Research methodology vs. research methods
The research methodology or design is the overall strategy and rationale that you used to carry out the research. Whereas, research methods are the specific tools and processes you use to gather and understand the data you need to test your hypothesis.
To further understand research methodology, let’s explore some examples of research methodology:
a. Qualitative research methodology example: A study exploring the impact of author branding on author popularity might utilize in-depth interviews to gather personal experiences and perspectives.
b. Quantitative research methodology example: A research project investigating the effects of a book promotion technique on book sales could employ a statistical analysis of profit margins and sales before and after the implementation of the method.
c. Mixed-Methods research methodology example: A study examining the relationship between social media use and academic performance might combine both qualitative and quantitative approaches. It could include surveys to quantitatively assess the frequency of social media usage and its correlation with grades, alongside focus groups or interviews to qualitatively explore students’ perceptions and experiences regarding how social media affects their study habits and academic engagement.
These examples highlight the meaning of methodology in research and how it guides the research process, from data collection to analysis, ensuring the study’s objectives are met efficiently.
When it comes to writing your study, the methodology in research papers or a dissertation plays a pivotal role. A well-crafted methodology section of a research paper or thesis not only enhances the credibility of your research but also provides a roadmap for others to replicate or build upon your work.
Wondering how to write the research methodology section? Follow these steps to create a strong methods chapter:
At the start of a research paper , you would have provided the background of your research and stated your hypothesis or research problem. In this section, you will elaborate on your research strategy.
Begin by restating your research question and proceed to explain what type of research you opted for to test it. Depending on your research, here are some questions you can consider:
a. Did you use qualitative or quantitative data to test the hypothesis?
b. Did you perform an experiment where you collected data or are you writing a dissertation that is descriptive/theoretical without data collection?
c. Did you use primary data that you collected or analyze secondary research data or existing data as part of your study?
These questions will help you establish the rationale for your study on a broader level, which you will follow by elaborating on the specific methods you used to collect and understand your data.
Now that you have told your reader what type of research you’ve undertaken for the dissertation, it’s time to dig into specifics. State what specific methods you used and explain the conditions and variables involved. Explain what the theoretical framework behind the method was, what samples you used for testing it, and what tools and materials you used to collect the data.
Once you have explained the data collection process, explain how you analyzed and studied the data. Here, your focus is simply to explain the methods of analysis rather than the results of the study.
Here are some questions you can answer at this stage:
a. What tools or software did you use to analyze your results?
b. What parameters or variables did you consider while understanding and studying the data you’ve collected?
c. Was your analysis based on a theoretical framework?
Your mode of analysis will change depending on whether you used a quantitative or qualitative research methodology in your study. If you’re working within the hard sciences or physical sciences, you are likely to use a quantitative research methodology (relying on numbers and hard data). If you’re doing a qualitative study, in the social sciences or humanities, your analysis may rely on understanding language and socio-political contexts around your topic. This is why it’s important to establish what kind of study you’re undertaking at the onset.
Now that you have gone through your research process in detail, you’ll also have to make a case for it. Justify your choice of methodology and methods, explaining why it is the best choice for your research question. This is especially important if you have chosen an unconventional approach or you’ve simply chosen to study an existing research problem from a different perspective. Compare it with other methodologies, especially ones attempted by previous researchers, and discuss what contributions using your methodology makes.
No matter how thorough a methodology is, it doesn’t come without its hurdles. This is a natural part of scientific research that is important to document so that your peers and future researchers are aware of it. Writing in a research paper about this aspect of your research process also tells your evaluator that you have actively worked to overcome the pitfalls that came your way and you have refined the research process.
1. Remember who you are writing for. Keeping sight of the reader/evaluator will help you know what to elaborate on and what information they are already likely to have. You’re condensing months’ work of research in just a few pages, so you should omit basic definitions and information about general phenomena people already know.
2. Do not give an overly elaborate explanation of every single condition in your study.
3. Skip details and findings irrelevant to the results.
4. Cite references that back your claim and choice of methodology.
5. Consistently emphasize the relationship between your research question and the methodology you adopted to study it.
To sum it up, what is methodology in research? It’s the blueprint of your research, essential for ensuring that your study is systematic, rigorous, and credible. Whether your focus is on qualitative research methodology, quantitative research methodology, or a combination of both, understanding and clearly defining your methodology is key to the success of your research.
Once you write the research methodology and complete writing the entire research paper, the next step is to edit your paper. As experts in research paper editing and proofreading services , we’d love to help you perfect your paper!
Here are some other articles that you might find useful:
What does research methodology mean, what types of research methodologies are there, what is qualitative research methodology, how to determine sample size in research methodology, what is action research methodology.
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This is very simplified and direct. Very helpful to understand the research methodology section of a dissertation
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Published on 25 February 2019 by Shona McCombes . Revised on 10 October 2022.
Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research.
It should include:
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How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, frequently asked questions about methodology.
Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .
It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.
You can start by introducing your overall approach to your research. You have two options here.
What research problem or question did you investigate?
And what type of data did you need to achieve this aim?
Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?
Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .
In order to be considered generalisable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.
Here, explain how you operationalised your concepts and measured your variables. Discuss your sampling method or inclusion/exclusion criteria, as well as any tools, procedures, and materials you used to gather your data.
Surveys Describe where, when, and how the survey was conducted.
Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.
Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.
The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.
The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on 4–8 July 2022, between 11:00 and 15:00.
Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.
In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.
Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)
Interviews or focus groups Describe where, when, and how the interviews were conducted.
Participant observation Describe where, when, and how you conducted the observation or ethnography .
Existing data Explain how you selected case study materials for your analysis.
In order to gain better insight into possibilities for future improvement of the fitness shop’s product range, semi-structured interviews were conducted with 8 returning customers.
Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.
Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.
Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.
Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods here.
Next, you should indicate how you processed and analysed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.
In quantitative research , your analysis will be based on numbers. In your methods section, you can include:
In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).
Specific methods might include:
Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.
Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.
In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .
Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.
The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .
Your methodology can be strengthened by referencing existing research in your field. This can help you to:
Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.
Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.
Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.
Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments , surveys , statistical tests ).
In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .
Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.
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.
Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.
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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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Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.
The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.
A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.
Having a good research methodology in place has the following advantages: 3
Types of research methodology.
There are three types of research methodology based on the type of research and the data required. 1
Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.
In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:
During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.
Qualitative research 5
Quantitative research 6
What are data analysis methods.
The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.
Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.
Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:
Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:
Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:
Here are some important factors to consider when choosing a research methodology: 8
How to write a research methodology .
A research methodology should include the following components: 3,9
The methods section is a critical part of the research papers, allowing researchers to use this to understand your findings and replicate your work when pursuing their own research. However, it is usually also the most difficult section to write. This is where Paperpal can help you overcome the writer’s block and create the first draft in minutes with Paperpal Copilot, its secure generative AI feature suite.
With Paperpal you can get research advice, write and refine your work, rephrase and verify the writing, and ensure submission readiness, all in one place. Here’s how you can use Paperpal to develop the first draft of your methods section.
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Q1. What are the key components of research methodology?
A1. A good research methodology has the following key components:
Q2. Why is ethical consideration important in research methodology?
A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10
Q3. What is the difference between methodology and method?
A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.
Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.
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This article was co-authored by Alexander Ruiz, M.Ed. and by wikiHow staff writer, Jennifer Mueller, JD . Alexander Ruiz is an Educational Consultant and the Educational Director of Link Educational Institute, a tutoring business based in Claremont, California that provides customizable educational plans, subject and test prep tutoring, and college application consulting. With over a decade and a half of experience in the education industry, Alexander coaches students to increase their self-awareness and emotional intelligence while achieving skills and the goal of achieving skills and higher education. He holds a BA in Psychology from Florida International University and an MA in Education from Georgia Southern University. wikiHow marks an article as reader-approved once it receives enough positive feedback. In this case, several readers have written to tell us that this article was helpful to them, earning it our reader-approved status. This article has been viewed 529,506 times.
The research methodology section of any academic research paper gives you the opportunity to convince your readers that your research is useful and will contribute to your field of study. An effective research methodology is grounded in your overall approach – whether qualitative or quantitative – and adequately describes the methods you used. Justify why you chose those methods over others, then explain how those methods will provide answers to your research questions. [1] X Research source
To write a research methodology, start with a section that outlines the problems or questions you'll be studying, including your hypotheses or whatever it is you're setting out to prove. Then, briefly explain why you chose to use either a qualitative or quantitative approach for your study. Next, go over when and where you conducted your research and what parameters you used to ensure you were objective. Finally, cite any sources you used to decide on the methodology for your research. To learn how to justify your choice of methods in your research methodology, scroll down! Did this summary help you? Yes No
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The research methodology is a part of your research paper that describes your research process in detail. It would help if you always tried to make the section of the research methodology enjoyable.
As you describe the procedure that has already been completed, you need to write it in the past tense.
Your research methodology should explain:
What was the purpose of your research?
What type of research method is used?
What were the data collecting methods?
How did you analyze the data?
What kind of resources has been used in your research?
Why did you choose these methods?
Start writing your research methodology with the research problem giving a clear picture of your study’s purpose. It’ll help your readers focus on the research objectives and understand the remaining procedure of your research.
You should explain:
What type of research have you conducted?
The types of research can be categorized from the following perspectives;
Application of the study
Aim of the research
Mode of inquiry
Research approach
While talking about the research methods, you should highlight the key points, such as:
What kind of data gathering methods you’ve used in your research?
There are three types of data collecting methods such as:
Qualitative research is based on quality, and it looks in-depth at non-numerical data. It enables us to understand the comprehensive details of the problem. The researcher prepares open-ended questions to gather as much information as possible.
The quantitative research is associated with the aspects of measurement, quantity, and extent. It follows the statistical, mathematical, and computational techniques in numerical data such as percentages and statistics. The research is conducted on a large group of population.
When you combine quantitative and qualitative methods of research, the resulting approach becomes mixed methods of research.
Example: In quantitative correlation research , you aim to identify the cause-and-effect relationship between two or more variables. It would help if you also focused on explaining the difference between correlation and causation.
Example: In a qualitative research case study , your research’s focus is to find answers to how and why questions. You need to collect data collection from multiple sources over time. You need to analyse real-world problems in-depth, then you can use the method of the case study.
After explaining the research method you have used, you should describe the data collection methods you used. Mention the procedure and materials you used in your research.
Interview/Focus Group Discussion
Describe the details and criteria of the interviews and. You should include the following points:
The type of questionnaire you have used in your interview.
The procedure for selecting participants.
The size of your sample (number of participation)
The duration and location of interviews.
Describe the procedure of your observation and include the following points:
Who were the participants of your observation?
