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How to Write the Methods Section of a Research Paper

Writing a research paper is both an art and a skill, and knowing how to write the methods section of a research paper is the first crucial step in mastering scientific writing. If, like the majority of early career researchers, you believe that the methods section is the simplest to write and needs little in the way of careful consideration or thought, this article will help you understand it is not 1 .

We have all probably asked our supervisors, coworkers, or search engines “ how to write a methods section of a research paper ” at some point in our scientific careers, so you are not alone if that’s how you ended up here.  Even for seasoned researchers, selecting what to include in the methods section from a wealth of experimental information can occasionally be a source of distress and perplexity.   

Additionally, journal specifications, in some cases, may make it more of a requirement rather than a choice to provide a selective yet descriptive account of the experimental procedure. Hence, knowing these nuances of how to write the methods section of a research paper is critical to its success. The methods section of the research paper is not supposed to be a detailed heavy, dull section that some researchers tend to write; rather, it should be the central component of the study that justifies the validity and reliability of the research.

Are you still unsure of how the methods section of a research paper forms the basis of every investigation? Consider the last article you read but ignore the methods section and concentrate on the other parts of the paper . Now think whether you could repeat the study and be sure of the credibility of the findings despite knowing the literature review and even having the data in front of you. You have the answer!   

Having established the importance of the methods section , the next question is how to write the methods section of a research paper that unifies the overall study. The purpose of the methods section , which was earlier called as Materials and Methods , is to describe how the authors went about answering the “research question” at hand. Here, the objective is to tell a coherent story that gives a detailed account of how the study was conducted, the rationale behind specific experimental procedures, the experimental setup, objects (variables) involved, the research protocol employed, tools utilized to measure, calculations and measurements, and the analysis of the collected data 2 .

In this article, we will take a deep dive into this topic and provide a detailed overview of how to write the methods section of a research paper . For the sake of clarity, we have separated the subject into various sections with corresponding subheadings.  

Table of Contents

What is the methods section of a research paper ?  

The methods section is a fundamental section of any paper since it typically discusses the ‘ what ’, ‘ how ’, ‘ which ’, and ‘ why ’ of the study, which is necessary to arrive at the final conclusions. In a research article, the introduction, which serves to set the foundation for comprehending the background and results is usually followed by the methods section, which precedes the result and discussion sections. The methods section must explicitly state what was done, how it was done, which equipment, tools and techniques were utilized, how were the measurements/calculations taken, and why specific research protocols, software, and analytical methods were employed.  

Why is the methods section important?  

The primary goal of the methods section is to provide pertinent details about the experimental approach so that the reader may put the results in perspective and, if necessary, replicate the findings 3 .  This section offers readers the chance to evaluate the reliability and validity of any study. In short, it also serves as the study’s blueprint, assisting researchers who might be unsure about any other portion in establishing the study’s context and validity. The methods plays a rather crucial role in determining the fate of the article; an incomplete and unreliable methods section can frequently result in early rejections and may lead to numerous rounds of modifications during the publication process. This means that the reviewers also often use methods section to assess the reliability and validity of the research protocol and the data analysis employed to address the research topic. In other words, the purpose of the methods section is to demonstrate the research acumen and subject-matter expertise of the author(s) in their field.  

Structure of methods section of a research paper  

Similar to the research paper, the methods section also follows a defined structure; this may be dictated by the guidelines of a specific journal or can be presented in a chronological or thematic manner based on the study type. When writing the methods section , authors should keep in mind that they are telling a story about how the research was conducted. They should only report relevant information to avoid confusing the reader and include details that would aid in connecting various aspects of the entire research activity together. It is generally advisable to present experiments in the order in which they were conducted. This facilitates the logical flow of the research and allows readers to follow the progression of the study design.   

It is also essential to clearly state the rationale behind each experiment and how the findings of earlier experiments informed the design or interpretation of later experiments. This allows the readers to understand the overall purpose of the study design and the significance of each experiment within that context. However, depending on the particular research question and method, it may make sense to present information in a different order; therefore, authors must select the best structure and strategy for their individual studies.   

In cases where there is a lot of information, divide the sections into subheadings to cover the pertinent details. If the journal guidelines pose restrictions on the word limit , additional important information can be supplied in the supplementary files. A simple rule of thumb for sectioning the method section is to begin by explaining the methodological approach ( what was done ), describing the data collection methods ( how it was done ), providing the analysis method ( how the data was analyzed ), and explaining the rationale for choosing the methodological strategy. This is described in detail in the upcoming sections.    

How to write the methods section of a research paper  

Contrary to widespread assumption, the methods section of a research paper should be prepared once the study is complete to prevent missing any key parameter. Hence, please make sure that all relevant experiments are done before you start writing a methods section . The next step for authors is to look up any applicable academic style manuals or journal-specific standards to ensure that the methods section is formatted correctly. The methods section of a research paper typically constitutes materials and methods; while writing this section, authors usually arrange the information under each category.

The materials category describes the samples, materials, treatments, and instruments, while experimental design, sample preparation, data collection, and data analysis are a part of the method category. According to the nature of the study, authors should include additional subsections within the methods section, such as ethical considerations like the declaration of Helsinki (for studies involving human subjects), demographic information of the participants, and any other crucial information that can affect the output of the study. Simply put, the methods section has two major components: content and format. Here is an easy checklist for you to consider if you are struggling with how to write the methods section of a research paper .   

• Explain the research design, subjects, and sample details  
• Include information on inclusion and exclusion criteria  
• Mention ethical or any other permission required for the study  
• Include information about materials, experimental setup, tools, and software  
• Add details of data collection and analysis methods  
• Incorporate how research biases were avoided or confounding variables were controlled  
• Evaluate and justify the experimental procedure selected to address the research question  
• Provide precise and clear details of each experiment  
• Flowcharts, infographics, or tables can be used to present complex information     
• Use past tense to show that the experiments have been done   
• Follow academic style guides (such as APA or MLA ) to structure the content  
• Citations should be included as per standard protocols in the field  

Now that you know how to write the methods section of a research paper , let’s address another challenge researchers face while writing the methods section —what to include in the methods section .  How much information is too much is not always obvious when it comes to trying to include data in the methods section of a paper. In the next section, we examine this issue and explore potential solutions.   

What to include in the methods section of a research paper  

The technical nature of the methods section occasionally makes it harder to present the information clearly and concisely while staying within the study context. Many young researchers tend to veer off subject significantly, and they frequently commit the sin of becoming bogged down in itty bitty details, making the text harder to read and impairing its overall flow. However, the best way to write the methods section is to start with crucial components of the experiments. If you have trouble deciding which elements are essential, think about leaving out those that would make it more challenging to comprehend the context or replicate the results. The top-down approach helps to ensure all relevant information is incorporated and vital information is not lost in technicalities. Next, remember to add details that are significant to assess the validity and reliability of the study. Here is a simple checklist for you to follow ( bonus tip: you can also make a checklist for your own study to avoid missing any critical information while writing the methods section ).  

• Structuring the methods section : Authors should diligently follow journal guidelines and adhere to the specific author instructions provided when writing the methods section . Journals typically have specific guidelines for formatting the methods section ; for example, Frontiers in Plant Sciences advises arranging the materials and methods section by subheading and citing relevant literature. There are several standardized checklists available for different study types in the biomedical field, including CONSORT (Consolidated Standards of Reporting Trials) for randomized clinical trials, PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analysis) for systematic reviews and meta-analysis, and STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) for cohort, case-control, cross-sectional studies. Before starting the methods section , check the checklist available in your field that can function as a guide.     
• Organizing different sections to tell a story : Once you are sure of the format required for structuring the methods section , the next is to present the sections in a logical manner; as mentioned earlier, the sections can be organized according to the chronology or themes. In the chronological arrangement, you should discuss the methods in accordance with how the experiments were carried out. An example of the method section of a research paper of an animal study should first ideally include information about the species, weight, sex, strain, and age. Next, the number of animals, their initial conditions, and their living and housing conditions should also be mentioned. Second, how the groups are assigned and the intervention (drug treatment, stress, or other) given to each group, and finally, the details of tools and techniques used to measure, collect, and analyze the data. Experiments involving animal or human subjects should additionally state an ethics approval statement. It is best to arrange the section using the thematic approach when discussing distinct experiments not following a sequential order.  
• Define and explain the objects and procedure: Experimental procedure should clearly be stated in the methods section . Samples, necessary preparations (samples, treatment, and drug), and methods for manipulation need to be included. All variables (control, dependent, independent, and confounding) must be clearly defined, particularly if the confounding variables can affect the outcome of the study.  
• Match the order of the methods section with the order of results: Though not mandatory, organizing the manuscript in a logical and coherent manner can improve the readability and clarity of the paper. This can be done by following a consistent structure throughout the manuscript; readers can easily navigate through the different sections and understand the methods and results in relation to each other. Using experiment names as headings for both the methods and results sections can also make it simpler for readers to locate specific information and corroborate it if needed.   
• Relevant information must always be included: The methods section should have information on all experiments conducted and their details clearly mentioned. Ask the journal whether there is a way to offer more information in the supplemental files or external repositories if your target journal has strict word limitations. For example, Nature communications encourages authors to deposit their step-by-step protocols in an open-resource depository, Protocol Exchange which allows the protocols to be linked with the manuscript upon publication. Providing access to detailed protocols also helps to increase the transparency and reproducibility of the research.  
• It’s all in the details: The methods section should meticulously list all the materials, tools, instruments, and software used for different experiments. Specify the testing equipment on which data was obtained, together with its manufacturer’s information, location, city, and state or any other stimuli used to manipulate the variables. Provide specifics on the research process you employed; if it was a standard protocol, cite previous studies that also used the protocol.  Include any protocol modifications that were made, as well as any other factors that were taken into account when planning the study or gathering data. Any new or modified techniques should be explained by the authors. Typically, readers evaluate the reliability and validity of the procedures using the cited literature, and a widely accepted checklist helps to support the credibility of the methodology. Note: Authors should include a statement on sample size estimation (if applicable), which is often missed. It enables the reader to determine how many subjects will be required to detect the expected change in the outcome variables within a given confidence interval.  
• Write for the audience: While explaining the details in the methods section , authors should be mindful of their target audience, as some of the rationale or assumptions on which specific procedures are based might not always be obvious to the audience, particularly for a general audience. Therefore, when in doubt, the objective of a procedure should be specified either in relation to the research question or to the entire protocol.  
• Data interpretation and analysis : Information on data processing, statistical testing, levels of significance, and analysis tools and software should be added. Mention if the recommendations and expertise of an experienced statistician were followed. Also, evaluate and justify the preferred statistical method used in the study and its significance.  

What NOT to include in the methods section of a research paper  

To address “ how to write the methods section of a research paper ”, authors should not only pay careful attention to what to include but also what not to include in the methods section of a research paper . Here is a list of do not’s when writing the methods section :  

• Do not elaborate on specifics of standard methods/procedures: You should refrain from adding unnecessary details of experiments and practices that are well established and cited previously.  Instead, simply cite relevant literature or mention if the manufacturer’s protocol was followed.  
• Do not add unnecessary details : Do not include minute details of the experimental procedure and materials/instruments used that are not significant for the outcome of the experiment. For example, there is no need to mention the brand name of the water bath used for incubation.    
• Do not discuss the results: The methods section is not to discuss the results or refer to the tables and figures; save it for the results and discussion section. Also, focus on the methods selected to conduct the study and avoid diverting to other methods or commenting on their pros or cons.  
• Do not make the section bulky : For extensive methods and protocols, provide the essential details and share the rest of the information in the supplemental files. The writing should be clear yet concise to maintain the flow of the section.  

We hope that by this point, you understand how crucial it is to write a thoughtful and precise methods section and the ins and outs of how to write the methods section of a research paper . To restate, the entire purpose of the methods section is to enable others to reproduce the results or verify the research. We sincerely hope that this post has cleared up any confusion and given you a fresh perspective on the methods section .

As a parting gift, we’re leaving you with a handy checklist that will help you understand how to write the methods section of a research paper . Feel free to download this checklist and use or share this with those who you think may benefit from it.  

References  

• Bhattacharya, D. How to write the Methods section of a research paper. Editage Insights, 2018. https://www.editage.com/insights/how-to-write-the-methods-section-of-a-research-paper (2018).
• Kallet, R. H. How to Write the Methods Section of a Research Paper. Respiratory Care 49, 1229–1232 (2004). https://pubmed.ncbi.nlm.nih.gov/15447808/
• Grindstaff, T. L. & Saliba, S. A. AVOIDING MANUSCRIPT MISTAKES. Int J Sports Phys Ther 7, 518–524 (2012). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3474299/

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• How to Write Your Methods

Ensure understanding, reproducibility and replicability

What should you include in your methods section, and how much detail is appropriate?

Why Methods Matter

The methods section was once the most likely part of a paper to be unfairly abbreviated, overly summarized, or even relegated to hard-to-find sections of a publisher’s website. While some journals may responsibly include more detailed elements of methods in supplementary sections, the movement for increased reproducibility and rigor in science has reinstated the importance of the methods section. Methods are now viewed as a key element in establishing the credibility of the research being reported, alongside the open availability of data and results.

A clear methods section impacts editorial evaluation and readers’ understanding, and is also the backbone of transparency and replicability.

For example, the Reproducibility Project: Cancer Biology project set out in 2013 to replicate experiments from 50 high profile cancer papers, but revised their target to 18 papers once they understood how much methodological detail was not contained in the original papers.

What to include in your methods section

What you include in your methods sections depends on what field you are in and what experiments you are performing. However, the general principle in place at the majority of journals is summarized well by the guidelines at PLOS ONE : “The Materials and Methods section should provide enough detail to allow suitably skilled investigators to fully replicate your study. ” The emphases here are deliberate: the methods should enable readers to understand your paper, and replicate your study. However, there is no need to go into the level of detail that a lay-person would require—the focus is on the reader who is also trained in your field, with the suitable skills and knowledge to attempt a replication.

