women, healthcare professionals, managers and system-level stakeholders. The remote interviewing and survey approach supported the development of an ethnically and geographically diverse sample. The remote approach, however, favoured those we could reach with our study information as well as those with the resources, capacity and time to engage and take part in the survey and/or interviews. While efforts were made to mitigate against these barriers, inevitably we have not been able to capture all perspectives. Thus, the very nature of remote research, compelled by the pandemic, may have also created a self-selecting sample of more digitally-enabled participants. It was not possible to estimate a survey response rate owing to the recruitment methods used. Further, we were unable to measure clinical outcomes or to infer causal relationships. Accordingly, this paper does not make recommendations about the role of the routine physical and mental checks that should be maintained in future antenatal pathways.

The lure of digital transformation is powerful and hard to resist, 76 77 but introducing major changes into healthcare systems is rarely straightforward 78–82 and requires a systematic approach to quality and safety. Our study offers a provisional framework that can help in structuring thinking, policy and practice and, by drawing attention to the range of domains relevant to remote antenatal care, will help support the development of a codesigned evidence-base. Our findings suggest that a hybrid model should be on offer, but one that has sufficient flexibility to accommodate the needs and priorities of different groups and that is highly sensitised to equity and inclusion. Key areas for development and testing include the extent to which transactional and relational aspects of care are interlinked, the significance of continuity as a feature of quality in remote care and outcomes and experiences of different modes of remote antenatal care.

Details of ethics approval

All participants were provided with information about the study and gave consent (see Consent form in online supplemental file S3 File (redacted)). We followed the Standards for Reporting Qualitative Research recommendations ( online supplemental file S4 ). 48

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

Ethical approval for the study was obtained from the NHS HRA West Midlands—Coventry and Warwickshire Research Ethics Committee on 22 July 2020 (20/WM/0204). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

We are grateful to the study's Expert Collaborative Group for their input and guidance. The group included: Filsan H Ali, Nicky J Lyon, Dr Christine I Ekechi, Jane Fisher, Emma M Crookes, Dr Sharon Dixon, Joyce Darko, Lia Brigante, Jane Brewin, Nadia Brobbey, Marcus E Green, Professor Sara Kenyon and Michele Upton.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
• Data supplement 2
• Data supplement 3
• Data supplement 4

X @LisaHinton4, @dakinfrancesca, @K_Kuberska, @richardjmcmanus

Contributors The study was conceived by LH, MD-W and TD. Study setup for the survey and interview phases (including planning and approvals) were led by LH and FD, with design input from KK, MD-W and coinvestigators (TD, CW, RJM, LC, SC, EH). KK conducted a literature review to ascertain the existing evidence-base for remote care. The survey was built in Qualtrics by FD with support from Thiscovery team members. Researchers at RAND Europe undertook an initial analysis of the free text responses, with additional analysis by LH and FHD. Interviews were conducted by LH, FD, KK and JW. Interview analysis was completed by LH, FD, KK and NB using a coding framework developed by LH in discussion with FHD, KK and NB. LH, FD, KK and NB met frequently during analysis to discuss the results and confirm the reliability of each researcher’s analyses before discussion with MDW, co-investigators and the Expert Collaborative Contributorship Group. MD-W is the guarantor.

Funding This project is funded by THIS Institute’s grant from the Health Foundation. The Health Foundation is an independent charity committed to bringing about better health and health care for people in the UK. Mary Dixon-Woods is an NIHR Senior Investigator (NF-SI-0617-10026). Richard McManus and Lucy Chappell are NIHR Research Professors ((NIHR-RP-R2-12-015, NIHR -RP-2014-05-019) and NIHR Senior Investigators.

Competing interests TD is Vice President of the Royal College of Obstetricians and Gynaecologists. RJM has previously received BP monitors from Omron Healthcare for research purposes and is working with them on a telemonitoring system.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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A survey is a method of gathering information using relevant questions from a sample of people with the aim of understanding populations as a whole. Surveys provide a critical source of data and insights for everyone engaged in the information economy, from businesses to media, to government and academics.

