Learning Materials
Features
Discover
As a high school student, you should be no stranger to research. You might have had to carry out research for any number of essays, projects, or assignments during your studies.
Millions of flashcards designed to help you ace your studies
Sign up for free
Achieve better grades quicker with Premium
Geld-zurück-Garantie, wenn du durch die Prüfung fällst
Review generated flashcards
Start learning or create your own AI flashcards
Team Ethical Issues In Data Collection Teachers
No matter the purpose of the research, it's essential to ensure that your research and data collection methods are as effective and ethical as possible. That's what this article is all about!
On that note, let's dive in and learn about ethical issues in data collection.
Data collection is vital to any research, no matter your topic. As mentioned briefly above, ensuring that the data collection you do as part of your research is as ethical and 'above board' as possible is essential.
What does it mean to be ethical, and why is it so important where research is concerned? The following section will help to fill in these blanks.
When discussing being ethical, is it as simple as just doing things the right way? Let's take a look at a definition:
'Ethical' refers to the understanding of the principles of morality. If someone is ethical, they understand the difference between what is morally right and wrong and decide to behave in a morally correct way. The opposite would be someone understanding the difference between right and wrong and deciding to do the wrong thing.
What are some examples of general ethical behavior?
Handing in a dropped wallet to a police station
Whistleblowing within a company or industry to ensure unjust acts can be addressed appropriately
Being loyal in a relationship
Maintaining confidentiality and privacy within professional and personal situations
Letting a server know you've been undercharged in a restaurant
Showing compassion to others
Not telling lies
Can you think of any times when you acted ethically in a situation? Think about any scenarios where you might have been faced with a difficult choice and decided to do 'the right thing.'
Fig 1. Being ethical is all about knowing the difference between what's morally right and wrong
Now that we've got a better understanding of what it means to be ethical, how do we apply this to data collection?
Being ethical in data collection simply means using data collection methods that will not have a negative impact on anyone or anything or have as little negative impact as possible.
We'll now look at different kinds of data collection activities before exploring the ethical issues that might arise during data collection.
What kinds of activities count as data collection methods? In other words, what techniques or strategies can we use to collect data?
First, let's establish the two main types of data collection: qualitative and quantitative.
Qualitative data collection gathers descriptive or quality-related information, such as people's feelings or opinions on a particular topic. Qualitative data is less concerned with statistics or numbers and focuses more on people's experiences, judgments, feelings, and understandings.
Some examples of qualitative data collection methods include:
Quantitative data collection refers to gathering quantifiable or countable information. Quantitative data is heavily influenced by numbers and statistics and often relates to things that follow patterns, trends, or correlations.
Some examples of quantitative data collection methods include:
These are examples of data collection activities you can carry out during your research phase. The methods you choose will depend on the kind of data you're trying to gather (i.e., qualitative or quantitative).
Fig 2. Make sure you know what kind of research you need before starting data collection
Now that we know a little more about the different kinds of data collection and a few examples of data collection activities, we can get into the real meat of the article.
Here are some of the most common ethical issues that arise during data collection:
Lack of consent. When collecting data, regardless of your method, you must obtain consent from your participants. This could be as simple as asking for their permission to be observed or getting them to sign a consent form before partaking in a study.
When you visit a website, you've probably noticed a little box that pops up asking you to consent to cookies. 'Cookies' on the internet are small data files that track your search and browsing habits so websites can remember your patterns and preferences. The little box that pops up usually asks you to accept or reject that website's cookies - this is an example of a data collection process asking for consent.
Being unclear about intent. If you are collecting data, you need to let your participants know what the collected data will be used for. This ties in closely with consent as it allows the participants to make a more informed decision about the extent of their involvement.
Lack of confidentiality or anonymity. In most cases, people are happy to give their answers to surveys, questionnaires, or interviews if they remain anonymous or if the information they provide will be kept confidential. Sharing identifying information about your participants without them knowing is a significant ethical issue.
You should let participants know of any scenarios that would lead you to share confidential information (e.g., if you were worried about their safety or thought they posed a risk to themselves or others).