A constant principle of rigorous science

A methods section that enables other researchers to understand and replicate your results is a constant principle of rigorous, transparent, and Open Science. Aim to be thorough, even if a particular journal doesn’t require the same level of detail . Reproducibility is all of our responsibility. You cannot create any problems by exceeding a minimum standard of information. If a journal still has word-limits—either for the overall article or specific sections—and requires some methodological details to be in a supplemental section, that is OK as long as the extra details are searchable and findable .

Imagine replicating your own work, years in the future

As part of PLOS’ presentation on Reproducibility and Open Publishing (part of UCSF’s Reproducibility Series ) we recommend planning the level of detail in your methods section by imagining you are writing for your future self, replicating your own work. When you consider that you might be at a different institution, with different account logins, applications, resources, and access levels—you can help yourself imagine the level of specificity that you yourself would require to redo the exact experiment. Consider:

• Which details would you need to be reminded of? 
• Which cell line, or antibody, or software, or reagent did you use, and does it have a Research Resource ID (RRID) that you can cite?
• Which version of a questionnaire did you use in your survey? 
• Exactly which visual stimulus did you show participants, and is it publicly available? 
• What participants did you decide to exclude? 
• What process did you adjust, during your work? 

Tip: Be sure to capture any changes to your protocols

You yourself would want to know about any adjustments, if you ever replicate the work, so you can surmise that anyone else would want to as well. Even if a necessary adjustment you made was not ideal, transparency is the key to ensuring this is not regarded as an issue in the future. It is far better to transparently convey any non-optimal methods, or methodological constraints, than to conceal them, which could result in reproducibility or ethical issues downstream.

Visual aids for methods help when reading the whole paper

Consider whether a visual representation of your methods could be appropriate or aid understanding your process. A visual reference readers can easily return to, like a flow-diagram, decision-tree, or checklist, can help readers to better understand the complete article, not just the methods section.

Ethical Considerations

In addition to describing what you did, it is just as important to assure readers that you also followed all relevant ethical guidelines when conducting your research. While ethical standards and reporting guidelines are often presented in a separate section of a paper, ensure that your methods and protocols actually follow these guidelines. Read more about ethics .

Existing standards, checklists, guidelines, partners

While the level of detail contained in a methods section should be guided by the universal principles of rigorous science outlined above, various disciplines, fields, and projects have worked hard to design and develop consistent standards, guidelines, and tools to help with reporting all types of experiment. Below, you’ll find some of the key initiatives. Ensure you read the submission guidelines for the specific journal you are submitting to, in order to discover any further journal- or field-specific policies to follow, or initiatives/tools to utilize.

Tip: Keep your paper moving forward by providing the proper paperwork up front

Be sure to check the journal guidelines and provide the necessary documents with your manuscript submission. Collecting the necessary documentation can greatly slow the first round of peer review, or cause delays when you submit your revision.

Randomized Controlled Trials – CONSORT The Consolidated Standards of Reporting Trials (CONSORT) project covers various initiatives intended to prevent the problems of  inadequate reporting of randomized controlled trials. The primary initiative is an evidence-based minimum set of recommendations for reporting randomized trials known as the CONSORT Statement . 

Systematic Reviews and Meta-Analyses – PRISMA The Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) is an evidence-based minimum set of items focusing  on the reporting of  reviews evaluating randomized trials and other types of research.

Research using Animals – ARRIVE The Animal Research: Reporting of In Vivo Experiments ( ARRIVE ) guidelines encourage maximizing the information reported in research using animals thereby minimizing unnecessary studies. (Original study and proposal , and updated guidelines , in PLOS Biology .) 

Laboratory Protocols Protocols.io has developed a platform specifically for the sharing and updating of laboratory protocols , which are assigned their own DOI and can be linked from methods sections of papers to enhance reproducibility. Contextualize your protocol and improve discovery with an accompanying Lab Protocol article in PLOS ONE .

Consistent reporting of Materials, Design, and Analysis – the MDAR checklist A cross-publisher group of editors and experts have developed, tested, and rolled out a checklist to help establish and harmonize reporting standards in the Life Sciences . The checklist , which is available for use by authors to compile their methods, and editors/reviewers to check methods, establishes a minimum set of requirements in transparent reporting and is adaptable to any discipline within the Life Sciences, by covering a breadth of potentially relevant methodological items and considerations. If you are in the Life Sciences and writing up your methods section, try working through the MDAR checklist and see whether it helps you include all relevant details into your methods, and whether it reminded you of anything you might have missed otherwise.

Summary Writing tips

The main challenge you may find when writing your methods is keeping it readable AND covering all the details needed for reproducibility and replicability. While this is difficult, do not compromise on rigorous standards for credibility!

• Keep in mind future replicability, alongside understanding and readability.
• Follow checklists, and field- and journal-specific guidelines.
• Consider a commitment to rigorous and transparent science a personal responsibility, and not just adhering to journal guidelines.
• Establish whether there are persistent identifiers for any research resources you use that can be specifically cited in your methods section.
• Deposit your laboratory protocols in Protocols.io, establishing a permanent link to them. You can update your protocols later if you improve on them, as can future scientists who follow your protocols.
• Consider visual aids like flow-diagrams, lists, to help with reading other sections of the paper.
• Be specific about all decisions made during the experiments that someone reproducing your work would need to know.

Don’t

• Summarize or abbreviate methods without giving full details in a discoverable supplemental section.
• Presume you will always be able to remember how you performed the experiments, or have access to private or institutional notebooks and resources.
• Attempt to hide constraints or non-optimal decisions you had to make–transparency is the key to ensuring the credibility of your research.
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The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
• In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
• In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; 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.

Structure and Writing Style

I.  Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II.  Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

• Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
• Tools and methods used to identify and collect information, and how you identified relevant variables,
• The ways in which you processed the data and the procedures you used to analyze that data, and
• The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
• Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
• Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
• Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

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. If necessary, consider using appendices for raw data.

ANOTHER NOTE: If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE:   If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

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. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. 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 and of 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; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; 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; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; 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; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; 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.

Writing Tip

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.

To locate data and statistics, GO HERE .

Another Writing Tip

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 the application of 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.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

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Here's What You Need to Understand About Research Methodology

Table of Contents

Research methodology involves a systematic and well-structured approach to conducting scholarly or scientific inquiries. Knowing the significance of research methodology and its different components is crucial as it serves as the basis for any study.

Typically, your research topic will start as a broad idea you want to investigate more thoroughly. Once you’ve identified a research problem and created research questions , you must choose the appropriate methodology and frameworks to address those questions effectively.

What is the definition of a research methodology?

Research methodology is the process or the way you intend to execute your study. The methodology section of a research paper outlines how you plan to conduct your study. It covers various steps such as collecting data, statistical analysis, observing participants, and other procedures involved in the research process

The methods section should give a description of the process that will convert your idea into a study. Additionally, the outcomes of your process must provide valid and reliable results resonant with the aims and objectives of your research. This thumb rule holds complete validity, no matter whether your paper has inclinations for qualitative or quantitative usage.

Studying research methods used in related studies can provide helpful insights and direction for your own research. Now easily discover papers related to your topic on SciSpace and utilize our AI research assistant, Copilot , to quickly review the methodologies applied in different papers.

The need for a good research methodology

While deciding on your approach towards your research, the reason or factors you weighed in choosing a particular problem and formulating a research topic need to be validated and explained. A research methodology helps you do exactly that. Moreover, a good research methodology lets you build your argument to validate your research work performed through various data collection methods, analytical methods, and other essential points.

Just imagine it as a strategy documented to provide an overview of what you intend to do.

While undertaking any research writing or performing the research itself, you may get drifted in not something of much importance. In such a case, a research methodology helps you to get back to your outlined work methodology.

A research methodology helps in keeping you accountable for your work. Additionally, it can help you evaluate whether your work is in sync with your original aims and objectives or not. Besides, a good research methodology enables you to navigate your research process smoothly and swiftly while providing effective planning to achieve your desired results.

What is the basic structure of a research methodology?

Usually, you must ensure to include the following stated aspects while deciding over the basic structure of your research methodology:

1. Your research procedure

Explain what research methods you’re going to use. Whether you intend to proceed with quantitative or qualitative, or a composite of both approaches, you need to state that explicitly. The option among the three depends on your research’s aim, objectives, and scope.

2. Provide the rationality behind your chosen approach

Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome.

3. Explain your mechanism

The mechanism encompasses the research methods or instruments you will use to develop your research methodology. It usually refers to your data collection methods. You can use interviews, surveys, physical questionnaires, etc., of the many available mechanisms as research methodology instruments. The data collection method is determined by the type of research and whether the data is quantitative data(includes numerical data) or qualitative data (perception, morale, etc.) Moreover, you need to put logical reasoning behind choosing a particular instrument.

4. Significance of outcomes

The results will be available once you have finished experimenting. However, you should also explain how you plan to use the data to interpret the findings. This section also aids in understanding the problem from within, breaking it down into pieces, and viewing the research problem from various perspectives.

5. Reader’s advice

Anything that you feel must be explained to spread more awareness among readers and focus groups must be included and described in detail. You should not just specify your research methodology on the assumption that a reader is aware of the topic.  

All the relevant information that explains and simplifies your research paper must be included in the methodology section. If you are conducting your research in a non-traditional manner, give a logical justification and list its benefits.

6. Explain your sample space

Include information about the sample and sample space in the methodology section. The term "sample" refers to a smaller set of data that a researcher selects or chooses from a larger group of people or focus groups using a predetermined selection method. Let your readers know how you are going to distinguish between relevant and non-relevant samples. How you figured out those exact numbers to back your research methodology, i.e. the sample spacing of instruments, must be discussed thoroughly.

For example, if you are going to conduct a survey or interview, then by what procedure will you select the interviewees (or sample size in case of surveys), and how exactly will the interview or survey be conducted.

7. Challenges and limitations

This part, which is frequently assumed to be unnecessary, is actually very important. The challenges and limitations that your chosen strategy inherently possesses must be specified while you are conducting different types of research.

The importance of a good research methodology

You must have observed that all research papers, dissertations, or theses carry a chapter entirely dedicated to research methodology. This section helps maintain your credibility as a better interpreter of results rather than a manipulator.

A good research methodology always explains the procedure, data collection methods and techniques, aim, and scope of the research. In a research study, it leads to a well-organized, rationality-based approach, while the paper lacking it is often observed as messy or disorganized.

You should pay special attention to validating your chosen way towards the research methodology. This becomes extremely important in case you select an unconventional or a distinct method of execution.

Curating and developing a strong, effective research methodology can assist you in addressing a variety of situations, such as:

• When someone tries to duplicate or expand upon your research after few years.
• If a contradiction or conflict of facts occurs at a later time. This gives you the security you need to deal with these contradictions while still being able to defend your approach.
• Gaining a tactical approach in getting your research completed in time. Just ensure you are using the right approach while drafting your research methodology, and it can help you achieve your desired outcomes. Additionally, it provides a better explanation and understanding of the research question itself.
• Documenting the results so that the final outcome of the research stays as you intended it to be while starting.

Instruments you could use while writing a good research methodology

As a researcher, you must choose which tools or data collection methods that fit best in terms of the relevance of your research. This decision has to be wise.

There exists many research equipments or tools that you can use to carry out your research process. These are classified as:

a. Interviews (One-on-One or a Group)

An interview aimed to get your desired research outcomes can be undertaken in many different ways. For example, you can design your interview as structured, semi-structured, or unstructured. What sets them apart is the degree of formality in the questions. On the other hand, in a group interview, your aim should be to collect more opinions and group perceptions from the focus groups on a certain topic rather than looking out for some formal answers.

In surveys, you are in better control if you specifically draft the questions you seek the response for. For example, you may choose to include free-style questions that can be answered descriptively, or you may provide a multiple-choice type response for questions. Besides, you can also opt to choose both ways, deciding what suits your research process and purpose better.

c. Sample Groups

Similar to the group interviews, here, you can select a group of individuals and assign them a topic to discuss or freely express their opinions over that. You can simultaneously note down the answers and later draft them appropriately, deciding on the relevance of every response.

d. Observations

If your research domain is humanities or sociology, observations are the best-proven method to draw your research methodology. Of course, you can always include studying the spontaneous response of the participants towards a situation or conducting the same but in a more structured manner. A structured observation means putting the participants in a situation at a previously decided time and then studying their responses.

Of all the tools described above, it is you who should wisely choose the instruments and decide what’s the best fit for your research. You must not restrict yourself from multiple methods or a combination of a few instruments if appropriate in drafting a good research methodology.

Types of research methodology

A research methodology exists in various forms. Depending upon their approach, whether centered around words, numbers, or both, methodologies are distinguished as qualitative, quantitative, or an amalgamation of both.

1. Qualitative research methodology

When a research methodology primarily focuses on words and textual data, then it is generally referred to as qualitative research methodology. This type is usually preferred among researchers when the aim and scope of the research are mainly theoretical and explanatory.

The instruments used are observations, interviews, and sample groups. You can use this methodology if you are trying to study human behavior or response in some situations. Generally, qualitative research methodology is widely used in sociology, psychology, and other related domains.

2. Quantitative research methodology

If your research is majorly centered on data, figures, and stats, then analyzing these numerical data is often referred to as quantitative research methodology. You can use quantitative research methodology if your research requires you to validate or justify the obtained results.

In quantitative methods, surveys, tests, experiments, and evaluations of current databases can be advantageously used as instruments If your research involves testing some hypothesis, then use this methodology.

3. Amalgam methodology

As the name suggests, the amalgam methodology uses both quantitative and qualitative approaches. This methodology is used when a part of the research requires you to verify the facts and figures, whereas the other part demands you to discover the theoretical and explanatory nature of the research question.

The instruments for the amalgam methodology require you to conduct interviews and surveys, including tests and experiments. The outcome of this methodology can be insightful and valuable as it provides precise test results in line with theoretical explanations and reasoning.

The amalgam method, makes your work both factual and rational at the same time.

Final words: How to decide which is the best research methodology?

If you have kept your sincerity and awareness intact with the aims and scope of research well enough, you must have got an idea of which research methodology suits your work best.

