Introduction to qualitative research methods
Introduction to qualitative research methods
This introductory guide will help you learn about common data collection methods, when to use them, and how to collect your own qualitative data.
Find out how to analyse your data and ensure that the results of your real-world research are credible and trustworthy.
1. A quick look at qualitative research
There are two main forms of research: qualitative and quantitative.
- Qualitative research focuses on deeply understanding people, including their thoughts, feelings, actions, and experiences.
- Researchers ask exploratory, open-ended questions like, “why do you feel that way?”, “what is going on for you?” and “tell me more about …?”
- Qualitative researchers mainly gather people’s words, such as what someone says in an interview, shares during a focus group, writes on social media, or says in everyday conversations. They observe people in real-life settings.
- Sometimes qualitative researchers also collect images, such as photos, drawings, or videos, and they explore what these might reveal or represent.
- Qualitative research findings are rich and descriptive, like a window into people’s lives. They help us understand how people think, feel, and behave, and this can guide the design of products, services, and experiences that better meet people’s needs.
- On the other hand, quantitative research studies things by collecting and analysing numerical data to find patterns and draw conclusions.
In summary, qualitative research focuses on:
- Depth over breadth: It’s about deeply understanding a specific group of people or a particular experience, rather than looking for broad patterns that apply to the general population.
- Building understanding, not testing assumptions: It’s used to uncover how and why things happen, rather than to check if a specific idea is true.
- Words over numbers: Your research questions are best answered through people’s words, themes, and insights, rather than through numbers, statistics, graphs, or numerical trends.
Deciding whether to run a qualitative or a quantitative study
Before choosing your research approach, it’s important to first understand what you’re trying to learn:
- Outline your primary objective: What is the main goal of your study?
- Write your high-level research questions: What do you want to find out?
- Choose your research approach: Decide whether to use qualitative, quantitative, or ‘mixed methods’; primary (new data you collect) or secondary (existing data).
- Collect your data: Use methods that suit your approach, like interviews or surveys.
- Analyse your data and present your findings: Look for patterns, themes, or trends.
This guide focuses on qualitative data collection and analysis.
2. How to collect qualitative data
Identifying and recruiting respondents
Qualitative research aims to deeply understand people, so ask yourself who it is you are trying to understand. Who is best to help you answer your research questions? There are many ways to categorise people, for instance, by:
- Sporting code
- Level of sporting participation/engagement
- Demographic group: age, gender, ethnicity, location, etc.
- Key relationships, for example, parents of children who play sport
- Role within the sporting ecosystem, such as coach, funder, facility provider, etc.
- Seniority or level in a hierarchy
Often, the details of who you want to study will affect how you conduct your study. For instance, if you want to recruit people from a very specific niche, it might be easier to conduct one-on-one interviews rather than gathering a focus group. Or if you’re studying young people who don’t visit sport facilities, you may choose to ‘go where the people are’ and gather insights from social media.
Recruiting respondents
Once you know who you want to talk to, you need to decide how you’ll engage with them.
With some research methods, like interviews and focus groups, you’ll need to explicitly invite people to participate and share their views. So think about how you can contact these people. For instance:
- Use an email list from your sports club
- Recruit via your social media page
- Directly approach people at sports grounds
| Hot tip: Snowball sampling Sometimes you will be trying to recruit a very specific type of person, for instance adolescent boys who play rugby and identify as LGBTQI+. In these situations, you may like to use ‘snowball sampling’. This is asking people who have signed up to your study if they know anybody else you could speak to. It’s an effective, low-cost way of recruiting the respondents you need. |
With other research methods, such as observation or analysis of social media commentary, you may not need to invite people to participate in the research because you will be studying them at arm’s length.
Choosing your data collection method
There is a range of methods to collect qualitative data. In this guide we provide an overview of some common approaches, including:
- Interviews
- Focus groups
- Observation
- Audio-visual techniques
- Social media analysis
Before you begin, be aware of the challenges and trade-offs that qualitative research brings. For instance, if you use a recording device and transcribe verbatim, you may become bogged down with the volume of words. But if you take notes as you go, you could miss important details and have trouble staying in the flow of the session.
