- Accessibility of clinics
- Incentives to continue
For a comprehensive collection, see catalogofbias.org .
Here are some noteworthy examples of study bias from the literature: An example of information bias was observed when in 1998 an alleged association between the measles, mumps, and rubella (MMR) vaccine and autism was reported. Recall bias (a subtype of information bias) emerged when parents of autistic children recalled the onset of autism after an MMR vaccination more often than parents of similar children who were diagnosed prior to the media coverage of that controversial and meanwhile retracted study [ 51 ]. A study from 2001 showed better survival for academy award-winning actors, but this was due to immortal time bias that favors the treatment or exposure group [ 52 , 53 ]. A study systematically investigated self-reports about musculoskeletal symptoms and found the presence of information bias. The reason was that participants with little computer-time overestimated, and participants with a lot of computer-time spent underestimated their computer usage [ 54 ].
Information bias can be mitigated by using objective rather than subjective measurements. Standardized operating procedures (SOP) and electronic lab notebooks additionally help to follow well-designed protocols for data collection and handling [ 55 ]. Despite the failure to mitigate bias in studies, complete descriptions of data and methods can at least allow the assessment of risk of bias.
Rule 6: avoid questionable research practices.
Questionable research practices (QRPs) can lead to exaggerated findings and false conclusions and thus lead to irreproducible research. Often, QRPs are used with no bad intentions. This becomes evident when methods sections explicitly describe such procedures, for example, to increase the number of samples until statistical significance is reached that supports the hypothesis. Therefore, it is important that researchers know about QRPs in order to recognize and avoid them.
Several questionable QRPs have been named [ 56 , 57 ]. Among them are low statistical power, pseudoreplication, repeated inspection of data, p -hacking [ 58 ], selective reporting, and hypothesizing after the results are known (HARKing).
The first 2 QRPs, low statistical power and pseudoreplication, can be prevented by proper planning and designing of studies, including sample size calculation and appropriate statistical methodology to avoid treating data as independent when in fact they are not. Statistical power is not equal to reproducibility, but statistical power is a precondition of reproducibility as the lack thereof can result in false negative as well as false positive findings (see Rule 3 ).
In fact, a lot of QRP can be avoided with a study protocol and statistical analysis plan. Preregistration, as described in Rule 2, is considered best practice for this purpose. However, many of these issues can additionally be rooted in institutional incentives and rewards. Both funding and promotion are often tied to the quantity rather than the quality of the research output. At universities, still only few or no rewards are given for writing and registering protocols, sharing data, publishing negative findings, and conducting replication studies. Thus, a wider “culture change” is needed.
It would help if more researchers were familiar with correct interpretations and possible misinterpretations of statistical tests, p -values, confidence intervals, and statistical power [ 59 , 60 ]. A statistically significant p -value does not necessarily mean that there is a clinically or biologically relevant effect. Specifically, the traditional dichotomization into statistically significant ( p < 0.05) versus statistically nonsignificant ( p ≥ 0.05) results is seldom appropriate, can lead to cherry-picking of results and may eventually corrupt science [ 61 ]. We instead recommend reporting exact p -values and interpreting them in a graded way in terms of the compatibility of the null hypothesis with the data [ 62 , 63 ]. Moreover, a p -value around 0.05 (e.g., 0.047 or 0.055) provides only little information, as is best illustrated by the associated replication power: The probability that a hypothetical replication study of the same design will lead to a statistically significant result is only 50% [ 64 ] and is even lower in the presence of publication bias and regression to the mean (the phenomenon that effect estimates in replication studies are often smaller than the estimates in the original study) [ 65 ]. Claims of novel discoveries should therefore be based on a smaller p -value threshold (e.g., p < 0.005) [ 66 ], but this really depends on the discipline (genome-wide screenings or studies in particle physics often apply much lower thresholds).
Generally, there is often too much emphasis on p -values. A statistical index such as the p -value is just the final product of an analysis, the tip of the iceberg [ 67 ]. Statistical analyses often include many complex stages, from data processing, cleaning, transformation, addressing missing data, modeling, to statistical inference. Errors and pitfalls can creep in at any stage, and even a tiny error can have a big impact on the result [ 68 ]. Also, when many hypothesis tests are conducted (multiple testing), false positive rates may need to be controlled to protect against wrong conclusions, although adjustments for multiple testing are debated [ 69 – 71 ].