Before deciding which research methodology answers your research question, you must invest significant time in reading and doing your homework for that. Taking references that yield relevant results should be your first approach to establishing a research methodology.

Moreover, you should never refrain from exploring other options. Before setting your work in stone, you must try all the available options as it explains why the choice of research methodology that you finally make is more appropriate than the other available options.

You should always go for a quantitative research methodology if your research requires gathering large amounts of data, figures, and statistics. This research methodology will provide you with results if your research paper involves the validation of some hypothesis.

Whereas, if  you are looking for more explanations, reasons, opinions, and public perceptions around a theory, you must use qualitative research methodology.The choice of an appropriate research methodology ultimately depends on what you want to achieve through your research.

Frequently Asked Questions (FAQs) about Research Methodology

1. how to write a research methodology.

You can always provide a separate section for research methodology where you should specify details about the methods and instruments used during the research, discussions on result analysis, including insights into the background information, and conveying the research limitations.

2. What are the types of research methodology?

There generally exists four types of research methodology i.e.

• Observation
• Experimental
• Derivational

3. What is the true meaning of research methodology?

The set of techniques or procedures followed to discover and analyze the information gathered to validate or justify a research outcome is generally called Research Methodology.

4. Where lies the importance of research methodology?

Your research methodology directly reflects the validity of your research outcomes and how well-informed your research work is. Moreover, it can help future researchers cite or refer to your research if they plan to use a similar research methodology.

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The Ultimate Guide To Research Methodology

Research methodology is a crucial aspect of any investigative process, serving as the blueprint for the entire research journey. If you are stuck in the methodology section of your research paper , then this blog will guide you on what is a research methodology, its types and how to successfully conduct one. 

Table of Contents

What Is Research Methodology?

Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings. 

Research methodology is not confined to a singular approach; rather, it encapsulates a diverse range of methods tailored to the specific requirements of the research objectives.

Here is why Research methodology is important in academic and professional settings.

Facilitating Rigorous Inquiry

Research methodology forms the backbone of rigorous inquiry. It provides a structured approach that aids researchers in formulating precise thesis statements , selecting appropriate methodologies, and executing systematic investigations. This, in turn, enhances the quality and credibility of the research outcomes.

Ensuring Reproducibility And Reliability

In both academic and professional contexts, the ability to reproduce research outcomes is paramount. A well-defined research methodology establishes clear procedures, making it possible for others to replicate the study. This not only validates the findings but also contributes to the cumulative nature of knowledge.

Guiding Decision-Making Processes

In professional settings, decisions often hinge on reliable data and insights. Research methodology equips professionals with the tools to gather pertinent information, analyze it rigorously, and derive meaningful conclusions.

This informed decision-making is instrumental in achieving organizational goals and staying ahead in competitive environments.

Contributing To Academic Excellence

For academic researchers, adherence to robust research methodology is a hallmark of excellence. Institutions value research that adheres to high standards of methodology, fostering a culture of academic rigour and intellectual integrity. Furthermore, it prepares students with critical skills applicable beyond academia.

Enhancing Problem-Solving Abilities

Research methodology instills a problem-solving mindset by encouraging researchers to approach challenges systematically. It equips individuals with the skills to dissect complex issues, formulate hypotheses , and devise effective strategies for investigation.

Understanding Research Methodology

In the pursuit of knowledge and discovery, understanding the fundamentals of research methodology is paramount. 

Basics Of Research

Research, in its essence, is a systematic and organized process of inquiry aimed at expanding our understanding of a particular subject or phenomenon. It involves the exploration of existing knowledge, the formulation of hypotheses, and the collection and analysis of data to draw meaningful conclusions. 

Research is a dynamic and iterative process that contributes to the continuous evolution of knowledge in various disciplines.

Types of Research

Research takes on various forms, each tailored to the nature of the inquiry. Broadly classified, research can be categorized into two main types:

• Quantitative Research: This type involves the collection and analysis of numerical data to identify patterns, relationships, and statistical significance. It is particularly useful for testing hypotheses and making predictions.
• Qualitative Research: Qualitative research focuses on understanding the depth and details of a phenomenon through non-numerical data. It often involves methods such as interviews, focus groups, and content analysis, providing rich insights into complex issues.

Components Of Research Methodology

To conduct effective research, one must go through the different components of research methodology. These components form the scaffolding that supports the entire research process, ensuring its coherence and validity.

Research Design

Research design serves as the blueprint for the entire research project. It outlines the overall structure and strategy for conducting the study. The three primary types of research design are:

• Exploratory Research: Aimed at gaining insights and familiarity with the topic, often used in the early stages of research.
• Descriptive Research: Involves portraying an accurate profile of a situation or phenomenon, answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.
• Explanatory Research: Seeks to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how.’

Data Collection Methods

Choosing the right data collection methods is crucial for obtaining reliable and relevant information. Common methods include:

• Surveys and Questionnaires: Employed to gather information from a large number of respondents through standardized questions.
• Interviews: In-depth conversations with participants, offering qualitative insights.
• Observation: Systematic watching and recording of behaviour, events, or processes in their natural setting.

Data Analysis Techniques

Once data is collected, analysis becomes imperative to derive meaningful conclusions. Different methodologies exist for quantitative and qualitative data:

• Quantitative Data Analysis: Involves statistical techniques such as descriptive statistics, inferential statistics, and regression analysis to interpret numerical data.
• Qualitative Data Analysis: Methods like content analysis, thematic analysis, and grounded theory are employed to extract patterns, themes, and meanings from non-numerical data.

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Choosing a Research Method

Selecting an appropriate research method is a critical decision in the research process. It determines the approach, tools, and techniques that will be used to answer the research questions. 

Quantitative Research Methods

Quantitative research involves the collection and analysis of numerical data, providing a structured and objective approach to understanding and explaining phenomena.

Experimental Research

Experimental research involves manipulating variables to observe the effect on another variable under controlled conditions. It aims to establish cause-and-effect relationships.

Key Characteristics:

• Controlled Environment: Experiments are conducted in a controlled setting to minimize external influences.
• Random Assignment: Participants are randomly assigned to different experimental conditions.
• Quantitative Data: Data collected is numerical, allowing for statistical analysis.

Applications: Commonly used in scientific studies and psychology to test hypotheses and identify causal relationships.

Survey Research

Survey research gathers information from a sample of individuals through standardized questionnaires or interviews. It aims to collect data on opinions, attitudes, and behaviours.

• Structured Instruments: Surveys use structured instruments, such as questionnaires, to collect data.
• Large Sample Size: Surveys often target a large and diverse group of participants.
• Quantitative Data Analysis: Responses are quantified for statistical analysis.

Applications: Widely employed in social sciences, marketing, and public opinion research to understand trends and preferences.

Descriptive Research

Descriptive research seeks to portray an accurate profile of a situation or phenomenon. It focuses on answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.

• Observation and Data Collection: This involves observing and documenting without manipulating variables.
• Objective Description: Aim to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: T his can include both types of data, depending on the research focus.

Applications: Useful in situations where researchers want to understand and describe a phenomenon without altering it, common in social sciences and education.

Qualitative Research Methods

Qualitative research emphasizes exploring and understanding the depth and complexity of phenomena through non-numerical data.

A case study is an in-depth exploration of a particular person, group, event, or situation. It involves detailed, context-rich analysis.

• Rich Data Collection: Uses various data sources, such as interviews, observations, and documents.
• Contextual Understanding: Aims to understand the context and unique characteristics of the case.
• Holistic Approach: Examines the case in its entirety.

Applications: Common in social sciences, psychology, and business to investigate complex and specific instances.

Ethnography

Ethnography involves immersing the researcher in the culture or community being studied to gain a deep understanding of their behaviours, beliefs, and practices.

• Participant Observation: Researchers actively participate in the community or setting.
• Holistic Perspective: Focuses on the interconnectedness of cultural elements.
• Qualitative Data: In-depth narratives and descriptions are central to ethnographic studies.

Applications: Widely used in anthropology, sociology, and cultural studies to explore and document cultural practices.

Grounded Theory

Grounded theory aims to develop theories grounded in the data itself. It involves systematic data collection and analysis to construct theories from the ground up.

• Constant Comparison: Data is continually compared and analyzed during the research process.
• Inductive Reasoning: Theories emerge from the data rather than being imposed on it.
• Iterative Process: The research design evolves as the study progresses.

Applications: Commonly applied in sociology, nursing, and management studies to generate theories from empirical data.

Research design is the structural framework that outlines the systematic process and plan for conducting a study. It serves as the blueprint, guiding researchers on how to collect, analyze, and interpret data.

Exploratory, Descriptive, And Explanatory Designs

Exploratory design.

Exploratory research design is employed when a researcher aims to explore a relatively unknown subject or gain insights into a complex phenomenon.

• Flexibility: Allows for flexibility in data collection and analysis.
• Open-Ended Questions: Uses open-ended questions to gather a broad range of information.
• Preliminary Nature: Often used in the initial stages of research to formulate hypotheses.

Applications: Valuable in the early stages of investigation, especially when the researcher seeks a deeper understanding of a subject before formalizing research questions.

Descriptive Design

Descriptive research design focuses on portraying an accurate profile of a situation, group, or phenomenon.

• Structured Data Collection: Involves systematic and structured data collection methods.
• Objective Presentation: Aims to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: Can incorporate both types of data, depending on the research objectives.

Applications: Widely used in social sciences, marketing, and educational research to provide detailed and objective descriptions.

Explanatory Design

Explanatory research design aims to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how’ behind observed relationships.

• Causal Relationships: Seeks to establish causal relationships between variables.
• Controlled Variables : Often involves controlling certain variables to isolate causal factors.
• Quantitative Analysis: Primarily relies on quantitative data analysis techniques.

Applications: Commonly employed in scientific studies and social sciences to delve into the underlying reasons behind observed patterns.

Cross-Sectional Vs. Longitudinal Designs

Cross-sectional design.

Cross-sectional designs collect data from participants at a single point in time.

• Snapshot View: Provides a snapshot of a population at a specific moment.
• Efficiency: More efficient in terms of time and resources.
• Limited Temporal Insights: Offers limited insights into changes over time.

Applications: Suitable for studying characteristics or behaviours that are stable or not expected to change rapidly.

Longitudinal Design

Longitudinal designs involve the collection of data from the same participants over an extended period.

• Temporal Sequence: Allows for the examination of changes over time.
• Causality Assessment: Facilitates the assessment of cause-and-effect relationships.
• Resource-Intensive: Requires more time and resources compared to cross-sectional designs.

Applications: Ideal for studying developmental processes, trends, or the impact of interventions over time.

Experimental Vs Non-experimental Designs

Experimental design.

Experimental designs involve manipulating variables under controlled conditions to observe the effect on another variable.

• Causality Inference: Enables the inference of cause-and-effect relationships.
• Quantitative Data: Primarily involves the collection and analysis of numerical data.

Applications: Commonly used in scientific studies, psychology, and medical research to establish causal relationships.

Non-Experimental Design

Non-experimental designs observe and describe phenomena without manipulating variables.

• Natural Settings: Data is often collected in natural settings without intervention.
• Descriptive or Correlational: Focuses on describing relationships or correlations between variables.
• Quantitative or Qualitative Data: This can involve either type of data, depending on the research approach.

Applications: Suitable for studying complex phenomena in real-world settings where manipulation may not be ethical or feasible.

Effective data collection is fundamental to the success of any research endeavour. 

Designing Effective Surveys

Objective Design:

• Clearly define the research objectives to guide the survey design.
• Craft questions that align with the study’s goals and avoid ambiguity.

Structured Format:

• Use a structured format with standardized questions for consistency.
• Include a mix of closed-ended and open-ended questions for detailed insights.

Pilot Testing:

• Conduct pilot tests to identify and rectify potential issues with survey design.
• Ensure clarity, relevance, and appropriateness of questions.

Sampling Strategy:

• Develop a robust sampling strategy to ensure a representative participant group.
• Consider random sampling or stratified sampling based on the research goals.

Conducting Interviews

Establishing Rapport:

• Build rapport with participants to create a comfortable and open environment.
• Clearly communicate the purpose of the interview and the value of participants’ input.

Open-Ended Questions:

• Frame open-ended questions to encourage detailed responses.
• Allow participants to express their thoughts and perspectives freely.

Active Listening:

• Practice active listening to understand areas and gather rich data.
• Avoid interrupting and maintain a non-judgmental stance during the interview.

Ethical Considerations:

• Obtain informed consent and assure participants of confidentiality.
• Be transparent about the study’s purpose and potential implications.

Observation

1. participant observation.

Immersive Participation:

• Actively immerse yourself in the setting or group being observed.
• Develop a deep understanding of behaviours, interactions, and context.

Field Notes:

• Maintain detailed and reflective field notes during observations.
• Document observed patterns, unexpected events, and participant reactions.

Ethical Awareness:

• Be conscious of ethical considerations, ensuring respect for participants.
• Balance the role of observer and participant to minimize bias.

2. Non-participant Observation

Objective Observation:

• Maintain a more detached and objective stance during non-participant observation.
• Focus on recording behaviours, events, and patterns without direct involvement.

Data Reliability:

• Enhance the reliability of data by reducing observer bias.
• Develop clear observation protocols and guidelines.

Contextual Understanding:

• Strive for a thorough understanding of the observed context.
• Consider combining non-participant observation with other methods for triangulation.

Archival Research

1. using existing data.

Identifying Relevant Archives:

• Locate and access archives relevant to the research topic.
• Collaborate with institutions or repositories holding valuable data.

Data Verification:

• Verify the accuracy and reliability of archived data.
• Cross-reference with other sources to ensure data integrity.

Ethical Use:

• Adhere to ethical guidelines when using existing data.
• Respect copyright and intellectual property rights.

2. Challenges and Considerations

Incomplete or Inaccurate Archives:

• Address the possibility of incomplete or inaccurate archival records.
• Acknowledge limitations and uncertainties in the data.