You will need to decide how extensively you’ll summarise your themes. How will you strike the balance between high-level findings and real-life quotes that bring your research to life?
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Interviews
What they are:
Interviews are in-depth conversations between the researcher and one person, or a small number of people. They can be in-person, online, or over the phone, and they usually last 30 minutes to 1 hour.When to use them:
Interviews are a good option if:- You need to discuss sensitive or confidential topics that people wouldn’t feel comfortable talking about in a larger group
- You have group dynamics or power dynamics that would prevent open sharing in a larger group (e.g., managers and staff members both present)
- It is logistically difficult to organise a meeting with a larger group
Be aware:
Interviews can be time-consuming and therefore costly. You will need to ask your questions in a neutral way, to avoid leading people to certain responses, and producing biased results.Method
Before the interview:
- Draft your questions with your objectives and the ‘big picture’ in mind.
- Write open-ended questions. Start with prompts like who, what, when, where, how, tell me about…, can you describe a time when… and so on.
- Write your questions in plain language. Avoid asking two questions in one, to avoid confusion.
- Start with a question that is easy to answer to put your respondents at ease.
- Ask your questions in a logical order, so there is a natural flow to the conversation.
- Don’t plan too many questions. As a rule of thumb, allow 3 to 5 minutes per question. So, in a 30-minute interview you would ask 6 to 10 questions. (Use your first interview as a test-run, and if you need to cut down on your list of questions, do so.)
- You may like to send your interview questions to participants in advance, so they can prepare.
- It’s a good idea to print out a copy of your questions, even if you are conducting an online interview.
Hot tip
Avoid asking questions that start with ‘why’ because they can sound challenging. Instead, ask the same question using ‘what’ language. For instance, rather than asking ‘why did you stop playing sport after high school?’ ask ‘what led you to stop playing sport after high school?’During the interview:
- Conduct your interview in a quiet place that is free from distractions.
- Have a recording device such as a mobile phone or dictaphone. Ask for permission before recording the interview.
- Start by building rapport with your interviewees. Put them at ease with some friendly conversation. Ensure participants are comfortable and offer them a glass of water.
- Share a bit about yourself and why you’re interested in speaking with them. Ask them if they have any questions.
- Ask your questions. It’s good practice to jot down important themes or notes by hand, to help at the data analysis stage.
- Don’t just mechanically work through your questions. Really listen, and ask appropriate follow-up questions. You may find you jump between topics, so it can be helpful to cross off each question as it’s answered.
- If you notice themes coming through you can start testing some of your ideas with respondents. For instance, “I’m hearing that people’s early experiences in sport can have an influence on whether they keep playing. Does that ring true to you?” Accept whatever people have to say, and stay open to changing your mind.
After the interview:
- It’s good practice to immediately jot down the key things that you heard, and any early themes or insights you notice. Three to five bullet points per interview is enough.
- Use a free or low-cost transcription tool to convert the audio into a written transcript. Otter AI and Contented AI are good options.
- You’re now ready to conduct the data analysis.
Key output: High-level notes and written transcript
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Focus groups
What they are:
Focus groups are group discussions between a facilitator and 6 to 8 participants with something in common, such as shared team membership or shared demographics. Focus groups can be conducted in-person or online, and they usually run for 1 to 2 hours. Researchers may compare and contrast across focus groups, for instance, exploring what people most want from a sports service, or comparing experienced and new players’ perspectives.When to use them:
Focus groups can help researchers understand the views of a group, such as what people value in a coach, differing attitudes towards a proposed change, or the main motivations for engaging in sport. They are more efficient and cost-effective than interviews.Be aware:
Group dynamics can be a challenge. Individuals may dominate the discussion while others stay quiet, and early contributions may set the tone. Participants may exhibit groupthink, and appear more unified than they really are. Facilitators need to manage these dynamics by including everyone, keeping the discussion on track, and drawing out differing opinions and points of consensus.How to do it
Before the focus group:
- Decide how many focus groups you need to run, and who will be in each session.
