Transcription is the first part of analysis of your data. It involves listening to the audio (or video) recording of an interview and typing what is said. The level of detail required for transcription will vary depending on the type of analysis you are doing- for example, discourse analysis will require more detail about non-verbal speech components (e.g. pauses, um etc.). You can use software such as VLC media player or Olympus DSS player to manage and store your audio-files, and slow down the speed at which the audio is played. Qualitative data analysis software may also have options for transcribing, for example NVivo 11.

If you don't do the transcribing yourself it is important to validate the transcripts (by listening to the audio and reading the transcript concurrently). This serves two functions: familiarising yourself with the data and checking for any errors in the transcript. During transcription, it may be necessary to remove identifying information from the transcripts, for example, names, places and age, so that your participants cannot be identified.

Resources for transcription


Coding is the second step in analysing qualitative data. In coding, you assign phrases which identify key features of your data. Once you have coded your data, you then identify themes or categories (depending on the type of analysis you choose to use). You may manage the coding process manually, using highlighting on paper or in word processing software (as Leonie did-see Box 1 below) or using qualitative data analysis software such as NVivo. To improve the rigour of your research, you may co-code your data with your supervisors to reach agreement on codes and themes.

When you code your data, it is important to remember reflexivity as something that may influence your interpretation and analysis. This is particularly important if you are a stakeholder in your research. For example, Leonie worked in the same area as her research subjects and therefore needed to make sure she didn't just assume what the interviewee was meaning when discussing practice situations.

Example of highlighting text and developing early codes from Leonie's research

  • CASE examples
  • Team issues
  • Values held by VET
  • Principles and norms which guide practice
  • Pivotal experiences
  • How to support students/new graduate
  • Strategies for dealing with ethical challenges

Greeting & explanation of project.

L So I want you to just have a little think, um and think back over your career um where they might've been some situations that it was difficult for you to make a decision either ethically or morally and you weren't quite sure what the right thing to do was, or you wanted to do the right thing but you felt you couldn't in your own mind. Can you think of some situations like that?

J Um I, I don't think, pause, wow. Pause, yes, I've occasionally come across ethical dilemmas, um, in work. For example, the typical ones that we tend to face are, what do you do when you've got a sick or injured animal and the owner has no money, you know, that's a typical, I imagine, that's an ethical dilemma
Sometimes, um, for example, euthanasia for reasons that you may not agree with
However, those types of ethical dilemmas, and again I don't want to pre-empt your questions too much

L Yeah, no no, just keep going

J Those types of ethical dilemmas don't generally cause me, personally, a lot of um anxiety or, or, emotional, pause, issues or anything like that. As a matter of fact, the, pause, and I don't know if you'd class this as ethical, but it certainly would relate to students and employees

But the biggest ethical dilemmas that I tend to face, pause, are where I want to do something for my client or I, or I wish to um do something but my business partner or my team disagree with me
alright, let's use specifically euthanasia as a moral dilemma or ethical dilemma. I was raised in a family where animals were considered utility and not members of the family, um, I have never felt it a challenge, I've never got an emotional fatigue, I've never felt an issue, um, with euthanasia. For, pause, for, pause, um, whatever reason.

Because I've always practiced as a veterinarian from the viewpoint that the, the, animal is an object that is owned by the owner and they have the ultimate say, and, by having that philosophy, I've never really had any confronting issue.

Where I get confronted is if I have got a dog that's presented to me because it's barking 24 hours a day, it's a well looked after pet, but the owners have had neighbour complaints, they've had, pause, the council come around, um these are actual examples of what have happened, and they've presented it to me saying we cannot live with this dog anymore, this dog cannot live with us anymore, we've tried to rehouse it, um, we don't want to go down the path of debarking, it's an illegal procedure but we, you know, we've had that discussion, and I've gone, I get that

J I get that, this dog is no suitable, not suitable for your family anymore, um, it is what my staff and my colleagues will say about me, pause, or to me that gives me the anxiety

because I will often have my business partner or my staff go, you can't put down a healthy dog so the moral dilemma is, you know, part of me is, pause, the ethical dilemma is, part of me is, this dog is owned by this owner, I understand this owner is having great difficulty, I don't have an emotional connection with this patient
I am legally allowed to put this dog to sleep, and therefore, I'm happy to do it

but, my colleagues, pause, would, pause, look at me and go, that's a healthy dog, you know, have you given this owner all their options, pause, you know, shouldn't you try to rehouse it, shouldn't you get them to surrender it, well they don't want to surrender it, they want, they don't want to think that it might get put to sleep after 8 days, or not know which home

So, if I may summarise, I think where I tend to face ethical dilemmas in my, pause, personal practice, pause, is, pause, not my interaction with clients or what they might want to do, but with my concerns of how I might appear or be viewed, pause, by my peers.

and that tends to cause me more ethical dilemma. I'll give you another example, pause, client comes in and can't pay for something, it, pause, certainly if it's something major, broken leg, thankfully in our profession we can refer those to animal welfare organisations

but if it's something like you know, er, something small, you asked, you know, big or small, something small, the client who may have been to us 5 times, who's never had a problem paying the bill, who wants to have an account for a minor procedure, you know the dog has to be stitched up and they don't have the money with them right now

another example, I came in, you know, the leg was obviously fractured, the owner would not let me put the cat to sleep and had no money to treat the cat and I got the distinct impression that they were literally going to go home and, and kill this cat

so I have said to these people in those circumstances, I will put your cat to sleep 'cause your cat is in pain, your cat is suffering, I will put your cat to sleep at no charge to you, so that your cat doesn't have to suffer anymore and these people have declined that
and said, nah we'll we'll take it home 20 cent bullet will fix it

under those circumstances, I have said to people, if you leave this premises, you need to tell me where you're going, and I will follow up and make sure you get there, because if you don't, I will be calling the RSPCA and making an investigation in cruelty against your treatment, now, in those circumstances, I have not forcibly taken the cat.

Resources for coding

Developing themes

Two common types of data analysis beyond coding are thematic and inductive content analysis. Both of these approaches focus on identifying patterns and meanings across a data set. Nabreesa and Leonie used thematic analysis to initially code the content of their transcript data and then to cluster that content into themes.

Resources for developing themes

Data saturation

Achieving saturation in data analysis is often referred to as the point in your analysis when no new themes or ideas or patterns are emerging. However it is a concept that is hard to define and some argue it is difficult to ever claim to have reached absolute saturation from data analysis because there are always new ideas that could emerge from your data depending on your own understanding and ways of thinking about your data.

Resources for data saturation

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