How did you get access to that specific group?
How did you record the data? (written form, audio or video recording)
Archival Data
Here you have to describe the existing data you’ve’ used. You should explain:
What type of resources have you used? (texts, images, audio, videos)
To seek in-depth information about the stress level among men and women, semi-structured interviews were conducted with ten men and ten women of company X. The participants were aged between 20-40. The interviews were held in the canteen to create a stress-free environment that lasted 15 minutes each. The responses were written and filmed.
Describe the entire procedure of your survey. Include the following points:
What type of survey have you conducted? (Questionnaire/interview/ rating scale/ Online Survey)
Who were the participants of your survey? How did you select them?
What was the sample size ?
What type of questions you’ve used in your survey? (open-ended/closed-ended)
How many questions have you used?
What was the response rate of the participants?
Experiments
Explain the detailed procedure you have followed in your experiment. Try to provide as much information you can provide. Include the following points:
The type of your experimental design .
Sampling method you’ve used to select subjects.
Tools and techniques used in the experiment.
The way you identified a cause-and-effect relationship between the variables.
Describe the existing data you’ve used in your research. Include the following points:
The survey included ten multiple-choice questions and ten open-ended questions. The survey’s objective is to determine the stress level of working women who have to deal with household responsibilities. From 17-20 Jan 2018, between 11:00 to 13:00, the survey questionnaire was distributed among the women at the working counters. The participants were given 10 minutes to fill the questionnaire. Out of 500 participants, 450 responded, and 350 were included in the analysis.
In this section, you should briefly describe the methods you’ve used to analyse the data you’ve collected.
The qualitative method includes analysing language, images, audio, videos, or any textual data (textual analysis). The following types of methods are used in textual analysis .
Discourse analysis : Discourse analysis is an essential aspect of studying a language and its uses in day-to-day life.
Content analysis : It is a method of studying and retrieving meaningful information from documents
Thematic analysis: It’s a method of identifying patterns of themes in the collected information, such as face-to-face interviews, texts, and transcripts.
Example: After collecting the data, it was checked thoroughly to find the missing information. The interviews were transcribed, and textual analysis was conducted. The repetitions of the text, types of colours displayed, the tone of the speakers was measured.
Quantitative data analysis is used for analysing numerical data. Include the following points:
The methods of preparing data before analysing it.
Which statistical test you have used? (one-ended test, two-ended test)
The type of software you’ve used.
After collecting the data, it was checked thoroughly to find out the missing information. The coding system was used to interpret the data.
Provide Background and Justification
Many research methods are available, from standard to an averaged approach based on the requirements and abilities. In the research methodology section, it’s essential to mention the reasons behind selecting a specific research method.
You should also explain why you did not choose any other standard approach to your topic when it fits your requirements. Talk about your research objectives and highlight the points that could affect your research procedure if you select another research method.
You can discuss the limitations of other research methods compared to your research requirements and the method you’ve used.
Ethnographic research requires a lot of time, and one has to struggle a lot to gain access to the community. A researcher has to spend time with the target group in their natural environment. Sometimes, it’s difficult for a researcher to introduce himself as a researcher/participant with the community.
The online survey does not provide reliable responses. The only benefit of conducting an online survey would be its quick response rate and cost-effectiveness.
While writing your methodology, you need to keep in mind that you don’t need to make it complicated with unnecessary details.
The aim of your writing a research methodology is not merely discussing the methods and techniques you’ve used.
You have to provide a detailed account of the procedure you’ve followed, the obstacles you faced, and the way you overcome them.
Your research question and objectives of the research are the base of your research. You should discuss the objectives and explain how this specific method helped you answer your research question. You can use goals and outcomes as evidence to support your discussion.
If you’ve used any standard method in your research, you don’t need to provide many details about it as it would be common in your field. However, if you’ve used any specific approach rarely used in your field, you should explain it in detail. Your explanation and information can help other researchers in their research.
Your methodology should be well-structured and easy to understand, with all the necessary information, evidence to support your argument.
After gathering the data, it’s essential to credit the sources you have used in your research. Mention the resources you’ve used, the way you got access to those resources. Use any suitable referencing style to cite sources such as APA, MLA, and Chicago, etc.
Research methods for dissertation – types with comparison, qualitative vs quantitative research – a comprehensive guide, types of variables – a comprehensive guide, a complete guide to experimental research, ethnographic research – complete guide with examples, a quick guide to case study with examples, discourse analysis – a definitive guide with steps & types, action research for my dissertation – the do’s and the don’ts, methods of data collection – guide with tips, inductive and deductive reasoning – examples & limitations, hypothesis testing – a complete guide with examples, correlational research – steps & examples, how to conduct surveys – guide with examples, a quick guide to textual analysis – definition & steps, thematic analysis – a guide with examples, historical research – a guide based on its uses & steps, types of research – tips and examples, reliability and validity – definitions, types & examples, sampling methods – a guide with examples, a quick guide to descriptive research, tips to transcribe an interview – a guide with tips & examples, what is content analysis – steps & examples, primary vs secondary research – a guide with examples, what are confounding variables, advantages of secondary research – a definitive guide, disadvantages of secondary research – a definitive guide, advantages of primary research – types & advantages, disadvantages of primary research, meta-analysis – guide with definition, steps & examples, qualitative research questionnaire – types & examples, quantitative research questionnaire – types & examples, popular articles in this category.