- Think about who should facilitate the session. For instance, if the discussion is about the experiences of females in sport, it would be helpful to have a female facilitator.
- Find a comfortable venue. This could be a meeting room or a club room; anywhere that people feel at home, as this will help them to open up.
- You may need to incentivise people to participate, for instance by providing afternoon tea or a gift card. Make sure you follow your organisation’s guidelines when it comes to offering gifts.
- Write your questions in advance, making sure to ask open-ended questions that will help you to achieve the objectives of your research. As with interviews, it’s good practice to start with some simple questions to get people talking, before getting into more complex or personal topics.
- Decide how you will record the session, for instance, with a mobile phone or dictaphone. Also, be aware that because focus groups have more participants, your audio recordings will have people interrupting or talking over each other. This makes it harder to get a ‘clean’ transcript. You may like to bring along a colleague to take notes, allowing you to focus on running the conversation. Try to jot down the key things you hear.
During the focus group:
- Note people’s names before the session, either on name tags or on your own piece of paper, so you can easily address people by name during the session.
- Introduce yourself and give a brief overview of your role and of the study. Explain why you are speaking to this group.
- Give people time to introduce themselves and get comfortable. Make small talk or have a warm-up activity.
- Ask your questions and listen carefully to the responses. Ask good follow-up prompts.
- Consider incorporating creative activities to encourage people to share. You could ask people to represent their experiences on paper, and then describe them. Use tactile materials like playdough or Lego to illustrate a challenge. Brainstorm on Post-it notes. Show photos and ask people to respond to what they see.
- Try to notice who is talking more, and who is talking less. You may need to directly ask questions of quieter people, to draw them in. Do this in a gentle way, e.g., “What do you think about that, Sam?”
- Try to organise what you’re hearing, during the session. For instance, summarise what you hear, then check, “Is this what you’re saying? Have I missed anything?”
- Keep one eye on the clock so that you have enough time to cover all your questions and topics. Know in advance what your critical questions are, so if you’re running short of time, you can focus on those.
After the focus group:
- Jot down some bullet points about what you heard, focusing on answering your key research questions, and identifying points of consensus and contention.
- If a colleague was with you, debrief together.
- Upload and transcribe the audio.
Key output: High-level notes and written transcript
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Observation
What it is:
Observing people in the environment or context that relates to your study. For example, observing people as they move through a sports facility, complete a registration process, interact with their coach, and so on.When to use it:
Observation is a great option if you’re interested in understanding how people act in a certain context or environment, and you can get first-hand access to that environment. It’s particularly useful if people can’t clearly share their thoughts and feelings (e.g., in a focus group), or if they don’t want to be part of your study.Observation is a good adjunct to interviews, because you can cross-check what you’ve heard in interviews with what you observe in real life.
Be aware:
Observation can be time-consuming. It takes time to settle into a site and see what’s happening. It also takes skill and practice to extract insights from an observation.How to do it
Before the observation:
- Identify your site. Arrange for permission to visit and observe.
- Decide what type of observer you will be. Will you stay at arm’s length, taking notes? Will you be a partial participant by getting close to participants and asking questions? Or will you join in? If you haven’t conducted an observation before, it’s best to be a pure observer rather than a participant, because it will allow you to focus on taking notes.
- Have clear questions you want to answer, or behaviours you want to observe. Decide how you will record what you’re seeing. It could be useful to develop a simple sheet that notes:
- Location, date and time of your visit
- The questions you want to answer
- Descriptive notes of what you take in with your senses
- Key behaviours you notice. You could include a simple table to note the types of behaviour that relate to your questions
- Reflective notes to describe what you think and feel about what you observe. Are there any early themes coming through?
