Data Analysis for Qualitative Research: 6 Step Guide (2023)

Data analysis for qualitative research is not intuitive. This is because qualitative data stands in opposition to traditional data analysis methodologies: while data analysis is concerned with quantities, qualitative data is by definition unquantified. But there is an easy, methodical approach that anyone can take use to get reliable results when performing data analysis for qualitative research. The process consists of 6 steps that I’ll break down in this article:

  1. Perform interviews(if necessary)
  2. Gather all documents and transcribe any non-paper records
  3. Decide whether to either code analytical data, analyze word frequencies, or both
  4. Decide what interpretive angle you want to take: content analysis, narrative analysis, discourse analysis, framework analysis, and/or grounded theory
  5. Compile your data in a spreadsheet using document saving techniques (windows and mac)
  6. Identify trends in words, themes, metaphors, natural patterns, and more

To complete these steps, you will need:

  1. Microsoft word
  2. Microsoft excel
  3. Internet access

You can get the free Intro to Data Analysis eBook to cover the fundamentals and ensure strong progression in all your data endeavors.


What is qualitative research?

Qualitative research is not the same as quantitative research. In short, qualitative research is the interpretation of non-numeric data. It usually aims at drawing conclusions that explain why a phenomenon occurs, rather than that one does occur. Here’s a great quote from a nursing magazine about quantitative vs qualitative research:

“A traditional quantitative study… uses a predetermined (and auditable) set of steps to confirm or refute [a] hypothesis.

“In contrast, qualitative research often takes the position that an interpretive understanding is only possible by way of uncovering or deconstructing the meanings of a phenomenon.

Thus, a distinction between explaining how something operates (explanation) and why it operates in the manner that it does (interpretation) may be [an] effective way to distinguish quantitative from qualitative analytic processes involved in any particular study.” (bold added)


Learn to Interpret Your Qualitative Data

This article explain what data analysis is and how to do it. To learn how to interpret the results, visualize, and write an insightful report, sign up for our handbook below.

(Video) Qualitative Data Analysis 101 Tutorial: 6 Analysis Methods + Examples

Step 1a: Data collection methods and techniques in qualitative research: interviews and focus groups

Step 1 is collecting the data that you will need for the analysis. If you are not performing any interviews or focus groups to gather data, then you can skip this step. It’s for people who need to go into the field and collect raw information as part of their qualitative analysis.


Since the whole point of an interview and of qualitative analysis in general is to understand a research question better, you should start by making sure you have a specific, refined research question. Whether you’re a researcher by trade or a data analyst working on one-time project, you must know specifically what you want to understand in order to get results.

Good research questions are specific enough to guide action but open enough to leave room for insight and growth. Examples of good research questions include:

  • Good: To what degree does living in a city impact the quality of a person’s life? (open-ended, complex)
  • Bad: Does living in a city impact the quality of a person’s life? (closed, simple)

Once you understand the research question, you need to develop a list of interview questions. These questions should likewise be open-ended and provide liberty of expression to the responder. They should support the research question in an active way without prejudicing the response. Examples of good interview questions include:

  • Good: Tell me what it’s like to live in a city versus in the country. (open, not leading)
  • Bad: Don’t you prefer the city to the country because there are more people? (closed, leading)

Some additional helpful tips include:

  • Begin each interview with a neutral question to get the person relaxed
  • Limit each question to a single idea
  • If you don’t understand, ask for clarity
  • Do not pass any judgements
  • Do not spend more than 15m on an interview, lest the quality of responses drop

Focus groups

The alternative to interviews is focus groups. Focus groups are a great way for you to get an idea for how people communicate their opinions in a group setting, rather than a one-on-one setting as in interviews.

(Video) Thematic Analysis | 6 Steps to Perform Thematic analysis [ Definition, Purposes, Steps, Example ]

In short, focus groups are gatherings of small groups of people from representative backgrounds who receive instruction, or “facilitation,” from a focus group leader. Typically, the leader will ask questions to stimulate conversation, reformulate questions to bring the discussion back to focus, and prevent the discussion from turning sour or giving way to bad faith.

Focus group questions should be open-ended like their interview neighbors, and they should stimulate some degree of disagreement. Disagreement often leads to valuable information about differing opinions, as people tend to say what they mean if contradicted.

