[Auto-generated transcript. Edits may have been applied for clarity.]
Hi everyone, and welcome to this lecture which is an introduction to ethical research Practice.

So the aim of this, what I'm focusing on today, and that I hope you can take away, is an understanding of the core principles of ethical research,

an awareness of some of the key ethical practice that we use throughout the research cycle,

for you to think critically about who is included and represented in research and our research findings,

as well as appreciating the wider impact of how we interpret and communicate

our research findings and what those impacts can have on people and society.

So ethics isn't just something that happens before we start the research.

So we often think think of ethics as an application that we make before the research study starts.

And also. Informed consent, which takes place before someone takes part in the research.

But as researchers, we have to make ethical decisions that run all through the research cycle.

So this can include what questions to ask, making sure that these are theoretically grounded and ethically sound,

as well as how we design our study, how we'll measure these concepts, how we will recruit people and who we will recruit when we collect data,

how we collect that, how we safely store that,

as well as making decisions around the best and most appropriate ways to analyse our data as well as, and interpret and report it appropriately.

So to guide all these ethical decisions that we have to make,

we follow some key principles that are outlined by the British Psychological Society are the principles of ethical research.

So there are four key principles of the Code of human research ethics.

And the first one is respect for autonomy, privacy and dignity.

So this means that participants in our research, it reminds us that they are people.

So they're not data subjects. We don't just gain data from them, but they have a right to choose what they do or do not take part in,

as well as their right to privacy of their information and their dignity as well.

Okay. So in terms of scientific integrity,

we've spoken about this earlier on in the module around things like making sure our claims are rooted in solid theory and evidence,

as well as conducting and communicating research in an honest and rigorous way.

And for that to be transparent so that other researchers and people can really interrogate it.

For social responsibility, this really asks us to step out of our small niche little research,

study or area and consider the wider consequences of our research.

So that is our responsibility as psychological researchers to really educate ourselves and to

be thinking about the societal context and the potential impact that our work could have.

And lastly, maximising benefit and minimising harm.

So we are often rightly focussed on minimising harm.

So reducing any risks to participants, of course.

But we should also be thinking about how we can maximise the benefits of our research as well.

So really, um, helping and making sure that we pull out any potential impacts that come from our findings

so that we do the best work possible for the people who we actually do research for,

which is our participants, and making sure that benefit comes out of our research.

And hopefully you'll be able to see how those principles I've just spoken about are embedded and align with these practices.

So firstly informed consent. So you may all be familiar with this.

Informed consent in its simplest and practical sense, is when a participant thinks about taking part in a research study,

they will read a participant information sheet, and that information sheet will tell them what they will do and what the risks?

And the benefits are of the research?

Um, whether they can change their mind and how they can change their mind and withdraw their data at any point in the study.

And, um, when the participant has read and understood these, these things in the participant information sheet,

then they will be asked to sign a consent form to say that they agree to take part, and then they're happy with all of this.

But beyond kind of what's going to happen to them in the study, they also have the right to understand why the study is being conducted.

So why we are doing this research and how their data will be used.

Okay. So it's our responsibility to make sure that our potential participants have all the all of this information so that they can decide,

um, if they want their information to be, to be used that way.

So I'll just give you a brief example here from my research.

So I do research with people who are profoundly deaf and also people who are profoundly deaf and use cochlear implants to access sound.

So cochlear implantation can be quite a controversial topic in the deaf community.

And I won't go into all those reasons why. But when I conduct research with people who are deaf and who don't have a cochlear implant,

even though my study isn't directly about cochlear implants,

I have to think about whether their data could inform cochlear implant care in the future.

So can the scientific findings from a group of deaf individuals inform this area in the future?

And so it's my responsibility to think about that,

and to explain the wider purpose sufficiently so that participants can decide if they want to take part or not.

So in terms of the information that you give your participants,

this also needs to be provided in an accessible format, so using plain language that everyone can understand.

And in my example, I would need to provide this information in British Sign Language rather than written English.

So my key takeaway for you here is that meaningful,

informed consent really involves providing both full and accessible information

to participants so that they can decide whether they want to take part.