During the observation:
- Take time to get used to your environment. It’s easy to get overwhelmed by everything that’s going on. Try to take in the whole scene, and then after a few minutes, focus your attention on the parts that relate to your research.
- Start taking notes, draw the environment, describe the scene using your senses. Note who is there, what they are doing, how they are interacting, and the themes of what they are saying. Try to write in full sentences, capturing rich details.
- Note your reflections and any early themes you can identify.
- When you’re ready, quietly withdraw from the observation site. Thank people for letting you visit, if that’s appropriate.
After the observation:
- Tidy up your notes while things are fresh in your mind.
- Nobody needs to see your notes, so jot down any thoughts and questions that have cropped up.
Example of observation notes
Observation site: Southwest hockey turf, Waikato
Date and time: 5 May 2025, 8pmGuiding research questions:
- How do coaches and players foster an inclusive team culture?
- How do coaches and players accidentally or intentionally prevent inclusion?
Descriptive notes:
- The turf is floodlit and it’s chilly. Players gather outside the dugout because the turf is busy with another practice.
- The players put their shinpads on, moving in and out of conversation. Most people are in groups of 2–4. Some are laughing. A few players arrive just before the practice.
- One player walks up and says hello loudly with a smile; all groups turn and welcome this person.
- Another couple of players show up around the same time. One does a small smile and waves at a specific person in a circle, but then moves to the side to put her shinpads on and checks her phone.
- Another player avoids eye contact with other players, puts on her shinpads, and goes for a solo run around the perimeter of the turf.
Key behaviours observed:
- Coach inclusion: Huddle in for instructions.
- Coach exclusion: Certain drills don’t involve everyone.
- Player inclusion: Group greeting at the start.
- Player exclusion: Closed circles of discussion.
Reflective notes:
- Is there any link between how long people have been in the team, and how connected they are to other team members?
- Are more extroverted players more likely to be included?
- Is the person who went for a solo run more serious about the game than the others – is there potential tension between socialising (seen as fun) and serious practice?
- Is the person who was warmly greeted by everyone the captain – and if not, what generates the warmth from the rest of the team? What makes them want to include her?
- Phones seem to serve as a defence mechanism for people who feel excluded and uncomfortable.
- Team members don’t seem to make strong efforts to include ‘outsiders’ before or during practice.
- What makes the bigger difference for feelings of inclusion – the coach or the team?
Key output: Observation notes
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Audio-visual techniques
What these are:
There are three kinds of audio-visual data: (1) audio only; (2) visual only; (3) audio-visual files. Audio-visual (AV) techniques relate to the capture and analysis of AV data.When to use them:
AV inputs can be used to supplement other types of qualitative research, such as taking photos as part of an observational study; including voice memos as part of research findings; or including video footage in a case study.AV data is valuable because it can help people who are absorbing your research to ‘get closer’ to the original data. For instance, an audio file lets you hear people’s ideas as they express them, with their original tone of voice and emphasis.
Be aware:
There is a balance to be struck between sharing ‘original’ or ‘untouched’ data such as photos and spoken quotes from participants, and the distillation and theming of that data, so you can tell a clear and compelling story. The AV data should help to illustrate your main points, but you still need to make those points. Only collect a small amount of AV data, rather than conducting a large-scale study based on AV inputs.How to do it
Beforehand:
- Decide how you will capture any AV data. If you are filming or photographing people, ask for their consent before capturing and using their image.
During:
- Capture any AV data that you feel will help you to answer your research questions or build people’s understanding of the topic. For instance, you could ask people to record video diaries after key moments at an event or across their sporting season. Or you could set up a photo booth in your sport facility, and get people to answer a simple research question on a whiteboard that they hold up in the photo.
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After:
- Review your data and extract the clips or images that you feel are most powerful, and best answer your research question.
- Conduct high-level coding and thematic analysis of these clips. We describe the process below.