The methodology is perhaps the most challenging and laborious part of research work. Here is a guide on how to write the methodology chapter for the dissertation!
Struggling to figure out “whether I should choose primary research or secondary research in my dissertation?” Here are some tips to help you decide.
The authenticity of the dissertation is largely influenced by the research method employed. Here we present the most notable research methods for dissertation.
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Content analysis is a research method you might come across when analyzing data. Learn what a content analysis is and how to do one in this step-by-step guide.
A thematic analysis is a research method you might come across when analyzing qualitative data. Learn what a thematic analysis is and how to write one in this step-by-step guide.
A rhetorical analysis explores the goals and motivations of an author, the techniques they’ve used to reach their audience, and how successful these techniques were. Learn how to write an excellent rhetorical analysis in this guide.
Qualitative and quantitative research are effective but very different approaches to study a subject. Learn the difference between them, what they are used for, and how to analyze qualitative and quantitative research in this guide.
Having the right research methodology can be a make-or-break factor for your academic work. What is research methodology, and how can you get ahead?
The methods section of a research paper provides the information by which a study’s validity is judged. The method section answers two main questions: 1) How was the data collected or generated? 2) How was it analyzed? The writing should be direct and precise and written in the past tense.
You must explain how you obtained and analyzed your results for the following reasons:
Bem, Daryl J. Writing the Empirical Journal Article . Psychology Writing Center. University of Washington; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.
I. Groups of Research Methods
There are two main groups of research methods in the social sciences:
II. Content
An effectively written methodology section should:
NOTE : Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic.
III. Problems to Avoid
Irrelevant Detail The methodology section of your paper should be thorough but to the point. Don’t provide any background information that doesn’t directly help the reader to understand why a particular method was chosen, how the data was gathered or obtained, and how it was analyzed. Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. NOTE: An exception to this rule is if you select an unconventional approach to doing the method; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall research process. Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose. Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].
It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.
Azevedo, L.F. et al. How to Write a Scientific Paper: Writing the Methods Section. Revista Portuguesa de Pneumologia 17 (2011): 232-238; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section . The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Writing the Experimental Report: Methods, Results, and Discussion . The Writing Lab and The OWL. Purdue University; Methods and Materials . The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.
Statistical Designs and Tests? Do Not Fear Them!
Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.
Knowing the Relationship Between Theories and Methods
There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.
Introspectively engage in an ongoing dialectic between theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.
Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics. Part 1, Chapter 3. Boise State University; The Theory-Method Relationship . S-Cool Revision. United Kingdom.
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Researching the white paper:.
The process of researching and composing a white paper shares some similarities with the kind of research and writing one does for a high school or college research paper. What’s important for writers of white papers to grasp, however, is how much this genre differs from a research paper. First, the author of a white paper already recognizes that there is a problem to be solved, a decision to be made, and the job of the author is to provide readers with substantive information to help them make some kind of decision--which may include a decision to do more research because major gaps remain.
Thus, a white paper author would not “brainstorm” a topic. Instead, the white paper author would get busy figuring out how the problem is defined by those who are experiencing it as a problem. Typically that research begins in popular culture--social media, surveys, interviews, newspapers. Once the author has a handle on how the problem is being defined and experienced, its history and its impact, what people in the trenches believe might be the best or worst ways of addressing it, the author then will turn to academic scholarship as well as “grey” literature (more about that later). Unlike a school research paper, the author does not set out to argue for or against a particular position, and then devote the majority of effort to finding sources to support the selected position. Instead, the author sets out in good faith to do as much fact-finding as possible, and thus research is likely to present multiple, conflicting, and overlapping perspectives. When people research out of a genuine desire to understand and solve a problem, they listen to every source that may offer helpful information. They will thus have to do much more analysis, synthesis, and sorting of that information, which will often not fall neatly into a “pro” or “con” camp: Solution A may, for example, solve one part of the problem but exacerbate another part of the problem. Solution C may sound like what everyone wants, but what if it’s built on a set of data that have been criticized by another reliable source? And so it goes.
For example, if you are trying to write a white paper on the opioid crisis, you may focus on the value of providing free, sterilized needles--which do indeed reduce disease, and also provide an opportunity for the health care provider distributing them to offer addiction treatment to the user. However, the free needles are sometimes discarded on the ground, posing a danger to others; or they may be shared; or they may encourage more drug usage. All of those things can be true at once; a reader will want to know about all of these considerations in order to make an informed decision. That is the challenging job of the white paper author. The research you do for your white paper will require that you identify a specific problem, seek popular culture sources to help define the problem, its history, its significance and impact for people affected by it. You will then delve into academic and grey literature to learn about the way scholars and others with professional expertise answer these same questions. In this way, you will create creating a layered, complex portrait that provides readers with a substantive exploration useful for deliberating and decision-making. You will also likely need to find or create images, including tables, figures, illustrations or photographs, and you will document all of your sources.
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Artificial intelligence and organoid advances hold new promise for reducing the number of laboratory animals used in studies..
By Jack McGovan
A round 348 B.C., Aristotle took a two-year trip to the eastern Aegean island of Lesbos to study animals in a lagoon. Along with observing the creatures in their natural habitat and surmising, among other things, that dolphins were not fish, he dissected smaller animals to try and understand their internal workings. When he cut open eels, abundant in the lagoon, he was puzzled to find no evidence of reproductive tissue and made the false assumption that they generated spontaneously from the mud.
