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Ethical Considerations – Types, Examples and Writing Guide

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Ethical Considerations

Ethical considerations in research refer to the principles and guidelines that researchers must follow to ensure that their studies are conducted in an ethical and responsible manner. These considerations are designed to protect the rights, safety, and well-being of research participants, as well as the integrity and credibility of the research itself

Some of the key ethical considerations in research include:

• Informed consent: Researchers must obtain informed consent from study participants, which means they must inform participants about the study’s purpose, procedures, risks, benefits, and their right to withdraw at any time.
• Privacy and confidentiality : Researchers must ensure that participants’ privacy and confidentiality are protected. This means that personal information should be kept confidential and not shared without the participant’s consent.
• Harm reduction : Researchers must ensure that the study does not harm the participants physically or psychologically. They must take steps to minimize the risks associated with the study.
• Fairness and equity : Researchers must ensure that the study does not discriminate against any particular group or individual. They should treat all participants equally and fairly.
• Use of deception: Researchers must use deception only if it is necessary to achieve the study’s objectives. They must inform participants of the deception as soon as possible.
• Use of vulnerable populations : Researchers must be especially cautious when working with vulnerable populations, such as children, pregnant women, prisoners, and individuals with cognitive or intellectual disabilities.
• Conflict of interest : Researchers must disclose any potential conflicts of interest that may affect the study’s integrity. This includes financial or personal relationships that could influence the study’s results.
• Data manipulation: Researchers must not manipulate data to support a particular hypothesis or agenda. They should report the results of the study objectively, even if the findings are not consistent with their expectations.
• Intellectual property: Researchers must respect intellectual property rights and give credit to previous studies and research.
• Cultural sensitivity : Researchers must be sensitive to the cultural norms and beliefs of the participants. They should avoid imposing their values and beliefs on the participants and should be respectful of their cultural practices.

Types of Ethical Considerations

Types of Ethical Considerations are as follows:

Research Ethics:

This includes ethical principles and guidelines that govern research involving human or animal subjects, ensuring that the research is conducted in an ethical and responsible manner.

Business Ethics :

This refers to ethical principles and standards that guide business practices and decision-making, such as transparency, honesty, fairness, and social responsibility.

Medical Ethics :

This refers to ethical principles and standards that govern the practice of medicine, including the duty to protect patient autonomy, informed consent, confidentiality, and non-maleficence.

Environmental Ethics :

This involves ethical principles and values that guide our interactions with the natural world, including the obligation to protect the environment, minimize harm, and promote sustainability.

Legal Ethics

This involves ethical principles and standards that guide the conduct of legal professionals, including issues such as confidentiality, conflicts of interest, and professional competence.

Social Ethics

This involves ethical principles and values that guide our interactions with other individuals and society as a whole, including issues such as justice, fairness, and human rights.

Information Ethics

This involves ethical principles and values that govern the use and dissemination of information, including issues such as privacy, accuracy, and intellectual property.

Cultural Ethics

This involves ethical principles and values that govern the relationship between different cultures and communities, including issues such as respect for diversity, cultural sensitivity, and inclusivity.

Technological Ethics

This refers to ethical principles and guidelines that govern the development, use, and impact of technology, including issues such as privacy, security, and social responsibility.

Journalism Ethics

This involves ethical principles and standards that guide the practice of journalism, including issues such as accuracy, fairness, and the public interest.

Educational Ethics

This refers to ethical principles and standards that guide the practice of education, including issues such as academic integrity, fairness, and respect for diversity.

Political Ethics

This involves ethical principles and values that guide political decision-making and behavior, including issues such as accountability, transparency, and the protection of civil liberties.

Professional Ethics

This refers to ethical principles and standards that guide the conduct of professionals in various fields, including issues such as honesty, integrity, and competence.

Personal Ethics

This involves ethical principles and values that guide individual behavior and decision-making, including issues such as personal responsibility, honesty, and respect for others.

Global Ethics

This involves ethical principles and values that guide our interactions with other nations and the global community, including issues such as human rights, environmental protection, and social justice.

Applications of Ethical Considerations

Ethical considerations are important in many areas of society, including medicine, business, law, and technology. Here are some specific applications of ethical considerations:

• Medical research : Ethical considerations are crucial in medical research, particularly when human subjects are involved. Researchers must ensure that their studies are conducted in a way that does not harm participants and that participants give informed consent before participating.
• Business practices: Ethical considerations are also important in business, where companies must make decisions that are socially responsible and avoid activities that are harmful to society. For example, companies must ensure that their products are safe for consumers and that they do not engage in exploitative labor practices.
• Environmental protection: Ethical considerations play a crucial role in environmental protection, as companies and governments must weigh the benefits of economic development against the potential harm to the environment. Decisions about land use, resource allocation, and pollution must be made in an ethical manner that takes into account the long-term consequences for the planet and future generations.
• Technology development : As technology continues to advance rapidly, ethical considerations become increasingly important in areas such as artificial intelligence, robotics, and genetic engineering. Developers must ensure that their creations do not harm humans or the environment and that they are developed in a way that is fair and equitable.
• Legal system : The legal system relies on ethical considerations to ensure that justice is served and that individuals are treated fairly. Lawyers and judges must abide by ethical standards to maintain the integrity of the legal system and to protect the rights of all individuals involved.

Examples of Ethical Considerations

Here are a few examples of ethical considerations in different contexts:

• In healthcare : A doctor must ensure that they provide the best possible care to their patients and avoid causing them harm. They must respect the autonomy of their patients, and obtain informed consent before administering any treatment or procedure. They must also ensure that they maintain patient confidentiality and avoid any conflicts of interest.
• In the workplace: An employer must ensure that they treat their employees fairly and with respect, provide them with a safe working environment, and pay them a fair wage. They must also avoid any discrimination based on race, gender, religion, or any other characteristic protected by law.
• In the media : Journalists must ensure that they report the news accurately and without bias. They must respect the privacy of individuals and avoid causing harm or distress. They must also be transparent about their sources and avoid any conflicts of interest.
• In research: Researchers must ensure that they conduct their studies ethically and with integrity. They must obtain informed consent from participants, protect their privacy, and avoid any harm or discomfort. They must also ensure that their findings are reported accurately and without bias.
• In personal relationships : People must ensure that they treat others with respect and kindness, and avoid causing harm or distress. They must respect the autonomy of others and avoid any actions that would be considered unethical, such as lying or cheating. They must also respect the confidentiality of others and maintain their privacy.

How to Write Ethical Considerations

When writing about research involving human subjects or animals, it is essential to include ethical considerations to ensure that the study is conducted in a manner that is morally responsible and in accordance with professional standards. Here are some steps to help you write ethical considerations:

• Describe the ethical principles: Start by explaining the ethical principles that will guide the research. These could include principles such as respect for persons, beneficence, and justice.
• Discuss informed consent : Informed consent is a critical ethical consideration when conducting research. Explain how you will obtain informed consent from participants, including how you will explain the purpose of the study, potential risks and benefits, and how you will protect their privacy.
• Address confidentiality : Describe how you will protect the confidentiality of the participants’ personal information and data, including any measures you will take to ensure that the data is kept secure and confidential.
• Consider potential risks and benefits : Describe any potential risks or harms to participants that could result from the study and how you will minimize those risks. Also, discuss the potential benefits of the study, both to the participants and to society.
• Discuss the use of animals : If the research involves the use of animals, address the ethical considerations related to animal welfare. Explain how you will minimize any potential harm to the animals and ensure that they are treated ethically.
• Mention the ethical approval : Finally, it’s essential to acknowledge that the research has received ethical approval from the relevant institutional review board or ethics committee. State the name of the committee, the date of approval, and any specific conditions or requirements that were imposed.

When to Write Ethical Considerations

Ethical considerations should be written whenever research involves human subjects or has the potential to impact human beings, animals, or the environment in some way. Ethical considerations are also important when research involves sensitive topics, such as mental health, sexuality, or religion.

In general, ethical considerations should be an integral part of any research project, regardless of the field or subject matter. This means that they should be considered at every stage of the research process, from the initial planning and design phase to data collection, analysis, and dissemination.

Ethical considerations should also be written in accordance with the guidelines and standards set by the relevant regulatory bodies and professional associations. These guidelines may vary depending on the discipline, so it is important to be familiar with the specific requirements of your field.

Purpose of Ethical Considerations

Ethical considerations are an essential aspect of many areas of life, including business, healthcare, research, and social interactions. The primary purposes of ethical considerations are:

• Protection of human rights: Ethical considerations help ensure that people’s rights are respected and protected. This includes respecting their autonomy, ensuring their privacy is respected, and ensuring that they are not subjected to harm or exploitation.
• Promoting fairness and justice: Ethical considerations help ensure that people are treated fairly and justly, without discrimination or bias. This includes ensuring that everyone has equal access to resources and opportunities, and that decisions are made based on merit rather than personal biases or prejudices.
• Promoting honesty and transparency : Ethical considerations help ensure that people are truthful and transparent in their actions and decisions. This includes being open and honest about conflicts of interest, disclosing potential risks, and communicating clearly with others.
• Maintaining public trust: Ethical considerations help maintain public trust in institutions and individuals. This is important for building and maintaining relationships with customers, patients, colleagues, and other stakeholders.
• Ensuring responsible conduct: Ethical considerations help ensure that people act responsibly and are accountable for their actions. This includes adhering to professional standards and codes of conduct, following laws and regulations, and avoiding behaviors that could harm others or damage the environment.

Advantages of Ethical Considerations

Here are some of the advantages of ethical considerations:

• Builds Trust : When individuals or organizations follow ethical considerations, it creates a sense of trust among stakeholders, including customers, clients, and employees. This trust can lead to stronger relationships and long-term loyalty.
• Reputation and Brand Image : Ethical considerations are often linked to a company’s brand image and reputation. By following ethical practices, a company can establish a positive image and reputation that can enhance its brand value.
• Avoids Legal Issues: Ethical considerations can help individuals and organizations avoid legal issues and penalties. By adhering to ethical principles, companies can reduce the risk of facing lawsuits, regulatory investigations, and fines.
• Increases Employee Retention and Motivation: Employees tend to be more satisfied and motivated when they work for an organization that values ethics. Companies that prioritize ethical considerations tend to have higher employee retention rates, leading to lower recruitment costs.
• Enhances Decision-making: Ethical considerations help individuals and organizations make better decisions. By considering the ethical implications of their actions, decision-makers can evaluate the potential consequences and choose the best course of action.
• Positive Impact on Society: Ethical considerations have a positive impact on society as a whole. By following ethical practices, companies can contribute to social and environmental causes, leading to a more sustainable and equitable society.

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Muhammad Hassan

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Ethical Considerations

Ethical Considerations can be specified as one of the most important parts of the research. Dissertations may even be doomed to failure if this part is missing.

According to Bryman and Bell (2007) [1] the following ten points represent the most important principles related to ethical considerations in dissertations:

• Research participants should not be subjected to harm in any ways whatsoever.
• Respect for the dignity of research participants should be prioritised.
• Full consent should be obtained from the participants prior to the study.
• The protection of the privacy of research participants has to be ensured.
• Adequate level of confidentiality of the research data should be ensured.
• Anonymity of individuals and organisations participating in the research has to be ensured.
• Any deception or exaggeration about the aims and objectives of the research must be avoided.
• Affiliations in any forms, sources of funding, as well as any possible conflicts of interests have to be declared.
• Any type of communication in relation to the research should be done with honesty and transparency.
• Any type of misleading information, as well as representation of primary data findings in a biased way must be avoided.

In order to address ethical considerations aspect of your dissertation in an effective manner, you will need to expand discussions of each of the following points to at least one paragraph:

1. Voluntary participation of respondents in the research is important. Moreover, participants have rights to withdraw from the study at any stage if they wish to do so.

2. Respondents should participate on the basis of informed consent. The principle of informed consent involves researchers providing sufficient information and assurances about taking part to allow individuals to understand the implications of participation and to reach a fully informed, considered and freely given decision about whether or not to do so, without the exercise of any pressure or coercion. [2]

3. The use of offensive, discriminatory, or other unacceptable language needs to be avoided in the formulation of Questionnaire/Interview/Focus group questions.

4. Privacy and anonymity or respondents is of a paramount importance.

5. Acknowledgement of works of other authors used in any part of the dissertation with the use of Harvard/APA/Vancouver referencing system according to the Dissertation Handbook

6. Maintenance of the highest level of objectivity in discussions and analyses throughout the research

7. Adherence to Data Protection Act (1998) if you are studying in the UK

In studies that do not involve primary data collection, on the other hand, ethical issues are going to be limited to the points d) and e) above.

Most universities have their own Code of Ethical Practice. It is critically important for you to thoroughly adhere to this code in every aspect of your research and declare your adherence in ethical considerations part of your dissertation.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline. John Dudovskiy

[1] Bryman, A. &  Bell, E. (2007) “Business Research Methods”, 2nd edition. Oxford University Press.

[2] Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6th edition, Pearson Education Limited.

Ethical considerations in research: Best practices and examples

To conduct responsible research, you’ve got to think about ethics. They protect participants’ rights and their well-being - and they ensure your findings are valid and reliable. This isn’t just a box for you to tick. It’s a crucial consideration that can make all the difference to the outcome of your research.

In this article, we'll explore the meaning and importance of research ethics in today's research landscape. You'll learn best practices to conduct ethical and impactful research.

Examples of ethical considerations in research

As a researcher, you're responsible for ethical research alongside your organization. Fulfilling ethical guidelines is critical. Organizations must ensure employees follow best practices to protect participants' rights and well-being.

Keep these things in mind when it comes to ethical considerations in research:

Voluntary participation

Voluntary participation is key. Nobody should feel like they're being forced to participate or pressured into doing anything they don't want to. That means giving people a choice and the ability to opt out at any time, even if they've already agreed to take part in the study.

Informed consent

Informed consent isn't just an ethical consideration. It's a legal requirement as well. Participants must fully understand what they're agreeing to, including potential risks and benefits.

The best way to go about this is by using a consent form. Make sure you include:

• A brief description of the study and research methods.
• The potential benefits and risks of participating.
• The length of the study.
• Contact information for the researcher and/or sponsor.
• Reiteration of the participant’s right to withdraw from the research project at any time without penalty.

Anonymity means that participants aren't identifiable in any way. This includes:

• Email address
• Photographs
• Video footage

You need a way to anonymize research data so that it can't be traced back to individual participants. This may involve creating a new digital ID for participants that can’t be linked back to their original identity using numerical codes.

Confidentiality

Information gathered during a study must be kept confidential. Confidentiality helps to protect the privacy of research participants. It also ensures that their information isn't disclosed to unauthorized individuals.

Some ways to ensure confidentiality include:

• Using a secure server to store data.
• Removing identifying information from databases that contain sensitive data.
• Using a third-party company to process and manage research participant data.
• Not keeping participant records for longer than necessary.
• Avoiding discussion of research findings in public forums.

Potential for harm

​​The potential for harm is a crucial factor in deciding whether a research study should proceed. It can manifest in various forms, such as:

• Psychological harm
• Social harm
• Physical harm

Conduct an ethical review to identify possible harms. Be prepared to explain how you’ll minimize these harms and what support is available in case they do happen.