However, focus group leaders must be careful not to let disagreements escalate, as anger can make people lie to be hurtful or simply to win an argument. And lies are not helpful in data analysis for qualitative research.

Step 1b: Tools for qualitative data collection

When it comes to data analysis for qualitative analysis, the tools you use to collect data should align to some degree with the tools you will use to analyze the data.

As mentioned in the intro, you will be focusing on analysis techniques that only require the traditional Microsoft suite programs: Microsoft Excel and Microsoft Word. At the same time, you can source supplementary tools from various websites, like Text Analyzer and WordCounter.

In short, the tools for qualitative data collection that you need are Excel and Word, as well as web-based free tools like Text Analyzer and WordCounter. These online tools are helpful in the quantitative part of your qualitative research.

Step 2: Gather all documents & transcribe non-written docs

Once you have your interviews and/or focus group transcripts, it’s time to decide if you need other documentation. If you do, you’ll need to gather it all into one place first, then develop a strategy for how to transcribe any non-written documents.

When do you need documentation other than interviews and focus groups? Two situations usually call for documentation. First, if you have little funding, then you can’t afford to run expensive interviews and focus groups.

Second, social science researchers typically focus on documents since their research questions are less concerned with subject-oriented data, while hard science and business researchers typically focus on interviews and focus groups because they want to know what people think, and they want to know today.

Non-written records

Other factors at play include the type of research, the field, and specific research goal. For those who need documentation and to describe non-written records, there are some steps to follow:

  1. Put all hard copy source documents into a sealed binder (I use plastic paper holders with elastic seals).
  2. If you are sourcing directly from printed books or journals, then you will need to digitalize them by scanning them and making them text readable by the computer. To do so, turn all PDFs into Word documents using online tools such as PDF to Word Converter. This process is never full-proof, and it may be a source of error in the data collection, but it’s part of the process.
  3. If you are sourcing online documents, try as often as possible to get computer-readable PDF documents that you can easily copy/paste or convert. Locked PDFs are essentially a lost cause.
  4. Transcribe any audio files into written documents. There are free online tools available to help with this, such as 360converter. If you run a test through the system, you’ll see that the output is not 100%. The best way to use this tool is as a first draft generator. You can then correct and complete it with old fashioned, direct transcription.

Step 3: Decide on the type of qualitative research

Before step 3 you should have collected your data, transcribed it all into written-word documents, and compiled it in one place. Now comes the interesting part. You need to decide what you want to get out of your research by choosing an analytic angle, or type of qualitative research.

(Video) Thematic Analysis | Explanation and Step by Step Example

The available types of qualitative research are as follows. Each of them takes a unique angle that you must choose to get what information you want from the analysis. In addition, each of them has a different impact on the data analysis for qualitative research (coding vs word frequency) that we use.

  1. Content analysis
  2. Narrative analysis
  3. Discourse analysis
  4. Framework analysis, and/or
  5. Grounded theory

From a high level, content, narrative, and discourse analysis are actionable independent tactics, whereas framework analysis and grounded theory are ways of honing and applying the first three.

Content analysis

  • Definition: Content analysis is identify and labelling themes of any kind within a text.
  • Focus: Identifying any kind of pattern in written text, transcribed audio, or transcribed video. This could be thematic, word repetition, idea repetition. Most often, the patterns we find are idea that make up an argument.
  • Goal: To simplify, standardize, and quickly reference ideas from any given text. Content analysis is a way to pull the main ideas from huge documents for comparison. In this way, it’s more a means to an end.
  • Pros: The huge advantage of doing content analysis is that you can quickly process huge amounts of texts using simple coding and word frequency techniques we will look at below. To use a metaphore, it is to qualitative analysis documents what Spark notes are to books.
  • Cons: The downside to content analysis is that it’s quite general. If you have a very specific, narrative research question, then tracing “any and all ideas” will not be very helpful to you.

Narrative analysis

  • Definition: Narrative analysis is the reformulation and simplification of interview answers or documentation into small narrative components to identify story-like patterns.
  • Focus: Understanding the text based on its narrative components as opposed to themes or other qualities.
  • Goal: To reference the text from an angle closer to the nature of texts in order to obtain further insights.
  • Pros: Narrative analysis is very useful for getting perspective on a topic in which you’re extremely limited. It can be easy to get tunnel vision when you’re digging for themes and ideas from a reason-centric perspective. Turning to a narrative approach will help you stay grounded. More importantly, it helps reveal different kinds of trends.
  • Cons: Narrative analysis adds another layer of subjectivity to the instinctive nature of qualitative research. Many see it as too dependent on the researcher to hold any critical value.