Aristotle's dissections were some of the first documented experiments on animals. Initially a practice aimed at understanding anatomy, these experiments evolved as biology and medicine progressed. For example, the Roman physician Galen of Pergamon developed techniques for dissection and vivisection of animals, which informed his treatises on medicine that remained canonical until the 14th century, when the Renaissance began in Italy.
It wasn't until the late 1930s that rigorous animal testing became a standard part of the drug development process . A U.S. pharmaceutical company had created an elixir with a raspberry aroma that promised to work as an antibiotic. The solution contained diethylene glycol. Unbeknownst to the company's chief chemist, the chemical proved poisonous to humans, and over 100 people died after the elixir hit store shelves. The resulting outcry led to the passage of the 1938 Federal Food, Drug, and Cosmetic Act, which required that drugs be tested on animals before being marketed.
Without animal testing, many of the medicines and procedures we take for granted wouldn't exist today. Transplantation of skin, corneas, and internal organs became possible owing to knowledge acquired through experimentation on animals. And polio—the devastating, paralysis-causing virus that was once one of the most feared diseases in the world—has been nearly eradicated because of a vaccine that was developed through experiments on monkeys. The number of drugs failing to make it to market that have passed animal testing reached an all-time high of 95% in 2021.
Today, animals continue to be widely used in the biomedical sciences. One paper published in Sage Journals found that 79.9 million animals were used in scientific procedures in 2015, an estimated 37% increase from 2005. There have also been unprecedented levels of funding in drug development in the last decade. However, the number of drugs failing to make it to market that have passed animal testing reached an all-time high of 95% in 2021, according to a review paper in the journal Nature . Thomas Hartung , a professor of toxicology and director of the Johns Hopkins Center for Alternatives to Animal Testing, wants better science—and more options.
Sometimes the consequences of animal experiments can go beyond a couple of failed experiments. In France in 2016, six people were hospitalized and one man died during a clinical trial. The drug in question had been tested in mice, rats, dogs, and monkeys with dosages 400 times stronger than those given to the human volunteers, and no ill effects were recorded.
Hartung's research has found that there are cases where animal models may no longer be necessary. In a paper published in a 2018 edition of the journal Toxicological Sciences , he and his team found they were able to predict—using a computer model that combed through a massive chemical hazard database—whether a particular chemical would be toxic to humans in more cases than animal models could. "The publication was a turning point," says the German-born Hartung, who has led the center since 2009.
The finding effectively put the center at the heart of a revolution in toxicology to move away from decades-old animal tests to the use of artificial intelligence and organoid cultures, 3D tissue models grown from stem cells programmed to mimic a specific organ. In the not-so-distant future, Hartung hopes, this emerging and quickly evolving technology and science could render many animal tests a remnant of the past.
T hat 2018 paper was a breakthrough in the use of machine learning to approach toxicology. One of Hartung's PhD students had built a database that could be used to predict—better than animal models could—how toxic a certain chemical would be to humans. More than 10,000 chemicals and their properties, provided by the European Chemical Agency, were reviewed.
The structure of a chemical determines whether it would be toxic to humans. So, when researchers want to determine the toxicology of a chemical, they can look at those with a similar structure known to produce a negative reaction. Manually assessing each chemical on a case-by-case basis would be time consuming, limiting its usefulness. What the researchers at the center, known as CAAT, did was to automate and accelerate that process, using big data to examine potential human interactions such as acute oral and dermal toxicity, eye and skin irritation, and mutagenicity (ability to induce a genetic mutation).
Computational tools are just one of the ways Hartung and other researchers at CAAT are attempting to move away from animal testing. The lab where Hartung is based is filled with brains, and that's not a reference to the staff located there. Little clumps of brain tissue, barely visible to the human eye, are grown in incubators every week by the thousands. Referred to as organoids, these clumps of tissue can't think or feel, but they can be used to see how brain cells respond to stimuli in a lab setting.
Brain organoids are made from pluripotent stem cells, which can produce any cell or tissue a body may need. The cells are placed in a matrix that helps them connect with each other and form larger tissues. They're then added to an incubator and allowed to grow for eight weeks, at which point they are essentially miniaturized, 3D versions of organs able to be used for testing. "Once you have mastered production, it is a very robust and reasonably cheap process," Hartung says.
By combining brain organoids with AI, Hartung hopes to develop what he calls organoid intelligence, a major step forward "to make brain cell cultures do what the [human] brain is supposed to." Although it is currently still science fiction, he says, the organoid intelligence project (running since January 2023) recently produced a technical paper describing how to build such a system.
Hartung moved to the United States in 2009 to take over from Alan Goldberg as director at CAAT. Founded in 1981 with a $1 million grant from the Cosmetic, Toiletry, and Fragrance Association, the center and its researchers for the next few decades worked to harness scientific advances, such as in vitro experiments using human cell lines, where once mice and rats were used. Advances in biostatistics and computer modeling of biological systems enabled researchers to construct experiments using only a fraction of the animals they would otherwise have needed.
When Hartung arrived, the center was "an information hub of six people" down by Baltimore's Inner Harbor. He promptly moved activities to the university, under the auspices of the Bloomberg School of Public Health, because he wanted to have a larger lab space and students to work with. Today, CAAT involves more than 30 researchers. His background, he says, has also helped bring some diversity to the center. "We have an unusual number of expats and people from all over the world," he says.