Temporal Bias:

• Recognize potential temporal biases in archived data.
• Consider the historical context and changes that may impact interpretation.

Access Limitations:

• Address potential limitations in accessing certain archives.
• Seek alternative sources or collaborate with institutions to overcome barriers.

Common Challenges in Research Methodology

Conducting research is a complex and dynamic process, often accompanied by a myriad of challenges. Addressing these challenges is crucial to ensure the reliability and validity of research findings.

Sampling Issues

Sampling bias:.

• The presence of sampling bias can lead to an unrepresentative sample, affecting the generalizability of findings.
• Employ random sampling methods and ensure the inclusion of diverse participants to reduce bias.

Sample Size Determination:

• Determining an appropriate sample size is a delicate balance. Too small a sample may lack statistical power, while an excessively large sample may strain resources.
• Conduct a power analysis to determine the optimal sample size based on the research objectives and expected effect size.

Data Quality And Validity

Measurement error:.

• Inaccuracies in measurement tools or data collection methods can introduce measurement errors, impacting the validity of results.
• Pilot test instruments, calibrate equipment, and use standardized measures to enhance the reliability of data.

Construct Validity:

• Ensuring that the chosen measures accurately capture the intended constructs is a persistent challenge.
• Use established measurement instruments and employ multiple measures to assess the same construct for triangulation.

Time And Resource Constraints

Timeline pressures:.

• Limited timeframes can compromise the depth and thoroughness of the research process.
• Develop a realistic timeline, prioritize tasks, and communicate expectations with stakeholders to manage time constraints effectively.

Resource Availability:

• Inadequate resources, whether financial or human, can impede the execution of research activities.
• Seek external funding, collaborate with other researchers, and explore alternative methods that require fewer resources.

Managing Bias in Research

Selection bias:.

• Selecting participants in a way that systematically skews the sample can introduce selection bias.
• Employ randomization techniques, use stratified sampling, and transparently report participant recruitment methods.

Confirmation Bias:

• Researchers may unintentionally favour information that confirms their preconceived beliefs or hypotheses.
• Adopt a systematic and open-minded approach, use blinded study designs, and engage in peer review to mitigate confirmation bias.

Tips On How To Write A Research Methodology

Conducting successful research relies not only on the application of sound methodologies but also on strategic planning and effective collaboration. Here are some tips to enhance the success of your research methodology:

Tip 1. Clear Research Objectives

Well-defined research objectives guide the entire research process. Clearly articulate the purpose of your study, outlining specific research questions or hypotheses.

Tip 2. Comprehensive Literature Review

A thorough literature review provides a foundation for understanding existing knowledge and identifying gaps. Invest time in reviewing relevant literature to inform your research design and methodology.

Tip 3. Detailed Research Plan

A detailed plan serves as a roadmap, ensuring all aspects of the research are systematically addressed. Develop a detailed research plan outlining timelines, milestones, and tasks.

Tip 4. Ethical Considerations

Ethical practices are fundamental to maintaining the integrity of research. Address ethical considerations early, obtain necessary approvals, and ensure participant rights are safeguarded.

Tip 5. Stay Updated On Methodologies

Research methodologies evolve, and staying updated is essential for employing the most effective techniques. Engage in continuous learning by attending workshops, conferences, and reading recent publications.

Tip 6. Adaptability In Methods

Unforeseen challenges may arise during research, necessitating adaptability in methods. Be flexible and willing to modify your approach when needed, ensuring the integrity of the study.

Tip 7. Iterative Approach

Research is often an iterative process, and refining methods based on ongoing findings enhance the study’s robustness. Regularly review and refine your research design and methods as the study progresses.

Frequently Asked Questions

What is the research methodology.

Research methodology is the systematic process of planning, executing, and evaluating scientific investigation. It encompasses the techniques, tools, and procedures used to collect, analyze, and interpret data, ensuring the reliability and validity of research findings.

What are the methodologies in research?

Research methodologies include qualitative and quantitative approaches. Qualitative methods involve in-depth exploration of non-numerical data, while quantitative methods use statistical analysis to examine numerical data. Mixed methods combine both approaches for a comprehensive understanding of research questions.

How to write research methodology?

To write a research methodology, clearly outline the study’s design, data collection, and analysis procedures. Specify research tools, participants, and sampling methods. Justify choices and discuss limitations. Ensure clarity, coherence, and alignment with research objectives for a robust methodology section.

How to write the methodology section of a research paper?

In the methodology section of a research paper, describe the study’s design, data collection, and analysis methods. Detail procedures, tools, participants, and sampling. Justify choices, address ethical considerations, and explain how the methodology aligns with research objectives, ensuring clarity and rigour.

What is mixed research methodology?

Mixed research methodology combines both qualitative and quantitative research approaches within a single study. This approach aims to enhance the details and depth of research findings by providing a more comprehensive understanding of the research problem or question.

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Research Methods | Definition, Types, Examples

Research methods are specific procedures for collecting and analysing 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 :

• Qualitative vs quantitative : Will your data take the form of words or numbers?
• Primary vs secondary : Will you collect original data yourself, or will you use data that have already been collected by someone else?
• Descriptive vs experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyse the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

Data are 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.

Qualitative vs quantitative data

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 .

You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.

Primary vs secondary data

Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information 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 synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs experimental data

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.

Prevent plagiarism, run a free check.

Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.

Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:

• From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
• Using non-probability sampling methods .

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.

Quantitative analysis methods

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 were collected either:

• During an experiment.
• Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

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.

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.

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.

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

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

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 analyse data (e.g. 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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How to write the methods section of a research paper

Affiliation.

• 1 Respiratory Care Services, San Francisco General Hospital, NH:GA-2, 1001 Potrero Avenue, San Francisco, CA 94110, USA. [email protected]
• PMID: 15447808

The methods section of a research paper provides the information by which a study's validity is judged. Therefore, it requires a clear and precise description of how an experiment was done, and the rationale for why specific experimental procedures were chosen. The methods section should describe what was done to answer the research question, describe how it was done, justify the experimental design, and explain how the results were analyzed. Scientific writing is direct and orderly. Therefore, the methods section structure should: describe the materials used in the study, explain how the materials were prepared for the study, describe the research protocol, explain how measurements were made and what calculations were performed, and state which statistical tests were done to analyze the data. Once all elements of the methods section are written, subsequent drafts should focus on how to present those elements as clearly and logically as possibly. The description of preparations, measurements, and the protocol 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. Material in each section should be organized by topic from most to least important.

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Advanced Research Methods

Writing the research paper.

• What Is Research?
• Library Research
• Writing a Research Proposal

Before Writing the Paper

Methods, thesis and hypothesis, clarity, precision and academic expression, format your paper, typical problems, a few suggestions, avoid plagiarism.

• Presenting the Research Paper

Find a topic.

• Try to find a subject that really interests you.
• While you explore the topic, narrow or broaden your target and focus on something that gives the most promising results.
• Don't choose a huge subject if you have to write a 3 page long paper, and broaden your topic sufficiently if you have to submit at least 25 pages.
• Consult your class instructor (and your classmates) about the topic.

Explore the topic.

• Find primary and secondary sources in the library.
• Read and critically analyse them.
• Take notes.
• Compile surveys, collect data, gather materials for quantitative analysis (if these are good methods to investigate the topic more deeply).
• Come up with new ideas about the topic. Try to formulate your ideas in a few sentences.
• Review your notes and other materials and enrich the outline.
• Try to estimate how long the individual parts will be.
• Do others understand what you want to say?
• Do they accept it as new knowledge or relevant and important for a paper?
• Do they agree that your thoughts will result in a successful paper?
• Qualitative: gives answers on questions (how, why, when, who, what, etc.) by investigating an issue
• Quantitative:requires data and the analysis of data as well
• the essence, the point of the research paper in one or two sentences.
• a statement that can be proved or disproved.
• Be specific.
• Avoid ambiguity.
• Use predominantly the active voice, not the passive.
• Deal with one issue in one paragraph.
• Be accurate.
• Double-check your data, references, citations and statements.

Academic Expression

• Don't use familiar style or colloquial/slang expressions.
• Write in full sentences.
• Check the meaning of the words if you don't know exactly what they mean.
• Avoid metaphors.
• Almost the rough content of every paragraph.
• The order of the various topics in your paper.
• On the basis of the outline, start writing a part by planning the content, and then write it down.
• Put a visible mark (which you will later delete) where you need to quote a source, and write in the citation when you finish writing that part or a bigger part.
• Does the text make sense?
• Could you explain what you wanted?
• Did you write good sentences?
• Is there something missing?
• Check the spelling.
• Complete the citations, bring them in standard format.

Use the guidelines that your instructor requires (MLA, Chicago, APA, Turabian, etc.).

• Adjust margins, spacing, paragraph indentation, place of page numbers, etc.
• Standardize the bibliography or footnotes according to the guidelines.

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(Based on English Composition 2 from Illinois Valley Community College):

• Weak organization
• Poor support and development of ideas
• Weak use of secondary sources
• Excessive errors
• Stylistic weakness

When collecting materials, selecting research topic, and writing the paper:

• Be systematic and organized (e.g. keep your bibliography neat and organized; write your notes in a neat way, so that you can find them later on.
• Use your critical thinking ability when you read.
• Write down your thoughts (so that you can reconstruct them later).
• Stop when you have a really good idea and think about whether you could enlarge it to a whole research paper. If yes, take much longer notes.
• When you write down a quotation or summarize somebody else's thoughts in your notes or in the paper, cite the source (i.e. write down the author, title, publication place, year, page number).
• If you quote or summarize a thought from the internet, cite the internet source.
• Write an outline that is detailed enough to remind you about the content.
• Read your paper for yourself or, preferably, somebody else. 
• When you finish writing, check the spelling;
• Use the citation form (MLA, Chicago, or other) that your instructor requires and use it everywhere.

Plagiarism : somebody else's words or ideas presented without citation by an author

• Cite your source every time when you quote a part of somebody's work.
• Cite your source  every time when you summarize a thought from somebody's work.
• Cite your source  every time when you use a source (quote or summarize) from the Internet.

Consult the Citing Sources research guide for further details.

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How to Write a Methods Section for a Psychology Paper

Tips and Examples of an APA Methods Section

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Verywell / Brianna Gilmartin 

The methods section of an APA format psychology paper provides the methods and procedures used in a research study or experiment . This part of an APA paper is critical because it allows other researchers to see exactly how you conducted your research.

Method refers to the procedure that was used in a research study. It included a precise description of how the experiments were performed and why particular procedures were selected. While the APA technically refers to this section as the 'method section,' it is also often known as a 'methods section.'

The methods section ensures the experiment's reproducibility and the assessment of alternative methods that might produce different results. It also allows researchers to replicate the experiment and judge the study's validity.

This article discusses how to write a methods section for a psychology paper, including important elements to include and tips that can help.

What to Include in a Method Section

So what exactly do you need to include when writing your method section? You should provide detailed information on the following:

• Research design
• Participants
• Participant behavior

The method section should provide enough information to allow other researchers to replicate your experiment or study.

Components of a Method Section

The method section should utilize subheadings to divide up different subsections. These subsections typically include participants, materials, design, and procedure.

Participants 

In this part of the method section, you should describe the participants in your experiment, including who they were (and any unique features that set them apart from the general population), how many there were, and how they were selected. If you utilized random selection to choose your participants, it should be noted here.

For example: "We randomly selected 100 children from elementary schools near the University of Arizona."

At the very minimum, this part of your method section must convey:

• Basic demographic characteristics of your participants (such as sex, age, ethnicity, or religion)
• The population from which your participants were drawn
• Any restrictions on your pool of participants
• How many participants were assigned to each condition and how they were assigned to each group (i.e., randomly assignment , another selection method, etc.)
• Why participants took part in your research (i.e., the study was advertised at a college or hospital, they received some type of incentive, etc.)

Information about participants helps other researchers understand how your study was performed, how generalizable the result might be, and allows other researchers to replicate the experiment with other populations to see if they might obtain the same results.

In this part of the method section, you should describe the materials, measures, equipment, or stimuli used in the experiment. This may include:

• Testing instruments
• Technical equipment
• Any psychological assessments that were used
• Any special equipment that was used

For example: "Two stories from Sullivan et al.'s (1994) second-order false belief attribution tasks were used to assess children's understanding of second-order beliefs."

For standard equipment such as computers, televisions, and videos, you can simply name the device and not provide further explanation.

Specialized equipment should be given greater detail, especially if it is complex or created for a niche purpose. In some instances, such as if you created a special material or apparatus for your study, you might need to include an illustration of the item in the appendix of your paper.

In this part of your method section, describe the type of design used in the experiment. Specify the variables as well as the levels of these variables. Identify:

• The independent variables
• Dependent variables
• Control variables
• Any extraneous variables that might influence your results.

Also, explain whether your experiment uses a  within-groups  or between-groups design.

For example: "The experiment used a 3x2 between-subjects design. The independent variables were age and understanding of second-order beliefs."

The next part of your method section should detail the procedures used in your experiment. Your procedures should explain:

• What the participants did
• How data was collected
• The order in which steps occurred

For example: "An examiner interviewed children individually at their school in one session that lasted 20 minutes on average. The examiner explained to each child that he or she would be told two short stories and that some questions would be asked after each story. All sessions were videotaped so the data could later be coded."

Keep this subsection concise yet detailed. Explain what you did and how you did it, but do not overwhelm your readers with too much information.

Tips for How to Write a Methods Section

In addition to following the basic structure of an APA method section, there are also certain things you should remember when writing this section of your paper. Consider the following tips when writing this section:

• Use the past tense : Always write the method section in the past tense.
• Be descriptive : Provide enough detail that another researcher could replicate your experiment, but focus on brevity. Avoid unnecessary detail that is not relevant to the outcome of the experiment.
• Use an academic tone : Use formal language and avoid slang or colloquial expressions. Word choice is also important. Refer to the people in your experiment or study as "participants" rather than "subjects."
• Use APA format : Keep a style guide on hand as you write your method section. The Publication Manual of the American Psychological Association is the official source for APA style.
• Make connections : Read through each section of your paper for agreement with other sections. If you mention procedures in the method section, these elements should be discussed in the results and discussion sections.
• Proofread : Check your paper for grammar, spelling, and punctuation errors.. typos, grammar problems, and spelling errors. Although a spell checker is a handy tool, there are some errors only you can catch.