Fair payment

One of the most crucial aspects of setting up a research study is deciding on fair compensation for your participants. Underpayment is a common ethical issue that shouldn't be overlooked. Properly rewarding participants' time is critical for boosting engagement and obtaining high-quality data. While Prolific requires a minimum payment of £6.00 / $8.00 per hour, there are other factors you need to consider when deciding on a fair payment.

First, check your institution's reimbursement guidelines to see if they already have a minimum or maximum hourly rate. You can also use the national minimum wage as a reference point.

Next, think about the amount of work you're asking participants to do. The level of effort required for a task, such as producing a video recording versus a short survey, should correspond with the reward offered.

You also need to consider the population you're targeting. To attract research subjects with specific characteristics or high-paying jobs, you may need to offer more as an incentive.

We recommend a minimum payment of £9.00 / $12.00 per hour, but we understand that payment rates can vary depending on a range of factors. Whatever payment you choose should reflect the amount of effort participants are required to put in and be fair to everyone involved.

Ethical research made easy with Prolific

At Prolific, we believe in making ethical research easy and accessible. The findings from the Fairwork Cloudwork report speak for themselves. Prolific was given the top score out of all competitors for minimum standards of fair work.

With over 25,000 researchers in our community, we're leading the way in revolutionizing the research industry. If you're interested in learning more about how we can support your research journey, sign up to get started now.

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Is Quantitative Research Ethical? Tools for Ethically Practicing, Evaluating, and Using Quantitative Research

• Original Paper
• Published: 28 April 2017
• Volume 143 , pages 1–16, ( 2017 )

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• Michael J. Zyphur 1 &
• Dean C. Pierides 2  

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This editorial offers new ways to ethically practice, evaluate, and use quantitative research (QR). Our central claim is that ready-made formulas for QR, including ‘best practices’ and common notions of ‘validity’ or ‘objectivity,’ are often divorced from the ethical and practical implications of doing, evaluating, and using QR for specific purposes. To focus on these implications, we critique common theoretical foundations for QR and then recommend approaches to QR that are ‘built for purpose,’ by which we mean designed to ethically address specific problems or situations on terms that are contextually relevant. For this, we propose a new tool for evaluating the quality of QR, which we call ‘relational validity.’ Studies, including their methods and results, are relationally valid when they ethically connect researchers’ purposes with the way that QR is oriented and the ways that it is done—including the concepts and units of analysis invoked, as well as what its ‘methods’ imply more generally. This new way of doing QR can provide the liberty required to address serious worldly problems on terms that are both practical and ethically informed in relation to the problems themselves rather than the confines of existing QR logics and practices.

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Abrahamson, E., Berkowitz, H., & Dumez, H. (2016). A more relevant approach to relevance in management studies: An essay on performativity. Academy of Management Review, 41, 367–381.

Article   Google Scholar  

American Psychological Association. (2009). Publication manual of the American Psychological Association (6th ed.). Washington, DC: American Psychological Association.

Google Scholar  

Bettis, R. A., Ethiraj, S., Gambardella, A., Helfat, C., & Mitchell, W. (2016). Creating repeatable cumulative knowledge in strategic management. Strategic Management Journal, 37 (2), 257–261.

*Buchholz, R. A., & Rosenthal, S. B. (2008). The unholy alliance of business and science. Journal of Business Ethics, 78 (1), 199–206.

Campbell, D. T. (1957). Factors relevant to the validity of experiments in social settings. Psychological Bulletin, 54, 297–312.

Campbell, D. T. (1991). Methods for the experimenting society. Evaluation Practice, 12 (3), 223–260.

Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research on teaching. In N. L. Gage (Ed.), Handbook of research on teaching (pp. 171–246). Chicago: Rand McNally.

Cartwright, N. (1993). In defence of this worldly’causality: Comments on van Fraassen’s laws and symmetry. Philosophy and Phenomenological Research, 53 (2), 423–429.

Cartwright, N. (2004). Causation: One word, many things. Philosophy of Science, 71 (5), 805–819.

Cartwright, N. (2006). Well-ordered science: Evidence for use. Philosophy of Science, 73 (5), 981–990.

Cartwright, N. (2007). Hunting causes and using them: Approaches in philosophy and economics . Cambridge: Cambridge University Press.

Book   Google Scholar  

*Collison, D., Cross, S., Ferguson, J., Power, D., & Stevenson, L. (2012). Legal determinants of external finance revisited: The inverse relationship between investor protection and societal well-being. Journal of Business Ethics, 108 (3), 393–410.

Cunliffe, A. L. (2003). Reflexive inquiry in organizational research: Questions and possibilities. Human Relations, 56, 983–1003.

Daston, L. (1995). The moral economy of science. Osiris, 10, 2–24.

Daston, L. (2005). Scientific error and the ethos of belief. Social Research, 72, 1–28.

Davies, W. (2017, January 19). How statistics lost their power—And why we should fear what comes next. The Guardian . Retrieved from https://www.theguardian.com/politics/2017/jan/19/crisis-of-statistics-big-data-democracy .

Davis, M. S. (1971). That’s interesting! Towards a phenomenology of sociology and a sociology of phenomenology. Philosophy of the Social Sciences, 1 (4), 309–344.

Deetz, S. (1996). Describing differences in approaches to organization science: Rethinking Burrell and Morgan and their legacy. Organization Science, 7, 191–207.

Dewey, J. (1929). The quest for certainty . New York: Minton, Balch, & Co.

Dunn, W. N. (1982). Reforms as arguments. Knowledge, 3 (3), 293–326.

Erturk, I., Froud, J., Johal, S., Leaver, A., & Williams, K. (2013). (How) do devices matter in finance? Journal of Cultural Economy, 6 (3), 336–352.

Ezzamel, M., & Willmott, H. (2014). Registering ‘the ethical’ in organization theory formation: Towards the disclosure of an ‘invisible force’. Organization Studies, 35, 1013–1039.

Falleti, T. G., & Lynch, J. F. (2009). Context and causal mechanisms in political analysis. Comparative Political Studies, 42 (9), 1143–1166.

Farjoun, M., Ansell, C., & Boin, A. (2015). Pragmatism in organization studies: Meeting the challenges of a dynamic and complex world. Organization Science, 26 (6), 1787–1804.

Feldman, M. S., & Orlikowski, W. J. (2011). Theorizing practice and practicing theory. Organization science .

Freeman, R. E. (2002). Toward a new vision for management research: A commentary on “Organizational researcher values, ethical responsibility, and the committed-to-participant research perspective”. Journal of Management Inquiry, 11 (2), 186–189.

Gabbay, D. M., Hartmann, S., & Woods, J. (2011). Handbook of the history of logic: Inductive logic (Vol. 10). Oxford: Elsevier.

Gelman, A. (2015). The connection between varying treatment effects and the crisis of unreplicable research a Bayesian perspective. Journal of Management, 41, 632–643.

Gigerenzer, G., & Marewski, J. N. (2015). Surrogate science the idol of a universal method for scientific inference. Journal of Management, 41, 421–440.

Gigerenzer, G., Swijtink, Z. G., Porter, T. M., Daston, L., Beatty, J., & Krüger, L. (1989). The empire of chance: How probability changed science and everyday life . Cambridge: Cambridge University Press.

*Greenwood, M. (2016). Approving or improving research ethics in management journals. Journal of Business Ethics , 137 , 1–14.

Hacking, I. (1990). The taming of chance . Cambridge: Cambridge University Press.

Hacking, I. (1992a). Statistical language, statistical truth and statistical reason: The self-authentification of a style of scientific reasoning. In E. McMullin (Ed.), The social dimensions of science (Vol. 3, pp. 130–157). Notre Dame: University of Notre Dame Press.

Hacking, I. (1992b). The self-vindication of the laboratory sciences. In A. Pickering (Ed.), Science as practice and culture (pp. 29–64). Chicago: Chicago Unviersity Press.

Hacking, I. (1999). The social construction of what? . Cambridge: Harvard University Press.

Hacking, I. (2001). An introduction to probability and inductive logic . Cambridge: Cambridge University Press.

Hacking, I. (2002). Historical Ontology . Cambridge: Harvard University Press.

Hacking, I. (2006). The emergence of probability: A philosophical study of early ideas about probability, induction and statistical inference . Cambridge: Cambridge University Press.

Hakala, J., & Ylijoki, O.-H. (2001). Research for whom? Research orientations in three academic cultures. Organization, 8 (2), 373–380.

Hardy, C., & Clegg, S. (1997). Relativity without relativism: Reflexivity in post-paradigm organization studies. British Journal of Management, 8, 5–17.

Hardy, C., Phillips, N., & Clegg, S. (2001). Reflexivity in organization and management theory: A study of the production of the research “subject”. Human Relations, 54, 531–560.

*Hill, R. P. (2002). Stalking the poverty consumer a retrospective examination of modern ethical dilemmas. Journal of Business Ethics, 37 (2), 209–219.

*Holland, D., & Albrecht, C. (2013). The worldwide academic field of business ethics: Scholars’ perceptions of the most important issues. Journal of Business Ethics, 117 (4), 777–788.

Howie, D. (2002). Interpreting probability: Controversies and developments in the early twentieth century . Cambridge: Cambridge University Press.

Huhtala, M., Feldt, T., Lämsä, A. M., Mauno, S., & Kinnunen, U. (2011). Does the ethical culture of organisations promote managers’ occupational well-being? Investigating indirect links via ethical strain. Journal of Business Ethics, 101 (2), 231–247.

Jeanes, E. (2016). Are we ethical? Approaches to ethics in management and organisation research. Organization . doi: 10.1177/1350508416656930 .

*Kaptein, M., & Schwartz, M. S. (2008). The effectiveness of business codes: A critical examination of existing studies and the development of an integrated research model. Journal of Business Ethics, 77 (2), 111–127.

*Keeble, J. J., Topiol, S., & Berkeley, S. (2003). Using indicators to measure sustainability performance at a corporate and project level. Journal of Business Ethics, 44 (2), 149–158.

*Kerssens-van Drongelen, I. C., & Fisscher, O. A. (2003). Ethical dilemmas in performance measurement. Journal of Business Ethics, 45 (1), 51–63.

*Knox, S., & Gruar, C. (2007). The application of stakeholder theory to relationship marketing strategy development in a non-profit organization. Journal of Business Ethics, 75 (2), 115–135.

Kozlowski, S. W. J., & Klein, K. J. (2000). A multilevel approach to theory and research in organizations: Contextual, temporal, and emergent processes. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (pp. 3–90). San Francisco: Jossey-Bass.

Latour, B., & Woolgar, S. (1986). Laboratory life: The construction of scientific facts . Beverly Hills: Sage.

Law, J. (2009). Seeing like a survey. Cultural Sociology, 3 (2), 239–256.

MacKenzie, D. A., Muniesa, F., & Siu, L. (2007). Do economists make markets? On the performativity of economics . Princeton: Princeton University Press.

Martela, F. (2015). Fallible inquiry with ethical ends-in-view: A pragmatist philosophy of science for organizational research. Organization Studies, 36, 537–563.

*Michalos, A. C. (1988). Editorial. Journal of Business Ethics, 1, 1.

Misangyi, V. F., Greckhamer, T., Furnari, S., Fiss, P. C., Crilly, D., & Aguilera, R. (2017). Embracing causal complexity the emergence of a neo-configurational perspective. Journal of Management, 43 (1), 255–282.

Morgan, G. (2006). Images of organization . Thousand Oaks: Sage.

OED Online. Oxford University Press, (June 2016). Retrieved June 10, 2016, from http://www.oxforddictionaries.com/definition/english/orient .

*Orlitzky, M., Louche, C., Gond, J. P., & Chapple, W. (2015). Unpacking the drivers of corporate social performance: A multilevel, multistakeholder, and multimethod analysis. Journal of Business Ethics . doi: 10.1007/s10551-015-2822-y .

*Painter-Morland, M. (2011). Rethinking responsible agency in corporations: Perspectives from Deleuze and Guattari. Journal of Business Ethics, 101 (1), 83–95.

Panter, A. T., & Sterba, S. K. (Eds.). (2011). Handbook of ethics in quantitative methodology . New York: Routledge.

Parkhurst, J. O., & Abeysinghe, S. (2016). What constitutes “good” evidence for public health and social policy-making? From hierarchies to appropriateness. Social Epistemology, 30 (5–6), 665–679.

Pashler, H., & Wagenmakers, E. J. (2012). Editors’ introduction to the special section on replicability in psychological science a crisis of confidence? Perspectives on Psychological Science, 7 (6), 528–530.

Pedhazur, E. J., & Schmelkin, L. P. (2013). Measurement, design, and analysis: An integrated approach . Washington, DC: Psychology Press.

*Prado, A. M., & Woodside, A. G. (2015). Deepening understanding of certification adoption and non-adoption of international-supplier ethical standards. Journal of Business Ethics, 132 (1), 105–125.

*Ralston, D. A., Egri, C. P., Furrer, O., Kuo, M. H., Li, Y., Wangenheim, F., et al. (2014). Societal-level versus individual-level predictions of ethical behavior: A 48-society study of collectivism and individualism. Journal of Business Ethics, 122 (2), 283–306.

*Rathner, S. (2013). The influence of primary study characteristics on the performance differential between socially responsible and conventional investment funds: A meta-analysis. Journal of Business Ethics, 118 (2), 349–363.

Rorty, R. (2009). Philosophy and the mirror of nature . Princeton: Princeton University Press.

Rose, N. (1985). The psychological complex . London: Routledge Kegan.

*Rousseau, D. M., Manning, J., & Denyer, D. (2008). Evidence in management and organizational science: Assembling the field’s full weight of scientific knowledge through syntheses. Academy of Management Annals, 2 (1), 475–515.

Russell, J., Greenhalgh, T., Byrne, E., & McDonnell, J. (2008). Recognizing rhetoric in health care policy analysis. Journal of Health Services Research and Policy, 13, 40–46.

Schön, D. A. (1992). The theory of inquiry: Dewey’s legacy to education. Curriculum Inquiry, 22 (2), 119–139.

Scott, J. C. (1998). Seeing like a state: How certain schemes to improve the human condition have failed . New Haven: Yale University Press.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference . New York: Wadsworth Cengage learning.

Shapin, S., & Schaffer, S. (1985). Leviathan and the air pump: Hobbes, Boyle and the experimental life . Princeton: Princeton University Press.

Singleton, V., & Law, J. (2013). Devices as rituals: Notes on enacting resistance. Journal of Cultural Economy, 6 (3), 259–277.

*Soares, C. (2003). Corporate versus individual moral responsibility. Journal of Business Ethics, 46 (2), 143–150.

Stone, D. A. (1989). Causal stories and the formation of policy agendas. Political Science Quarterly, 104 (2), 281–300.

Tuck, E., & McKenzie, M. (2015). Relational validity and the “where” of inquiry: Place and land in qualitative research. Qualitative Inquiry, 21 (7), 633–638.

Turker, D. (2009). Measuring corporate social responsibility: A scale development study. Journal of business ethics, 85 (4), 411–427.

Wasserman, L. (2013). All of statistics: A concise course in statistical inference . New York: Springer.