Thus, a p -value alone is not a measure of how credible a scientific finding is [ 72 ]. Instead, the quality of the research must be considered, including the study design, the quality of the measurement, and the validity of the assumptions that underlie the data analysis [ 60 , 73 ]. Frameworks exist that help to systematically and transparently assess the certainty in evidence; the most established and widely used one is Grading of Recommendations, Assessment, Development and Evaluations (GRADE; www.gradeworkinggroup.org ) [ 74 ].
Training in basic statistics, statistical programming, and reproducible analyses and better involvement of data professionals in academia is necessary. University departments sometimes have statisticians that can support researchers. Importantly, statisticians need to be involved early in the process and on an equal footing and not just at the end of a project to perform the final data analysis.
In reality, science often lacks transparency. Open science makes the process of producing evidence and claims transparent and accessible to others [ 75 ]. Several universities and research funders have already implemented open science roadmaps to advocate free and public science as well as open access to scientific knowledge, with the aim of further developing the credibility of research. Open research allows more eyes to see it and critique it, a principle similar to the “Linus’s law” in software development, which says that if there are enough people to test a software, most bugs will be discovered.
As science often progresses incrementally, writing and sharing a study protocol and making data and methods readily available is crucial to facilitate knowledge building. The Open Science Framework (osf.io) is a free and open-source project management tool that supports researchers throughout the entire project life cycle. OSF enables preregistration of study protocols and sharing of documents, data, analysis code, supplementary materials, and preprints.
To facilitate reproducibility, a research paper can link to data and analysis code deposited on OSF. Computational notebooks are now readily available that unite data processing, data transformations, statistical analyses, figures and tables in a single document (e.g., R Markdown, Jupyter); see also the 10 simple rules for reproducible computational research [ 76 ]. Making both data and code open thus minimizes waste of funding resources and accelerates science.
Open science can also advance researchers’ careers, especially for early-career researchers. The increased visibility, retrievability, and citations of datasets can all help with career building [ 77 ]. Therefore, institutions should provide necessary training, and hiring committees and journals should align their core values with open science, to attract researchers who aim for transparent and credible research [ 78 ].
Rule 9: report all findings.
Publication bias occurs when the outcome of a study influences the decision whether to publish it. Researchers, reviewers, and publishers often find nonsignificant study results not interesting or worth publishing. As a consequence, outcomes and analyses are only selectively reported in the literature [ 79 ], also known as the file drawer effect [ 80 ].
The extent of publication bias in the literature is illustrated by the overwhelming frequency of statistically significant findings [ 81 ]. A study extracted p -values from MEDLINE and PubMed Central and showed that 96% of the records reported at least 1 statistically significant p -value [ 82 ], which seems implausible in the real world. Another study plotted the distribution of more than 1 million z -values from Medline, revealing a huge gap from −2 to 2 [ 83 ]. Positive studies (i.e., statistically significant, perceived as striking or showing a beneficial effect) were 4 times more likely to get published than negative studies [ 84 ].
Often a statistically nonsignificant result is interpreted as a “null” finding. But a nonsignificant finding does not necessarily mean a null effect; absence of evidence is not evidence of absence [ 85 ]. An individual study may be underpowered, resulting in a nonsignificant finding, but the cumulative evidence from multiple studies may indeed provide sufficient evidence in a meta-analysis. Another argument is that a confidence interval that contains the null value often also contains non-null values that may be of high practical importance. Only if all the values inside the interval are deemed unimportant from a practical perspective, then it may be fair to describe a result as a null finding [ 61 ]. We should thus never report “no difference” or “no association” just because a p -value is larger than 0.05 or, equivalently, because a confidence interval includes the “null” [ 61 ].
On the other hand, studies sometimes report statistically nonsignificant results with “spin” to claim that the experimental treatment is beneficial, often by focusing their conclusions on statistically significant differences on secondary outcomes despite a statistically nonsignificant difference for the primary outcome [ 86 , 87 ].