After writing a draft of your method section, be sure to get a second opinion. You can often become too close to your work to see errors or lack of clarity. Take a rough draft of your method section to your university's writing lab for additional assistance.

A Word From Verywell

The method section is one of the most important components of your APA format paper. The goal of your paper should be to clearly detail what you did in your experiment. Provide enough detail that another researcher could replicate your study if they wanted.

Finally, if you are writing your paper for a class or for a specific publication, be sure to keep in mind any specific instructions provided by your instructor or by the journal editor. Your instructor may have certain requirements that you need to follow while writing your method section.

Frequently Asked Questions

While the subsections can vary, the three components that should be included are sections on the participants, the materials, and the procedures.

• Describe who the participants were in the study and how they were selected.
• Define and describe the materials that were used including any equipment, tests, or assessments
• Describe how the data was collected

To write your methods section in APA format, describe your participants, materials, study design, and procedures. Keep this section succinct, and always write in the past tense. The main heading of this section should be labeled "Method" and it should be centered, bolded, and capitalized. Each subheading within this section should be bolded, left-aligned and in title case.

The purpose of the methods section is to describe what you did in your experiment. It should be brief, but include enough detail that someone could replicate your experiment based on this information. Your methods section should detail what you did to answer your research question. Describe how the study was conducted, the study design that was used and why it was chosen, and how you collected the data and analyzed the results.

Erdemir F. How to write a materials and methods section of a scientific article ? Turk J Urol . 2013;39(Suppl 1):10-5. doi:10.5152/tud.2013.047

Kallet RH. How to write the methods section of a research paper . Respir Care . 2004;49(10):1229-32. PMID: 15447808.

American Psychological Association.  Publication Manual of the American Psychological Association  (7th ed.). Washington DC: The American Psychological Association; 2019.

American Psychological Association. APA Style Journal Article Reporting Standards . Published 2020.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

• Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
• Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
• Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
• Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
• Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
• Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

• Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
• Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
• Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
• Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
• Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
• Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

• Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
• Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
• Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
• Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
• Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
• Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

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Researcher, Academic Writer, Web developer

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How to Write a Methods Section for a Research Paper

A common piece of advice for authors preparing their first journal article for publication is to start with the methods section: just list everything that was done and go from there. While that might seem like a very practical approach to a first draft, if you do this without a clear outline and a story in mind, you can easily end up with journal manuscript sections that are not logically related to each other. 

Since the methods section constitutes the core of your paper, no matter when you write it, you need to use it to guide the reader carefully through your story from beginning to end without leaving questions unanswered. Missing or confusing details in this section will likely lead to early rejection of your manuscript or unnecessary back-and-forth with the reviewers until eventual publication. Here, you will find some useful tips on how to make your methods section the logical foundation of your research paper.

Not just a list of experiments and methods

While your introduction section provides the reader with the necessary background to understand your rationale and research question (and, depending on journal format and your personal preference, might already summarize the results), the methods section explains what exactly you did and how you did it. The point of this section is not to list all the boring details just for the sake of completeness. The purpose of the methods sections is to enable the reader to replicate exactly what you did, verify or corroborate your results, or maybe find that there are factors you did not consider or that are more relevant than expected. 

To make this section as easy to read as possible, you must clearly connect it to the information you provide in the introduction section before and the results section after, it needs to have a clear structure (chronologically or according to topics), and you need to present your results according to the same structure or topics later in the manuscript. There are also official guidelines and journal instructions to follow and ethical issues to avoid to ensure that your manuscript can quickly reach the publication stage.

Table of Contents:

• General Methods Structure: What is Your Story? 
• What Methods Should You Report (and Leave Out)? 
• Details Frequently Missing from the Methods Section

More Journal Guidelines to Consider 

• Accurate and Appropriate Language in the Methods

General Methods Section Structure: What Is Your Story? 

You might have conducted a number of experiments, maybe also a pilot before the main study to determine some specific factors or a follow-up experiment to clarify unclear details later in the process. Throwing all of these into your methods section, however, might not help the reader understand how everything is connected and how useful and appropriate your methodological approach is to investigate your specific research question. You therefore need to first come up with a clear outline and decide what to report and how to present that to the reader.

The first (and very important) decision to make is whether you present your experiments chronologically (e.g., Experiment 1, Experiment 2, Experiment 3… ), and guide the reader through every step of the process, or if you organize everything according to subtopics (e.g., Behavioral measures, Structural imaging markers, Functional imaging markers… ). In both cases, you need to use clear subheaders for the different subsections of your methods, and, very importantly, follow the same structure or focus on the same topics/measures in the results section so that the reader can easily follow along (see the two examples below).

If you are in doubt which way of organizing your experiments is better for your study, just ask yourself the following questions:

• Does the reader need to know the timeline of your study? 
• Is it relevant that one experiment was conducted first, because the outcome of this experiment determined the stimuli or factors that went into the next?
• Did the results of your first experiment leave important questions open that you addressed in an additional experiment (that was maybe not planned initially)?
• Is the answer to all of these questions “no”? Then organizing your methods section according to topics of interest might be the more logical choice.

If you think your timeline, protocol, or setup might be confusing or difficult for the reader to grasp, consider adding a graphic, flow diagram, decision tree, or table as a visual aid.

What Methods Should You Report (and Leave Out)?

The answer to this question is quite simple–you need to report everything that another researcher needs to know to be able to replicate your study. Just imagine yourself reading your methods section in the future and trying to set up the same experiments again without prior knowledge. You would probably need to ask questions such as:

• Where did you conduct your experiments (e.g., in what kind of room, under what lighting or temperature conditions, if those are relevant)? 
• What devices did you use? Are there specific settings to report?
• What specific software (and version of that software) did you use?
• How did you find and select your participants?
• How did you assign participants into groups?  
• Did you exclude participants from the analysis? Why and how?
• Where did your reagents or antibodies come from? Can you provide a Research Resource Identifier (RRID) ?
• Did you make your stimuli yourself or did you get them from somewhere?
• Are the stimuli you used available for other researchers?
• What kind of questionnaires did you use? Have they been validated?
• How did you analyze your data? What level of significance did you use?
• Were there any technical issues and did you have to adjust protocols?

Note that for every experimental detail you provide, you need to tell the reader (briefly) why you used this type of stimulus/this group of participants/these specific amounts of reagents. If there is earlier published research reporting the same methods, cite those studies. If you did pilot experiments to determine those details, describe the procedures and the outcomes of these experiments. If you made assumptions about the suitability of something based on the literature and common practice at your institution, then explain that to the reader.

In a nutshell, established methods need to be cited, and new methods need to be clearly described and briefly justified. However, if the fact that you use a new approach or a method that is not traditionally used for the data or phenomenon you study is one of the main points of your study (and maybe already reflected in the title of your article), then you need to explain your rationale for doing so in the introduction already and discuss it in more detail in the discussion section .

Note that you also need to explain your statistical analyses at the end of your methods section. You present the results of these analyses later, in the results section of your paper, but you need to show the reader in the methods section already that your approach is either well-established or valid, even if it is new or unusual. 

When it comes to the question of what details you should leave out, the answer is equally simple ‒ everything that you would not need to replicate your study in the future. If the educational background of your participants is listed in your institutional database but is not relevant to your study outcome, then don’t include that. Other things you should not include in the methods section:

• Background information that you already presented in the introduction section.
• In-depth comparisons of different methods ‒ these belong in the discussion section.
• Results, unless you summarize outcomes of pilot experiments that helped you determine factors for your main experiment.

Also, make sure your subheadings are as clear as possible, suit the structure you chose for your methods section, and are in line with the target journal guidelines. If you studied a disease intervention in human participants, then your methods section could look similar to this:

Since the main point of interest here are your patient-centered outcome variables, you would center your results section on these as well and choose your headers accordingly (e.g., Patient characteristics, Baseline evaluation, Outcome variable 1, Outcome variable 2, Drop-out rate ). 

If, instead, you did a series of visual experiments investigating the perception of faces including a pilot experiment to create the stimuli for your actual study, you would need to structure your methods section in a very different way, maybe like this:

Since here the analysis and outcome of the pilot experiment are already described in the methods section (as the basis for the main experimental setup and procedure), you do not have to mention it again in the results section. Instead, you could choose the two main experiments to structure your results section ( Discrimination and classification, Familiarization and adaptation ), or divide the results into all your test measures and/or potential interactions you described in the methods section (e.g., Discrimination performance, Classification performance, Adaptation aftereffects, Correlation analysis ).

Details Commonly Missing from the Methods Section

Manufacturer information.

For laboratory or technical equipment, you need to provide the model, name of the manufacturer, and company’s location. The usual format for these details is the product name (company name, city, state) for US-based manufacturers and the product name (company name, city/town, country) for companies outside the US.

Sample size and power estimation

Power and sample size estimations are measures for how many patients or participants are needed in a study in order to detect statistical significance and draw meaningful conclusions from the results. Outside of the medical field, studies are sometimes still conducted with a “the more the better” approach in mind, but since many journals now ask for those details, it is better to not skip this important step.

Ethical guidelines and approval

In addition to describing what you did, you also need to assure the editor and reviewers that your methods and protocols followed all relevant ethical standards and guidelines. This includes applying for approval at your local or national ethics committee, providing the name or location of that committee as well as the approval reference number you received, and, if you studied human participants, a statement that participants were informed about all relevant experimental details in advance and signed consent forms before the start of the study. For animal studies, you usually need to provide a statement that all procedures included in your research were in line with the Declaration of Helsinki. Make sure you check the target journal guidelines carefully, as these statements sometimes need to be placed at the end of the main article text rather than in the method section.

Structure & word limitations

While many journals simply follow the usual style guidelines (e.g., APA for the social sciences and psychology, AMA for medical research) and let you choose the headers of your method section according to your preferred structure and focus, some have precise guidelines and strict limitations, for example, on manuscript length and the maximum number of subsections or header levels. Make sure you read the instructions of your target journal carefully and restructure your method section if necessary before submission. If the journal does not give you enough space to include all the details that you deem necessary, then you can usually submit additional details as “supplemental” files and refer to those in the main text where necessary.

Standardized checklists

In addition to ethical guidelines and approval, journals also often ask you to submit one of the official standardized checklists for different study types to ensure all essential details are included in your manuscript. For example, there are checklists for randomized clinical trials, CONSORT (Consolidated Standards of Reporting Trials) , cohort, case-control, cross‐sectional studies, STROBE (STrengthening the Reporting of OBservational studies in Epidemiology ), diagnostic accuracy, STARD (STAndards for the Reporting of Diagnostic accuracy studies) , systematic reviews and meta‐analyses PRISMA (Preferred Reporting Items for Systematic reviews and Meta‐Analyses) , and Case reports, CARE (CAse REport) .

Make sure you check if the manuscript uses a single- or double-blind review procedure , and delete all information that might allow a reviewer to guess where the authors are located from the manuscript text if necessary. This means that your method section cannot list the name and location of your institution, the names of researchers who conducted specific tests, or the name of your institutional ethics committee.  

Accurate and Appropriate Language in the Methods Section

Like all sections of your research paper, your method section needs to be written in an academic tone . That means it should be formal, vague expressions and colloquial language need to be avoided, and you need to correctly cite all your sources. If you describe human participants in your method section then you should be especially careful about your choice of words. For example, “participants” sounds more respectful than “subjects,” and patient-first language, that is, “patients with cancer,” is considered more appropriate than “cancer patients” by many journals.

Passive voice is often considered the standard for research papers, but it is completely fine to mix passive and active voice, even in the method section, to make your text as clear and concise as possible. Use the simple past tense to describe what you did, and the present tense when you refer to diagrams or tables. Have a look at this article if you need more general input on which verb tenses to use in a research paper . 

Lastly, make sure you label all the standard tests and questionnaires you use correctly (look up the original publication when in doubt) and spell genes and proteins according to the common databases for the species you studied, such as the HUGO Gene Nomenclature Committee database for human studies .  

Visit Wordvice AI’s AI Text Editor to receive a free grammar check and English editing services (including manuscript editing , paper editing , and dissertation editing ) before submitting your manuscript to journal editors.

Organizing Academic Research Papers: 6. The Methodology

• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• How to Manage Group Projects
• Multiple Book Review Essay
• Reviewing Collected Essays
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Acknowledgements

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.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you choose affects the results and, by extension, how you likely interpreted those results.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and it misappropriates interpretations of findings .
• In most cases, there are a variety of different methods you can choose to investigate a research problem. Your methodology section of your paper should make clear the reasons why you chose a particular method or procedure .
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The research method must be appropriate to the objectives of the study . For example, be sure you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring . For any problems that did arise, you must describe the ways in which their impact was minimized or why these problems do not affect the findings in any way that impacts your interpretation of the data.
• Often in social science research, it is useful for other researchers to adapt or replicate your methodology. Therefore, it is important to always provide sufficient information to allow others to use or replicate the study . This information is particularly important when a new method had been developed or an innovative use of an existing method has been utilized.

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.

Structure and Writing Style

I. Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The empirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences. This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for hypotheses that need to be tested. This approach is focused on explanation .
• The interpretative group is focused on understanding phenomenon in a comprehensive, holistic way . This research method allows you to recognize your connection to the subject under study. Because the interpretative group focuses more on subjective knowledge, it requires careful interpretation of variables.

II. Content

An effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods should have a clear connection with your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is unsuited to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors?
• Provide background and rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a rationale for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of statisics being used? If other data sources exist, explain why the data you chose is most appropriate.
• Address potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

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.

Writing Tip

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.