Alternatives to animal testing would get a real boost when the COVID-19 pandemic hit. Scientists were desperate for answers, and fast, so they turned to nonanimal models to understand the virus . Similarly, researchers had to dramatically cut down the time it took to develop a vaccine, typically in the range of five to 10 years. "I'm not saying that animal studies don't give us good answers, but they're expensive and lengthy, and they're not for something that you need answers quickly on," says Suzanne Fitzpatrick, a toxicologist at the FDA's Center for Food Safety and Applied Nutrition.
In the years since, there has been growing support for alternatives to animal testing. Maryland became the first state to require animal testing labs to contribute money to nonanimal research. Monica Bertagnolli, the director of the National Institutes of Health, announced in February that it would prioritize the development and use of combinatorial NAMs. NAMs refers to new approach methods, another term for alternatives to animal testing.
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In January 2023, the FDA Modernization Act took things a step further, declaring that animal testing was no longer required as evidence before clinical trials.
CAAT recently announced that it would collaborate with the FDA's Center for Food Safety and Applied Nutrition to discuss and share the latest developments in the field of animal testing alternatives. "There's so many papers coming out now in this area, it's hard to keep up with the science," Fitzpatrick says. The collaboration, she says, makes it easier for scientists to keep up with advances while "we're still doing our regular jobs."
Soon, Fitzpatrick expects "more and more methods coming in that might be of use to the FDA" as nonanimal models mature. But, she cautions, "I don't think we're at the point where we're not going to have animal testing."
Of the $42 billion of funding the NIH awarded in 2020, 47% went to projects based on animal testing. But it should be pointed out that there are many laws, regulations, and policies that protect animals used in federally funded research. According to the National Institutes of Health, these protections include considering nonanimal alternatives to meet the scientific objectives and using the fewest subjects needed for thorough and repeatable results. They also outline standards that reflect a commitment to animal care.
In short, animals are still a vital part of science and public health. Reporting in Fast Company from earlier this year highlighted a shortage of long-tailed macaques during the COVID-19 pandemic. A panel assembled by the National Academies of Sciences, Engineering, and Medicine concluded that a lack of nonhuman primates in research would "severely limit the ability of National Institutes of Health–supported research programs to respond adequately to public health emergencies, as well as to carry out high-impact biomedical research."
The panel also said: "While no model, animal or otherwise, can fully mimic the complexities of the human body, there remain research questions that currently cannot be answered outside of the context of a living organism."
"In an animal, you have the systemic interaction of multiple organs, says Eva-Maria Dehne, a senior scientist at TissUse , a biotechnology company in Germany. "This is what you need to replace [animal models]." Her work focuses on organ-on-a-chip systems, small chips roughly the size of a computer memory stick. Organoids are added to chambers on the chips, lined with canals along which liquid can flow, mimicking blood vessels. Valves allow researchers to control the rate of flow.
Different chips can be connected so that a researcher could, for example, end up with a brain-heart-liver system. TissUse develops these organ-on-a-chip systems and sells them to researchers in the biomedical field.
Dehne, who was initially interested in the field owing to an ethical opposition to animal testing, has become more and more convinced by the scientific arguments to move away from the practice.
In making the case for nonanimal testing, Hartung thinks it's best to focus on arguments around efficiency. He adds that in his experience people tend not to respond positively to ethical arguments. When you highlight the efficiency of alternatives, that can open more doors. Data suggests that organ-on-a-chip systems could reduce research and development costs by 26%. "It is much more powerful than saying you have to protect these animals," he says.
Dehne worked with others on a brain-to-liver chip to test the blood-brain barrier permeation of the drugs atenolol and propranolol, the results of which were published in the journal Cells in 2022. Not only did the drugs match the results from human clinical trials, so, too, did the metabolites. In another paper, cosmetic ingredients were tested on a skin-liver-thyroid chip , with results predicting safe dosages within a fraction of current safety standards.
Researchers from Columbia University tested the cancer drug doxorubicin on a heart-liver-bone-skin chip , which matched the results found from clinical trials of the drug. Emulate, a spinoff from Harvard's Wyss Institute that is also developing organ-on-a-chip systems, works with top pharmaceutical companies, such as AstraZeneca, Johnson & Johnson, and Roche.
Hartung says the main message he wants to get across is that today there are simply fewer reasons—scientifically, economically, and ethically—to keep experimenting on animals to the same degree as we have done historically. In his view, "it is time to complement and then to replace the animal tests where we can do better," he says.
There are, however, still a lot of technological hurdles to cross before nonanimal testing can become more prevalent. Unless organoid intelligence, or something similar, comes to fruition, running experiments that involve a conscious response may always have to be done on animals. For example, if testing the effects of a pain relief drug, you need a conscious being.
Organ-on-a-chip systems are also quite complex, and that limits their usefulness. Chengpeng Chen, an assistant professor of analytical chemistry at the University of Maryland, Baltimore County, has experienced that firsthand. He remembers setting up a chip system, but when it came to adding the cells, they were either contaminated or hadn't grown properly, and so he had to discard the whole configuration. "It takes days to have a setup ready," he says. "Any mistake or any problem in any of the steps can mess up the whole setup."
Chen himself is running a lab focused on organ-on-a-chip systems. One of his aims is to try and make the use of these alternatives to animal testing easier. "A lab has to have very well-trained personnel to fabricate and maintain such organs-on-a-chip," Chen says. If we want the technology to be more widely used, then it must become easier to handle. Focusing on gains in efficiency in lieu of accessibility, while still useful, will mean the technology remains largely in academia, he says.