Findings that are not being published have a tremendous impact on the research ecosystem, distorting our knowledge of the scientific landscape by perpetuating misconceptions, and jeopardizing judgment of researchers and the public trust in science. In clinical research, publication bias can mislead care decisions and harm patients, for example, when treatments appear useful despite only minimal or even absent benefits reported in studies that were not published and thus are unknown to physicians [ 88 ]. Moreover, publication bias also directly affects the formulation and proliferation of scientific theories, which are taught to students and early-career researchers, thereby perpetuating biased research from the core. It has been shown in modeling studies that unless a sufficient proportion of negative studies are published, a false claim can become an accepted fact [ 89 ] and the false positive rates influence trustworthiness in a given field [ 90 ].
In sum, negative findings are undervalued. They need to be more consistently reported at the study level or be systematically investigated at the systematic review level. Researchers have their share of responsibilities, but there is clearly a lack of incentives from promotion and tenure committees, journals, and funders.
Study reports need to faithfully describe the aim of the study and what was done, including potential deviations from the original protocol, as well as what was found. Yet, there is ample evidence of discrepancies between protocols and research reports, and of insufficient quality of reporting [ 79 , 91 – 95 ]. Reporting deficiencies threaten our ability to clearly communicate findings, replicate studies, make informed decisions, and build on existing evidence, wasting time and resources invested in the research [ 96 ].
Reporting guidelines aim to provide the minimum information needed on key design features and analysis decisions, ensuring that findings can be adequately used and studies replicated. In 2008, the Enhancing the QUAlity and Transparency Of Health Research (EQUATOR) network was initiated to provide reporting guidelines for a variety of study designs along with guidelines for education and training on how to enhance quality and transparency of health research. Currently, there are 468 reporting guidelines listed in the network; see the most prominent guidelines in Table 2 . Furthermore, following the ICMJE recommendations, medical journals are increasingly endorsing reporting guidelines [ 97 ], in some cases making it mandatory to submit the appropriate reporting checklist along with the manuscript.
Guideline name | Study type |
---|---|
ARRIVE | Animal experiments |
CONSORT | Randomized trials |
STROBE | Observational studies |
PRISMA | Systematic reviews |
SPIRIT | Study protocols |
STARD/TRIPOID | Diagnostic/prognostic studies |
The EQUATOR Network is a library with more than 400 reporting guidelines in health research ( www.equator-network.org ).
The use of reporting guidelines and journal endorsement has led to a positive impact on the quality and transparency of research reporting, but improvement is still needed to maximize the value of research [ 98 , 99 ].
Originally, this paper targeted early-career researchers; however, throughout the development of the rules, it became clear that the present recommendations can serve all researchers irrespective of their seniority. We focused on practical guidelines for planning, conducting, and reporting of research. Others have aligned GRP with similar topics [ 100 , 101 ]. Even though we provide 10 simple rules, the word “simple” should not be taken lightly. Putting the rules into practice usually requires effort and time, especially at the beginning of a research project. However, time can also be redeemed, for example, when certain choices can be justified to reviewers by providing a study protocol or when data can be quickly reanalyzed by using computational notebooks and dynamic reports.
Researchers have field-specific research skills, but sometimes are not aware of best practices in other fields that can be useful. Universities should offer cross-disciplinary GRP courses across faculties to train the next generation of scientists. Such courses are an important building block to improve the reproducibility of science.
This article was written along the Good Research Practice (GRP) courses at the University of Zurich provided by the Center of Reproducible Science ( www.crs.uzh.ch ). All materials from the course are available at https://osf.io/t9rqm/ . We appreciated the discussion, development, and refinement of this article within the working group “training” of the SwissRN ( www.swissrn.org ). We are grateful to Philip Bourne for a lot of valuable comments on the earlier versions of the manuscript.
S.S. received funding from SfwF (Stiftung für wissenschaftliche Forschung an der Universität Zürich; grant no. STWF-19-007). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Among all things you’re going to learn throughout graduate school, there is a really important skill you didn’t hope to gain: proper writing. You’re not expected to become a novelist, but it’s certainly important for you to develop the skill of clear, convincing argumentation. According to EduGeeksClub’s guide to dissertation writing , most grad students have an impression that academic papers are completely irrelevant to their future careers. They are wrong. Writing skills are valuable not only for the completion of a thesis and dissertation project, but also for the overall career development upon graduation.