Another Writing Tip

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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How to Write the Methods Section of a Research Paper

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The methods section of a research paper provides the information by which a study's validity is judged. Therefore, it requires a clear and precise description of how an experiment was done, and the rationale for why specific experimental procedures were chosen. The methods section should describe what was done to answer the research question, describe how it was done, justify the experimental design, and explain how the results were analyzed. Scientific writing is direct and orderly. Therefore, the methods section structure should: describe the materials used in the study, explain how the materials were prepared for the study, describe the research protocol, explain how measurements were made and what calculations were performed, and state which statistical tests were done to analyze the data. Once all elements of the methods section are written, subsequent drafts should focus on how to present those elements as clearly and logically as possibly. The description of preparations, measurements, and the protocol 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. Material in each section should be organized by topic from most to least important.

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The Role of Fintech in Promoting Financial Inclusion to Achieve Sustainable Development: An Integrated Bibliometric Analysis and Systematic Literature Review

• Published: 03 July 2024

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• Kriti Kishor   ORCID: orcid.org/0009-0006-2808-1633 1 ,
• Sanjeev K. Bansal   ORCID: orcid.org/0009-0009-8585-0429 1 &
• Roshan Kumar 2  

Fintech’s ability to enhance efficiency and reduce costs in financial services can promote greater financial inclusion (FI), which in turn serves as a foundation for sustainable and equitable development. Due to the dearth of thorough summaries in the body of existing literature, this systematic review and bibliometric analysis aim to present quantitative and qualitative information about the comprehensive relationship between fintech, FI, and sustainability development in an organised way. The review includes 189 publications from peer-reviewed journals of Scopus and Web of Science (WoS) databases up to 2023. The article was compiled based on the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR‐4‐SLR) protocol and the theory-context-characteristics-methodology (TCCM) framework. Bibliometric analysis has identified the leading journals, authors, nations, articles, and themes. A conceptual model has been designed to illustrate the entire scope, following which potential study areas have been proposed. This study aims to provide academic researchers, policymakers, and regulators with a detailed understanding of the relationship between fintech, financial inclusion, and sustainable development. The analysis demonstrates that FI is an essential requirement of our society and a vital pathway to achieve sustainable development. In the content analysis, we identify an integrative framework of four variables on this nexus. We found a very few conceptual, qualitative, and mixed method papers on this interaction, which provide potential avenues for further research. We recommend that scholars consider adopting a multi-theory perspective. We propose a comprehensive framework on this nexus. It will also pinpoint specific areas that require further investigation.

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• Published: 05 January 2024

Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing

• Yusong Wang 1 , 2   na1 ,
• Tong Wang   ORCID: orcid.org/0000-0002-9483-0050 1   na1 ,
• Shaoning Li 1   na1 ,
• Xinheng He 1 , 3 , 4 ,
• Mingyu Li 1 , 5 ,
• Zun Wang   ORCID: orcid.org/0000-0002-8763-8327 1 ,
• Nanning Zheng 2 ,
• Bin Shao   ORCID: orcid.org/0000-0002-9790-5687 1 &
• Tie-Yan Liu   ORCID: orcid.org/0000-0002-0476-8020 1  

Nature Communications volume  15 , Article number:  313 ( 2024 ) Cite this article

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• Chemical biology
• Computational biology and bioinformatics
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• Molecular modelling
• Protein structure predictions

Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular property prediction, their applications, such as drug discovery and molecular dynamics (MD) simulation, have been hindered by insufficient utilization of geometric information and high computational costs. Here we propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures with low computational costs. Our proposed ViSNet outperforms state-of-the-art approaches on multiple MD benchmarks, including MD17, revised MD17 and MD22, and achieves excellent chemical property prediction on QM9 and Molecule3D datasets. Furthermore, through a series of simulations and case studies, ViSNet can efficiently explore the conformational space and provide reasonable interpretability to map geometric representations to molecular structures.

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Introduction.

Molecular modeling plays a crucial role in modern scientific and engineering fields, aiding in the understanding of chemical reactions, facilitating new drug development, and driving scientific and technological advancements 1 , 2 , 3 , 4 . One commonly used method in molecular modeling is density functional theory (DFT). DFT enables accurate calculations of energy, forces, and other chemical properties of molecules 5 , 6 . However, due to the large computational requirements, DFT calculations often demand significant computational resources and time, particularly for large molecular systems or high-precision calculations. Machine learning (ML) offers an alternative solution by learning from reference data with ab initio accuracy and high computational efficiency 7 , 8 . Gradient-domain machine learning (GDML) 9 constructs accurate molecular force fields using conservation of energy and limited samples from ab initio molecular dynamics trajectories, enabling cost-effective simulations while maintaining accuracy. Symmetric GDML (sGDML) 10 further improves force field construction by incorporating physical symmetries, achieving CCSD(T)-level accuracy for flexible molecules. An exact iterative approach (Global sGDML) 11 extends sGDML to global force fields for molecules with several hundred atoms, maintaining correlations of atomic degree and accurately describing complex molecules and materials. In recent years, deep learning (DL) has demonstrated its powerful ability to learn from raw data without any hand-crafted features in many fields and thus attracted more and more attention. However, the inherent drawback of deep learning, which requires large amounts of data, has become a bottleneck for its application to more scenarios 12 . To alleviate the dependency on data for DL potentials, recent works have incorporated the inductive bias of symmetry into neural network design, known as geometric deep learning (GDL). Symmetry describes the conservation of physical laws, i.e., the unchanged physical properties with any transformations such as translations or rotations. It allows GDL to be extended to limited data scenarios without any data augmentation.

Equivariant graph neural network (EGNN) is one of the representative approaches in GDL, which has extensive capability to model molecular geometry 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 . A popular kind of EGNN conducts equivariance from directional information and involves geometric features to predict molecular properties. GemNet 20 extends the invariant DimeNet/DimeNet++ 16 , 17 with dihedral information. They explicitly extract geometric information in the Euclidean space with first-order geometric tensor, i.e., setting l max  = 1. PaiNN 18 and equivariant transformer 19 further adopt vector embedding and scalarize the angular representation implicitly via the inner product of the vector embedding itself. They reduce the complexity of explicit geometry extraction by taking the angular information into consideration. Another mainstream approach to achieving equivariance is through group representation theory, which can achieve higher accuracy but comes with large computational costs. NequIP, Allegro, and MACE 12 , 22 , 23 achieve state-of-the-art performance on several molecular dynamics simulation datasets leveraging high-order geometric tensors. On the one hand, algorithms based on group representation theory have strong mathematical foundations and are able to fully utilize geometric information using high-order geometric tensors. On the other hand, these algorithms often require computationally expensive operations such as the Clebsch–Gordan product (CG-product) 24 , making them possibly suitable for periodic systems with elaborate model design but impractical for large molecular systems such as chemical and biological molecules without periodic boundary conditions.

In this study, we propose ViSNet (short for “Vector-Scalar interactive graph neural Network"), which alleviates the dilemma between computational costs and sufficient utilization of geometric information. By incorporating an elaborate runtime geometry calculation (RGC) strategy, ViSNet implicitly extracts various geometric features, i.e., angles, dihedral torsion angles, and improper angles in accordance with the force field of classical MD with linear time complexity, thus significantly accelerating model training and inference while reducing the memory consumption. To extend the vector representation, we introduce spherical harmonics and simplify the computationally expensive Clebsch–Gordan product with the inner product. Furthermore, we present a well-designed vector–scalar interactive equivariant message passing (ViS-MP) mechanism, which fully utilizes the geometric features by interacting vector hidden representations with scalar ones. When comprehensively evaluated on some benchmark datasets, ViSNet outperforms all state-of-the-art algorithms on all molecules in MD17, revised MD17 and MD22 datasets and shows superior performance on QM9, Molecule3D dataset indicating the powerful capability of molecular geometric representation. ViSNet also has won the PCQM4Mv2 track in the OGB-LCS@NeurIPS2022 competition ( https://ogb.stanford.edu/neurips2022/results/ ). We then performed molecular dynamics simulations for each molecule on MD17 driven by ViSNet trained only with limited data (950 samples). The highly consistent interatomic distance distributions and the explored potential energy surfaces between ViSNet and quantum simulation illustrate that ViSNet is genuinely data-efficient and can perform simulations with high fidelity. To further explore the usefulness of ViSNet to real-world applications, we used an in-house dataset that consists of about 10,000 different conformations of the 166-atom mini-protein Chignolin derived from replica exchange molecular dynamics and calculated at the DFT level. When evaluated on the dataset, ViSNet also achieved significantly better performance than empirical force fields, and the simulations performed by ViSNet exhibited very close force calculation to DFT. In addition, ViSNet exhibits reasonable interpretability to map geometric representation to molecular structures. The contributions of ViSNet can be summarized as follows:

Proposing an RGC module that utilizes high-order geometric tensors to implicitly extract various geometric features, including angles, dihedral torsion angles, and improper angles, with linear time complexity.

Introducing ViS-MP mechanism to enable efficient interaction between vector hidden representations and scalar ones and fully exploit the geometric information.

Achieving state-of-the-art performance in six benchmarks for predicting energy, forces, HOMO-LUMO gap, and other quantum properties of molecules.

Performing molecular dynamics simulations driven by ViSNet on both small molecules and 166-atom Chignolin with high fidelity.

Demonstrating reasonable model interpretability between geometric features and molecular structures.

Overview of ViSNet

ViSNet is a versatile EGNN that predicts potential energy, atomic forces as well as various quantum chemical properties by taking atomic coordinates and numbers as inputs. As shown in Fig.  1 a, the model is composed of an embedding block and multiple stacked ViSNet blocks, followed by an output block. The atomic number and coordinates are fed into the embedding block followed by ViSNet blocks to extract and encode geometric representations. The geometric representations are then used to predict molecular properties through the output block. It is worth noting that ViSNet is an energy-conserving potential, i.e., the predicted atomic forces are derived from the negative gradients of the potential energy with respect to the coordinates 9 , 10 .

a Model sketch of ViSNet. ViSNet embeds the 3D structures of molecules and extracts the geometric information through a series of ViSNet blocks and outputs the molecule properties such as energy, forces, and HOMO–LUMO gap through an output block. b Flowchart of one ViSNet Block. One ViSNet block consists of two modules: (i) Scalar2Vec , responsible for attaching scalar embeddings to vectors.; (ii) Vec2Scalar , renovates scalar embeddings built on RGC strategy. The inputs of Scalar2Vec are the node embedding h i , edge embedding f i j , direction unit \({\overrightarrow{v}}_{i}\) and the relative positions between two atoms. The edge-fusion graph attention module (serves as \({\phi }_{{\rm {m}}}^{{\rm {s}}}\) ) takes as input h i and the output of the dense layer following f i j , and outputs scalar messages. Before aggregation, each scalar message is transformed through a dense layer, and then fused with the unit of the relative position \({\overrightarrow{u}}_{ij}\) and its own direction unit \({\overrightarrow{v}}_{j}\) . We further compute the vector messages and aggregate them all among the neighborhood. Through a gated residual connection, the final residual \({{\Delta }}{\overrightarrow{v}}_{i}\) is produced. In Vec2Scalar module, by Hadamard production of aggregated scalar messages and the output of RGC-Angle calculation and adding a gated residual connection, the final Δ h i is figured out. Likewise, combining the projected f i j and the output of RGC-Dihedral calculation, the final Δ f i j is determined.

The success of classical force fields shows that geometric features such as interatomic distances, angles, dihedral torsion angles, and improper angles in Fig.  2 are essential to determine the total potential energy of molecules. The explicit extraction of invariant geometric representations in previous studies often suffers from a large amount of time or memory consumption during model training and inference. Given an atom, the calculation of angular information scales \({{{{{{{\mathcal{O}}}}}}}}({{{{{{{{\mathcal{N}}}}}}}}}^{2})\) with the number of neighboring atoms, while the computational complexity is even \({{{{{{{\mathcal{O}}}}}}}}({{{{{{{{\mathcal{N}}}}}}}}}^{3})\) for dihedrals 20 . To alleviate this problem, inspired by Sch¨utt et al. 18 , we propose runtime geometry calculation (RGC), which uses an equivariant vector representation (termed as direction unit) for each node to preserve its geometric information. RGC directly calculates the geometric information from the direction unit which only sums the vectors from the target node to its neighbors once. Therefore, the computational complexity can be reduced to \({{{{{{{\mathcal{O}}}}}}}}({{{{{{{\mathcal{N}}}}}}}})\) . Notably, beyond employing angular information that has been used in PaiNN 18 and ET 19 , ViSNet further considers the dihedral torsion and improper angle calculation with higher geometric tensors.

The bonded terms consist of bond length, bond angle, dihedral torsion, and improper angle. The RGC module depicts all bonded terms of classical MD as model operations in linear time complexity. Yellow arrow \({\overrightarrow{v}}_{i}\) denotes the direction unit in Eq. ( 1 ).

Considering the sub-structure of a toy molecule with four atoms shown in Fig.  2 , the angular information of the target node i could be conducted from the vector \({\overrightarrow{r}}_{ij}\) as follows:

where \({\overrightarrow{r}}_{ij}\) is the vector from node i to its neighboring node j , \({\overrightarrow{u}}_{ij}\) is the unit vector of \({\overrightarrow{r}}_{ij}\) . Here, we define the direction unit \({\overrightarrow{v}}_{i}\) as the sum of all unit vectors from node i to its all neighboring nodes j , where node i is the intersection of all unit vectors. As shown in Eq. ( 2 ), we calculate the inner product of the direction unit \({\overrightarrow{v}}_{i}\) which represents the sum of the inner products of unit vectors from node i to all its neighboring nodes. Combining with Eq. ( 1 ), the inner product of direction \({\overrightarrow{v}}_{i}\) finally stands for the sum of cosine values of all angles formed by node i and any two of its neighboring nodes.