W hen Hartung went to Germany's University of Tübingen in the 1980s, animal testing was the norm. "I really needed a big glass of whiskey in the evening when I had done an experiment on mice and rats," he says. Feeling uneasy with the prospect of a career in a field where he had to keep testing on animals, Hartung managed to convince his mentor at the time to let him run experiments on cell cultures instead.
"I got a lot of feedback from some fellow scientists who told me: 'How can you waste your beautiful career with this alternative nonsense?'" he says. Nevertheless, Hartung continued with his research into cell cultures until, in 1996, he made a breakthrough when he designed an in vitro version of a pyrogen test—a test, traditionally done on rabbits, to find out whether a product is clean of bacterial contamination.
Ecstatic to have made a contribution that could save animal lives, Hartung was disappointed when the test was finally approved in 2006 alongside a host of others with the same function. Almost nobody seemed interested in adopting them. "The appetite by both the regulators and the regulated industry to make changes is often not very big," Hartung says.
One moment that really highlighted this resistance to change for Hartung was when he was on a panel discussing his pyrogen test. An employee from a big pharmaceutical company opposed the test "harshly," yet away from the spotlight, in the safety of a private conversation, the employee said he thought the test was good; it's just that his company had taken the stance to oppose it as it might impact their profits.
A 2022 paper published in the Journal of the Royal Society of Medicine noted that the beneficial effects of "tissue plasminogen activator for stroke had been well documented in animal models by 2001, but research using several thousand animals continued for several years afterward." An analysis published in BMJ Open in 2020 found that most stroke researchers recognized that animal models had not been successful in the field, yet they were reluctant to relinquish them. The analysis looked at opinion papers published in journals from 1979 to 2018 and found that only one author out of 80 had advocated for alternatives to animal testing.
Others, however, seem to be embracing alternatives. Last year, CAAT, together with TissUse, organized a conference in Berlin based on alternatives to animal testing. Hartung said that while they initially expected around 700–1,000 guests, the capacity of 1,300 was reached months before the conference occurred.
Could ai put an end to animal testing, could the next blockbuster drug be lab-rat free, fda no longer needs to require animal tests before human drug trials.
CAAT recently received $17 million for a seven-year project called IMPACT. With the money, Hartung and his team hope to further refine the alternatives to animal testing they've developed and create the Human Exposome Project, a database cataloging chemical exposures a person might face over their lifetime, along with potential harmful side effects.
Dehne thinks the community building around nonanimal models is great, and she is happy that more companies and researchers are entering the space. "There will never be one system that can answer all the questions, and therefore there will always be a need for different systems," she says.
Hartung has a similar view. "We're trying to form communities, … hundreds of people ultimately collaborating because they buy in" to the mission, he says. "It's more important that things are being done than who does them."
Fitzpatrick from the FDA says that alternatives to animal testing will likely help reduce the number of animals used but not necessarily fully replace them. She suggests that alternatives could be used to study a particular chemical, so researchers would have a better understanding of what to look for in animal tests, meaning fewer overall tests, and therefore fewer test subjects, would be necessary. "Our responsibility is to put safe and effective products on the market—not ending animal testing," she said. "So, we have to do that however we can."
Almost three decades after Hartung designed his pyrogen test, the European Union finally decided to outlaw the industry standard rabbit pyrogen test by 2026. "If you would have told me as a young postdoc how long this might take, I probably would have gone into another field," he says. Though the U.S. is still lagging in that regard, Hartung is enjoying the moment of having finally pushed through a replacement that he says could save up to 170,000 rabbits from unnecessary suffering every year.
Jack McGovan is a freelance writer based in Berlin.
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To assess the yield strength of gradient-nanostructured (GNS) material by small punch test, an analytical method is proposed in this paper. The constructed theoretical model of GNS material is integrated into the classical circular plate bending theory, and an analytical formula specific to GNS material with different microstructures is derived. In order to mitigate the influence of stress state, the analytical formula is modified. The yield strength obtained by the revised analytical formula is close to the standard tensile test results, with errors falling within permissible range. Additionally, the relationship between elastic modulus and surface microstructure of GNS material is established. Thus, the yield strength of GNS material could be evaluated by the surface grain size without measuring of the surface elastic modulus.
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This work was supported by The National Key Research and Development Program of China (2018YFA0704604).
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Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology, Wuxi, China
Xiangwei Liao, Xinjun Yang, Dongxiang Wang, Jiyun Du, Fangyang Yuan & Wei Yu
School of Mechanical Engineering, Jiangnan University, Wuxi, 214122, China
Xinjun Yang, Dongxiang Wang, Jiyun Du, Fangyang Yuan & Wei Yu
College of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, 211816, China
Qingsheng Li
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Xiangwei Liao was involved in methodology, investigation, formal derivation, validation, writing original—draft. Xinjun Yang helped in experiments, funding acquisition, review & editing. Dongxiang Wang contributed to supervision, resources. Jiyun Du helped in review & editing, supervision. Fangyang Yuan was involved in supervision, resources. Wei Yu contributed to supervision, review & editing. Qingsheng Li assisted in experiments, funding acquisition.
Correspondence to Xinjun Yang .