Do you know who gets a call for a job interview? The candidate with the best CV/resume and cover letter. You’ll need to showcase some writing skills as soon as you get out of grad school. Do you know who gets the best chances for career progress? The worker who writes great reports, research articles, evaluations, and other types of projects related to a particular position. Whatever career you choose, writing skills will make you better at it.
At this point, it’s important for you to focus on academic writing. A high-quality paper needs to be based on trustworthy resources, but it should also expose your own opinions. There are 5 fundamental principles you need to maintain when writing an academic paper. When you keep them in mind, the entire writing and research process will be much simpler.
Many students mistake complexity for being the most fundamental principle in academic writing. Yes, academic content is really complex because a single paper contains many layers and arguments. However, the aim for complexity should not take you in the wrong direction. Many students are using long, unusual words just because they make the content seem more eloquent. That’s a wrong strategy. The structure of the paper and the concept it elaborates may be complicated, but the language itself needs to be as clear as possible.
Try to use fewer passive verbs, participles, adjectives, and words you just looked up in the dictionary. If there are simple words that convey your arguments, then use them. Use Hemingway Editor – a nifty tool that shows where you got carried away. Try to get rid of long sequences of prepositional phrases, passives, and other structures that make the paper unreadable.
This is the main rule to remember: you don’t have to limit yourself to fewer words when you want to be clear; but you need to use the strongest ones.
For example, let’s say that you’re trying to prove that a specific leadership skill (like the ability to motivate other people) influences the success of the overall organization. How are you going to prove that? You need specific statistics and examples from real-life situations, which make your arguments trustworthy.
An academic paper may contain abstract theoretical concepts, especially when the topic is related to philosophy or social sciences. However, you have to clarify those concepts through concrete examples that help a reader understand what you’re talking about. Examples work in every part of the paper, so make sure to use them when you want to attract the attention of the reader and clarify a particular concept.
Of course you have your own opinions and you want to stand strong behind them. You will emphasize your point of view and you’ll try to prove the thesis statement with strong arguments, but that doesn’t mean you can simply neglect the opposing side. When you’re working on a grad-level paper, you have to prove you’ve studied all sides of the issue and you understand the opposing arguments.
If, for example, you’re writing a paper on the health benefits of marijuana and you really want to prove its value for patients with various diseases, you also have to pay attention to the warnings and side effects. Don’t neglect the studies that showcase warning results; just make sure to explain why your arguments beat the ones on the opposing side.
When you’re writing an academic paper, you have to be very precise with the information you use. After the research stage, you will have a pile of resources that can prove your arguments. It’s really important to use them in the right manner. For example, you can’t just say “many cancer patients have found relief in treatment with medical marijuana.” You need to give precise information: how many patients? What studies offer such proof? You will use such information in the paper, and you need to reference it. Google may trick you into using unreliable resources, so you have to be sure you’re getting the data from reliable websites before referencing any online source.
There are three major citation styles , which are usually requested in academic writing: APA, MLA, and Chicago. Make sure to understand the requirements of the style you implement, since each comma and capital letter makes a difference.
Consistency refers to two different aspects of the academic paper: the uniformity of your writing style and the logical flow of your arguments. For example, you cannot use both email and e-mail in your paper; you need to commit to one form. Your style should be consistent: if you’re expressing yourself through complex sentences in the introduction, it would be wise to maintain such writing throughout the entire paper.
An academic paper with a flawless logical flow leads the reader from the introduction to the very last sentence without causing any confusion. When you’re done with the first draft, you need to read and revise the paper to make sure there are no information gaps. Read it from the position of someone who doesn’t understand anything about the particular topic. Then, make sure that all arguments are consistent and related to the thesis statement. Don’t think twice before you get rid of repetitive or unnecessary sentences and paragraphs. Each piece of the puzzle has to be relevant to the main impression you want to achieve.
You can’t become a talented academic writer overnight. The principles of skilled writing are basic, so you understood them well when you read the descriptions above. However, you need to put them into practice! You’ll become a better writer if you keep working on your papers and you invest a lot of energy into every single stage of the process.