Similar to runtime angle calculation, we also calculate the vector rejection 25 of the direction unit \({\overrightarrow{v}}_{i}\) of node i and \({\overrightarrow{v}}_{j}\) of node j on the vector \({\overrightarrow{u}}_{ij}\) and \({\overrightarrow{u}}_{ji}\) , respectively.

where \({{{{{{{{\rm{Rej}}}}}}}}}_{\overrightarrow{b}}(\overrightarrow{a})\) represents the vector component of \(\overrightarrow{a}\) perpendicular to \(\overrightarrow{b}\) , termed as the vector rejection. \({\overrightarrow{u}}_{ij}\) and \({\overrightarrow{v}}_{i}\) are defined in Eq. ( 1 ). \({\overrightarrow{w}}_{ij}\) represents the sum of the vector rejection \({{{{{{{{\rm{Rej}}}}}}}}}_{{\overrightarrow{u}}_{ij}}({\overrightarrow{u}}_{im})\) and \({\overrightarrow{w}}_{ji}\) represents the sum of the vector rejection \({{{{{{{{\rm{Rej}}}}}}}}}_{{\overrightarrow{u}}_{ji}}({\overrightarrow{u}}_{jn})\) . The inner product between \({\overrightarrow{w}}_{ij}\) and \({\overrightarrow{w}}_{ji}\) is then calculated to conduct dihedral torsion angle information of the intersecting edge e i j as follows:

The improper angle is derived from a pyramid structure forming by 4 nodes. As the last toy molecule shown in Fig.  2 , node i is the vertex of the pyramid, and the improper torsion angle is formed by two adjacent planes with an intersecting edge e i j . We can also calculate the improper angle by vector rejection:

In the same way, the inner product between \({\overrightarrow{t}}_{ij}\) and \({\overrightarrow{t}}_{ji}\) indicates the summation of improper angle information formed by e i j :

Multiple works have shown the effectiveness of high-order geometric tensors for molecular modeling 12 , 22 , 26 , 27 . However, the computational overheads of these approaches are generally expansive due to the CG-product, impeding their further application for large systems. In this work, we convert the vectors to high-order representation with spherical harmonics but discard CG-product with the inner product following the idea of RGC. We find that the extended high-order geometric tensors can still represent the above angular information in the form of Legendre polynomials according to the addition theorem:

where the P l is the Legendre polynomial of degree l , Y l , m denotes the spherical harmonics function and \({Y}_{l,m}^{*}\) denotes its complex conjugation. We sum the product of different order l to obtain the scalar angular representation, which is the same operation as the inner product. It is worth noting that such an extension does not increase the model size and keeps the model architecture unchanged. We also provide proof about the rotational invariance of the RGC strategy in the section “Proofs of the rotational invariance of RGC ”.

In order to make full use of geometric information and enhance the interaction between scalars and vectors, we designed an effective vector–scalar interactive message-passing mechanism with respect to the intersecting nodes and edges for angles and dihedrals, respectively. It is important to note that previous studies 18 , 19 primarily focused on updating node features, whereas our approach updates both node and edge features during message passing, leading to a more comprehensive geometric representation. The key operations in ViS-MP are given as follows:

where h i denotes the scalar embedding of node i , f i j stands for the edge feature between node i and node j . \({\overrightarrow{v}}_{i}\) represents the embedding of the direction unit mentioned in RGC. The superscript of variables indicates the index of the block that the variables belong to. We omit the improper angle here for brevity. A comprehensive version is depicted in Supplementary. ViS-MP extends the conventional message passing, aggregation, and update processes with vector–scalar interactions. Eqs. ( 8 ) and ( 9 ) depict our message-passing and aggregation processes. To be concrete, scalar messages m i j incorporating scalar embedding h j , h i , and f i j are passed and then aggregated to node i through a message function \({\phi }_{m}^{s}\) (Eq. ( 8 )). Similar operations are applied for vector messages \({\overrightarrow{m}}_{i}^{l}\) of node i that incorporates scalar message m i j , vector \({\overrightarrow{r}}_{ij}\) and vector embedding \({\overrightarrow{v}}_{j}\) (Eq. ( 9 )). Equations ( 10 ) and ( 11 ) demonstrate the update processes. h i is updated by the aggregated scalar message output m i while the inner product of \({\overrightarrow{v}}_{i}\) is updated through an update function \({\phi }_{un}^{s}\) . Then \({\overrightarrow{f}}_{ij}\) is updated by the inner product of the rejection of the vector embedding \({\overrightarrow{v}}_{i}\) and \({\overrightarrow{v}}_{j}\) through an update function \({\phi }_{ue}^{s}\) . Finally, the vector embedding \({\overrightarrow{v}}_{i}\) is updated by both scalar and vector messages through an update function \({\phi }_{un}^{v}\) . Notably, the vectors update function, i.e., ϕ v require to be equivariant. The detailed message and update functions can be found in the Methods section. A proof about the equivariance of ViS-MP can be found in Supplementary Methods.

In summary, the geometric features are extracted by inner products in the RGC strategy and the scalar and vector embeddings are cyclically updating each other in ViS-MP so as to learn a comprehensive geometric representation from molecular structures.

Accurate quantum chemical property predictions

We evaluated ViSNet on several prevailing benchmark datasets including MD17 9 , 10 , 28 , revised MD17 29 , MD22 30 , QM9 31 , Molecule3D 32 , and OGB-LSC PCQM4Mv2 33 for energy, force, and other molecular property prediction. MD17 consists of the MD trajectories of seven small organic molecules; the number of conformations in each molecule dataset ranges from 133,700 to 993,237. The dataset rMD17 is a reproduced version of MD17 with higher accuracy. MD22 is a recently proposed MD trajectories dataset that presents challenges with respect to larger system sizes (42–370 atoms). Large molecules such as proteins, lipids, carbohydrates, nucleic acids, and supramolecules are included in MD22. QM9 consists of 12 kinds of quantum chemical properties of 133,385 small organic molecules with up to 9 heavy atoms. Molecule3D is a recently proposed dataset including 3,899,647 molecules collected from PubChemQC with their ground-state structures and corresponding properties calculated by DFT. We focus on the prediction of the HOMO–LUMO gap following ComENet 34 . OGB-LSC PCQM4Mv2 is a quantum chemistry dataset originally curated under the PubChemQC including a DFT-calculated HOMO–LUMO gap of 3,746,619 molecules. The 3D conformations are provided for 3,378,606 training molecules but not for the validation and test sets. The training details of ViSNet on each benchmark are described in the “Methods” section.

We compared ViSNet with the state-of-the-art algorithms, including DimeNet 16 , PaiNN 18 , SpookyNet 21 , ET 19 , GemNet 20 , UNiTE 35 , NequIP 12 , SO3KRATES 36 , Allegro 22 , MACE 23 and so on. As shown in Table  1 (MD17), Table  2 (rMD17), and Table  3 (MD22), it is remarkable that ViSNet outperformed the compared algorithms for both small (MD17 and rMD17) and large molecules (MD22) with the lowest mean absolute errors (MAE) of predicted energy and forces. On the one hand, compared with PaiNN, ET, and GemNet, ViSNet incorporated more geometric information and made full use of geometric information in ViS-MP, which contributes to the performance gains. On the other hand, compared with NequIP, Allegro, SO3KRATES, MACE, etc., ViSNet testified the effect of introducing spherical harmonics in the RGC module.

As shown in Table  4 , ViSNet also achieved superior performance for chemical property predictions on QM9. It outperformed the compared algorithms for 9 of 12 chemical properties and achieved comparable results on the remaining properties. Elaborated evaluations on Molecule3D confirmed the high prediction accuracy of ViSNet as shown in Table  5 . ViSNet achieved 33.6% and 6.51% improvements than the second-best for random split and scaffold split, respectively. Furthermore, ViSNet exhibited good portability to other multimodality methods, e.g., Transformer-M 37 and outperformed other approaches on OGB-LSC PCQM4Mv2 (see Supplementary Fig.  S1) . ViSNet also achieved the winners of PCQM4Mv2 track in the OGB-LCS@NeurIPS2022 competition when testing on unseen molecules 38 ( https://ogb.stanford.edu/neurips2022/results/ ).

To evaluate the computational efficiency of our ViSNet, following 23 , we compare the time latency of ViSNet with prevailing models in Supplementary Fig.  S2 . The latency is defined as the time it takes to compute forces on a structure (i.e., the gradient calculation for a set of input coordinates through the whole deep neural network). As shown in Supplementary Fig.  S2 , ViSNet ( L  = 2) saved 42.8% time latency compared with MACE ( L  = 2). Notably, despite the use of CG-product, Allegro had a significant speed improvement compared to NequIP and BOTNet. However, ViSNet still saved 6.1%, 4.1%, and 61% time latency compared to Allegro with L  = 1, 2, and 3, respectively.

Efficient molecular dynamics simulations

To evaluate ViSNet as the potential for MD simulations, we incorporated ViSNet that trained only with 950 samples on MD17 into the ASE simulation framework 39 to perform MD simulations for all seven kinds of organic molecules. All simulations are run with a time step τ  = 0.5 fs under the Berendsen thermostat with the other settings the same as those of the MD17 dataset. As shown in Fig.  3 , we analyzed the interatomic distance distributions derived from both AIMD simulations with ViSNet as the potential and ab initio molecular dynamics simulations at the DFT level for all seven molecules, respectively. As shown in Fig.  3 a, the interatomic distance distribution h ( r ) is defined as the ensemble average of atomic density at a radius r 9 . Figure  3 b–h illustrates the distributions derived from ViSNet are very close to those generated by DFT. We also compared the potential energy surfaces sampled by ViSNet and DFT for these molecules, respectively (Supplementary Fig.  S3 ). The consistent potential energy surfaces suggest that ViSNet can recover the conformational space from the simulation trajectories. Moreover, compared to DFT, numerous groundbreaking machine learning force fields (MLFFs), including sGDML 10 , ANI 40 , DPMD 41 , and PhysNet 42 have proven their exceptional speeds in MD simulations. Similar to such algorithms, ViSNet also exhibited significant computational cost reduction compared to DFT as shown in Supplementary Fig.  S4 and Table  S2 .

a An illustration about the atomic density at a radius r with the arbitrary atom as the center. The interatomic distance distribution is defined as the ensemble average of atomic density. b – h The interatomic distance distributions comparison between simulations by ViSNet and DFT for all seven organic molecules in MD17. The curve of ViSNet is shown using a solid blue line, while the dashed orange line is used for the DFT curve. The structures of the corresponding molecules are shown in the upper right corner. Source data are provided as a Source Data file.

To further examine the molecular properties derived from simulations driven by ViSNet, we performed 500 ps MD simulations at a constant energy ensemble (NVE) for ethanol in the MD17 dataset with a time step of τ  = 0.5 fs and 200 ps Ac-Ala3-NHMe in the MD22 dataset with a time step of τ  = 1 fs. The simulations were driven by ViSNet, sGDML, and DFT, respectively. For ethanol, we analyzed its vibrational spectra and the probability distribution of dihedral angles. For Ac-Ala3-NHMe, we investigated its vibrational spectra and potential energy surface (PES) via the Ramachandran plot. To analyze the Ramachandran plot of different simulations, the free energy value was estimated using the potential of mean force (PMF). ϕ and ψ were set as two reaction coordinates ( x , y ). All three ϕ and ψ dihedrals in Ac-Ala3-NHMe were calculated and plotted. The relative free energy value was calculated and referred to with the minimum value. To generate the landscape, 40 bins were used in both the x and y directions. Supplementary Fig.  S5 a and b demonstrate that both ViSNet and sGDML generate similar vibrational spectra, with slight differences in peak intensities compared to DFT. The probability distribution of hydroxyl angles in ethanol (Supplementary Fig.  S5 c) reveals three minima: gauche ± ( M g ± ) and trans ( M t ). Furthermore, even though ViSNet showed better performance than sGDML for various conformations in the MD22 dataset, starting from the same structure of the alanine tetrapeptide, the performance difference may not have a notable impact on the sampling efficiency for such small molecules, and thus may also lead to similar dynamics on the Ramachandran plots as shown in the Supplementary Fig.  S5 d–f. These results demonstrate that with only a few training samples, ViSNet can act with the potential to perform high-fidelity molecular dynamics simulations with much less computational cost and higher accuracy.

Applications for real-world full-atom proteins

To examine the usefulness of ViSNet in real-world applications, we made evaluations on the 166-atom mini-protein Chignolin (Fig.  4 a). Based on a Chignolin dataset consisting of about 10,000 conformations that sampled by replica exchange MD 43 and calculated at DFT level by Gaussian 16 44 in our another study 45 , 46 , we split it as training, validation, and test sets by the ratio of 8:1:1. We trained ViSNet as well as other prevailing MLFFs including ET 19 , PaiNN 18 , GemNet-OC 47 , MACE 23 , NequIP 12 and Allegro 22 and compared them with molecular mechanics (MM) 48 . The DFT results were used as the ground truth. Figure  4 b shows the free energy landscape of Chignolin and is depicted by d D3−G7 (the distance between carbonyl oxygen on the D3 backbone and nitrogen on the G7 backbone) and d E5−T8 (the distance between carbonyl oxygen on the E5 backbone and nitrogen on T8 backbone). The concentrated energy basin on the left shows the folded state and the scattered energy basin on the right shows the unfolded state. We randomly selected six structures from different regions of the potential energy surface for visualization. Among them, four structures were predicted by the model with smaller errors than the MAE while the other two with larger errors. Interestingly, all models consistently performed poorly on the structures with high potential energies (low probability of sampling) and performed well on the other structures. This implies that the sampling of conformations with high potential energies could be enhanced to ensure the generalization ability of the models.

a The visualization of Chignolin structure. The backbone is colored grey while the side chains of each residue in Chignolin are highlighted with a ball and stick. b The energy landscape of Chignolin sampled by REMD. The x -axis of the landscape is the distance between carbonyl oxygen on the D3 backbone and nitrogen on the G7 backbone, while the y -axis is the distance between carbonyl oxygen on the E5 backbone and nitrogen on the T8 backbone. Six structures were then selected for visualization. Each structure is shown as a cartoon and residues are depicted in sticks. The histograms show the absolute error between the energy difference predicted by MLFFs including ViSNet, ET, PaiNN, GemNet-OC, NequIP, Allegro, and MACE or calculated by MM, and the ground truth calculated by DFT on the corresponding structure. c The average root mean square deviation (RMSD) of the Chignolin trajectories simulated by ViSNet was calculated from 10 different trajectories. The shaded areas indicate the standard deviation range. d The MAE of each component of atomic forces during the simulations driven by ViSNet. The ground truth energies and forces were calculated using Gaussian 16. The shaded areas indicate the standard deviation range. Source data are provided as a Source Data file.