Discourse analysis

  • Definition: Discourse analysis is the textual analysis of naturally occurring speech. Any oral expression must be transcribed before undergoing legitimate discourse analysis.
  • Focus: Understanding ideas and themes through language communicated orally rather than pre-processed on paper.
  • Goal: To obtain insights from an angle outside the traditional content analysis on text.
  • Pros: Provides a considerable advantage in some areas of study in order to understand how people communicate an idea, versus the idea itself. For example, discourse analysis is important in political campaigning. People rarely vote for the candidate who most closely corresponds to his/her beliefs, but rather for the person they like the most.
  • Cons: As with narrative analysis, discourse analysis is more subjective in nature than content analysis, which focuses on ideas and patterns. Some do not consider it rigorous enough to be considered a legitimate subset of qualitative analysis, but these people are few.

Framework analysis

  • Definition: Framework analysis is a kind of qualitative analysis that includes 5 ordered steps: coding, indexing, charting, mapping, and interpreting. In most ways, framework analysis is a synonym for qualitative analysis — the same thing. The significant difference is the importance it places on the perspective used in the analysis.
  • Focus: Understanding patterns in themes and ideas.
  • Goal: Creating one specific framework for looking at a text.
  • Pros: Framework analysis is helpful when the researcher clearly understands what he/she wants from the project, as it’s a limitation approach. Since each of its step has defined parameters, framework analysis is very useful for teamwork.
  • Cons: It can lead to tunnel vision.

Grounded theory

  • Definition: The use of content, narrative, and discourse analysis to examine a single case, in the hopes that discoveries from that case will lead to a foundational theory used to examine other like cases.
  • Focus: A vast approach using multiple techniques in order to establish patterns.
  • Goal: To develop a foundational theory.
  • Pros: When successful, grounded theories can revolutionize entire fields of study.
  • Cons: It’s very difficult to establish ground theories, and there’s an enormous amount of risk involved.

Step 4: Coding, word frequency, or both

Coding in data analysis for qualitative research is the process of writing 2-5 word codes that summarize at least 1 paragraphs of text (not writing computer code). This allows researchers to keep track of and analyze those codes. On the other hand, word frequency is the process of counting the presence and orientation of words within a text, which makes it the quantitative element in qualitative data analysis.

Video example of coding for data analysis in qualitative research

In short, coding in the context of data analysis for qualitative research follows 2 steps (video below):

  1. Reading through the text one time
  2. Adding 2-5 word summaries each time a significant theme or idea appears

Let’s look at a brief example of how to code for qualitative research in this video:

Click here for a link to the source text.1

Example of word frequency processing

And word frequency is the process of finding a specific word or identifying the most common words through 3 steps:

  1. Decide if you want to find 1 word or identify the most common ones
  2. Use word’s “Replace” function to find a word or phrase
  3. Use Text Analyzer to find the most common terms

Here’s another look at word frequency processing and how you to do it. Let’s look at the same example above, but from a quantitative perspective.

Imagine we are already familiar with melanoma and KITs, and we want to analyze the text based on these keywords. One thing we can do is look for these words using the Replace function in word

(Video) Thematic Analysis In Qualitative Research: 6 Time-Saving Tips (+ Examples)

  1. Locate the search bar
  2. Click replace
  3. Type in the word
  4. See the total results

Here’s a brief video example:

Another option is to use an online Text Analyzer. This methodology won’t help us find a specific word, but it will help us discover the top performing phrases and words. All you need to do it put in a link to a target page or paste a text. I pasted the abstract from our source text, and what turns up is as expected. Here’s a picture:

Data Analysis for Qualitative Research: 6 Step Guide (2)

Step 5: Compile your data in a spreadsheet

After you have some coded data in the word document, you need to get it into excel for analysis. This process requires saving the word doc as an .htm extension, which makes it a website. Once you have the website, it’s as simple as opening that page, scrolling to the bottom, and copying/pasting the comments, or codes, into an excel document.