Werhane, P. H., & Freeman, R. E. (1999). Business ethics: The state of the art. International Journal of Management Reviews, 1 (1), 1–16.

Wicks, A. C., & Freeman, R. E. (1998). Organizational studies and the new pragmatism: Positivism, anti-positivism, and the search for ethics. Organization Science, 9, 123–140.

Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data . Cambridge: MIT press.

Young, I. M. (2011). Justice and the politics of difference . Princeton: Princeton University Press.

Zyphur, M. J., Pierides, D. C., & Roffe, J. (2016a). Measurement and statistics in ‘organization science’: Philosophical, sociological, and historical perspectives. In R. Mir, H. Willmott, & M. Greenwood (Eds.), The Routledge companion to philosophy in organization studies (pp. 474–482). Abingdon: Routledge.

Zyphur, M. J., Zammuto, R. F., & Zhang, Z. (2016b). Multilevel latent polynomial regression for modeling (in) congruence across organizational groups: The case of organizational culture research. Organizational Research Methods, 19 (1), 53–79.

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Acknowledgements

This research was supported by Australian Research Council’s Future Fellowship scheme (project FT140100629).

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Typical regression methods minimize the residual variance of outcome variables by predicting the mean (or statistical ‘expectation’) of an outcome. This can be shown by a simple regression model as follows:

wherein \(y_{i}\) is an outcome for some unit i , \(a\) is a regression intercept, \(\beta\) is a slope linking a predictor \(x_{i}\) to the outcome, and \(e_{i}\) is a residual. Typical regression assumptions pertain to \(e\) because this is parameterized as a random variable for estimation and inference, typically with a normal distribution such that:

wherein the residual variable has zero mean and variance \(\sigma^{2}\) .

However, if the outcome variable y is parameterized using the regression equation, the prediction of the outcome enters as the variable’s average. Specifically:

wherein all terms are as before, but the focus on the average of the outcome \(y\) at each level of the predictor \(x\) is clarified by showing how what is predicted are average levels of the outcomes \(y\) at different values of the predictor \(x\) .

The implication is that most regression methods implicitly assume that predicting averages are what is of greatest interest to researchers. With a focus on reducing errors in inference, the best way to do this probabilistically is to predict averages, but this is only true to the extent that a single numerical prediction of an assumedly homogenous group is desired based on the group’s average standing along an outcome \(y\) at a specific value of a predictor \(x\) . However, whether or not (and to what extent) averages may be relevant for a specific purpose and research orientation is typically left unclarified in QR, and we propose that this should be examined on a case-by-case basis with an eye to the ethics this or other QR practices.

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Zyphur, M.J., Pierides, D.C. Is Quantitative Research Ethical? Tools for Ethically Practicing, Evaluating, and Using Quantitative Research. J Bus Ethics 143 , 1–16 (2017). https://doi.org/10.1007/s10551-017-3549-8

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Issue Date : June 2017

DOI : https://doi.org/10.1007/s10551-017-3549-8

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A guide to ethical considerations in research

Last updated

12 March 2023

Reviewed by

Miroslav Damyanov

Whether you are conducting a survey, running focus groups , doing field research, or holding interviews, the chances are participants will be a part of the process.

Taking ethical considerations into account and following all obligations are essential when people are involved in your research. Upholding academic integrity is another crucial ethical concern in all research types. 

So, how can you protect your participants and ensure that your research is ethical? Let’s take a closer look at the ethical considerations in research and the best practices to follow.

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• The importance of ethical research

Research ethics are integral to all forms of research. They help protect participants’ rights, ensure that the research is valid and accurate, and help minimize any risk of harm during the process.

When people are involved in your research, it’s particularly important to consider whether your planned research method follows ethical practices.

You might ask questions such as:

Will our participants be protected?

Is there a risk of any harm?

Are we doing all we can to protect the personal data and information we collect?

Does our study include any bias?

How can we ensure that the results will be accurate and valid?

Will our research impact public safety?

Is there a more ethical way to complete the research?

Conducting research unethically and not protecting participants’ rights can have serious consequences. It can discredit the entire study. Human rights, dignity, and research integrity should all be front of mind when you are conducting research.

• How to conduct ethical research

Before kicking off any project, the entire team must be familiar with ethical best practices. These include the considerations below.

Voluntary participation

In an ethical study, all participants have chosen to be part of the research. They must have voluntarily opted in without any pressure or coercion to do so. They must be aware that they are part of a research study. Their information must not be used against their will. 

To ensure voluntary participation, make it clear at the outset that the person is opting into the process.

While participants may agree to be part of a study for a certain duration, they are allowed to change their minds. Participants must be free to leave or withdraw from the study at any time. They don’t need to give a reason.

Informed consent

Before kicking off any research, it’s also important to gain consent from all participants. This ensures participants are clear that they are part of a research study and understand all of the information related to it.

Gaining informed consent usually involves a written consent form—physical or digital—that participants can sign.

Best practice informed consent generally includes the following:

An explanation of what the study is

The duration of the study

The expectations of participants

Any potential risks

An explanation that participants are free to withdraw at any time

Contact information for the research supervisor

When obtaining informed consent, you should ensure that all parties truly understand what they are signing and their obligations as a participant. There should never be any coercion to sign.

Anonymity is key to ensuring that participants cannot be identified through their data. Personal information includes things like participants’ names, addresses, emails, phone numbers, characteristics, and photos.

However, making information truly anonymous can be challenging, especially if personal information is a necessary part of the research.

To maintain a degree of anonymity, avoid gathering any information you don’t need. This will minimize the risk of participants being identified.

Another useful tool is data pseudonymization, which makes it harder to directly link information to a real person. Data pseudonymization means giving participants fake names or mock information to protect their identity. You could, for example, replace participants’ names with codes.

Confidentiality

Keeping data confidential is a critical aspect of all forms of research. You should communicate to all participants that their information will be protected and then take active steps to ensure that happens.

Data protection has become a serious topic in recent years and should be taken seriously. The more information you gather, the more important it is to heavily protect that data.

There are many ways to protect data, including the following:

Restricted access: Information should only be accessible to the researchers involved in the project to limit the risk of breaches.

Password protection : Information should not be accessible without access via a password that complies with secure password guidelines.

Encrypted data: In this day and age, password protection isn’t usually sufficient. Encrypting the data can help ensure its security.

Data retention: All organizations should uphold a data retention policy whereby data gathered should only be held for a certain period of time. This minimizes the risk of breaches further down the line.

In research where participants are grouped together (such as in focus groups), ask participants not to pass on what has been discussed. This helps maintain the group’s privacy.

Data falsification

Regardless of what your study is about or whether it involves humans, it’s always unethical to falsify data or information. That means editing or changing any data that has been gathered or gathering data in ways that skew the results.

Bias in research is highly problematic and can significantly impact research integrity. Data falsification or misrepresentation can have serious consequences.

Take the case of Korean researcher Hwang Woo-suk, for example. Woo-suk, once considered a scientific leader in stem-cell research, was found guilty of fabricating experiments in the field and making ethical violations. Once discovered, he was fired from his role and sentenced to two years in prison.

All conflicts of interest should be declared at the outset to avoid any bias or risk of fabrication in the research process. Data must be collected and recorded accurately, and analysis must be completed impartially.

If conflicts do arise during the study, researchers may need to step back to maintain the study’s integrity. Outsourcing research to neutral third parties is necessary in some cases.

Potential for harm

Another consideration is the potential for harm. When completing research, it’s important to ensure that your participants will be safe throughout the study’s duration. 

Harm during research could occur in many forms.

Physical harm may occur if your participants are asked to perform a physical activity, or if they are involved in a medical study.

Psychological harm can occur if questions or activities involve triggering or sensitive topics, or if participants are asked to complete potentially embarrassing tasks.

Harm can be caused through a data breach or privacy concern.

A study can cause harm if the participants don’t feel comfortable with the study expectations or their supervisors.

Maintaining the physical and mental well-being of all participants throughout studies is an essential aspect of ethical research.

• Gaining ethical approval

Gaining ethical approval may be necessary before conducting some types of research. 

The US Department of Health and Human Services (HHS) and the US Food and Drug Administration (FDA) advise that approval is likely required for studies involving people.

To gain approval, it’s necessary to submit a proposal to an Institutional Review Board (IRB). The board will check the proposal and ensure that the research aligns with ethical practices. It will allow the project to proceed if it meets requirements.

Not gaining appropriate approval could invalidate your study, so it’s essential to pay attention to all local guidelines and laws.

• The dangers of unethical practices

Not maintaining ethical standards in research isn’t just questionable—it can be dangerous too. Many historical cases show just how widespread the ramifications can be.

The case of Korean researcher Hwang Woo-suk shows just how critical it is to obtain information ethically and accurately represent findings.

A case in 1998, which involved fraudulent data reporting, further proves this point.

The study, now debunked, was completed by Andrew Wakefield. It suggested there may be a link between the measles, mumps, and rubella (MMR) vaccine and autism in children. It was later found that the data was manipulated to show a causal link when there wasn’t one. Wakefield’s medical license was removed as a result, but the fraudulent study was still widely cited and continues to cause vaccine hesitancy among many parents.

Large organizational bodies have also been a part of unethical research. The alcohol industry, for example, was found to be highly influential in a major public health study in an attempt to prove that moderate alcohol consumption had health benefits. Five major alcohol companies pledged approximately $66 million to fund the study.

However, the World Health Organization (WHO) is clear that research shows there is no safe level of alcohol consumption. After pressure from many organizations, the study was eventually pulled due to biasing by the alcohol industry. Despite this, the idea that moderate alcohol consumption is better than abstaining may still appear in public discourse.

In more extreme cases, unethical research has led to medical studies being completed on people without their knowledge and against their will. The atrocities committed in Nazi Germany during World War II are an example.

Unethical practices in research are not just problematic or in conflict with academic integrity; they can seriously harm public health and safety.

• The ethical way to research

Considering ethical concerns and adopting best practices throughout studies is essential when conducting research.

When people are involved in studies, it’s important to consider their rights. They must not be coerced into participating, and they should be protected throughout the process.

Accurate reporting, unbiased results, and a genuine interest in answering questions rather than confirming assumptions are all essential aspects of ethical research.

Ethical research ultimately means producing true and valuable results for the benefit of everyone impacted by your study.

What are ethical considerations in research?

Ethical research involves a series of guidelines and considerations to ensure that the information gathered is valid and reliable. These guidelines ensure that:

People are not harmed during research

Participants have data protection and anonymity

Academic integrity is upheld

Not maintaining ethics in research can have serious consequences for those involved in the studies, the broader public, and policymakers.

What are the most common ethical considerations?

To maintain integrity and validity in research, all biases must be removed, data should be reported accurately, and studies must be clearly represented.

Some of the most common ethical guidelines when it comes to humans in research include avoiding harm, data protection, anonymity, informed consent, and confidentiality.

What are the ethical issues in secondary research?

Using secondary data is generally considered an ethical practice. That’s because the use of secondary data minimizes the impact on participants, reduces the need for additional funding, and maximizes the value of the data collection.

However, secondary research still has risks. For example, the risk of data breaches increases as more parties gain access to the information.

To minimize the risk, researchers should consider anonymity or data pseudonymization before the data is passed on. Furthermore, using the data should not cause any harm or distress to participants.

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Ethical Considerations in Research | Types & Examples

Published on 7 May 2022 by Pritha Bhandari .

Ethical considerations in research are a set of principles that guide your research designs and practices. Scientists and researchers must always adhere to a certain code of conduct when collecting data from people.

The goals of human research often include understanding real-life phenomena, studying effective treatments, investigating behaviours, and improving lives in other ways. What you decide to research and how you conduct that research involve key ethical considerations.

These considerations work to:

• Protect the rights of research participants
• Enhance research validity
• Maintain scientific integrity

Table of contents

Why do research ethics matter, getting ethical approval for your study, types of ethical issues, voluntary participation, informed consent, confidentiality, potential for harm, results communication, examples of ethical failures, frequently asked questions about research ethics.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe for research subjects.

You’ll balance pursuing important research aims with using ethical research methods and procedures. It’s always necessary to prevent permanent or excessive harm to participants, whether inadvertent or not.

Defying research ethics will also lower the credibility of your research because it’s hard for others to trust your data if your methods are morally questionable.

Even if a research idea is valuable to society, it doesn’t justify violating the human rights or dignity of your study participants.

Prevent plagiarism, run a free check.

Before you start any study involving data collection with people, you’ll submit your research proposal to an institutional review board (IRB) .

An IRB is a committee that checks whether your research aims and research design are ethically acceptable and follow your institution’s code of conduct. They check that your research materials and procedures are up to code.

If successful, you’ll receive IRB approval, and you can begin collecting data according to the approved procedures. If you want to make any changes to your procedures or materials, you’ll need to submit a modification application to the IRB for approval.

If unsuccessful, you may be asked to re-submit with modifications or your research proposal may receive a rejection. To get IRB approval, it’s important to explicitly note how you’ll tackle each of the ethical issues that may arise in your study.

There are several ethical issues you should always pay attention to in your research design, and these issues can overlap with each other.

You’ll usually outline ways you’ll deal with each issue in your research proposal if you plan to collect data from participants.

Voluntary participation means that all research subjects are free to choose to participate without any pressure or coercion.

All participants are able to withdraw from, or leave, the study at any point without feeling an obligation to continue. Your participants don’t need to provide a reason for leaving the study.

It’s important to make it clear to participants that there are no negative consequences or repercussions to their refusal to participate. After all, they’re taking the time to help you in the research process, so you should respect their decisions without trying to change their minds.

Voluntary participation is an ethical principle protected by international law and many scientific codes of conduct.

Take special care to ensure there’s no pressure on participants when you’re working with vulnerable groups of people who may find it hard to stop the study even when they want to.

Informed consent refers to a situation in which all potential participants receive and understand all the information they need to decide whether they want to participate. This includes information about the study’s benefits, risks, funding, and institutional approval.

• What the study is about
• The risks and benefits of taking part
• How long the study will take
• Your supervisor’s contact information and the institution’s approval number

Usually, you’ll provide participants with a text for them to read and ask them if they have any questions. If they agree to participate, they can sign or initial the consent form. Note that this may not be sufficient for informed consent when you work with particularly vulnerable groups of people.

If you’re collecting data from people with low literacy, make sure to verbally explain the consent form to them before they agree to participate.

For participants with very limited English proficiency, you should always translate the study materials or work with an interpreter so they have all the information in their first language.

In research with children, you’ll often need informed permission for their participation from their parents or guardians. Although children cannot give informed consent, it’s best to also ask for their assent (agreement) to participate, depending on their age and maturity level.

Anonymity means that you don’t know who the participants are and you can’t link any individual participant to their data.

You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, and videos.

In many cases, it may be impossible to truly anonymise data collection. For example, data collected in person or by phone cannot be considered fully anonymous because some personal identifiers (demographic information or phone numbers) are impossible to hide.

You’ll also need to collect some identifying information if you give your participants the option to withdraw their data at a later stage.

Data pseudonymisation is an alternative method where you replace identifying information about participants with pseudonymous, or fake, identifiers. The data can still be linked to participants, but it’s harder to do so because you separate personal information from the study data.