You don’t have a specific assignment to work on? Then, pick a topic you like and practice writing! The above-listed principles stand for academic writing of all types, so they will work if you’re dealing with an essay, thesis, dissertation, research paper, or any other type of content. Once you get them right, you’ll be ready to deal with any writing challenge that gets in your way.
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Methodology
Published on October 18, 2021 by Pritha Bhandari . Revised on May 9, 2024.
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 behaviors, 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
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, other interesting articles, 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 objectives 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.
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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 | Your participants are free to opt in or out of the study at any point in time. |
---|---|
Informed consent | Participants know the purpose, benefits, risks, and funding behind the study before they agree or decline to join. |
Anonymity | You don’t know the identities of the participants. Personally identifiable data is not collected. |
Confidentiality | You know who the participants are but you keep that information hidden from everyone else. You anonymize personally identifiable data so that it can’t be linked to other data by anyone else. |
Potential for harm | Physical, social, psychological and all other types of harm are kept to an absolute minimum. |
Results communication | You ensure your work is free of or research misconduct, and you accurately represent your results. |
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.
You make sure to provide all potential participants with all the relevant information about
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 anonymize 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 pseudonymization 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.
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 or counseling 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 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 academic 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.
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Research bias
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.
If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
Bhandari, P. (2024, May 09). Ethical Considerations in Research | Types & Examples. Scribbr. Retrieved August 26, 2024, from https://www.scribbr.com/methodology/research-ethics/
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Thesis dialogue blueprint, writing wizard's template, research proposal compass.
Starting a research project can be a bit overwhelming, especially if it's your first time. But don't worry! This guide will walk you through each step, making the process easier and more manageable. By breaking down the project into smaller tasks, you'll find it much simpler to handle. Let's dive into how you can go from an idea to a well-organized research proposal.
Selecting a topic of interest.
The first step in starting your research project is to choose a topic that genuinely interests you. Selecting a topic that excites you will keep you motivated throughout the research process. Begin by brainstorming broad areas of interest and then narrow them down to a specific niche. Consider the practicalities, such as the availability of resources and the scope of your project. If you're struggling to find a topic, consult with your instructor or peers for guidance.
Once you have a general topic, it's essential to narrow it down to a more specific focus. This involves conducting an initial literature review to identify gaps, debates, and questions within your chosen field. By doing so, you can pinpoint a unique angle for your research. Remember, a well-defined focus will make your research more manageable and impactful.
It's crucial to ensure that your chosen topic aligns with the assignment instructions provided by your instructor. Review the guidelines carefully to understand the requirements and limitations. This alignment will not only help you meet academic expectations but also make your research more relevant and structured. If in doubt, seek clarification from your instructor to avoid any misunderstandings.
Identifying key stakeholders.
Before starting your research, it's crucial to identify the key stakeholders involved. These are the people who have a vested interest in your project. They can include supervisors, funding bodies, and even the target audience of your research. Understanding who your stakeholders are will help you align your research goals with their expectations.
Once you've identified your stakeholders, the next step is to conduct initial meetings . These meetings are essential for gathering input and setting expectations. During these meetings, discuss the scope of your research, the methodologies you plan to use, and any potential challenges. This is also a good time to ask for any resources or support you might need.
After the initial meetings, gather all the input and expectations from your stakeholders. This will help you refine your research plan and ensure that it meets everyone's needs. Create a summary document that outlines the key points discussed and any agreed-upon actions. This document will serve as a reference throughout your research project.
Formulating the main question.
Creating a strong research statement starts with formulating the main question . This question will guide your entire project. Make sure it is clear and specific. For example, if you're studying the impact of WhatsApp on communication, your main question could be, "How does WhatsApp influence daily communication habits?"
Your research statement should be both clear and concise. Avoid using complex words or jargon. Instead, focus on making your statement easy to understand. A clear and concise statement helps keep your research focused and on track.
Finally, ensure that your research statement aligns with your overall research goals. This means that your statement should directly relate to what you aim to achieve with your study. For instance, if your goal is to understand user behavior on WhatsApp, your research statement should reflect this aim.