You will need to wrangle the data slightly in order to make it readable in excel. I’ve made a video to explain this process and places it below.

Step 6: Identify trends & analyze!

There are literally thousands of different ways to analyze qualitative data, and in most situations, the best technique depends on the information you want to get out of the research.

Nevertheless, there are a few go-to techniques. The most important of this is occurrences. In this short video, we finish the example from above by counting the number of times our codes appear. In this way, it’s very similar to word frequency (discussed above).

A few other options include:

(Video) Qualitative analysis of interview data: A step-by-step guide for coding/indexing

  1. Ranking each code on a set of relevant criteria and clustering
  2. Pure cluster analysis
  3. Causal analysis

We cover different types of analysis like this on the website, so be sure to check out other articles on the home page.

How to analyze qualitative data from an interview

To analyze qualitative data from an interview, follow the same 6 steps for quantitative data analysis:

  1. Perform the interviews
  2. Transcribe the interviews onto paper
  3. Decide whether to either code analytical data (open, axial, selective), analyze word frequencies, or both
  4. Decide what interpretive angle you want to take: content analysis, narrative analysis, discourse analysis, framework analysis, and/or grounded theory
  5. Compile your data in a spreadsheet using document saving techniques (for windows and mac)
  6. Identify trends in words, themes, metaphors, natural patterns, and more
  1. Source text []


What are the 6 steps of qualitative analysis? ›

Step 1: Become familiar with the data, Step 2: Generate initial codes, Step 3: Search for themes, Step 4: Review themes, Step 5: Define themes, Step 6: Write-up. 3.3 Step 1: Become familiar with the data. The first step in any qualitative analysis is reading, and re-reading the transcripts.

What are the 6 generic steps in data analysis? ›

According to Google, there are six data analysis phases or steps: ask, prepare, process, analyze, share, and act. Following them should result in a frame that makes decision-making and problem solving a little easier.

What are the steps in analyzing data for qualitative research? ›

Qualitative data analysis requires a 5-step process:
  1. Prepare and organize your data. Print out your transcripts, gather your notes, documents, or other materials. ...
  2. Review and explore the data. ...
  3. Create initial codes. ...
  4. Review those codes and revise or combine into themes. ...
  5. Present themes in a cohesive manner.

What are the 6 steps of thematic analysis Braun and Clarke? ›

This paper illustrates our experience in applying the six phases of reflexive thematic analysis as described by Braun and Clarke: (1) familiarizing oneself with the data, (2) generating codes, (3) constructing themes, (4) reviewing potential themes, (5) defining and naming themes, and (6) producing the report.

What is the 6 types of qualitative research? ›

Six common types of qualitative research are phenomenological, ethnographic, grounded theory, historical, case study, and action research.

What are the 6 steps of the quantitative research model? ›

  • state the research problem. Often stated as a question, the problem should be focused narrowly on theproblem being studied. ...
  • define the purpose of the study. ...
  • review related literature. ...
  • formulate hypotheses and variables. ...
  • select the research design. ...
  • select the population and sample.

What are the 6 data processing cycle? ›

The raw data is collected, filtered, sorted, processed, analyzed, stored, and then presented in a readable format. Data processing is essential for organizations to create better business strategies and increase their competitive edge.

What are the 6 V's of data? ›

One that I've used is the 6 Vs of data. Those are volume, variety, velocity, value, veracity, and variability, let's cover each of them.

What are the 7 stages of data analysis? ›

Diagnostic Analysis, Predictive Analysis, Prescriptive Analysis, Text Analysis, and Statistical Analysis are the most commonly used data analytics types. Statistical analysis can be further broken down into Descriptive Analytics and Inferential Analysis.

What are the 8 steps of content analysis? ›

  • Step 1: Prepare the Data. ...
  • Step 2: Define the Unit of Analysis. ...
  • Step 3: Develop Categories and a Coding Scheme. ...
  • Step 4: Test Your Coding Scheme on a Sample of Text. ...
  • Step 5: Code All the Text. ...
  • Step 6: Assess Your Coding Consistency. ...
  • Step 7: Draw Conclusions from the Coded Data. ...
  • Step 8: Report Your Methods and Findings.