Key output: Small selection of AV files – audio-only, visual-only, or audio-visual
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Social media analysis
What it is:
Social media analysis involves gathering content from platforms such as Facebook, Instagram, TikTok, or LinkedIn, and analysing the contents.When to use it:
This is useful when you want to understand the views of a large number of people, quickly and cost-effectively, and when you want to hear from people who don’t participate in traditional studies, like surveys or focus groups. Social media analysis can reveal people’s true thoughts and feelings, whereas people may hold back when they are aware they’re being studied.Be aware:
Social media analysis can be challenging because there can be so much data to consider. You’ll need to draw a clear boundary around what is in scope and what is not. The ethics can be a grey area. You’ll need to take care to anonymise comments if you intend to analyse content.How to do it
Beforehand:
- Be very clear why you are conducting analysis and how it will help you to answer your research question.
- Identify the best platform and any tools you can use to focus and limit your data collection. For example, you could focus your search by:
- Searching for a specific hashtag
- Searching in a defined geographic area
- Filtering according to audience demographics (e.g., females based in a particular region and age group)
- Only looking at responses to your club/organisation page.
During:
- You can choose one of two key pathways to gather data:
- Be passive and simply observe discussions as they happen. For instance, you may analyse tweets, comments, or long-form posts.
- Directly interact with people. For example, ask people to respond to a specific question, post, photo, or hashtag.
- Determine how to save and organise the data:
- If you have gathered written responses, decide whether to download these in an Excel or Word file.
- If you’ve gathered images, download and save them.
After:
- You may wish to tidy up your initial ‘scrape’ of the data and eliminate content that doesn’t relate to your research. Alternatively, you could clean the data at the next stage (data analysis).
Key output: Raw data from social media, such as written comments, imagery, or video.
How many people should you include?
This is a really important question in quantitative research, because those studies need to engage with a sample group to draw conclusions about the whole population. For example, before an election, pollsters will speak to a small percentage of voters and then predict how all voters will behave, and therefore, which party will win the election.
Qualitative research is different, because you’re aiming for deep understanding of a specific population, not generalised results. As such, there are fewer guidelines about the ideal number of participants.
As a rule of thumb, qualitative researchers recommend recruiting between 10 to 50 participants. The ideal number will depend on your research question, your method, your timeline, and your budget. For instance, if your research question is about very personal experiences, then it may be best to interview people, and you may only have time to engage with 6 to 8 participants. But if your research question is about group views, and you have a little more time, you could run three focus groups with 6 people per session, therefore recruiting 18 participants.
It’s recommended to make a judgement call at the outset, and then again at the data analysis stage, asking: have I reached saturation point? In other words, are you seeing recurring themes? If so, you’ve talked to enough people. If not, keep recruiting.
Remember, you need to reach saturation point for each sub-group in your study. That way, you can be confident in drawing conclusions about each of the sub-groups involved. For instance:
- If you were studying the views of all teenagers aged 13–18 years old, then you would only have one group of interest, and you’d reach saturation point when you heard similar themes coming through (e.g., two focus groups of 6 people per session = 12 participants).
- If you were studying the views of teenagers aged 13–18 years old, but you wanted to compare boys and girls, and those who play sport versus those who don’t, then you have four sub-groups (female participants, male participants, female non-participants, male non-participants). You’d need to run two focus groups for every sub-population, so your study would be much larger (e.g., 4 x 2 focus groups with 6 people per session = 48 participants).
In a nutshell:
- Recruit at least 10–12 people in your study but aim for more if you can.
- Keep gathering data until you reach saturation point and you’re not hearing new key themes or ideas.
3. How to analyse qualitative data
You’ve gathered your raw qualitative data, and you may have jotted down some early ideas about the themes coming through. You’re ready to get into data analysis. Here’s how:
Coding and thematic analysis
Braun and Clarke (2006) developed a useful framework for conducting qualitative analysis, which is summarised here:
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Get to know your data:
Start by reading through your full data set, ideally a few times. Jot down your early thoughts and impressions. -
Generate early codes:
A code is like a basic descriptive ‘tag’ that you add to qualitative comments. Coding helps you to boil down your data into little nuggets of meaning.For instance, if someone said, “I love team sport because I’m competitive and I like being part of a group,” you may add the tags:
- team sport
- pro-competition
- pro-groups
You don’t need to code every single piece of text – just focus on the quotes or content that feels relevant to your research question.