Setting clear research goals is a crucial step in any research project. These goals guide your study and help you stay focused on what you aim to achieve. Here’s how to establish effective research goals:
Start by pinpointing the main areas you want to explore. These should be directly related to your research statement. Identifying these key areas will help you stay organized and ensure that your research is comprehensive.
Once you have identified the key areas, the next step is to set specific objectives. These objectives should be clear, measurable, and achievable. Pinpointing the major focus of your research will help you stay on track and make your study more manageable.
It's important to ensure that your research goals align with the expectations of your stakeholders. This alignment will help you gather the necessary support and resources for your project. Conducting initial meetings with stakeholders can provide valuable input and help you refine your goals.
Gathering relevant sources.
Before diving into your research, it's crucial to gather all the relevant sources. Start by doing a preliminary search to see if there's enough information available. Use libraries, online databases, and academic journals to find books, articles, and papers related to your topic. This step ensures you have a solid foundation for your research .
Once you have your sources, the next step is to analyze them. Skim through the materials to identify key points and different viewpoints. This will help you understand the current state of research in your field. Pay attention to how these sources relate to your research question.
Finally, look for gaps in the existing research. These are areas that haven't been explored or questions that haven't been answered. Identifying these gaps can provide a direction for your own research and make your study more valuable. Conducting a comprehensive literature review is vital for putting your research in context and highlighting what your research will add to the field.
When starting your research, you need to decide whether to use qualitative or quantitative methods . Qualitative methods involve first-hand observations like interviews, focus groups, and case studies. These methods are great for exploring complex issues in depth. On the other hand, quantitative methods deal with numbers and logic, focusing on statistics and numerical patterns. They are ideal for testing hypotheses and making generalizable conclusions. Sometimes, a mixed-method approach, combining both qualitative and quantitative methods, can be the best choice.
Choosing the right tools for data collection is crucial. For qualitative research, you might use interviews, focus groups, or open-ended surveys. For quantitative research, tools like structured surveys, experiments, and statistical software are more appropriate. Make sure your tools align with your research questions and objectives.
Once you have collected your data, the next step is to analyze it. For qualitative data, look for patterns and themes. Coding and thematic analysis are common techniques. For quantitative data, use statistical methods to test your hypotheses. Software like SPSS or R can help you manage and analyze large datasets. Proper planning of your data analysis techniques ensures that your findings are reliable and valid.
Creating a detailed research plan is essential for the success of your project. It helps you stay organized and ensures that you cover all necessary aspects of your research. Here are the key steps to follow:
Start by outlining the methodology you will use. This includes deciding on qualitative or quantitative methods, selecting tools for data collection, and determining how you will analyze the data. A clear methodology is essential for the credibility of your research.
Next, create a timeline for your research activities. Break down your tasks into manageable steps and assign deadlines to each. This will help you stay on track and ensure that you complete your project on time. Use a table to organize your timeline:
Task | Deadline |
---|---|
Literature Review | Month 1 |
Data Collection | Month 2-3 |
Data Analysis | Month 4 |
Writing Draft | Month 5 |
Revisions | Month 6 |
Finally, allocate your resources effectively. This includes budgeting for any costs, such as software, travel, or materials, and ensuring you have access to necessary resources like libraries or labs. Proper resource allocation can make a significant difference in the quality and feasibility of your research.
Structuring the proposal.
When structuring your research proposal, it's essential to include several key components. Start with a clear title that reflects the main focus of your study. Follow this with an abstract that provides a brief summary of your research objectives, methods, and expected outcomes. The introduction should set the context for your research, explaining the background and significance of your study. Make sure to include a literature review that highlights existing research and identifies gaps your study aims to fill. Finally, outline your research design, detailing the methods and procedures you will use to collect and analyze data.
A comprehensive literature review is crucial for situating your research within the existing body of knowledge. Begin by gathering relevant sources from academic journals, books, and other credible publications. Summarize and synthesize these sources to show how they relate to your research question. Highlight any gaps or inconsistencies in the current literature that your study will address. This section not only demonstrates your understanding of the field but also justifies the need for your research.