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Liao, X., Yang, X., Wang, D. et al. Research on Small Punch Test Method for Evaluating the Yield Strength of Gradient Nanostructured Material. J. of Materi Eng and Perform (2024). https://doi.org/10.1007/s11665-024-10072-x
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Received : 11 December 2023
Revised : 20 May 2024
Accepted : 26 June 2024
Published : 12 September 2024
DOI : https://doi.org/10.1007/s11665-024-10072-x
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Research on modification of oxygen-producing adsorbents for high-altitude and low-pressure environments.
Graphical Abstract
2. experimental section, 2.1. materials, 2.2. preparation of sorbents, 2.3. nitrogen and oxygen adsorption performance analysis, 3. results and discussion, 3.1. specific surface area and pore size analysis of modified adsorbents, 3.2. pore size distribution analysis, 3.3. study of nitrogen–oxygen separation performance of modified adsorbent, 3.4. nitrogen and oxygen adsorption and separation performance of adsorbent under high altitude and low air pressure, 4. future perspectives, 5. conclusions, supplementary materials, author contributions, data availability statement, conflicts of interest.
Click here to enlarge figure
Adsorbent | Adsorption Capacity (mL/g) | S1 | S2 | S3 | |||||
---|---|---|---|---|---|---|---|---|---|
N | O | ||||||||
Li-LSX | 24.68 | 3.48 | 7.09 | 11.58 | 11.23 | 2.5694 | 37.788 | 0.0074 | 0.00004 |
AgLi-LSX-1 | 27.92 | 4.56 | 6.12 | 30.82 | 18.77 | 1.5123 | 3.4369 | 0.0438 | 0.0006 |
AgLi-LSX-2 | 23.09 | 3.71 | 6.22 | 28.78 | 18.04 | 1.2676 | 2.8760 | 0.0397 | 0.0006 |
AgLi-LSX-3 | 22.79 | 4.82 | 4.73 | 23.06 | 10.63 | 1.2675 | 203.98 | 0.0376 | 0.00001 |
AgLi-LSX-4 | 10.32 | 2.99 | 3.45 | 10.24 | 6.92 | 0.6360 | 1.8434 | 0.0233 | 0.0008 |
CaLi-LSX-1 | 23.11 | 4.40 | 5.25 | 11.18 | 8.87 | 2.5607 | 5.5616 | 0.0070 | 0.0003 |
CaLi-LSX-2 | 25.83 | 4.22 | 6.12 | 12.21 | 11.74 | 2.1566 | 1.4702 | 0.0118 | 0.0016 |
CaLi-LSX-3 | 14.94 | 3.15 | 4.74 | 8.23 | 7.85 | 1.4178 | 1.2574 | 0.0227 | 0.0020 |
CaLi-LSX-4 | 9.13 | 2.86 | 3.20 | 5.15 | 4.32 | 1.2269 | 1.2594 | 0.0298 | 0.0013 |
Adsorbent | Working Capacities (mL/g) | S4 | |
---|---|---|---|
N | O | ||
Li-LSX | 18.80 | 5.43 | 40 |
AgLiLSX-1 | 9.31 | 5.86 | 44 |
AgLiLSX-2 | 6.34 | 5.07 | 35 |
AgLiLSX-3 | 6.53 | 6.92 | 22 |
AgLiLSX-4 | 4.06 | 4.05 | 10 |
CaLiLSX-1 | 13.92 | 6.15 | 25 |
CaLiLSX-2 | 14.91 | 6.47 | 25 |
CaLiLSX-3 | 10.56 | 4.09 | 21 |
CaLiLSX-4 | 7.51 | 4.02 | 9 |
Altitudes (m) | 50 | 2000 | 3000 | 4000 | 5000 | |
---|---|---|---|---|---|---|
S4 | Li-LSX | 24.44 | 31.13 | 38.53 | 44.27 | 49.36 |
AgLi-LSX-1 | 20.85 | 28.91 | 38.53 | 47.61 | 57.00 | |
AgLi-LSX-2 | 16.60 | 22.78 | 30.97 | 38.81 | 47.02 | |
CaLi-LSX-1 | 14.29 | 18.65 | 23.59 | 27.70 | 31.55 | |
CaLi-LSX-2 | 15.26 | 19.11 | 23.34 | 26.91 | 30.28 |
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Li, Y.; Yue, H.; Zhang, Q.; Yan, D.; Li, Z.; Liu, Z.; Liu, Y.; Wang, Y.; Wang, S.; Yang, X. Research on Modification of Oxygen-Producing Adsorbents for High-Altitude and Low-Pressure Environments. Inorganics 2024 , 12 , 250. https://doi.org/10.3390/inorganics12090250
Li Y, Yue H, Zhang Q, Yan D, Li Z, Liu Z, Liu Y, Wang Y, Wang S, Yang X. Research on Modification of Oxygen-Producing Adsorbents for High-Altitude and Low-Pressure Environments. Inorganics . 2024; 12(9):250. https://doi.org/10.3390/inorganics12090250
Li, Ye, Huiqing Yue, Quanli Zhang, Dumin Yan, Ziyi Li, Zhiwei Liu, Yingshu Liu, Yongyan Wang, Shifeng Wang, and Xiong Yang. 2024. "Research on Modification of Oxygen-Producing Adsorbents for High-Altitude and Low-Pressure Environments" Inorganics 12, no. 9: 250. https://doi.org/10.3390/inorganics12090250
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