Confidentiality means that you know who the participants are, but you remove all identifying information from your report.

All participants have a right to privacy, so you should protect their personal data for as long as you store or use it. Even when you can’t collect data anonymously, you should secure confidentiality whenever you can.

Some research designs aren’t conducive to confidentiality, but it’s important to make all attempts and inform participants of the risks involved.

As a researcher, you have to consider all possible sources of harm to participants. Harm can come in many different forms.

• Psychological harm: Sensitive questions or tasks may trigger negative emotions such as shame or anxiety.
• Social harm: Participation can involve social risks, public embarrassment, or stigma.
• Physical harm: Pain or injury can result from the study procedures.
• Legal harm: Reporting sensitive data could lead to legal risks or a breach of privacy.

It’s best to consider every possible source of harm in your study, as well as concrete ways to mitigate them. Involve your supervisor to discuss steps for harm reduction.

Make sure to disclose all possible risks of harm to participants before the study to get informed consent. If there is a risk of harm, prepare to provide participants with resources, counselling, or medical services if needed.

Some of these questions may bring up negative emotions, so you inform participants about the sensitive nature of the survey and assure them that their responses will be confidential.

The way you communicate your research results can sometimes involve ethical issues. Good science communication is honest, reliable, and credible. It’s best to make your results as transparent as possible.

Take steps to actively avoid plagiarism and research misconduct wherever possible.

Plagiarism means submitting others’ works as your own. Although it can be unintentional, copying someone else’s work without proper credit amounts to stealing. It’s an ethical problem in research communication because you may benefit by harming other researchers.

Self-plagiarism is when you republish or re-submit parts of your own papers or reports without properly citing your original work.

This is problematic because you may benefit from presenting your ideas as new and original even though they’ve already been published elsewhere in the past. You may also be infringing on your previous publisher’s copyright, violating an ethical code, or wasting time and resources by doing so.

In extreme cases of self-plagiarism, entire datasets or papers are sometimes duplicated. These are major ethical violations because they can skew research findings if taken as original data.

You notice that two published studies have similar characteristics even though they are from different years. Their sample sizes, locations, treatments, and results are highly similar, and the studies share one author in common.

Research misconduct

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement about data analyses.

Research misconduct is a serious ethical issue because it can undermine scientific integrity and institutional credibility. It leads to a waste of funding and resources that could have been used for alternative research.

Later investigations revealed that they fabricated and manipulated their data to show a nonexistent link between vaccines and autism. Wakefield also neglected to disclose important conflicts of interest, and his medical license was taken away.

This fraudulent work sparked vaccine hesitancy among parents and caregivers. The rate of MMR vaccinations in children fell sharply, and measles outbreaks became more common due to a lack of herd immunity.

Research scandals with ethical failures are littered throughout history, but some took place not that long ago.

Some scientists in positions of power have historically mistreated or even abused research participants to investigate research problems at any cost. These participants were prisoners, under their care, or otherwise trusted them to treat them with dignity.

To demonstrate the importance of research ethics, we’ll briefly review two research studies that violated human rights in modern history.

These experiments were inhumane and resulted in trauma, permanent disabilities, or death in many cases.

After some Nazi doctors were put on trial for their crimes, the Nuremberg Code of research ethics for human experimentation was developed in 1947 to establish a new standard for human experimentation in medical research.

In reality, the actual goal was to study the effects of the disease when left untreated, and the researchers never informed participants about their diagnoses or the research aims.

Although participants experienced severe health problems, including blindness and other complications, the researchers only pretended to provide medical care.

When treatment became possible in 1943, 11 years after the study began, none of the participants were offered it, despite their health conditions and high risk of death.

Ethical failures like these resulted in severe harm to participants, wasted resources, and lower trust in science and scientists. This is why all research institutions have strict ethical guidelines for performing research.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

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Handbook of Ethics in Quantitative Methodology

DOI link for Handbook of Ethics in Quantitative Methodology

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This comprehensive Handbook is the first to provide a practical, interdisciplinary review of ethical issues as they relate to quantitative methodology including how to present evidence for reliability and validity, what comprises an adequate tested population, and what constitutes scientific knowledge for eliminating biases. The book uses an ethical framework that emphasizes the human cost of quantitative decision making to help researchers understand the specific implications of their choices. The order of the Handbook chapters parallels the chronology of the research process: determining the research design and data collection; data analysis; and communicating findings. Each chapter:

• Explores the ethics of a particular topic
• Identifies prevailing methodological issues
• Reviews strategies and approaches for handling such issues and their ethical implications
• Provides one or more case examples
• Outlines plausible approaches to the issue including best-practice solutions.

Part 1 presents ethical frameworks that cross-cut design, analysis, and modeling in the behavioral sciences. Part 2 focuses on ideas for disseminating ethical training in statistics courses. Part 3 considers the ethical aspects of selecting measurement instruments and sample size planning and explores issues related to high stakes testing, the defensibility of experimental vs. quasi-experimental research designs, and ethics in program evaluation. Decision points that shape a researchers’ approach to data analysis are examined in Part 4 – when and why analysts need to account for how the sample was selected, how to evaluate tradeoffs of hypothesis-testing vs. estimation, and how to handle missing data. Ethical issues that arise when using techniques such as factor analysis or multilevel modeling and when making causal inferences are also explored. The book concludes with ethical aspects of reporting meta-analyses, of cross-disciplinary statistical reform, and of the publication process.

This Handbook appeals to researchers and practitioners in psychology, human development, family studies, health, education, sociology, social work, political science, and business/marketing. This book is also a valuable supplement for quantitative methods courses required of all graduate students in these fields.

TABLE OF CONTENTS

Chapter | 11  pages, ethics in quantitative methodology: an introduction, chapter | 46  pages, developing an ethical framework for methodologists, chapter | 22  pages, ethics in quantitative professional practice, ethical principles in data analysis: an overview, chapter | 65  pages, teaching quantitative ethics, chapter | 64  pages, a statistical guide for the ethically perplexed, chapter | 139  pages, ethics and research design issues, chapter | 31  pages, measurement choices: reliability, validity, and generalizability, chapter | 26  pages, ethics and sample size planning, chapter | 25  pages, ethics and the conduct of randomized experiments and quasi-experiments in field settings, chapter | 30  pages, psychometric methods and high-stakes assessment: contexts and methods for ethical testing practice, chapter | 24  pages, ethics in program evaluation, chapter | 150  pages, ethics and data analysis issues, beyond treating complex sampling designs as simple random samples: data analysis and reporting, chapter | 20  pages, from hypothesis testing to parameter estimation: an example of evidence-based practice in statistics, chapter | 27  pages, some ethical issues in factor analysis, chapter | 15  pages, ethical aspects of multilevel modeling, the impact of missing data on the ethical quality of a research study, chapter | 32  pages, the science and ethics of causal modeling, chapter | 78  pages, ethics and communicating findings, ethical issues in the conduct and reporting of meta-analysis, chapter | 18  pages, ethics and statistical reform: lessons from medicine, ethical issues in professional research, writing, and publishing.

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Frequently asked questions

What are ethical considerations in research.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Ethical and Legal Considerations in Quantitative Research Essay (Critical Writing)

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

Introduction

Overview of the quantitative question, the irb process, ethical considerations, legal considerations, ethics in data collection and reporting results.

In quantitative research, much attention should be paid to addressing ethical and legal norms and rules. According to Wiles and Boddy (2013), research ethics can “encourage researchers not only to improve levels of ‘ethical literacy’ in the research community but more fundamentally, to reflect deeply on their research project and process from the perspective of all the possible stakeholders” (p. 1). As a result, the rights and interests of all individuals involved in the study can be discussed as protected if ethical and legal norms are followed. The purpose of this paper is to present the quantitative research question and discuss ethical and legal issues related to quantitative methodology.

The following research question was developed for the quantitative study: Is there a relationship between the amount of time which students from grades 6-7 spend playing computer and video games and their achievement at school? The independent variable in this study is the amount of time that students can spend while playing computer and video games.

The time is measured in minutes per week. The dependent variable is the students’ achievement that should be measured in weekly tests’ scores (from 0 to 100). Tests should cover the materials related to Language, Mathematics, and Science. The proposed research question is appropriate to be used in the study the aim of which is to find out how the students’ interest in playing computer and video games can predict their achievement at school.

The Institutional Review Board (IRB) is a committee that is responsible for controlling the research process in educational and other types of organizations. The IRB process means filling in the specific form and receiving the approval to conduct the planned study (Metro, 2014).

The IRB form includes the following information to mention: a purpose of the research, dates, a description of the process and procedures, a note regarding the voluntary participation in a study, principles of selecting participants for a study, a note on ethical issues and confidentiality, and a description of the withdrawal procedure (Metro, 2014). This information should be provided to the Institutional Review Board to guarantee that human participants’ rights and interests will be protected and non-violated concerning the proposed study.

The involvement of children in a quantitative study is associated with a range of ethical issues. Therefore, researchers can start conducting a study only when informed consent forms are signed, and the permissions of parents are received (Tangen, 2014). For this study, the permission of parents and their involvement in the research process are critical because they help report the actual time spent on playing video and computer games (Wouters, Van Nimwegen, Van Oostendorp, & Van Der Spek, 2013; Yang, 2012).

Thus, it is expected that the participation of students and their families is voluntary even though a random sampling technique can be used to determine the sample for the study (Smith, 2016). Also, it is important to guarantee that the test results of students and the private data are not shared publicly. Much attention should be paid to the issue of confidentiality to protect participants’ rights.

Legal issues associated with involving children in the proposed quantitative study are the following ones: the necessity of collecting the data with the help of parents as guardians; the necessity of protecting students’ anonymity; the impossibility to provide the plagiarized data; and the impossibility to make up data to address the purpose of the study (Doyle & Buckley, 2014; Smith, 2016). If the listed principles are ignored, it is possible to speak about the violation of legal norms associated with conducting quantitative research. Therefore, much attention should be paid to organizing quantitative research in the sphere of education.

The data related to quantitative studies should be effectively collected and reported. The sample size selected for the study should be appropriate to guarantee the ethical generalization of results. All the data necessary for the study should be collected concerning the guardians’ permission (Roberts & Allen, 2015). The gathered information needs to be protected with the help of passwords if the digital data are collected for the study.

When all the required data are stored appropriately to guarantee the confidentiality and privacy of participants, it is necessary to select the relevant statistical test to analyze the information (Tangen, 2014). Reporting results are the next stage at which researchers are expected to avoid presenting the made-up data or discussing only significant and positive results (Smith, 2016). In quantitative research, there are risks that hypotheses formulated with the focus on the research question cannot be supported concerning study results. The ethical behavior at this stage means reporting all findings and limitations associated with the study.

To conduct quantitative research, educators need to pay much attention to ethical and legal issues. It is important to guarantee that the participation of respondents is voluntary and that they understand their rights related to the study. Therefore, researchers should focus on protecting the interests of children when they conduct quantitative studies in the educational area. In this context, the focus should be on the researcher’s cooperation with parents to protect children’s interests.

Doyle, E., & Buckley, P. (2014). Research ethics in teaching and learning. Innovations in Education and Teaching International , 51 (2), 153-163.

Metro, R. (2014). From the form to the face to face: IRBs, ethnographic researchers, and human subjects translate consent. Anthropology & Education Quarterly , 45 (2), 167-184.

Roberts, L. D., & Allen, P. J. (2015). Exploring ethical issues associated with using online surveys in educational research. Educational Research and Evaluation , 21 (2), 95-108.

Smith, J. (2016). Reflections on teaching research ethics in education for international postgraduate students in the UK. Teaching in Higher Education , 21 (1), 94-105.

Tangen, R. (2014). Balancing ethics and quality in educational research – the ethical matrix method. Scandinavian Journal of Educational Research , 58 (6), 678-694.

Wiles, R., & Boddy, J. (2013). Introduction to the special issue: Research ethics in challenging contexts. Methodological Innovations Online , 8 (2), 1-5.

Wouters, P., Van Nimwegen, C., Van Oostendorp, H., & Van Der Spek, E. D. (2013). A meta-analysis of the cognitive and motivational effects of serious games. Journal of Educational Psychology , 105 (2), 249.

Yang, Y. T. (2012). Building virtual cities, inspiring intelligent citizens: Digital games for developing students’ problem solving and learning motivation. Computers & Education , 59 (2), 365-377.

• Ethics and Professional Responsibility
• Ethics of Testing Teacher Preparedness
• Ethical Considerations in Nursing
• Analysis, Evaluation and Synthesis Approaches
• Research Design: Medical and Psychological Practice
• Rapid Critical Appraisal Process
• Research Methods by Alwabel, Zairi, and Ahmed
• "What Theory Is Not, Theorizing Is" by Karl Weick
• Chicago (A-D)
• Chicago (N-B)

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Ethical Issues in Research: Perceptions of Researchers, Research Ethics Board Members and Research Ethics Experts

Marie-josée drolet.

1 Department of Occupational Therapy (OT), Université du Québec à Trois-Rivières (UQTR), Trois-Rivières (Québec), Canada

Eugénie Rose-Derouin

2 Bachelor OT program, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières (Québec), Canada

Julie-Claude Leblanc

Mélanie ruest, bryn williams-jones.

3 Department of Social and Preventive Medicine, School of Public Health, Université de Montréal, Montréal (Québec), Canada

In the context of academic research, a diversity of ethical issues, conditioned by the different roles of members within these institutions, arise. Previous studies on this topic addressed mainly the perceptions of researchers. However, to our knowledge, no studies have explored the transversal ethical issues from a wider spectrum, including other members of academic institutions as the research ethics board (REB) members, and the research ethics experts. The present study used a descriptive phenomenological approach to document the ethical issues experienced by a heterogeneous group of Canadian researchers, REB members, and research ethics experts. Data collection involved socio-demographic questionnaires and individual semi-structured interviews. Following the triangulation of different perspectives (researchers, REB members and ethics experts), emerging ethical issues were synthesized in ten units of meaning: (1) research integrity, (2) conflicts of interest, (3) respect for research participants, (4) lack of supervision and power imbalances, (5) individualism and performance, (6) inadequate ethical guidance, (7) social injustices, (8) distributive injustices, (9) epistemic injustices, and (10) ethical distress. This study highlighted several problematic elements that can support the identification of future solutions to resolve transversal ethical issues in research that affect the heterogeneous members of the academic community.

Introduction

Research includes a set of activities in which researchers use various structured methods to contribute to the development of knowledge, whether this knowledge is theoretical, fundamental, or applied (Drolet & Ruest, accepted ). University research is carried out in a highly competitive environment that is characterized by ever-increasing demands (i.e., on time, productivity), insufficient access to research funds, and within a market economy that values productivity and speed often to the detriment of quality or rigour – this research context creates a perfect recipe for breaches in research ethics, like research misbehaviour or misconduct (i.e., conduct that is ethically questionable or unacceptable because it contravenes the accepted norms of responsible conduct of research or compromises the respect of core ethical values that are widely held by the research community) (Drolet & Girard, 2020 ; Sieber, 2004 ). Problematic ethics and integrity issues – e.g., conflicts of interest, falsification of data, non-respect of participants’ rights, and plagiarism, to name but a few – have the potential to both undermine the credibility of research and lead to negative consequences for many stakeholders, including researchers, research assistants and personnel, research participants, academic institutions, and society as a whole (Drolet & Girard, 2020 ). It is thus evident that the academic community should be able to identify these different ethical issues in order to evaluate the nature of the risks that they pose (and for whom), and then work towards their prevention or management (i.e., education, enhanced policies and procedures, risk mitigation strategies).