The research design section should provide a detailed plan of how you will conduct your study. Start by explaining whether you will use qualitative, quantitative, or mixed methods. Describe the data collection tools you will use, such as surveys, interviews, or experiments. Outline your sampling methods and criteria for selecting participants or data sources. Finally, detail your data analysis techniques, explaining how you will interpret the results to answer your research question. This section should be thorough enough to convince reviewers that your methodology is sound and feasible.
Collecting data.
Once your research plan is in place, the next step is to start collecting data. This involves gathering the information you need to answer your research questions . Make sure to use the data collection tools you selected during your planning phase. Accurate data collection is crucial for the success of your project.
After collecting your data, the next step is to analyze it. This means looking for patterns, trends, and insights that will help you answer your research questions. Use the data analysis techniques you planned earlier. Remember, the goal is to make sense of the data and draw meaningful conclusions.
As you collect and analyze data, you might find that some parts of your plan need to be adjusted. This is normal and part of the research process. Be flexible and ready to make changes to your methodology or data collection methods if necessary. Staying adaptable will help you overcome any challenges that arise.
Organizing the presentation.
When presenting your research findings, it's crucial to structure your presentation logically. Start with an introduction that outlines the purpose of your research and the main questions you aimed to answer. Follow this with a summary of your methodology, highlighting the key methods used for data collection and analysis. Ensure your findings are presented clearly and concisely , using tables and graphs where appropriate to illustrate your points.
To keep your audience engaged, use a mix of visual aids and verbal explanations. Interactive elements like Q&A sessions or live demonstrations can also be effective. Make sure to explain the significance of your findings and how they contribute to the existing body of knowledge. This not only keeps the audience interested but also underscores the importance of your work.
Be prepared to handle questions and feedback from your audience. This is an opportunity to clarify any doubts and to demonstrate your deep understanding of the subject. Listen carefully to the questions, and take your time to provide thoughtful and well-reasoned answers. This will not only help in addressing any concerns but also in reinforcing the credibility of your research.
Sharing your research results is a crucial step in your academic journey. It can be tough, but you don't have to do it alone. Our Thesis Action Plan is here to guide you through every step. Ready to make your thesis writing stress-free? Visit our website now and claim your special offer!
In summary, starting a research project can seem overwhelming, but breaking it down into clear, manageable steps can make the process much more approachable. By carefully defining your research topic, engaging with stakeholders, crafting a precise research statement, and establishing clear goals and methodologies, you set a strong foundation for your project. Remember, a well-organized plan not only helps you manage your time and resources effectively but also enhances the credibility and impact of your research. As you embark on your research journey, keep these steps in mind to navigate the process smoothly and achieve your academic goals.
What is a research project.
A research project is a detailed study on a specific topic. It involves gathering information, analyzing data, and presenting findings to answer a particular question or solve a problem.
Pick a topic that interests you and has plenty of resources available. Make sure it aligns with your assignment guidelines and is neither too broad nor too narrow.
Defining the research subject helps you stay focused and organized. It ensures that you have a clear direction and don't get lost in too many ideas.
Stakeholders are people who have an interest in your research. They can include funders, academic supervisors, or anyone affected by your study.
A research statement is a clear and concise description of the main question or problem your research aims to address.
Research goals are the specific objectives you aim to achieve with your study. They guide your research and help you stay focused on your main question.
Choosing the right methodology involves deciding how you will collect and analyze data. Consider whether you need qualitative or quantitative data and choose tools and techniques that best suit your study.
A research proposal should include the research subject, a literature review, research questions, methodology, and a timeline. It outlines what you plan to study and how you will do it.
How do i start writing my thesis: a step-by-step guide.
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The benefits of this new 2nd edition. Restructured content for a better learning experience. Enriched content with extensive real-world examples. Bite-size lessons on each topic to fit busy schedules. Strategies to apply narrative tools when writing research papers. Detailed examples for explaining concepts, taken from real papers where possible.
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Unit 4: Principles of Research and Report Writing. 6019 Course Learning Outcomes. This unit supports the following course learning outcomes (CLOs): Prepare and evaluate professional business documents in common formats, using appropriate tone, structure/format (including headings and graphics) and patterns of development (direct/indirect ...