What are the 5 phases of qualitative research? ›

A popular and helpful categorization separate qualitative methods into five groups: ethnography, narrative, phenomenological, grounded theory, and case study. John Creswell outlines these five methods in Qualitative Inquiry and Research Design.

What are the 4 components of qualitative data analysis? ›

Integral to the quality framework is the idea that all qualitative research must be: credible, analyzable, transparent, and useful. These four components or criteria are fundamental to the quality framework and its ability to guide researchers in designing their qualitative research studies.

Which of the following is step 6 of the research process mainly about? ›

Step 6: Analyzing the Data

In traditional qualitative research studies, data analysis typically begins during data collection, continues throughout the remainder of the process of collecting data, and is completed following data collection.

What are the steps in content analysis? ›

The content analysis process can be broken down into 5 steps.
  • Step 1: Identify and Collect Data. ...
  • Step 2: Determine Coding Categories. ...
  • Step 3: Code the Content. ...
  • Step 4: Check Validity and Reliability. ...
  • Step 5: Analyze and Present Results.

What is Chapter 6 in qualitative research? ›

Chapter 6 is the summary, conclusions and recommendations of all the previous chapters as well as an evaluation of the research goal, objectives and hypothesis.

What are the 6 quantitative research? ›

There are four main types of Quantitative research: Descriptive, Correlational, Causal-Comparative/Quasi-Experimental, and Experimental Research.

What are 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, surveys, and observations and interpreting it. The method aims to identify patterns and themes behind textual data.

What are the six 6 steps in implementing the research plan? ›

Market Research Process: 6 Steps to Project Success
  • Identify and define the problem. Before you start any web survey project, you should identify the key issues you hope to be able to solve. ...
  • Develop the approach. ...
  • Research design. ...
  • Collect the data. ...
  • Analyze the Data. ...
  • Report, Present, Take Action.
Aug 20, 2019

What are the 6 sections of the research methodology? ›

Identify the key components of the methodology chapter: (a) Introduction and overview,(b) research sample, (c) overview of information needed, (d) research design, (e) methods of data collection, (f) methods for data analysis and synthesis, (g) ethical considerations, (h) issues of trustworthiness, (i) limitations of ...

What are the 5 parts of data processing? ›

The data processing is broadly divided into 6 basic steps as Data collection, storage of data, Sorting of data, Processing of data, Data analysis, Data presentation, and conclusions. There are mainly three methods used to process that are Manual, Mechanical, and Electronic.

What are the 5 methods of data processing? ›

In this article, we are going to discuss the five main types of data processing.
  • Commercial Data Processing. ...
  • Scientific Data Processing. ...
  • Batch Processing. ...
  • Online Processing. ...
  • Real-Time Processing.

What are the 7 V's of data? ›

After addressing volume, velocity, variety, variability, veracity, and visualization — which takes a lot of time, effort, and resources —, you want to be sure your organization is getting value from the data.

What are the 5 C's of data? ›

The five C's pertaining to data analytics soft skills—many of which are interrelated—are communication, collaboration, critical thinking, curiosity and creativity.

What are the 5 V's of data analytics? ›

Big data is a collection of data from many different sources and is often describe by five characteristics: volume, value, variety, velocity, and veracity.

What are the 10 steps in analyzing data? ›

What is a data analysis method?
  • Collaborate your needs. ...
  • Establish your questions. ...
  • Harvest your data. ...
  • Set your KPIs. ...
  • Omit useless data. ...
  • Conduct statistical analysis. ...
  • Build a data management roadmap. ...
  • Integrate technology.

What are the six elements of analysis? ›

Firstly, the “six-element” analysis method for terrorist activities based on social network is proposed in this paper, namely, a variety of sub-networks are constructed according to the correlation among the six elements—people, organization, time, location, manner and event.

What are the 4 steps to write an analysis? ›

Choose your argument. Define your thesis. Write the introduction. Write the body paragraphs.

What are the 7 qualitative research? ›

Grounded theory, ethnographic, narrative research, historical, case studies, and phenomenology are several types of qualitative research designs. The proceeding paragraphs give a brief over view several of these qualitative methods.

What is the core of qualitative data analysis? ›

The emphasis in qualitative analysis is “sense making” or understanding a phenomenon, rather than predicting or explaining. A creative and investigative mindset is needed for qualitative analysis, based on an ethically enlightened and participant-in-context attitude, and a set of analytic strategies.