In this article, we define an “ethical issue” as any situation that may compromise, in whole or in part, the respect of at least one moral value (Swisher et al., 2005 ) that is considered socially legitimate and should thus be respected. In general, ethical issues occur at three key moments or stages of the research process: (1) research design (i.e., conception, project planning), (2) research conduct (i.e., data collection, data analysis) and (3) knowledge translation or communication (e.g., publications of results, conferences, press releases) (Drolet & Ruest, accepted ). According to Sieber ( 2004 ), ethical issues in research can be classified into five categories, related to: (a) communication with participants and the community, (b) acquisition and use of research data, (c) external influence on research, (d) risks and benefits of the research, and (e) selection and use of research theories and methods. Many of these issues are related to breaches of research ethics norms, misbehaviour or research misconduct. Bruhn et al., ( 2002 ) developed a typology of misbehaviour and misconduct in academia that can be used to judge the seriousness of different cases. This typology takes into consideration two axes of reflection: (a) the origin of the situation (i.e., is it the researcher’s own fault or due to the organizational context?), and (b) the scope and severity (i.e., is this the first instance or a recurrent behaviour? What is the nature of the situation? What are the consequences, for whom, for how many people, and for which organizations?).

A previous detailed review of the international literature on ethical issues in research revealed several interesting findings (Beauchemin et al., 2021 ). Indeed, the current literature is dominated by descriptive ethics, i.e., the sharing by researchers from various disciplines of the ethical issues they have personally experienced. While such anecdotal documentation is relevant, it is insufficient because it does not provide a global view of the situation. Among the reviewed literature, empirical studies were in the minority (Table  1 ) – only about one fifth of the sample (n = 19) presented empirical research findings on ethical issues in research. The first of these studies was conducted almost 50 years ago (Hunt et al., 1984 ), with the remainder conducted in the 1990s. Eight studies were conducted in the United States (n = 8), five in Canada (n = 5), three in England (n = 3), two in Sweden (n = 2) and one in Ghana (n = 1).

Summary of Empirical Studies on Ethical Issues in Research by the year of publication

Further, the majority of studies in our sample (n = 12) collected the perceptions of a homogeneous group of participants, usually researchers (n = 14) and sometimes health professionals (n = 6). A minority of studies (n = 7) triangulated the perceptions of diverse research stakeholders (i.e., researchers and research participants, or students). To our knowledge, only one study has examined perceptions of ethical issues in research by research ethics board members (REB; Institutional Review Boards [IRB] in the USA), and none to date have documented the perceptions of research ethics experts. Finally, nine studies (n = 9) adopted a qualitative design, seven studies (n = 7) a quantitative design, and three (n = 3) a mixed-methods design.

More studies using empirical research methods are needed to better identify broader trends, to enrich discussions on the values that should govern responsible conduct of research in the academic community, and to evaluate the means by which these values can be supported in practice (Bahn, 2012 ; Beauchemin et al., 2021 ; Bruhn et al., 2002 ; Henderson et al., 2013 ; Resnik & Elliot, 2016; Sieber 2004 ). To this end, we conducted an empirical qualitative study to document the perceptions and experiences of a heterogeneous group of Canadian researchers, REB members, and research ethics experts, to answer the following broad question: What are the ethical issues in research?

Research Methods

Research design.

A qualitative research approach involving individual semi-structured interviews was used to systematically document ethical issues (De Poy & Gitlin, 2010 ; Hammell et al., 2000 ). Specifically, a descriptive phenomenological approach inspired by the philosophy of Husserl was used (Husserl, 1970 , 1999 ), as it is recommended for documenting the perceptions of ethical issues raised by various practices (Hunt & Carnavale, 2011 ).

Ethical considerations

The principal investigator obtained ethics approval for this project from the Research Ethics Board of the Université du Québec à Trois-Rivières (UQTR). All members of the research team signed a confidentiality agreement, and research participants signed the consent form after reading an information letter explaining the nature of the research project.

Sampling and recruitment

As indicated above, three types of participants were sought: (1) researchers from different academic disciplines conducting research (i.e., theoretical, fundamental or empirical) in Canadian universities; (2) REB members working in Canadian organizations responsible for the ethical review, oversight or regulation of research; and (3) research ethics experts, i.e., academics or ethicists who teach research ethics, conduct research in research ethics, or are scholars who have acquired a specialization in research ethics. To be included in the study, participants had to work in Canada, speak and understand English or French, and be willing to participate in the study. Following Thomas and Polio’s (2002) recommendation to recruit between six and twelve participants (for a homogeneous sample) to ensure data saturation, for our heterogeneous sample, we aimed to recruit approximately twelve participants in order to obtain data saturation. Having used this method several times in related projects in professional ethics, data saturation is usually achieved with 10 to 15 participants (Drolet & Goulet, 2018 ; Drolet & Girard, 2020 ; Drolet et al., 2020 ). From experience, larger samples only serve to increase the degree of data saturation, especially in heterogeneous samples (Drolet et al., 2017 , 2019 ; Drolet & Maclure, 2016 ).

Purposive sampling facilitated the identification of participants relevant to documenting the phenomenon in question (Fortin, 2010 ). To ensure a rich and most complete representation of perceptions, we sought participants with varied and complementary characteristics with regards to the social roles they occupy in research practice (Drolet & Girard, 2020 ). A triangulation of sources was used for the recruitment (Bogdan & Biklen, 2006 ). The websites of Canadian universities and Canadian health institution REBs, as well as those of major Canadian granting agencies (i.e., the Canadian Institutes of Health Research, the Natural Sciences and Engineering Research Council of Canada, and the Social Sciences and Humanities Research Council of Canada, Fonds de recherche du Quebec), were searched to identify individuals who might be interested in participating in the study. Further, people known by the research team for their knowledge and sensitivity to ethical issues in research were asked to participate. Research participants were also asked to suggest other individuals who met the study criteria.

Data Collection

Two tools were used for data collecton: (a) a socio-demographic questionnaire, and (b) a semi-structured individual interview guide. English and French versions of these two documents were used and made available, depending on participant preferences. In addition, although the interview guide contained the same questions, they were adapted to participants’ specific roles (i.e., researcher, REB member, research ethics expert). When contacted by email by the research assistant, participants were asked to confirm under which role they wished to participate (because some participants might have multiple, overlapping responsibilities) and they were sent the appropriate interview guide.

The interview guides each had two parts: an introduction and a section on ethical issues. The introduction consisted of general questions to put the participant at ease (i.e., “Tell me what a typical day at work is like for you”). The section on ethical issues was designed to capture the participant’s perceptions through questions such as: “Tell me three stories you have experienced at work that involve an ethical issue?” and “Do you feel that your organization is doing enough to address, manage, and resolve ethical issues in your work?”. Although some interviews were conducted in person, the majority were conducted by videoconference to promote accessibility and because of the COVID-19 pandemic. Interviews were digitally recorded so that the verbatim could be transcribed in full, and varied between 40 and 120 min in duration, with an average of 90 min. Research assistants conducted the interviews and transcribed the verbatim.

Data Analysis

The socio-demographic questionnaires were subjected to simple descriptive statistical analyses (i.e., means and totals), and the semi-structured interviews were subjected to qualitative analysis. The steps proposed by Giorgi ( 1997 ) for a Husserlian phenomenological reduction of the data were used. After collecting, recording, and transcribing the interviews, all verbatim were analyzed by at least two analysts: a research assistant (2nd author of this article) and the principal investigator (1st author) or a postdoctoral fellow (3rd author). The repeated reading of the verbatim allowed the first analyst to write a synopsis, i.e., an initial extraction of units of meaning. The second analyst then read the synopses, which were commented and improved if necessary. Agreement between analysts allowed the final drafting of the interview synopses, which were then analyzed by three analysts to generate and organize the units of meaning that emerged from the qualitative data.

Participants

Sixteen individuals (n = 16) participated in the study, of whom nine (9) identified as female and seven (7) as male (Table  2 ). Participants ranged in age from 22 to 72 years, with a mean age of 47.5 years. Participants had between one (1) and 26 years of experience in the research setting, with an average of 14.3 years of experience. Participants held a variety of roles, including: REB members (n = 11), researchers (n = 10), research ethics experts (n = 4), and research assistant (n = 1). As mentioned previously, seven (7) participants held more than one role, i.e., REB member, research ethics expert, and researcher. The majority (87.5%) of participants were working in Quebec, with the remaining working in other Canadian provinces. Although all participants considered themselves to be francophone, one quarter (n = 4) identified themselves as belonging to a cultural minority group.

Description of Participants

With respect to their academic background, most participants (n = 9) had a PhD, three (3) had a post-doctorate, two (2) had a master’s degree, and two (2) had a bachelor’s degree. Participants came from a variety of disciplines: nine (9) had a specialty in the humanities or social sciences, four (4) in the health sciences and three (3) in the natural sciences. In terms of their knowledge of ethics, five (5) participants reported having taken one university course entirely dedicated to ethics, four (4) reported having taken several university courses entirely dedicated to ethics, three (3) had a university degree dedicated to ethics, while two (2) only had a few hours or days of training in ethics and two (2) reported having no knowledge of ethics.

Ethical issues

As Fig.  1 illustrates, ten units of meaning emerge from the data analysis, namely: (1) research integrity, (2) conflicts of interest, (3) respect for research participants, (4) lack of supervision and power imbalances, (5) individualism and performance, (6) inadequate ethical guidance, (7) social injustices, (8) distributive injustices, (9) epistemic injustices, and (10) ethical distress. To illustrate the results, excerpts from verbatim interviews are presented in the following sub-sections. Most of the excerpts have been translated into English as the majority of interviews were conducted with French-speaking participants.

Ethical issues in research according to the participants

Research Integrity

The research environment is highly competitive and performance-based. Several participants, in particular researchers and research ethics experts, felt that this environment can lead both researchers and research teams to engage in unethical behaviour that reflects a lack of research integrity. For example, as some participants indicated, competition for grants and scientific publications is sometimes so intense that researchers falsify research results or plagiarize from colleagues to achieve their goals.

Some people will lie or exaggerate their research findings in order to get funding. Then, you see it afterwards, you realize: “ah well, it didn’t work, but they exaggerated what they found and what they did” (participant 14). Another problem in research is the identification of authors when there is a publication. Very often, there are authors who don’t even know what the publication is about and that their name is on it. (…) The time that it surprised me the most was just a few months ago when I saw someone I knew who applied for a teaching position. He got it I was super happy for him. Then I looked at his publications and … there was one that caught my attention much more than the others, because I was in it and I didn’t know what that publication was. I was the second author of a publication that I had never read (participant 14). I saw a colleague who had plagiarized another colleague. [When the colleague] found out about it, he complained. So, plagiarism is a serious [ethical breach]. I would also say that there is a certain amount of competition in the university faculties, especially for grants (…). There are people who want to win at all costs or get as much as possible. They are not necessarily going to consider their colleagues. They don’t have much of a collegial spirit (participant 10).

These examples of research misbehaviour or misconduct are sometimes due to or associated with situations of conflicts of interest, which may be poorly managed by certain researchers or research teams, as noted by many participants.

Conflict of interest

The actors and institutions involved in research have diverse interests, like all humans and institutions. As noted in Chap. 7 of the Canadian Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS2, 2018),

“researchers and research students hold trust relationships, either directly or indirectly, with participants, research sponsors, institutions, their professional bodies and society. These trust relationships can be put at risk by conflicts of interest that may compromise independence, objectivity or ethical duties of loyalty. Although the potential for such conflicts has always existed, pressures on researchers (i.e., to delay or withhold dissemination of research outcomes or to use inappropriate recruitment strategies) heighten concerns that conflicts of interest may affect ethical behaviour” (p. 92).

The sources of these conflicts are varied and can include interpersonal conflicts, financial partnerships, third-party pressures, academic or economic interests, a researcher holding multiple roles within an institution, or any other incentive that may compromise a researcher’s independence, integrity, and neutrality (TCPS2, 2018). While it is not possible to eliminate all conflicts of interest, it is important to manage them properly and to avoid temptations to behave unethically.

Ethical temptations correspond to situations in which people are tempted to prioritize their own interests to the detriment of the ethical goods that should, in their own context, govern their actions (Swisher et al., 2005 ). In the case of researchers, this refers to situations that undermine independence, integrity, neutrality, or even the set of principles that govern research ethics (TCPS2, 2018) or the responsible conduct of research. According to study participants, these types of ethical issues frequently occur in research. Many participants, especially researchers and REB members, reported that conflicts of interest can arise when members of an organization make decisions to obtain large financial rewards or to increase their academic profile, often at the expense of the interests of members of their research team, research participants, or even the populations affected by their research.

A company that puts money into making its drug work wants its drug to work. So, homeopathy is a good example, because there are not really any consequences of homeopathy, there are not very many side effects, because there are no effects at all. So, it’s not dangerous, but it’s not a good treatment either. But some people will want to make it work. And that’s a big issue when you’re sitting at a table and there are eight researchers, and there are two or three who are like that, and then there are four others who are neutral, and I say to myself, this is not science. I think that this is a very big ethical issue (participant 14). There are also times in some research where there will be more links with pharmaceutical companies. Obviously, there are then large amounts of money that will be very interesting for the health-care institutions because they still receive money for clinical trials. They’re still getting some compensation because its time consuming for the people involved and all that. The pharmaceutical companies have money, so they will compensate, and that is sometimes interesting for the institutions, and since we are a bit caught up in this, in the sense that we have no choice but to accept it. (…) It may not be the best research in the world, there may be a lot of side effects due to the drugs, but it’s good to accept it, we’re going to be part of the clinical trial (participant 3). It is integrity, what we believe should be done or said. Often by the pressure of the environment, integrity is in tension with the pressures of the environment, so it takes resistance, it takes courage in research. (…) There were all the debates there about the problems of research that was funded and then the companies kept control over what was written. That was really troubling for a lot of researchers (participant 5).

Further, these situations sometimes have negative consequences for research participants as reported by some participants.

Respect for research participants

Many research projects, whether they are psychosocial or biomedical in nature, involve human participants. Relationships between the members of research teams and their research participants raise ethical issues that can be complex. Research projects must always be designed to respect the rights and interests of research participants, and not just those of researchers. However, participants in our study – i.e., REB members, researchers, and research ethics experts – noted that some research teams seem to put their own interests ahead of those of research participants. They also emphasized the importance of ensuring the respect, well-being, and safety of research participants. The ethical issues related to this unit of meaning are: respect for free, informed and ongoing consent of research participants; respect for and the well-being of participants; data protection and confidentiality; over-solicitation of participants; ownership of the data collected on participants; the sometimes high cost of scientific innovations and their accessibility; balance between the social benefits of research and the risks to participants (particularly in terms of safety); balance between collective well-being (development of knowledge) and the individual rights of participants; exploitation of participants; paternalism when working with populations in vulnerable situations; and the social acceptability of certain types of research. The following excerpts present some of these issues.