Survey software is a tool used to design, send and analyze surveys online. It’s the primary method of collecting feedback at scale whether that’s a simple questionnaire or a detailed study such as customer or employee feedback as part of a more structured experience management program. Cloud-based survey technology has revolutionized the ability to get data, quickly, from a large number of respondents by automating the process of sending out surveys across a variety of channels from websites and mobile to apps, email and even chatbots.

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How to Perform User Needs Analysis For SaaS

What’s the best way to ensure your product hits the right chord with users? A user needs analysis conducted with product analytics data lets you build products that address user pain points and exceed their expectations.

In this article, we dig deeper into what user needs analysis is and how you can get started with it.

• User needs analysis refers to using various techniques to understand users’ aspirations, goals, and pain points.
• It helps you make data-driven decisions to enhance your product and keep up with changing user needs to improve user satisfaction .
• You can perform a needs analysis using various methods, such as user feedback surveys, focus groups, and interviews. Usability testing can also come in handy.
• The first step to conducting this analysis is to set a SMART goal for your analysis.
• Then, select a user persona to drill into a particular segment’s preferences and problems.
• Analyze user behavior data collected to identify patterns and trends.
• Also, collect qualitative data through open and close-ended surveys from various user segments.
• Guide your product development process with the insights you’ve gathered.
• Userpilot can help you monitor user behavior and collect qualitative feedback with in-app surveys. Book a demo now.

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What is a user needs analysis?

User needs analysis employs various techniques to help you gain deeper insights into your user base’s aspirations , goals, and motivations. It also enables you to understand user pain points and preferences.

A needs analysis validates your assumptions regarding your users so you can better guide product development, marketing, and launches .

Why do you need to analyze user needs?

If you want to build a product that attracts, engages, and retains the right users, performing user needs analysis is non-negotiable. Let’s take a look at its key benefits.

• Understanding user pain points – Analyzing user needs helps you build a solid understanding of user challenges. It gives you an idea of what will motivate them to use your product, allowing you to build features shaped around their needs.
• Keeping up with changes in how users behave – With this analysis, you can stay on top of changing target audience preferences and behavior patterns . It ensures that your product adapts as your users’ needs change.
• Creating data-driven product strategies – It takes the guesswork out of product development. Instead, you can use concrete data to determine what features, functionalities, and upgrades will align with your audience’s requirements.
• Increasing user satisfaction – When you build a product tailored to your target user’s expectations and needs, it improves the overall user experience (UX). That, in turn, skyrockets user satisfaction levels and even helps you engage and retain more users.
• Identifying and removing friction – With user needs analysis, you can dig deep into their journey and identify the challenges your target audience faces when navigating your product. It comes in handy for pinpointing and eliminating areas of friction , resulting in enhanced UX.

User research methods to identify user requirements

Analyzing user needs is an integral part of building a product that stands out from the crowd. But what’s the right way to do it?

Here are a few user research techniques that’ll come in handy:

1. User feedback surveys

How do you know what users want? One of the most effective methods is to collect first-hand feedback using in-app surveys.

You can set up and trigger in-app surveys at specific points in a user’s journey to learn more about user requirements, needs, pain points, and preferences. For instance, when a new user logs in, you can use a welcome survey to ask about the tasks they want to accomplish.

On the other hand, if a user has been using your product for some time, you can use in-app surveys to dig deeper into their experience.

A tool like Userpilot comes in handy for creating and implementing feedback surveys. You can choose from several survey templates , such as NPS surveys and CES surveys, and include a mix of open-ended and close-ended questions. You can even use branching logic to modify the flow of questions based on previous responses.

In-app user surveys are a good way to collect feedback from active users. In order to collect feedback from inactive or potential users, you can deploy email surveys.

If you’re already using a CRM platform like HubSpot, you can integrate it with Userpilot . That way, you can leverage existing data from Userpilot to send relevant email surveys via HubSpot.

2. Focus groups

A focus group is an effective user research technique for collecting in-depth qualitative feedback , much like usability testing. It involves gathering a small group of users (usually 6-12 participants) and encouraging them to engage in discussions and debates.