In terms of practicalities, you can code:
- On a Word document or print-out. Put your quotes on the left side, line by line, and your codes on the right.
- On an Excel sheet, again putting one quote per line on the left, and then adding codes to the right.
- Using tools like Qualtrics (for text) or Transana (for images and audio data). However, these are advanced tools, and you can get robust results without them.
You can also use colours to help you code. For instance, if you are looking at the barriers and drivers to participation in sport, barriers could be highlighted in pink and drivers in green.
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Develop and refine themes
Qualitative data contains themes, and it’s your job to uncover them using thematic analysis. It can be useful to look at the data from both the bottom up and the top down.
Each theme is “a pattern that captures something significant or interesting about the data and/or research question” (Maguire and Delahunt, 2017, p. 3356).
How to approach theme development:
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Start with a bottom-up view:
Look over your summary codes and ask what patterns you can see. Consider the following:- How do the codes relate to each other?
- Can you group things into categories?
- Are there causes and effects?
- Are there overarching codes and sub-codes?
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Then try a top-down approach:
Step back, look over the data as a whole, and see what you notice. How would you summarise this data for someone else?
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Build a skeleton structure
At the end of this step, your data will be organised in a way that simplifies what you’ve heard but still captures the essence of what was shared. You’ll end up with a ‘skeleton structure’. There is no right or wrong way to develop this. Just keep playing with it until you have a version that fits.
Research question exampleResearch question: What are the barriers or drivers of participation in sport, for youth in Taranaki? Thematic analysis – DRAFTKey spectrums of experience:
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Flesh out the skeleton structure
It can be very helpful to flesh out your skeleton structure by copying and pasting key quotes or notes under each of your themes. For example, under “Early personal experiences – positive – scored goals” you may put this quote:
“I remember scoring a goal at netball, in my second-ever game. Everyone cheered and I won player of the day. It was a great feeling.” (15yo netball player)
This process will give you confidence that your themes are a good fit for the data. And if they’re not, keep adjusting them until they are suitable.
Stress-test your themes
Look over your thematic analysis and ask yourself some reflective questions (Maguire and Delahunt, 2017):
- Do the themes make sense?
- Does the data support the themes?
- Am I trying to fit too much into a theme?
- If themes overlap, are they really separate themes?
- Are there themes within themes (sub-themes)?
- Are there other themes within the data?
Convert your themes into one-liners
The final step in thematic analysis is to clearly identify the essence of each theme. This is what you’re going to report in your final write-up or presentation. For instance:
| High-level theme | One-liner |
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Early personal experiences - positive |
Positive early experiences have an outsize influence, and drive long-term participation in sport |
| Seen as ‘sporty’ | Children internalise feedback from others, and come to see themselves as sporty (or not) |
| Scored goals, won games | Early ‘wins’ create strong positive associations with sport |
Turn a high-level theme into a one-liner:
Early personal experiences – positive: Positive early experiences have an outsize influence and drive long-term participation in sport.
Seen as ‘sporty’: Children internalise feedback from others and come to see themselves as sporty (or not).
Scored goals, won games: Early ‘wins’ create strong positive associations with sport.
Keep in mind: Subjectivity
Qualitative analysis is subjective. This means it is affected by you, the researcher, and the ‘lens’ through which you view the world. Your lens is shaped by your history, your demographics, your personality, your hopes and fears and more. You can’t remove your lens, but you should think about how your perspective affects your findings. Ask yourself:
- What assumptions did I have before I started this study?
- How might my identity influence what people are choosing to share?
- How has my background affected how I am analysing this data? What am I paying most attention to, and why might that be?