Where it disturbs me ethically is in the medical field – because it’s more in the medical field that we’re going to see this – when consent forms are presented to patients to solicit them as participants, and then [these forms] have an average of 40 pages. That annoys me. When they say that it has to be easy to understand and all that, adapted to the language, and then the hyper-technical language plus there are 40 pages to read, I don’t understand how you’re going to get informed consent after reading 40 pages. (…) For me, it doesn’t work. I read them to evaluate them and I have a certain level of education and experience in ethics, and there are times when I don’t understand anything (participant 2). There is a lot of pressure from researchers who want to recruit research participants (…). The idea that when you enter a health care institution, you become a potential research participant, when you say “yes to a research, you check yes to all research”, then everyone can ask you. I think that researchers really have this fantasy of saying to themselves: “as soon as people walk through the door of our institution, they become potential participants with whom we can communicate and get them involved in all projects”. There’s a kind of idea that, yes, it can be done, but it has to be somewhat supervised to avoid over-solicitation (…). Researchers are very interested in facilitating recruitment and making it more fluid, but perhaps to the detriment of confidentiality, privacy, and respect; sometimes that’s what it is, to think about what type of data you’re going to have in your bank of potential participants? Is it just name and phone number or are you getting into more sensitive information? (participant 9).

In addition, one participant reported that their university does not provide the resources required to respect the confidentiality of research participants.

The issue is as follows: researchers, of course, commit to protecting data with passwords and all that, but we realize that in practice, it is more difficult. It is not always as protected as one might think, because professor-researchers will run out of space. Will the universities make rooms available to researchers, places where they can store these things, especially when they have paper documentation, and is there indeed a guarantee of confidentiality? Some researchers have told me: “Listen; there are even filing cabinets in the corridors”. So, that certainly poses a concrete challenge. How do we go about challenging the administrative authorities? Tell them it’s all very well to have an ethics committee, but you have to help us, you also have to make sure that the necessary infrastructures are in place so that what we are proposing is really put into practice (participant 4).

If the relationships with research participants are likely to raise ethical issues, so too are the relationships with students, notably research assistants. On this topic, several participants discussed the lack of supervision or recognition offered to research assistants by researchers as well as the power imbalances between members of the research team.

Lack of Supervision and Power Imbalances

Many research teams are composed not only of researchers, but also of students who work as research assistants. The relationship between research assistants and other members of research teams can sometimes be problematic and raise ethical issues, particularly because of the inevitable power asymmetries. In the context of this study, several participants – including a research assistant, REB members, and researchers – discussed the lack of supervision or recognition of the work carried out by students, psychological pressure, and the more or less well-founded promises that are sometimes made to students. Participants also mentioned the exploitation of students by certain research teams, which manifest when students are inadequately paid, i.e., not reflective of the number of hours actually worked, not a fair wage, or even a wage at all.

[As a research assistant], it was more of a feeling of distress that I felt then because I didn’t know what to do. (…) I was supposed to get coaching or be supported, but I didn’t get anything in the end. It was like, “fix it by yourself”. (…) All research assistants were supposed to be supervised, but in practice they were not (participant 1). Very often, we have a master’s or doctoral student that we put on a subject and we consider that the project will be well done, while the student is learning. So, it happens that the student will do a lot of work and then we realize that the work is poorly done, and it is not necessarily the student’s fault. He wasn’t necessarily well supervised. There are directors who have 25 students, and they just don’t supervise them (participant 14). I think it’s really the power relationship. I thought to myself, how I saw my doctorate, the beginning of my research career, I really wanted to be in that laboratory, but they are the ones who are going to accept me or not, so what do I do to be accepted? I finally accept their conditions [which was to work for free]. If these are the conditions that are required to enter this lab, I want to go there. So, what do I do, well I accepted. It doesn’t make sense, but I tell myself that I’m still privileged, because I don’t have so many financial worries, one more reason to work for free, even though it doesn’t make sense (participant 1). In research, we have research assistants. (…). The fact of using people… so that’s it, you have to take into account where they are, respect them, but at the same time they have to show that they are there for the research. In English, we say “carry” or take care of people. With research assistants, this is often a problem that I have observed: for grant machines, the person is the last to be found there. Researchers, who will take, use student data, without giving them the recognition for it (participant 5). The problem at our university is that they reserve funding for Canadian students. The doctoral clientele in my field is mostly foreign students. So, our students are poorly funded. I saw one student end up in the shelter, in a situation of poverty. It ended very badly for him because he lacked financial resources. Once you get into that dynamic, it’s very hard to get out. I was made aware of it because the director at the time had taken him under her wing and wanted to try to find a way to get him out of it. So, most of my students didn’t get funded (participant 16). There I wrote “manipulation”, but it’s kind of all promises all the time. I, for example, was promised a lot of advancement, like when I got into the lab as a graduate student, it was said that I had an interest in [this particular area of research]. I think there are a lot of graduate students who must have gone through that, but it is like, “Well, your CV has to be really good, if you want to do a lot of things and big things. If you do this, if you do this research contract, the next year you could be the coordinator of this part of the lab and supervise this person, get more contracts, be paid more. Let’s say: you’ll be invited to go to this conference, this big event”. They were always dangling something, but you have to do that first to get there. But now, when you’ve done that, you have to do this business. It’s like a bit of manipulation, I think. That was very hard to know who is telling the truth and who is not (participant 1).

These ethical issues have significant negative consequences for students. Indeed, they sometimes find themselves at the mercy of researchers, for whom they work, struggling to be recognized and included as authors of an article, for example, or to receive the salary that they are due. For their part, researchers also sometimes find themselves trapped in research structures that can negatively affect their well-being. As many participants reported, researchers work in organizations that set very high productivity standards and in highly competitive contexts, all within a general culture characterized by individualism.

Individualism and performance

Participants, especially researchers, discussed the culture of individualism and performance that characterizes the academic environment. In glorifying excellence, some universities value performance and productivity, often at the expense of psychological well-being and work-life balance (i.e., work overload and burnout). Participants noted that there are ethical silences in their organizations on this issue, and that the culture of individualism and performance is not challenged for fear of retribution or simply to survive, i.e., to perform as expected. Participants felt that this culture can have a significant negative impact on the quality of the research conducted, as research teams try to maximize the quantity of their work (instead of quality) in a highly competitive context, which is then exacerbated by a lack of resources and support, and where everything must be done too quickly.

The work-life balance with the professional ethics related to work in a context where you have too much and you have to do a lot, it is difficult to balance all that and there is a lot of pressure to perform. If you don’t produce enough, that’s it; after that, you can’t get any more funds, so that puts pressure on you to do more and more and more (participant 3). There is a culture, I don’t know where it comes from, and that is extremely bureaucratic. If you dare to raise something, you’re going to have many, many problems. They’re going to make you understand it. So, I don’t talk. It is better: your life will be easier. I think there are times when you have to talk (…) because there are going to be irreparable consequences. (…) I’m not talking about a climate of terror, because that’s exaggerated, it’s not true, people are not afraid. But people close their office door and say nothing because it’s going to make their work impossible and they’re not going to lose their job, they’re not going to lose money, but researchers need time to be focused, so they close their office door and say nothing (participant 16).

Researchers must produce more and more, and they feel little support in terms of how to do such production, ethically, and how much exactly they are expected to produce. As this participant reports, the expectation is an unspoken rule: more is always better.

It’s sometimes the lack of a clear line on what the expectations are as a researcher, like, “ah, we don’t have any specific expectations, but produce, produce, produce, produce.” So, in that context, it’s hard to be able to put the line precisely: “have I done enough for my work?” (participant 3).

Inadequate ethical Guidance

While the productivity expectation is not clear, some participants – including researchers, research ethics experts, and REB members – also felt that the ethical expectations of some REBs were unclear. The issue of the inadequate ethical guidance of research includes the administrative mechanisms to ensure that research projects respect the principles of research ethics. According to those participants, the forms required for both researchers and REB members are increasingly long and numerous, and one participant noted that the standards to be met are sometimes outdated and disconnected from the reality of the field. Multicentre ethics review (by several REBs) was also critiqued by a participant as an inefficient method that encumbers the processes for reviewing research projects. Bureaucratization imposes an ever-increasing number of forms and ethics guidelines that actually hinder researchers’ ethical reflection on the issues at stake, leading the ethics review process to be perceived as purely bureaucratic in nature.

The ethical dimension and the ethical review of projects have become increasingly bureaucratized. (…) When I first started working (…) it was less bureaucratic, less strict then. I would say [there are now] tons of forms to fill out. Of course, we can’t do without it, it’s one of the ways of marking out ethics and ensuring that there are ethical considerations in research, but I wonder if it hasn’t become too bureaucratized, so that it’s become a kind of technical reflex to fill out these forms, and I don’t know if people really do ethical reflection as such anymore (participant 10). The fundamental structural issue, I would say, is the mismatch between the normative requirements and the real risks posed by the research, i.e., we have many, many requirements to meet; we have very long forms to fill out but the research projects we evaluate often pose few risks (participant 8). People [in vulnerable situations] were previously unable to participate because of overly strict research ethics rules that were to protect them, but in the end [these rules] did not protect them. There was a perverse effect, because in the end there was very little research done with these people and that’s why we have very few results, very little evidence [to support practices with these populations] so it didn’t improve the quality of services. (…) We all understand that we have to be careful with that, but when the research is not too risky, we say to ourselves that it would be good because for once a researcher who is interested in that population, because it is not a very popular population, it would be interesting to have results, but often we are blocked by the norms, and then we can’t accept [the project] (participant 2).

Moreover, as one participant noted, accessing ethics training can be a challenge.

There is no course on research ethics. […] Then, I find that it’s boring because you go through university and you come to do your research and you know how to do quantitative and qualitative research, but all the research ethics, where do you get this? I don’t really know (participant 13).

Yet, such training could provide relevant tools to resolve, to some extent, the ethical issues that commonly arise in research. That said, and as noted by many participants, many ethical issues in research are related to social injustices over which research actors have little influence.

Social Injustices

For many participants, notably researchers, the issues that concern social injustices are those related to power asymmetries, stigma, or issues of equity, diversity, and inclusion, i.e., social injustices related to people’s identities (Blais & Drolet, 2022 ). Participants reported experiencing or witnessing discrimination from peers, administration, or lab managers. Such oppression is sometimes cross-sectional and related to a person’s age, cultural background, gender or social status.

I have my African colleague who was quite successful when he arrived but had a backlash from colleagues in the department. I think it’s unconscious, nobody is overtly racist. But I have a young person right now who is the same, who has the same success, who got exactly the same early career award and I don’t see the same backlash. He’s just as happy with what he’s doing. It’s normal, they’re young and they have a lot of success starting out. So, I think there is discrimination. Is it because he is African? Is it because he is black? I think it’s on a subconscious level (participant 16).

Social injustices were experienced or reported by many participants, and included issues related to difficulties in obtaining grants or disseminating research results in one’s native language (i.e., even when there is official bilingualism) or being considered credible and fundable in research when one researcher is a woman.

If you do international research, there are things you can’t talk about (…). It is really a barrier to research to not be able to (…) address this question [i.e. the question of inequalities between men and women]. Women’s inequality is going to be addressed [but not within the country where the research takes place as if this inequality exists elsewhere but not here]. There are a lot of women working on inequality issues, doing work and it’s funny because I was talking to a young woman who works at Cairo University and she said to me: “Listen, I saw what you had written, you’re right. I’m willing to work on this but guarantee me a position at your university with a ticket to go”. So yes, there are still many barriers [for women in research] (participant 16).

Because of the varied contextual characteristics that intervene in their occurrence, these social injustices are also related to distributive injustices, as discussed by many participants.

Distributive Injustices

Although there are several views of distributive justice, a classical definition such as that of Aristotle ( 2012 ), describes distributive justice as consisting in distributing honours, wealth, and other social resources or benefits among the members of a community in proportion to their alleged merit. Justice, then, is about determining an equitable distribution of common goods. Contemporary theories of distributive justice are numerous and varied. Indeed, many authors (e.g., Fraser 2011 ; Mills, 2017 ; Sen, 2011 ; Young, 2011 ) have, since Rawls ( 1971 ), proposed different visions of how social burdens and benefits should be shared within a community to ensure equal respect, fairness, and distribution. In our study, what emerges from participants’ narratives is a definite concern for this type of justice. Women researchers, francophone researchers, early career researchers or researchers belonging to racialized groups all discussed inequities in the distribution of research grants and awards, and the extra work they need to do to somehow prove their worth. These inequities are related to how granting agencies determine which projects will be funded.

These situations make me work 2–3 times harder to prove myself and to show people in power that I have a place as a woman in research (participant 12). Number one: it’s conservative thinking. The older ones control what comes in. So, the younger people have to adapt or they don’t get funded (participant 14).

Whether it is discrimination against stigmatized or marginalized populations or interest in certain hot topics, granting agencies judge research projects according to criteria that are sometimes questionable, according to those participants. Faced with difficulties in obtaining funding for their projects, several strategies – some of which are unethical – are used by researchers in order to cope with these situations.

Sometimes there are subjects that everyone goes to, such as nanotechnology (…), artificial intelligence or (…) the therapeutic use of cannabis, which are very fashionable, and this is sometimes to the detriment of other research that is just as relevant, but which is (…), less sexy, less in the spirit of the time. (…) Sometimes this can lead to inequities in the funding of certain research sectors (participant 9). When we use our funds, we get them given to us, we pretty much say what we think we’re going to do with them, but things change… So, when these things change, sometimes it’s an ethical decision, but by force of circumstances I’m obliged to change the project a little bit (…). Is it ethical to make these changes or should I just let the money go because I couldn’t use it the way I said I would? (participant 3).

Moreover, these distributional injustices are not only linked to social injustices, but also epistemic injustices. Indeed, the way in which research honours and grants are distributed within the academic community depends on the epistemic authority of the researchers, which seems to vary notably according to their language of use, their age or their gender, but also to the research design used (inductive versus deductive), their decision to use (or not use) animals in research, or to conduct activist research.

Epistemic injustices

The philosopher Fricker ( 2007 ) conceptualized the notions of epistemic justice and injustice. Epistemic injustice refers to a form of social inequality that manifests itself in the access, recognition, and production of knowledge as well as the various forms of ignorance that arise (Godrie & Dos Santos, 2017 ). Addressing epistemic injustice necessitates acknowledging the iniquitous wrongs suffered by certain groups of socially stigmatized individuals who have been excluded from knowledge, thus limiting their abilities to interpret, understand, or be heard and account for their experiences. In this study, epistemic injustices were experienced or reported by some participants, notably those related to difficulties in obtaining grants or disseminating research results in one’s native language (i.e., even when there is official bilingualism) or being considered credible and fundable in research when a researcher is a woman or an early career researcher.

