- 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.
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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.
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Methodology
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.
First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :
Second, decide how you will analyze the data .
Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.
Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.
Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.
For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .
If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .
Qualitative | to broader populations. . | |
---|---|---|
Quantitative | . |
You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.
Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).
If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.
Primary | . | methods. |
---|---|---|
Secondary |
In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .
In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .
To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.
Descriptive | . . | |
---|---|---|
Experimental |
Research method | Primary or secondary? | Qualitative or quantitative? | When to use |
---|---|---|---|
Primary | Quantitative | To test cause-and-effect relationships. | |
Primary | Quantitative | To understand general characteristics of a population. | |
Interview/focus group | Primary | Qualitative | To gain more in-depth understanding of a topic. |
Observation | Primary | Either | To understand how something occurs in its natural setting. |
Secondary | Either | To situate your research in an existing body of work, or to evaluate trends within a research topic. | |
Either | Either | To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study. |
Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.
Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.
Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:
Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .
Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).
You can use quantitative analysis to interpret data that was collected either:
Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.
Research method | Qualitative or quantitative? | When to use |
---|---|---|
Quantitative | To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations). | |
Meta-analysis | Quantitative | To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner. |
Qualitative | To analyze data collected from interviews, , or textual sources. To understand general themes in the data and how they are communicated. | |
Either | To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words). |
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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
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.
In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .
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.
The research methods you use depend on the type of data you need to answer your research question .
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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What is jaundice.
Jaundice is a condition that causes your baby’s skin to turn yellow in the first few days after birth. You may also notice that the sclera (white parts) of the baby’s eyes are yellow.
The yellow color of the skin and sclera in newborns with jaundice comes from a build up of bilirubin. Small to medium increases in bilirubin are normal in newborns and will not hurt your baby.
Very high levels of bilirubin can cause hearing loss, seizures and brain damage.
If your baby has jaundice, it is important that bilirubin levels are monitored closely. If your baby does not drink enough milk, this can lead to increased bilirubin. You should see a lactation consultant to get help with breastfeeding.
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Studies are conducted by most of the institutes and centers across the NIH. The Clinical Center hosts a wide range of studies from rare diseases to chronic health conditions, as well as studies for healthy volunteers. Visitors can search by diagnosis, sign, symptom or other key words. Join a National Registry of Research Volunteers
You can donate your medical data and health samples >. See how donating your existing medical records and health samples can save lives. Explains clinical trials, including what they are, why they are important, things to think about when deciding to take part, and questions to ask your doctor.
The Clinical Center provides hope through pioneering clinical research to improve human health. We rapidly translate scientific observations and laboratory discoveries into new ways to diagnose, treat and prevent disease. More than 500,000 people from around the world have participated in clinical research since the hospital opened in 1953.
Sometimes though, it is useful to use a more focused database, and that is where PubMed comes in. As its name suggests, PubMed is a repository for medical papers. It gets its papers both directly from journals and from author submissions. These submissions are checked to ensure that they are scientific papers.
There are various types of scientific studies such as experiments and comparative analyses, observational studies, surveys, or interviews. The choice of study type will mainly depend on the research question being asked. When making decisions, patients and doctors need reliable answers to a number of questions.
It is important to test drugs and medical products in the people they are meant to help. It is also important to conduct research in a variety of people, because different people may respond ...
The goal of clinical research is to develop knowledge that improves human health or increases understanding of human biology. People who take part in clinical research make it possible for this to occur. The path to finding out if a new drug is safe or effective is to test it on patients in clinical trials.
The basis of a scientific research study follows a common pattern: Define the question. Gather information and resources. Form hypotheses. Perform an experiment and collect data. Analyze the data ...
ResearchMatch helps you find a clinical trial or research study near you, or across the country, by matching you with researchers from leading medical research institutions. Whether you are a healthy volunteer or have a health condition, ResearchMatch connects you to research opportunities so you can make a difference and advance scientific discoveries by participating in research studies ...
Researchers often must choose one or a finite number of ways to investigate a question. It can take many different studies using different approaches to start piecing the whole picture together. Sometimes it might seem like research results contradict each other. But often, studies are just looking at different aspects of the same problem.
Research databases. You can search for scholarly sources online using databases and search engines like Google Scholar. These provide a range of search functions that can help you to find the most relevant sources. If you are searching for a specific article or book, include the title or the author's name. Alternatively, if you're just ...
Often, that means that it is hard to find qualitative studies in common health science databases like PubMed; On this page you'll find: articles that describe and evaluate search strategies for finding qualitative research; articles that provide search strategies for specific databases; web resources on search filters and finding qualitative ...
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This article takes you through the first steps of the research process, helping you narrow down your ideas and build up a strong foundation for your research project. Table of contents. Step 1: Choose your topic. Step 2: Identify a problem. Step 3: Formulate research questions. Step 4: Create a research design. Step 5: Write a research proposal.
Research: Where to Begin. Research isn't something that only scientists and professors do. Any time you use sources to investigate claims or reach new conclusions, you are performing research. Research happens in virtually all fields, so it's vitally important to know how to conduct research and navigate through source material regardless of ...
These studies involve completing a survey, questionnaire, phone, or in-person interview. Registries—research studies that collect patient data and observe how a person's health changes over time—may ask participants to provide access to their medical records, lab test results or other health information, and permission to contact them for ...
Qualitative Findings. Qualitative research is an exploratory research method used to understand the complexities of human behavior and experiences. Qualitative findings are non-numerical and descriptive data that describe the meaning and interpretation of the data collected. Examples of qualitative findings include quotes from participants ...
A research study must: Ask a research question. Identify a research population or group. Describe a research method. Test or measure something. Summarize the results. Research studies are almost always published in peer-reviewed (scholarly) journals. The articles often contain headings similar to these: Literature Review, Method, Results ...
Step 1: Identify and develop your topic. Selecting a topic can be the most challenging part of a research assignment. Since this is the very first step in writing a paper, it is vital that it be done correctly. Here are some tips for selecting a topic: Select a topic within the parameters set by the assignment.
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods.
3. I think that your first statement urgently calls for references. As for determining sources of funding, AFAIK, research papers typically include acknowledgement of funding sources and even specific grant references. Many authors also include conflict of interests statements. - Aleksandr Blekh.
Coming up with a research question is not always simple and may take time. A successful study requires a narrow and clear research question. In evidence-based research, prior studies are assessed in a systematic and transparent way to identify a research gap for a new study that answers a question that matters . Papers that provide a ...
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. ... It outlines what you plan to study and how you will do it. Share. Previous Article. एक शोध परियोजना कैसे शुरू करें: शुरुआती ...
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
Find a research study. Use this finder to learn more about the purpose of these studies and clinical trials, find out who can participate, and tell us you're interested in enrolling. Search now. Close Menu Patients & Families. Resources for families; Caring for your child;
A new study published in the journal Cancer is showing just how important cancer prevention and early detection efforts are. The study used data from the Global Cancer Observatory that took into ...