Lack of appropriate compensation. Where data collection is concerned, the researcher needs to understand that they are subjecting their participants to a certain degree of inconvenience. If a participant needs to travel to a certain location to take part in a focus group or interview, for example, they will have incurred some transport costs. It is then fair to offer logical and appropriate compensation to reimburse out-of-pocket expenses.
It won't always be necessary to compensate participants – for example, asking someone to fill in a quick, three-question poll with multiple choice answers provided would not impose a significant inconvenience upon them. However, participants should be reimbursed if your research incurs costs for them. Methods such as sleep studies and focus groups often provide financial compensation to participants.
Taking advantage of vulnerable people or groups that are easy to access. This probably goes without saying, but data collection should never take advantage of vulnerable people (data collection shouldn't take advantage of anyone). Data collection should be as representative as possible and shouldn't be gathered only from convenient or 'easy' sources.
Lack of relevance. It can be tempting to approach people or groups you know will give you the answers or data you're looking for, but you must ensure your data collection spans the relevant demographics. One person might give you an easy answer, but if they aren't part of your target demographic or if they aren't going to contribute to the relevance of your study, then they should be avoided.
Suppose you were gathering data about menstrual pain using a questionnaire. In that case, a cis-gendered man might be able to give the information he's learned from women he knows, but this would not be relevant or helpful as it is not a first-person account. Instead, asking people who have (or have had) periods would be more relevant.
Being biased. When collecting data, you need to be as impartial as possible. If the topic you're researching is too close to you or if your participants are people you know, it might be best to change your focus or find another group of participants. You should also ensure that your own beliefs and biases do not impact your perception of data.
This is not an exhaustive list of ethical issues to consider during data collection, but it should give you a decent idea of what to keep in mind.
Fig 3. Gaining consent and being respectful is important in data collection
It is best practice to ensure you have done everything in your power to consider all potential ethical risks before proceeding with your research.
If a complicated situation arises, and you aren't sure whether something is ethical or not, think to yourself, 'would I be happy with this if I were the participant?'. If the answer is 'no,' you probably shouldn't be doing it.
If you are required to collect data that requires input from other people, you must consider all the possible ethical implications of your research. As part of this consideration process, you will likely need to fill out an ethics form or application outlining all the factors you have taken into account. Some of the factors you will be asked about on the form include:
This is not an exhaustive list of considerations but should give you an idea of what to expect from an ethics application.
Sign up for free to gain access to all our flashcards.
Why do we need to consider ethical issues in collecting data explain?
It is important to consider ethical issues when collecting data collection because as researchers, we must ensure we are being as ethical and unbiased as possible. This will help us to ensure we are not causing undue harm, and that we are respecting our participants and sources properly.
What are the 3 ethical issues in research?
Three potential ethical issues in research include:
What are ethics in data collection?
In data collection, 'ethics' refers to the moral standards that researchers must adhere to, to ensure that their data collection is fair, thorough, and does not take advantage of participants or sources.
What are some examples of ethical issues?
Some examples of ethical issues include:
What are ethical concerns in research?
An ethical concern in research is when you are worried that a part of your research or data collection processes are unethical, harmful, biased, irrelevant, or unclear in intention. Research must aim to be purposeful, fair, and ethical.
At StudySmarter, we have created a learning platform that serves millions of students. Meet the people who work hard to deliver fact based content as well as making sure it is verified.
Lily Hulatt is a Digital Content Specialist with over three years of experience in content strategy and curriculum design. She gained her PhD in English Literature from Durham University in 2022, taught in Durham University’s English Studies Department, and has contributed to a number of publications. Lily specialises in English Literature, English Language, History, and Philosophy.
Get to know Lily
Gabriel Freitas is an AI Engineer with a solid experience in software development, machine learning algorithms, and generative AI, including large language models’ (LLMs) applications. Graduated in Electrical Engineering at the University of São Paulo, he is currently pursuing an MSc in Computer Engineering at the University of Campinas, specializing in machine learning topics. Gabriel has a strong background in software engineering and has worked on projects involving computer vision, embedded AI, and LLM applications.
Get to know Gabriel
Explanations 
Flashcards 
StudySmarter AI 
Notes 
Study Plans 
Study Sets 
Exams 
Find a job 
Student Deals 
Magazine 
Mobile App