I have never sent a grant application to the federal government in English. I have always done it in French, even though I know that when you receive the review, you can see that reviewers didn’t understand anything because they are English-speaking. I didn’t want to get in the boat. It’s not my job to translate, because let’s be honest, I’m not as good in English as I am in French. So, I do them in my first language, which is the language I’m most used to. Then, technically at the administrative level, they are supposed to be able to do it, but they are not good in French. (…) Then, it’s a very big Canadian ethical issue, because basically there are technically two official languages, but Canada is not a bilingual country, it’s a country with two languages, either one or the other. (…) So I was not funded (participant 14).

Researchers who use inductive (or qualitative) methods observed that their projects are sometimes less well reviewed or understood, while research that adopts a hypothetical-deductive (or quantitative) or mixed methods design is better perceived, considered more credible and therefore more easily funded. Of course, regardless of whether a research project adopts an inductive, deductive or mixed-methods scientific design, or whether it deals with qualitative or quantitative data, it must respect a set of scientific criteria. A research project should achieve its objectives by using proven methods that, in the case of inductive research, are credible, reliable, and transferable or, in the case of deductive research, generalizable, objective, representative, and valid (Drolet & Ruest, accepted ). Participants discussing these issues noted that researchers who adopt a qualitative design or those who question the relevance of animal experimentation or are not militant have sometimes been unfairly devalued in their epistemic authority.

There is a mini war between quantitative versus qualitative methods, which I think is silly because science is a method. If you apply the method well, it doesn’t matter what the field is, it’s done well and it’s perfect ” (participant 14). There is also the issue of the place of animals in our lives, because for me, ethics is human ethics, but also animal ethics. Then, there is a great evolution in society on the role of the animal… with the new law that came out in Quebec on the fact that animals are sensitive beings. Then, with the rise of the vegan movement, [we must ask ourselves]: “Do animals still have a place in research?” That’s a big question and it also means that there are practices that need to evolve, but sometimes there’s a disconnection between what’s expected by research ethics boards versus what’s expected in the field (participant 15). In research today, we have more and more research that is militant from an ideological point of view. And so, we have researchers, because they defend values that seem important to them, we’ll talk for example about the fight for equality and social justice. They have pressure to defend a form of moral truth and have the impression that everyone thinks like them or should do so, because they are defending a moral truth. This is something that we see more and more, namely the lack of distance between ideology and science (participant 8).

The combination or intersectionality of these inequities, which seems to be characterized by a lack of ethical support and guidance, is experienced in the highly competitive and individualistic context of research; it provides therefore the perfect recipe for researchers to experience ethical distress.

Ethical distress

The concept of “ethical distress” refers to situations in which people know what they should do to act ethically, but encounter barriers, generally of an organizational or systemic nature, limiting their power to act according to their moral or ethical values (Drolet & Ruest, 2021 ; Jameton, 1984 ; Swisher et al., 2005 ). People then run the risk of finding themselves in a situation where they do not act as their ethical conscience dictates, which in the long term has the potential for exhaustion and distress. The examples reported by participants in this study point to the fact that researchers in particular may be experiencing significant ethical distress. This distress takes place in a context of extreme competition, constant injunctions to perform, and where administrative demands are increasingly numerous and complex to complete, while paradoxically, they lack the time to accomplish all their tasks and responsibilities. Added to these demands are a lack of resources (human, ethical, and financial), a lack of support and recognition, and interpersonal conflicts.

We are in an environment, an elite one, you are part of it, you know what it is: “publish or perish” is the motto. Grants, there is a high level of performance required, to do a lot, to publish, to supervise students, to supervise them well, so yes, it is clear that we are in an environment that is conducive to distress. (…). Overwork, definitely, can lead to distress and eventually to exhaustion. When you know that you should take the time to read the projects before sharing them, but you don’t have the time to do that because you have eight that came in the same day, and then you have others waiting… Then someone rings a bell and says: “ah but there, the protocol is a bit incomplete”. Oh yes, look at that, you’re right. You make up for it, but at the same time it’s a bit because we’re in a hurry, we don’t necessarily have the resources or are able to take the time to do things well from the start, we have to make up for it later. So yes, it can cause distress (participant 9). My organization wanted me to apply in English, and I said no, and everyone in the administration wanted me to apply in English, and I always said no. Some people said: “Listen, I give you the choice”, then some people said: “Listen, I agree with you, but if you’re not [submitting] in English, you won’t be funded”. Then the fact that I am young too, because very often they will look at the CV, they will not look at the project: “ah, his CV is not impressive, we will not finance him”. This is complete nonsense. The person is capable of doing the project, the project is fabulous: we fund the project. So, that happened, organizational barriers: that happened a lot. I was not eligible for Quebec research funds (…). I had big organizational barriers unfortunately (participant 14). At the time of my promotion, some colleagues were not happy with the type of research I was conducting. I learned – you learn this over time when you become friends with people after you enter the university – that someone was against me. He had another candidate in mind, and he was angry about the selection. I was under pressure for the first three years until my contract was renewed. I almost quit at one point, but another colleague told me, “No, stay, nothing will happen”. Nothing happened, but these issues kept me awake at night (participant 16).

This difficult context for many researchers affects not only the conduct of their own research, but also their participation in research. We faced this problem in our study, despite the use of multiple recruitment methods, including more than 200 emails – of which 191 were individual solicitations – sent to potential participants by the two research assistants. REB members and organizations overseeing or supporting research (n = 17) were also approached to see if some of their employees would consider participating. While it was relatively easy to recruit REB members and research ethics experts, our team received a high number of non-responses to emails (n = 175) and some refusals (n = 5), especially by researchers. The reasons given by those who replied were threefold: (a) fear of being easily identified should they take part in the research, (b) being overloaded and lacking time, and (c) the intrusive aspect of certain questions (i.e., “Have you experienced a burnout episode? If so, have you been followed up medically or psychologically?”). In light of these difficulties and concerns, some questions in the socio-demographic questionnaire were removed or modified. Talking about burnout in research remains a taboo for many researchers, which paradoxically can only contribute to the unresolved problem of unhealthy research environments.

Returning to the research question and objective

The question that prompted this research was: What are the ethical issues in research? The purpose of the study was to describe these issues from the perspective of researchers (from different disciplines), research ethics board (REB) members, and research ethics experts. The previous section provided a detailed portrait of the ethical issues experienced by different research stakeholders: these issues are numerous, diverse and were recounted by a range of stakeholders.

The results of the study are generally consistent with the literature. For example, as in our study, the literature discusses the lack of research integrity on the part of some researchers (Al-Hidabi et al., 2018 ; Swazey et al., 1993 ), the numerous conflicts of interest experienced in research (Williams-Jones et al., 2013 ), the issues of recruiting and obtaining the free and informed consent of research participants (Provencher et al., 2014 ; Keogh & Daly, 2009 ), the sometimes difficult relations between researchers and REBs (Drolet & Girard, 2020 ), the epistemological issues experienced in research (Drolet & Ruest, accepted; Sieber 2004 ), as well as the harmful academic context in which researchers evolve, insofar as this is linked to a culture of performance, an overload of work in a context of accountability (Berg & Seeber, 2016 ; FQPPU; 2019 ) that is conducive to ethical distress and even burnout.

If the results of the study are generally in line with those of previous publications on the subject, our findings also bring new elements to the discussion while complementing those already documented. In particular, our results highlight the role of systemic injustices – be they social, distributive or epistemic – within the environments in which research is carried out, at least in Canada. To summarize, the results of our study point to the fact that the relationships between researchers and research participants are likely still to raise worrying ethical issues, despite widely accepted research ethics norms and institutionalized review processes. Further, the context in which research is carried out is not only conducive to breaches of ethical norms and instances of misbehaviour or misconduct, but also likely to be significantly detrimental to the health and well-being of researchers, as well as research assistants. Another element that our research also highlighted is the instrumentalization and even exploitation of students and research assistants, which is another important and worrying social injustice given the inevitable power imbalances between students and researchers.

Moreover, in a context in which ethical issues are often discussed from a micro perspective, our study helps shed light on both the micro- and macro-level ethical dimensions of research (Bronfenbrenner, 1979 ; Glaser 1994 ). However, given that ethical issues in research are not only diverse, but also and above all complex, a broader perspective that encompasses the interplay between the micro and macro dimensions can enable a better understanding of these issues and thereby support the identification of the multiple factors that may be at their origin. Triangulating the perspectives of researchers with those of REB members and research ethics experts enabled us to bring these elements to light, and thus to step back from and critique the way that research is currently conducted. To this end, attention to socio-political elements such as the performance culture in academia or how research funds are distributed, and according to what explicit and implicit criteria, can contribute to identifying the sources of the ethical issues described above.

Contemporary culture characterized by the social acceleration

The German sociologist and philosopher Rosa (2010) argues that late modernity – that is, the period between the 1980s and today – is characterized by a phenomenon of social acceleration that causes various forms of alienation in our relationship to time, space, actions, things, others and ourselves. Rosa distinguishes three types of acceleration: technical acceleration , the acceleration of social changes and the acceleration of the rhythm of life . According to Rosa, social acceleration is the main problem of late modernity, in that the invisible social norm of doing more and faster to supposedly save time operates unchallenged at all levels of individual and collective life, as well as organizational and social life. Although we all, researchers and non-researchers alike, perceive this unspoken pressure to be ever more productive, the process of social acceleration as a new invisible social norm is our blind spot, a kind of tyrant over which we have little control. This conceptualization of the contemporary culture can help us to understand the context in which research is conducted (like other professional practices). To this end, Berg & Seeber ( 2016 ) invite faculty researchers to slow down in order to better reflect and, in the process, take care of their health and their relationships with their colleagues and students. Many women professors encourage their fellow researchers, especially young women researchers, to learn to “say No” in order to protect their mental and physical health and to remain in their academic careers (Allaire & Descheneux, 2022 ). These authors also remind us of the relevance of Kahneman’s ( 2012 ) work which demonstrates that it takes time to think analytically, thoroughly, and logically. Conversely, thinking quickly exposes humans to cognitive and implicit biases that then lead to errors in thinking (e.g., in the analysis of one’s own research data or in the evaluation of grant applications or student curriculum vitae). The phenomenon of social acceleration, which pushes the researcher to think faster and faster, is likely to lead to unethical bad science that can potentially harm humankind. In sum, Rosa’s invitation to contemporary critical theorists to seriously consider the problem of social acceleration is particularly insightful to better understand the ethical issues of research. It provides a lens through which to view the toxic context in which research is conducted today, and one that was shared by the participants in our study.

Clark & Sousa ( 2022 ) note, it is important that other criteria than the volume of researchers’ contributions be valued in research, notably quality. Ultimately, it is the value of the knowledge produced and its influence on the concrete lives of humans and other living beings that matters, not the quantity of publications. An interesting articulation of this view in research governance is seen in a change in practice by Australia’s national health research funder: they now restrict researchers to listing on their curriculum vitae only the top ten publications from the past ten years (rather than all of their publications), in order to evaluate the quality of contributions rather than their quantity. To create environments conducive to the development of quality research, it is important to challenge the phenomenon of social acceleration, which insidiously imposes a quantitative normativity that is both alienating and detrimental to the quality and ethical conduct of research. Based on our experience, we observe that the social norm of acceleration actively disfavours the conduct of empirical research on ethics in research. The fact is that researchers are so busy that it is almost impossible for them to find time to participate in such studies. Further, operating in highly competitive environments, while trying to respect the values and ethical principles of research, creates ethical paradoxes for members of the research community. According to Malherbe ( 1999 ), an ethical paradox is a situation where an individual is confronted by contradictory injunctions (i.e., do more, faster, and better). And eventually, ethical paradoxes lead individuals to situations of distress and burnout, or even to ethical failures (i.e., misbehaviour or misconduct) in the face of the impossibility of responding to contradictory injunctions.

Strengths and Limitations of the study

The triangulation of perceptions and experiences of different actors involved in research is a strength of our study. While there are many studies on the experiences of researchers, rarely are members of REBs and experts in research ethics given the space to discuss their views of what are ethical issues. Giving each of these stakeholders a voice and comparing their different points of view helped shed a different and complementary light on the ethical issues that occur in research. That said, it would have been helpful to also give more space to issues experienced by students or research assistants, as the relationships between researchers and research assistants are at times very worrying, as noted by a participant, and much work still needs to be done to eliminate the exploitative situations that seem to prevail in certain research settings. In addition, no Indigenous or gender diverse researchers participated in the study. Given the ethical issues and systemic injustices that many people from these groups face in Canada (Drolet & Goulet, 2018 ; Nicole & Drolet, in press ), research that gives voice to these researchers would be relevant and contribute to knowledge development, and hopefully also to change in research culture.

Further, although most of the ethical issues discussed in this article may be transferable to the realities experienced by researchers in other countries, the epistemic injustice reported by Francophone researchers who persist in doing research in French in Canada – which is an officially bilingual country but in practice is predominantly English – is likely specific to the Canadian reality. In addition, and as mentioned above, recruitment proved exceedingly difficult, particularly amongst researchers. Despite this difficulty, we obtained data saturation for all but two themes – i.e., exploitation of students and ethical issues of research that uses animals. It follows that further empirical research is needed to improve our understanding of these specific issues, as they may diverge to some extent from those documented here and will likely vary across countries and academic research contexts.

Conclusions

This study, which gave voice to researchers, REB members, and ethics experts, reveals that the ethical issues in research are related to several problematic elements as power imbalances and authority relations. Researchers and research assistants are subject to external pressures that give rise to integrity issues, among others ethical issues. Moreover, the current context of social acceleration influences the definition of the performance indicators valued in academic institutions and has led their members to face several ethical issues, including social, distributive, and epistemic injustices, at different steps of the research process. In this study, ten categories of ethical issues were identified, described and illustrated: (1) research integrity, (2) conflicts of interest, (3) respect for research participants, (4) lack of supervision and power imbalances, (5) individualism and performance, (6) inadequate ethical guidance, (7) social injustices, (8) distributive injustices, (9) epistemic injustices, and (10) ethical distress. The triangulation of the perspectives of different members (i.e., researchers from different disciplines, REB members, research ethics experts, and one research assistant) involved in the research process made it possible to lift the veil on some of these ethical issues. Further, it enabled the identification of additional ethical issues, especially systemic injustices experienced in research. To our knowledge, this is the first time that these injustices (social, distributive, and epistemic injustices) have been clearly identified.

Finally, this study brought to the fore several problematic elements that are important to address if the research community is to develop and implement the solutions needed to resolve the diverse and transversal ethical issues that arise in research institutions. A good starting point is the rejection of the corollary norms of “publish or perish” and “do more, faster, and better” and their replacement with “publish quality instead of quantity”, which necessarily entails “do less, slower, and better”. It is also important to pay more attention to the systemic injustices within which researchers work, because these have the potential to significantly harm the academic careers of many researchers, including women researchers, early career researchers, and those belonging to racialized groups as well as the health, well-being, and respect of students and research participants.

Acknowledgements

The team warmly thanks the participants who took part in the research and who made this study possible. Marie-Josée Drolet thanks the five research assistants who participated in the data collection and analysis: Julie-Claude Leblanc, Élie Beauchemin, Pénéloppe Bernier, Louis-Pierre Côté, and Eugénie Rose-Derouin, all students at the Université du Québec à Trois-Rivières (UQTR), two of whom were active in the writing of this article. MJ Drolet and Bryn Williams-Jones also acknowledge the financial contribution of the Social Sciences and Humanities Research Council of Canada (SSHRC), which supported this research through a grant. We would also like to thank the reviewers of this article who helped us improve it, especially by clarifying and refining our ideas.