What is step 3 of qualitative data analysis? ›

Step 3: Make your story come to life.As the participants' comments are your “data,” you need to ensure you have enough room for these quotes. Break out your insights into short, digestible chunks, following each with three or four quotes.

What is the 6th steps in action research? ›

Step 6: Analyzing the Data

In traditional quantitative research studies, data analysis typically occurs following the completion of all data collection.

What are the 6 steps in developing a research question? ›

Steps to developing a research question:
  • Choose an interesting general topic. Most professional researchers focus on topics they are genuinely interested in studying. ...
  • Do some preliminary research on your general topic. ...
  • Consider your audience. ...
  • Start asking questions. ...
  • Evaluate your question.
Aug 8, 2018

Which of the following is 6 types of research? ›

The six critical types of research include exploratory research, descriptive research, explanatory research, correlational research, and causal research.

What is the aim of the process of qualitative data analysis? ›

Qualitative data analysis aims to make sense of the abundant, varied, mostly nonnumeric forms of information that accrue during an investigation.

What is the structure of qualitative research? ›

It suggests, at least, the following sections: introduction, aims of the study, review of the literature, sample, data collection methods, data analysis methods, findings, discussion, conclusion, abstract. Each of these sections is addressed along with many written-out examples.

What is qualitative data analysis PDF? ›

Qualitative data analysis is the classification and interpretation of linguistic (or visual) material to make statements about implicit and explicit dimensions and structures of meaning-making in the material and what is represented in it. Meaning-making can refer to subjective or social meanings.

Should I use thematic analysis or content analysis? ›

Thematic analysis helps researchers understand those aspects of a phenomenon that participants talk about frequently or in depth, and the ways in which those aspects of a phenomenon may be connected. Content analysis, on the other hand, can be used as a quantitative or qualitative method of data analysis.

How do you analyze data in research methods? ›

  1. Step 1: Write your hypotheses and plan your research design. ...
  2. Step 2: Collect data from a sample. ...
  3. Step 3: Summarize your data with descriptive statistics. ...
  4. Step 4: Test hypotheses or make estimates with inferential statistics. ...
  5. Step 5: Interpret your results.

What are the stages in data analysis? ›

These steps and many others fall into three stages of the data analysis process: evaluate, clean, and summarize.

What are the top 6 data collection methods? ›

6 methods of data collection
  • Observation. Observational methods focus on examining things and collecting data about them. ...
  • Survey. Survey methods focus on gathering written or multiple choice answers about various subjects from individuals. ...
  • Focus group. ...
  • Interview. ...
  • Design thinking. ...
  • User testing.
Jun 24, 2022

What are the 5 basic steps in data analysis? ›

In this post we'll explain five steps to get you started with data analysis.

What are the 5 levels of analysis? ›

Using five levels of analysis (explicit, implicit, theoretical, interpretive, and applicable) addresses this concern by challenging students to comprehend the central ideas of texts, interrogate in terms of social justice, connect concepts to their immediate realities and extrapolate useful ideas to apply to their ...

What are the 6 tools of research? ›

1.4 Identify examples of how six general research tools can play significant roles in a research project: (a) the li- brary and its resources, (b) computer technology, (c) measurement, (d) statistics, (e) language, and (f) the human mind.

How do you analyze data in research? ›

  1. Step 1: Write your hypotheses and plan your research design. ...
  2. Step 2: Collect data from a sample. ...
  3. Step 3: Summarize your data with descriptive statistics. ...
  4. Step 4: Test hypotheses or make estimates with inferential statistics. ...
  5. Step 5: Interpret your results.

What is data analysis with example? ›

The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis. A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision.

What is the data gathering tool in qualitative research? ›

Interviews are one of the most common qualitative data-collection methods, and they're a great approach when you need to gather highly personalized information. Informal, conversational interviews are ideal for open-ended questions that allow you to gain rich, detailed context.

What is data analysis explain in detail? ›

Data analysis, is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. Data, is collected and analyzed to answer questions, test hypotheses, or disprove theories.

What is an example of data collection in qualitative research? ›

Some of the most common qualitative data collection techniques include open-ended surveys and questionnaires, interviews, focus groups, observation, case studies, and so on.


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