Okay, so it's not for us to convince people to take part in our study,

but it's our role to give them the information in a way that they can understand so that they can make their own decision.

Okay, so debriefing and deception. So debriefing takes place when the participant has conducted or taking part in the study.

So it is our responsibility to explain what's happened to them, what they've just done, why they've done it.

And just kind of close, close the loop for them.

Remind them of key information, for example, um, how they can withdraw from the study and withdraw their data if they want to.

This is also the place where we can provide them with some signposting, for example to support services.

So if the research study brought up emotional topics, then signposting them to wellbeing services for example.

So debriefing is really valuable even when there's no deception involved.

Okay, so it matters even when we don't withhold information from participants.

So sometimes we, um, we might withhold some information from participants at the beginning of the study because it might bias their responses.

And it might mean that we can't answer our research question.

But this withholding of information must be justified, and the risks must be minimal to participants.

So with debriefing and deception, it's important to remember that the participants rights matter more than scientific convenience.

Okay, so if they want to withdraw, then that is absolutely their right.

Again, we shouldn't be trying to convince them to stay in the study.

Okay. So when we do collect data from our participants, it's our responsibility to protect this information that we collect.

And we have a legal obligation to do this responsibly.

In the UK we're governed by the UK GDPR, General Data Protection Regulation and the Data Protection Act.

So these are quite hefty pieces of legislation. So you don't need to, um, read and understand all of these.

But I'm just introducing you to these today because this is what we follow as researchers and the university help us to do this as well.

So they give us guidance and support as researchers to make sure that we are collecting

and storing data and using data responsibly and in line with these principles.

So there's a few principles of GDPR.

I'm just highlighting three for you today that I think are particularly relevant, especially at this point in your studies.

So firstly then transparency, which we've spoken about previously, this means being clear about what data are being collected and also why.

So how they're going to be used. Okay. And so you can see that this links in and is sort of covered by the informed consent and debriefing processes.

Secondly then data minimisation. So this means collecting only the information from participants that is necessary to answer your research question.

So we can't just collect everything and anything that we think might be interesting.

Um, we've had discussions, uh, earlier in the module about, for example, looking at the links between amount of screen time and mental health.

And we spoke about these being quite broad concepts.

And so for data minimisation, we really need to be thinking specifically about what it is exactly that we're going to measure.

So what is screen time for us. What is mental health for us.

And we need to be thinking about how to measure that in a way that answers your questions, but doesn't collect more data than you need.

Security and confidentiality. So this means protecting the data that we collect and limiting access to it so we can password protect, we can encrypt.

We can also make sure that only the research team has access to this data.

Okay, so one way and a common way that we help to protect participant information is by removing the participants name or not using their name at all.

So we often use participant ID codes such as this 10012.

And we might have a document where we have the participants names and then their codes linked to them.

And what this means. It doesn't mean that the data is anonymous, because we're only using the data code stored against their research data.

And this is because this this link, this key exists between their name and the code.

Okay. So all the time that this link exists, it means that the information that their research information,

their research responses to questionnaires, for example, it means that that information is pseudonymous and it still requires a protection.

So it's not fully anonymous. And typically, um, most data from humans is not ever fully anonymous.

And this is because even if we didn't collect a a participant's name and have this key linking the name to the code,

we often collect other data about other characteristics.

And when these are put together this could identify someone.

So for example date of birth ethnicity IP addresses which are commonly um acquired during online studies.

So it's just an important distinction here to make,

just to make you aware that just by giving someone a code doesn't necessarily make their data anonymous, if it can in any way be identified.

so we can think about who can actually access the study.

So, you know, we need to advertise our studies widely so that anyone who might be interested can access the opportunity to take part.

We need to think about things such as travel costs and reimbursing them to make sure that, again,

those people who want to take part can that there aren't any unfair barriers to taking part.

So advertising widely and thinking about appropriate measures like that.

We also need to ask who is included or excluded from our studies and whether this is intentional or unintentional.

So quite often this is intentional.

And when we think about inclusion and exclusion criteria for our studies, and this is because of the scientific question that we're asking.