Competing Interests and Funding

As noted in the Acknowledgements, this research was supported financially by the Social Sciences and Humanities Research Council of Canada (SSHRC).

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Al-Hidabi, Abdulmalek, M. D., & The, P. L. (2018). Multiple Publications: The Main Reason for the Retraction of Papers in Computer Science. In K. Arai, S. Kapoor, & R. Bhatia (eds), Future of Information and Communication Conference (FICC): Advances in Information and Communication, Advances in Intelligent Systems and Computing (AISC), Springer, vol. 886, pp. 511–526
• Allaire, S., & Deschenaux, F. (2022). Récits de professeurs d’université à mi-carrière. Si c’était à refaire… . Presses de l’Université du Québec
• Aristotle . Aristotle’s Nicomachean Ethics. Chicago: The University of Chicago Press; 2012. [ Google Scholar ]
• Bahn S. Keeping Academic Field Researchers Safe: Ethical Safeguards. Journal of Academic Ethics. 2012; 10 :83–91. doi: 10.1007/s10805-012-9159-2. [ CrossRef ] [ Google Scholar ]
• Balk DE. Bereavement Research Using Control Groups: Ethical Obligations and Questions. Death Studies. 1995; 19 :123–138. doi: 10.1080/07481189508252720. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Beauchemin, É., Côté, L. P., Drolet, M. J., & Williams-Jones, B. (2021). Conceptualizing Ethical Issues in the Conduct of Research: Results from a Critical and Systematic Literature Review. Journal of Academic Ethics , Early Online. 10.1007/s10805-021-09411-7
• Berg, M., & Seeber, B. K. (2016). The Slow Professor . University of Toronto Press
• Birchley G, Huxtable R, Murtagh M, Meulen RT, Flach P, Gooberman-Hill R. Smart homes, private homes? An empirical study of technology researchers’ perceptions of ethical issues in developing smart-home health technologies. BMC Medical Ethics. 2017; 18 (23):1–13. doi: 10.1186/s12910-017-0183-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Blais, J., & Drolet, M. J. (2022). Les injustices sociales vécues en camp de réfugiés: les comprendre pour mieux intervenir auprès de personnes ayant séjourné dans un camp de réfugiés. Recueil annuel belge d’ergothérapie , 14, 37–48
• Bogdan, R. C., & Biklen, S. K. (2006). Qualitative research in education: An introduction to theory and methods . Allyn & Bacon
• Bouffard C. Le développement des pratiques de la génétique médicale et la construction des normes bioéthiques. Anthropologie et Sociétés. 2000; 24 (2):73–90. doi: 10.7202/015650ar. [ CrossRef ] [ Google Scholar ]
• Bronfenbrenner, U. (1979). The Ecology of Human development. Experiments by nature and design . Harvard University Press
• Bruhn JG, Zajac G, Al-Kazemi AA, Prescott LD. Moral positions and academic conduct: Parameters of tolerance for ethics failure. Journal of Higher Education. 2002; 73 (4):461–493. doi: 10.1353/jhe.2002.0033. [ CrossRef ] [ Google Scholar ]
• Clark, A., & Sousa (2022). It’s time to end Canada’s obsession with research quantity. University Affairs/Affaires universitaires , February 14th. https://www.universityaffairs.ca/career-advice/effective-successfull-happy-academic/its-time-to-end-canadas-obsession-with-research-quantity/?utm_source=University+Affairs+e-newsletter&utm_campaign=276a847f 70-EMAIL_CAMPAIGN_2022_02_16&utm_medium=email&utm_term=0_314bc2ee29-276a847f70-425259989
• Colnerud G. Ethical dilemmas in research in relation to ethical review: An empirical study. Research Ethics. 2015; 10 (4):238–253. doi: 10.1177/1747016114552339. [ CrossRef ] [ Google Scholar ]
• Davison J. Dilemmas in Research: Issues of Vulnerability and Disempowerment for the Social Workers/Researcher. Journal of Social Work Practice. 2004; 18 (3):379–393. doi: 10.1080/0265053042000314447. [ CrossRef ] [ Google Scholar ]
• DePoy E, Gitlin LN. Introduction to Research. St. Louis: Elsevier Mosby; 2010. [ Google Scholar ]
• Drolet, M. J., & Goulet, M. (2018). Travailler avec des patients autochtones du Canada ? Perceptions d’ergothérapeutes du Québec des enjeux éthiques de cette pratique. Recueil annuel belge francophone d’ergothérapie , 10 , 25–56
• Drolet MJ, Girard K. Les enjeux éthiques de la recherche en ergothérapie: un portrait préoccupant. Revue canadienne de bioéthique. 2020; 3 (3):21–40. doi: 10.7202/1073779ar. [ CrossRef ] [ Google Scholar ]
• Drolet MJ, Girard K, Gaudet R. Les enjeux éthiques de l’enseignement en ergothérapie: des injustices au sein des départements universitaires. Revue canadienne de bioéthique. 2020; 3 (1):22–36. [ Google Scholar ]
• Drolet MJ, Maclure J. Les enjeux éthiques de la pratique de l’ergothérapie: perceptions d’ergothérapeutes. Revue Approches inductives. 2016; 3 (2):166–196. doi: 10.7202/1037918ar. [ CrossRef ] [ Google Scholar ]
• Drolet MJ, Pinard C, Gaudet R. Les enjeux éthiques de la pratique privée: des ergothérapeutes du Québec lancent un cri d’alarme. Ethica – Revue interdisciplinaire de recherche en éthique. 2017; 21 (2):173–209. [ Google Scholar ]
• Drolet MJ, Ruest M. De l’éthique à l’ergothérapie: un cadre théorique et une méthode pour soutenir la pratique professionnelle. Québec: Presses de l’Université du Québec; 2021. [ Google Scholar ]
• Drolet, M. J., & Ruest, M. (accepted). Quels sont les enjeux éthiques soulevés par la recherche scientifique? In M. Lalancette & J. Luckerhoff (dir). Initiation au travail intellectuel et à la recherche . Québec: Presses de l’Université du Québec, 18 p
• Drolet MJ, Sauvageau A, Baril N, Gaudet R. Les enjeux éthiques de la formation clinique en ergothérapie. Revue Approches inductives. 2019; 6 (1):148–179. doi: 10.7202/1060048ar. [ CrossRef ] [ Google Scholar ]
• Fédération québécoise des professeures et des professeurs d’université (FQPPU) Enquête nationale sur la surcharge administrative du corps professoral universitaire québécois. Principaux résultats et pistes d’action. Montréal: FQPPU; 2019. [ Google Scholar ]
• Fortin MH. Fondements et étapes du processus de recherche. Méthodes quantitatives et qualitatives. Montréal, QC: Chenelière éducation; 2010. [ Google Scholar ]
• Fraser DM. Ethical dilemmas and practical problems for the practitioner researcher. Educational Action Research. 1997; 5 (1):161–171. doi: 10.1080/09650799700200014. [ CrossRef ] [ Google Scholar ]
• Fraser, N. (2011). Qu’est-ce que la justice sociale? Reconnaissance et redistribution . La Découverte
• Fricker, M. (2007). Epistemic Injustice: Power and the Ethics of Knowing . Oxford University Press
• Giorgi A, et al. De la méthode phénoménologique utilisée comme mode de recherche qualitative en sciences humaines: théories, pratique et évaluation. In: Poupart J, Groulx LH, Deslauriers JP, et al., editors. La recherche qualitative: enjeux épistémologiques et méthodologiques. Boucherville, QC: Gaëtan Morin; 1997. pp. 341–364. [ Google Scholar ]
• Giorgini V, Mecca JT, Gibson C, Medeiros K, Mumford MD, Connelly S, Devenport LD. Researcher Perceptions of Ethical Guidelines and Codes of Conduct. Accountability in Research. 2016; 22 (3):123–138. doi: 10.1080/08989621.2014.955607. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Glaser, J. W. (1994). Three realms of ethics: Individual, institutional, societal. Theoretical model and case studies . Kansas Cuty, Sheed & Ward
• Godrie B, Dos Santos M. Présentation: inégalités sociales, production des savoirs et de l’ignorance. Sociologie et sociétés. 2017; 49 (1):7. doi: 10.7202/1042804ar. [ CrossRef ] [ Google Scholar ]
• Hammell KW, Carpenter C, Dyck I. Using Qualitative Research: A Practical Introduction for Occupational and Physical Therapists. Edinburgh: Churchill Livingstone; 2000. [ Google Scholar ]
• Henderson M, Johnson NF, Auld G. Silences of ethical practice: dilemmas for researchers using social media. Educational Research and Evaluation. 2013; 19 (6):546–560. doi: 10.1080/13803611.2013.805656. [ CrossRef ] [ Google Scholar ]
• Husserl E. The crisis of European sciences and transcendental phenomenology. Evanston, IL: Northwestern University Press; 1970. [ Google Scholar ]
• Husserl E. The train of thoughts in the lectures. In: Polifroni EC, Welch M, editors. Perspectives on Philosophy of Science in Nursing. Philadelphia, PA: Lippincott; 1999. [ Google Scholar ]
• Hunt SD, Chonko LB, Wilcox JB. Ethical problems of marketing researchers. Journal of Marketing Research. 1984; 21 :309–324. doi: 10.1177/002224378402100308. [ CrossRef ] [ Google Scholar ]
• Hunt MR, Carnevale FA. Moral experience: A framework for bioethics research. Journal of Medical Ethics. 2011; 37 (11):658–662. doi: 10.1136/jme.2010.039008. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jameton, A. (1984). Nursing practice: The ethical issues . Englewood Cliffs, Prentice-Hall
• Jarvis K. Dilemmas in International Research and the Value of Practical Wisdom. Developing World Bioethics. 2017; 17 (1):50–58. doi: 10.1111/dewb.12121. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kahneman D. Système 1, système 2: les deux vitesses de la pensée. Paris: Flammarion; 2012. [ Google Scholar ]
• Keogh B, Daly L. The ethics of conducting research with mental health service users. British Journal of Nursing. 2009; 18 (5):277–281. doi: 10.12968/bjon.2009.18.5.40539. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lierville AL, Grou C, Pelletier JF. Enjeux éthiques potentiels liés aux partenariats patients en psychiatrie: État de situation à l’Institut universitaire en santé mentale de Montréal. Santé mentale au Québec. 2015; 40 (1):119–134. doi: 10.7202/1032386ar. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lynöe N, Sandlund M, Jacobsson L. Research ethics committees: A comparative study of assessment of ethical dilemmas. Scandinavian Journal of Public Health. 1999; 27 (2):152–159. doi: 10.1177/14034948990270020401. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Malherbe JF. Compromis, dilemmes et paradoxes en éthique clinique. Anjou: Éditions Fides; 1999. [ Google Scholar ]
• McGinn R. Discernment and denial: Nanotechnology researchers’ recognition of ethical responsibilities related to their work. NanoEthics. 2013; 7 :93–105. doi: 10.1007/s11569-013-0174-6. [ CrossRef ] [ Google Scholar ]
• Mills, C. W. (2017). Black Rights / White rongs. The Critique of Racial Liberalism . Oxford University Press
• Miyazaki AD, Taylor KA. Researcher interaction biases and business ethics research: Respondent reactions to researcher characteristics. Journal of Business Ethics. 2008; 81 (4):779–795. doi: 10.1007/s10551-007-9547-5. [ CrossRef ] [ Google Scholar ]
• Mondain N, Bologo E. L’intentionnalité du chercheur dans ses pratiques de production des connaissances: les enjeux soulevés par la construction des données en démographie et santé en Afrique. Cahiers de recherche sociologique. 2009; 48 :175–204. doi: 10.7202/039772ar. [ CrossRef ] [ Google Scholar ]
• Nicole, M., & Drolet, M. J. (in press). Fitting transphobia and cisgenderism in occupational therapy, Occupational Therapy Now
• Pope KS, Vetter VA. Ethical dilemmas encountered by members of the American Psychological Association: A national survey. The American Psychologist. 1992; 47 (3):397–411. doi: 10.1037/0003-066X.47.3.397. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Provencher V, Mortenson WB, Tanguay-Garneau L, Bélanger K, Dagenais M. Challenges and strategies pertaining to recruitment and retention of frail elderly in research studies: A systematic review. Archives of Gerontology and Geriatrics. 2014; 59 (1):18–24. doi: 10.1016/j.archger.2014.03.006. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rawls, J. (1971). A Theory of Justice . Harvard University Press
• Resnik DB, Elliott KC. The Ethical Challenges of Socially Responsible Science. Accountability in Research. 2016; 23 (1):31–46. doi: 10.1080/08989621.2014.1002608. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rosa, H. (2010). Accélération et aliénation. Vers une théorie critique de la modernité tardive . Paris, Découverte
• Sen, A. K. (2011). The Idea of Justice . The Belknap Press of Harvard University Press
• Sen, A. K. (1995). Inegality Reexaminated . Oxford University Press
• Sieber JE. Empirical Research on Research Ethics. Ethics & Behavior. 2004; 14 (4):397–412. doi: 10.1207/s15327019eb1404_9. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sigmon ST. Ethical practices and beliefs of psychopathology researchers. Ethics & Behavior. 1995; 5 (4):295–309. doi: 10.1207/s15327019eb0504_1. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Swazey JP, Anderson MS, Lewis KS. Ethical Problems in Academic Research. American Scientist. 1993; 81 (6):542–553. [ Google Scholar ]
• Swisher LL, Arsalanian LE, Davis CM. The realm-individual-process-situation (RIPS) model of ethical decision-making. HPA Resource. 2005; 5 (3):3–8. [ Google Scholar ]
• Tri-Council Policy Statement (TCPS2) (2018). Ethical Conduct for Research Involving Humans . Government of Canada, Secretariat on Responsible Conduct of Research. https://ethics.gc.ca/eng/documents/tcps2-2018-en-interactive-final.pdf
• Thomas SP, Pollio HR. Listening to Patients: A Phenomenological Approach to Nursing Research and Practice. New York: Springer Publishing Company; 2002. [ Google Scholar ]
• Wiegand DL, Funk M. Consequences of clinical situations that cause critical care nurses to experience moral distress. Nursing Ethics. 2012; 19 (4):479–487. doi: 10.1177/0969733011429342. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Williams-Jones B, Potvin MJ, Mathieu G, Smith E. Barriers to research on research ethics review and conflicts of interest. IRB: Ethics & Human Research. 2013; 35 (5):14–20. [ PubMed ] [ Google Scholar ]
• Young, I. M. (2011). Justice and the Politics of difference . Princeton University Press

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Research design : qualitative, quantitative, and mixed methods approaches

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• The selection of a research approach
• Review of the literature
• The use of theory
• Writing strategies and ethical considerations
• The introduction
• The purpose statement
• Research questions and hypotheses
• Quantitative methods
• Qualitative methods
• Mixed methods procedures

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