Participants are free to share their experiences, ideas, and opinions about your product, and you can record their discussions. A moderator is present to steer conversations in the desired direction while the users interact.

Focus groups are a valuable resource for informing key product development decisions, such as the design process and UI layout. Also, you can use them for market research and validation.

3. User interviews

Interviews help you engage in one-on-one conversations with existing, new, and potential users. It makes them an excellent way to dig deeper into their minds and learn more about why they behave the way they do .

You can collect in-depth qualitative insights about a user’s journey and understand what works for them. Additionally, you can personalize your follow-up questions based on how the conversation is going.

But how do you encourage more people to participate in user research? A clever tactic is to offer an incentive, such as a discount or gift, in exchange.

4. Usability testing

Usability testing is a technique that helps measure how easy it is for people to use your product. It can help identify friction points users encounter when navigating your product. It also helps understand whether users can successfully realize their goals with your product.

If you want to assess product usability for user needs analysis, you can choose from the following types of tests:

• Guerilla testing – You go to a public location, ask people to try your product (or a part of it), and share their feedback.
• Remote usability testing – You use screen-sharing tools or video conferencing apps to observe how people use your product and collect feedback from them.
• 5-second test – You ask users to experience a part of your product for five seconds and share their feedback afterward.
• First-click testing – You focus on determining how easily users can identify the happy path to complete a given task.

How to perform a needs analysis to extract valuable insights

User needs analysis takes more than launching an in-app survey or conducting user interviews . You need a concrete plan to retrieve the right insights from the analysis, customer surveys, and more.

Here’s a step-by-step guide to help you get started:

1. Define your objectives for user needs analysis

First things first—you must understand why you want to perform a user needs analysis.

Are you looking to validate the idea for a new product? Do you want to identify ways to make users stick around for longer when trying your product? Or do you want to expand user accounts by tempting them with advanced features and add-ons?

Knowing the answers to these questions will give you a clear idea of your objectives . It’ll help you focus on the right user personas and research methods.

It’s a good idea to use the SMART (Specific, Measurable, Achievable, Realistic, Time-bound) goal-setting framework to define goals and KPIs .

2. Choose your user personas

Your customer base comprises different user segments, each with unique aspirations, motivations, pain points , and preferences. Selecting a specific user persona for your analysis will help you drill down on that particular segment’s needs.

The key is to outline detailed user personas , including their professional and socio-economic backgrounds, interests, and struggles.

3. Analyze user behavior data to identify patterns

Now that you’ve selected a user persona, it’s time to monitor and analyze their in-app behavior . A product analytics tool like Userpilot is handy for collecting user behavior data and identifying patterns .

You can leverage techniques, such as funnel and trend analysis to extract more in-depth insights . For instance, with funnel analysis , you can understand how users move from one step to another when completing a task and what causes them to drop off.

Similarly, trend analysis can help you identify and harness recurring patterns in user behavior.

4. Collect qualitative data with surveys to understand user expectations

Next, it’s time to dig deeper into user needs with in-app surveys. These surveys provide an effective way of collecting qualitative feedback from users about their experiences and expectations.

You can ask questions about the tasks they want to accomplish with your product and the challenges they face when using it. With Userpilot, you can choose from a variety of templates to implement different types of in-app surveys.

5. Guide the product development process

Comprehensive user needs analysis takes the guesswork out of product development . With all the qualitative and quantitative data collected in the previous steps, you’ll be better equipped to determine what users want. You can use these findings to guide design and product teams as they develop new features and product enhancements.

Create your product roadmap and add features and enhancements based on their priority. A plan allows multiple teams to collaborate in product development and ensure timely completion.

As you implement these changes, make sure you continue monitoring user activity and behavior. It’ll help you understand whether the modifications are meeting user needs and identify further areas of improvement .

Analyzing user needs offers several benefits, including improved user engagement and satisfaction levels. You can also leverage insights from user needs analysis to inform product design and development decisions.

A product analytics platform such as Userpilot helps SaaS companies collect feedback, monitor user activity, and analyze their behavior. These insights make data-driven product decisions possible. To see how Userpilot can help you with user needs analysis, book your demo today.

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