- What conclusions do I want to make, and how might that affect my interpretation?
A cheat sheet to minimise bias
Before you start your study, here’s a quick summary of the main types of bias that might creep into your work, and how to minimise them.
Researcher bias / Subjective bias
Your background, experiences, demographics etc will affect what you study and how.
Reduce the bias with reflexive questioning: Reflect on how your background and your biases may affect your study.
Subject bias / Withholding or distortion
People might not tell you the truth or might distort the truth in some way. For example, people in a sport team may not give genuine feedback about the quality of the coaching, for fear of retaliation.
Reduce the bias by building trust: Be clear about what you are studying and why. Engage with subjects over time so they get to know and trust you. This will encourage them to open up. Be clear about how you’ll protect privacy and confidentiality.
Contextual bias / Environmental influences on behaviour
People act differently, and share different information, in different contexts.
Reduce the bias by choosing a relevant context: Try to study people in the most relevant context, based on your research question. If you’re studying team dynamics, talk to players at practice. Spend time in that environment, so people get used to you being there.
Make it easy for people to share: Be considerate. Don’t ask personal questions in a crowded environment. Try to adjust the context or environment so that it’s easy for people to share.
Research method bias / Biased recruitment
No matter how you recruit people, there will be some inevitable bias in terms of who you reach and who decides to participate. For instance, recruiting via social media will skew towards a more tech-savvy audience.
Reduce the bias by aiming for balance: If you are aware that your recruitment process will favour one type of person (e.g., tech-savvy people), you can try to balance this with other forms of recruitment (e.g., in-person sign ups).
Be clear about the limitations: It’s difficult to eliminate all bias, so make sure to acknowledge the bias that exists.
Biased analysis
Bias can accidentally creep in at the data analysis stage, as you decide what to pay attention to, and what it all means.
Reduce the bias by getting a second opinion: If you can, get a second person to run over your data set. What do they notice?
Triangulate your data: Compare your findings with other findings. You could triangulate within your study, to see if what you heard in interviews aligns with what you heard in focus groups. Or you could triangulate with sources outside your study and see if your findings are generally consistent with what other researchers have found.
Try to prove yourself wrong: Humans are wired to seek out information that aligns with what we already think: confirmation bias. So it’s useful to actively try to find information that goes against what you believe. If you find data like this, don’t try to bury it. Instead, study it more deeply and see what you can learn.
“A proposition deserves some degree of trust only when it has survived serious attempts to falsify it.” (Chronbach cited in Brink, 1993)
Lack of transparency
Lack of transparency is an issue if you want your study to be ‘confirmable,’ or repeatable.
Reduce the bias by being crystal clear about your processes: Include very clear notes about how you’ve conducted your study, and the decisions you made at each stage.
| Source of bias | Description of bias | How to reduce bias |
|---|---|---|
| Research | Subjective bias As discussed - your background, experiences, demographics etc will affect what you study and how. |
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| Subjects | Withholding or distortion People might not tell you the truth, or might distort the truth in some way. (Eg, people in a sport team may not give genuine feedback about the quality of the coaching, for fear of retaliation.) |
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| The context | Environmental influences on behaviour People act differently, and share different information, in different contexts. |
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| The research method | Biased recruitment: No matter how you recruit people, there will be some forms of bias introduced, in terms of who you reach and who decides to participate. (Eg, recruiting via social media will skew towards a more tech-savvy audience.) |
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| Biased analysis | Bias can accidentally creep in at the data analysis stage, as you decide what to pay attention to, and what it all means. |
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| Lack of transparency | A lack of transparency is an issue if you want your study to be ‘confirmable,’ or able to be repeated by others. |
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You’re ready
You’ve now learned:
- The most common qualitative data collection methods, and when to use them
- How to collect qualitative data
- How to analyse your data and produce trustworthy results
You’re ready to embark on your own study.
If you’d like any additional research support, please contact the Sport NZ Research and Insights team via the Contact page.