So again, with my example, if I am conducting a study with people who are profoundly deaf,

then I will have exclusion criteria and inclusion criteria around levels of deafness and I won't be able to test.

Hearing people, for example, unless I want or need a control group.

So that's very intentional. But other times we often include or exclude people.

Somewhat unintentionally.

And this is because you haven't explicitly decided to do this, but they're excluded due to other systemic factors and barriers that exist.

And so it's our responsibility to be thinking about these barriers in our research and

how we can reduce them so that we ensure a large variety and representative sample.

So I just want to give you a Example from my research area where I've been working with my fantastic team

here to think about how we can make our neuroimaging practices more inclusive and not exclude people.

And even if that is unintentional exclusion. So very briefly, I use a neuroimaging method called functional near-infrared spectroscopy, or fNIRs.

And this uses light to measure participants brain activity. So the participant will will come in, they'll put this cap on their head.

And we'll clip these lights in all these optodes in.

And these optodes need to have good clear contact with the scalp in order to measure brain activation.

So what this means is that because of the way that the technology has actually been designed and with these lights and how thick they are,

it means that the technology doesn't work the same way for everyone.

And this is because the amount of contact that we can get between those optodes and the scalp depends on participants hair type,

hair volume, density, and current hairstyle. And so for example,

we can see in this image here an illustration that curly and coily hair types are more likely to push the optodes away from the scalp.

And so measurement quality and our ability to measure using this equipment can be reduced.

But this may not be the case for someone who has a straight, um, type one hair.

So what this means then, is that people with textured hair are often excluded from these types of neuroimaging studies,

and this disproportionately affects global majority ethnic groups because these physical hair characteristics are more prevalent in those groups.

And so that really matters on multiple levels, because not only does it weaken our science by not including a large population of people,

but it also perpetuates systemic marginalisation and leaves and leaves this data and people out of important scientific research.

So inclusion in research is a core ethical and scientific issue.

We really need to be considering whose data can enter the evidence base and how our methods and our processes impact that.

Okay. So lastly I'm going to highlight that we need to consider how we interpret our findings and

the impact that these interpretations and how we communicate our research findings can have.

Okay, so ethics doesn't end once we've collected the data.

So we need to select appropriate analysis methods.

And when we think about correlation for example we know that correlation does not establish causation.

And so if we have a finding such as this one here screen time and anxiety are positively correlated.

It would be an over claim for us to say that screen time causes anxiety.

Okay. So reporting that goes beyond what correlation analysis can tell us.

And that is an ethical concern. So we really shouldn't be making these claims as researchers.

These claims can shape media stories. They can shape policy.

They can shape public attitudes and views about people.

So in terms of when we think about the principles of ethical research, scientific integrity and social responsibility, again,

it's our responsibility as psychologists to both analyse the data appropriately, but also interpret it and report it appropriately.

So not selectively present data,

not exaggerate our results and distinguish association from causation and acknowledge any uncertainty and limitations of our findings.

So we really need to bear in mind that how we interpret and communicate findings has consequences and can affect individuals and communities.

So on a final note, I want to acknowledge and make you aware that some foundational statistical techniques that we use today,

such as correlation and the developers such as Karl Pearson, are um,

or used these methods historically in attempts to quantify human differences in support of eugenic ideas.

So the statistics and the mathematics that we use today are, you know, they haven't inherited those political aims as such.

But it is important that we're aware of how some of these techniques have been used.

And it's a really powerful reminder that statistics don't just describe society, but they can actively shape it.

And the responsibility lies with us as scientists and psychologists to make sure that we interpret and communicate these responsibilities responsibly,

because they do have and can have large consequences.

So I just wanted to point this out to you. I think it's important that we're aware of it.

And if you do want to find out more, then this link provided here to the UCL website is a good place to start to look at the history of that.

the message that I really want you to take away from all of this is that ethics isn't just a box to tick on a consent form before a study starts.

It's really part of how we design, how we conduct, how we analyse,

and how we communicate research as well as who we recruit and how we recruit people.

And so we really need to be thinking about the ethics of our research practice through the whole research cycle.