Supplementary Fig.  S6 shows the correlations between the energies predicted by MLFFs or MM and the ground truth values calculated by DFT for all conformations in the test set. ViSNet achieved a lower MAE and a higher R 2 score. From the violin plot of the absolute errors shown in Supplementary Fig.  S7 , ViSNet, PaiNN and ET exhibited smaller errors than other MLFFs while MM got a much wider range of prediction errors. Similar results can be seen in the force correlations in each component shown in Supplementary Fig.  S8 . Detailed settings about DFT and MM calculations are shown in Supplementary Materials. Furthermore, we also made a comprehensive comparison by taking model performance, training time consumption, and model size into consideration. ViSNet and other state-of-the-art algorithms such as PaiNN, ET, GemNet-OC, MACE, NequIP, and Allegro were analyzed on the Chignolin dataset and shown in Fig.  5 . Although ViSNet is marginally slower than ET and PaiNN, it introduces more geometric information, significantly enhancing its performance. When compared to GemNet, which also incorporates dihedral angles, ViSNet’s computational cost is significantly more affordable. Similarly, ViSNet proves to be computationally efficient when compared to models employing the CG-product method, such as MACE, Allegro, and NequIP.

PaiNN and ET are faster and smaller as ViSNet further incorporates dihedral calculation. ViSNet outperforms GemNet-OC due to its Runtime Geometry Calculation, reducing the explicit extraction of dihedral complexity from \({{{{{{{\mathcal{O}}}}}}}}({{{{{{{{\mathcal{N}}}}}}}}}^{3})\) to \({{{{{{{\mathcal{O}}}}}}}}({{{{{{{\mathcal{N}}}}}}}})\) . Additionally, ViSNet is also faster and smaller than MACE, Allegro, and NequIP for streamlining the CG-product. ViSNet achieves the best performance for its elaborate design, i.e., runtime geometric calculation and vector–scalar interactive message passing. Source data are provided as a Source Data file.

In addition, we performed MD simulations for Chignolin driven by ViSNet. 10 conformations were randomly selected as initial structures, and 100 ps simulations were run for each. As shown in Fig.  4 c, the RMSD for 10 simulation trajectories is shown against the simulation time. In Fig.  4 d, we displayed the MAE values of each component of the atomic forces between ViSNet and those calculated by Gaussian 16 44 at the DFT level. The simulation trajectory driven by ViSNet exhibited a small force difference for each component to quantum mechanics, which implies that ViSNet has no bias towards any force component, and thus consolidates the accuracy and potential usefulness for real-world applications.

Interpretability of ViSNet on molecular structures

Prior works have shown the effectiveness of incorporating geometric features, such as angles 16 , 20 . The primary method of geometry extraction utilized by ViSNet is the distinct inner product in its runtime geometry calculation. To this end, we illustrate a reasonable model interpretability of ViSNet by mapping the angle representations derived from the inner product of direction units in the model to the atoms in the molecular structure. We aim to bridge the gap between geometric representation in ViSNet and molecular structures. We visualized the embeddings after the inner product of direction units \(\langle {\overrightarrow{v}}_{i},{\overrightarrow{v}}_{i}\rangle\) extracted from 50 aspirin samples on the validation set. The high-dimensional embeddings were reduced to 2-dimensional space using T-SNE 49 and then clustered using DBSCAN 50 without the prior of number of clusters.

Supplementary Fig.  S9 exhibits the clustering results of nodes’ embeddings after the inner product of their corresponding direction units. We further map the clustered nodes to the atoms of aspirin chemical structure. Interestingly, the embeddings for these nodes could be distinctly gathered into several clusters shown in different colors. For example, although carbon atom C 11 and carbon atom C 12 possess different positions and connect with different atoms, their inner product \(\langle {\overrightarrow{v}}_{i},{\overrightarrow{v}}_{i}\rangle\) are clustered into the same class for holding similar substructures ({ C 11 − O 2 O 3 C 6 } and { C 12 − O 1 O 4 C 13 }). To summarize, ViSNet can discriminate different molecular substructures in the embedding space.

Ablation study

To further explore where the performance gains of ViSNet come from, we conducted a comprehensive ablation study. Specifically, we excluded the runtime angle calculation (w/o A), runtime dihedral calculation (w/o D), and both of them (w/o A&D) in ViSNet, in order to evaluate the usefulness of each part. ViSNet-improper denotes the additional improper angles and ViSNet l =1 uses the first-order spherical harmonics.

We designed some model variants with different message-passing mechanisms based on ViS-MP for scalar and vector interaction. ViSNet-N directly aggregates the dihedral information to intersecting nodes, and ViSNet-T leverages another form of dihedral calculation. The details of these model variants are elaborated in Supplementary. The results of the ablation study are shown in Supplementary Table  S3 and Supplementary Fig.  S10 . Based on the results, we can see that both kinds of directional geometric information are useful and the dihedral information contributes a little bit more to the final performance. The significant performance drop from ViSNet-N and ViSNet-T further validates the effectiveness of the ViS-MP mechanism. ViSNet-improper achieves similar performance to ViSNet for small molecules, but the contribution of improper angles is more obvious for large molecules (see Table  3 ). Furthermore, ViSNet using higher-order spherical harmonics achieves better performance.

We propose ViSNet, a geometric deep learning potential for molecular dynamics simulation. The group representation theory-based methods and the directional information-based methods are two mainstream classes of geometric deep learning potentials to enforce SE(3) equivariance 20 . ViSNet takes advantage of both sides in designing the RGC strategy and ViS-MP mechanism. On the one hand, the RGC strategy explicitly extracts and exploits the directional geometric information with computationally lightweight operations, making the model training and inference fast. On the other hand, ViS-MP employs a series of effective and efficient vector-scalar interactive operations, leading to the full use of geometric information. Furthermore, according to the many-body expansion theory 51 , 52 , 53 , the potential energy of the whole system equals the potential of each single atom plus the energy corrections from two-bodies to many-bodies. Most of the previous studies model the truncated energy correction terms hierarchically with k -hop information via stacking k message passing blocks. Different from these approaches, ViSNet encodes the angle, dihedral torsion, and improper information in a single block, which empowers the model to have a much more powerful representation ability. In addition, ViSNet’s universality or completeness is not validated by the geometric Weisfeiler–Leman (GWL) test 54 due to the inner product operation, which is computationally efficient but fails to distinguish certain atom reflection structures with the same angular information. To pass counterexamples or the GWL test, incorporating the CG-product with higher-order spherical harmonics is necessary in future studies.

Besides predicting energy, force, and chemical properties with high accuracy, performing molecular dynamics simulations with ab initio accuracy at the cost of the empirical force field is a grand challenge. ViSNet proves its usefulness in real-world ab initio molecular dynamics simulations with less computational costs and the ability of scaling to large molecules such as proteins. Extending ViSNet to support larger and more complex molecular systems will be our future research direction.

Equivariance

In the context of machine learning for atomic systems, equivariance is a pervasive concept. Specifically, the atomic vectors such as dipoles or forces must rotate in a manner consistent with the conformation coordinates. In molecular dynamics, such equivariance can be ensured by computing gradients based on a predicted conservative scalar energy. Formally, a function \({{{{{{{\mathcal{F}}}}}}}}:{{{{{{{\mathcal{X}}}}}}}}\to {{{{{{{\mathcal{Y}}}}}}}}\) is equivariant should guarantee:

where \({\rho }_{{{{{{{{\mathcal{X}}}}}}}}}(g)\) and \({\rho }_{{{{{{{{\mathcal{X}}}}}}}}}(g)\) are group representations in input and output spaces. The integration of equivariance into model parameterization has been shown to be effective, as seen in the implementation of shift-equivariance in CNNs, which is critical for enhancing the generalization capacity.

Proofs of the rotational invariance of RGC

Assume that the molecule rotates in 3D space, i.e.,

where, R   ∈   S O (3) is an arbitrary rotation matrix that satisfies:

The angular information after rotation is calculated as follows:

As shown in Eq. ( 18 ), the angle information does not change after rotation. The dihedral angular and improper information is also rotationally invariant since:

As Eq. ( 18 ) proved, the inner product has rotational invariance. Then, Eq. ( 19 ) can be further simplified as

The dihedral or improper angular information after rotation is calculated as:

As a result, Eqs. ( 18 ) and ( 21 ) have proved the rotational invariance of our proposed runtime geometry calculation (RGC).

We also provide proof of the equivariance of our ViS-MP in Supplementary Methods.

Detailed operations and modules in ViSNet

ViSNet predicts the molecular properties (e.g., energy \(\hat{E}\) , forces \(\overrightarrow{F}\in {{\mathbb{R}}}^{N\times 3}\) , dipole moment μ ) from the current states of atoms, including the atomic positions \(X\in {{\mathbb{R}}}^{N\times 3}\) and atomic numbers \(Z\in {{\mathbb{N}}}^{N}\) . The architecture of the proposed ViSNet is shown in Fig.  1 . The overall design of ViSNet follows the vector–scalar interactive message passing as illustrated from Eqs. ( 8 )–( 11 ). First, an embedding block encodes the atom numbers and edge distances into the embedding space. Then, a series of ViSNet blocks update the node-wise scalar and vector representations based on their interactions. A residual connection is placed between two ViSNet blocks. Finally, stacked corresponding gated equivariant blocks proposed by 18 are attached to the output block for specific molecular property prediction.

The embedding block

ViSNet expands the direct node and edge embedding with their neighbors. It first embeds atomic chemical symbol z i , and calculates the edge representation whose distances within the cutoff through radial basis functions (RBF). Then the initial embedding of the atom i , its 1-hop neighbors j and the directly connected edge e i j within cutoff are fused together as the initial node embedding \({h}_{i}^{0}\) and edge embedding \({f}_{ij}^{0}\) . In summary, the embedding block is given by:

\({{{{{{{\mathcal{N}}}}}}}}(i)\) denotes the set of 1-hop neighboring nodes of node i , and j is one of its neighbors. The embedding process is elaborated in Supplementary. The initial vector embedding \({\overrightarrow{v}}_{i}\) is set to \(\overrightarrow{0}\) . The vector embeddings \(\overrightarrow{v}\) are projected into the embedding space by following 18 ; \(\overrightarrow{v}\in {{\mathbb{R}}}^{N\times 3\times F}\) and F is the size of hidden dimension. The advantage of such projection is to assign a unique high-dimensional representation for each embedding to discriminate from each other. Further discussions on its effectiveness and interpretability are given in the Results section.

The Scalar2Vec module

In the Scalar2Vec module, the vector embedding \(\overrightarrow{v}\) is updated by both the scalar messages derived from node and edge scalar embeddings (Eq. ( 8 )) and the vector messages with inherent geometric information (Eq. ( 9 )). The message of each atom is calculated through an Edge-Fusion Graph Attention module, which fuses the node and edge embeddings and computes the attention scores. The fusion of the node and edge embeddings could be the concatenation operation, Hadamard product, or adding a learnable bias 55 . We leverage the Hadamard product and the vanilla multi-head attention mechanism borrowed from Transformer 56 for edge-node fusion.

Following 19 , we pass the fused representations through a nonlinear activation function as shown in Eq. ( 23 ). The value ( V ) in the attention mechanism is also fused by edge features before being multiplied by attention scores weighted by a cosine cutoff as shown in Eq. ( 24 ),

where l   ∈  {0, 1, 2,  ⋯   ,  L } is the index of the block, σ denotes the activation function (SiLU in this paper), W is the learnable weight matrix,  ⊙  represents the Hadamard product, ϕ (  ⋅  ) denotes the cosine cutoff and Dense(  ⋅  ) refers to one learnable weight matrix with an activation function. For brevity, we omit the learnable bias for linear transformation on scalar embedding in equations, and there is no bias for vector embedding to ensure the equivariance.

Then, the computed \({m}_{ij}^{l}\) is used to produce the geometric messages \({\overrightarrow{m}}_{ij}^{l}\) for vectors:

And the vector embedding \({\overrightarrow{v}}^{l}\) is updated by:

The Vec2Scalar module

In the Vec2Scalar module, the node embedding \({h}_{i}^{l}\) and edge embedding \({f}_{ij}^{l}\) are updated by the geometric information extracted by the RGC strategy, i.e., angles (Eq. ( 10 )) and dihedrals (Eq. ( 11 )), respectively. The residual node embedding \({{\Delta }}{h}_{i}^{l+1}\) , is calculated by a Hadamard product between the runtime angle information and the aggregated scalar messages with a gated residual connection:

To compute the residual edge embedding \({{\Delta }}{f}_{ij}^{l+1}\) , we perform the Hadamard product of the runtime dihedral information with the transformed edge embedding:

After the residual hidden representations are calculated, we add them to the original input of block l and feed them to the next block.

A comprehensive version that includes improper angles is depicted in Supplementary Methods.

The output block

Following PaiNN 18 , we update the scalar embedding and vector embedding of nodes with multiple gated equivariant blocks:

where [  ⋅  ,  ⋅  ] is the tensor concatenation operation. The final scalar embedding \({h}_{i}^{L}\in {{\mathbb{R}}}^{N\times 1}\) and vector embedding \({\overrightarrow{v}}_{i}^{L}\in {{\mathbb{R}}}^{N\times 3\times 1}\) are used to predict various molecular properties.

On QM9, the molecular dipole is calculated as follows:

where \({\overrightarrow{r}}_{c}\) denotes the center of mass. Similarly, for the prediction of electronic spatial extent 〈 R 2 〉, we use the following equation:

For the remaining 10 properties y , we simply aggregate the final scalar embedding of nodes as follows:

For models trained on the molecular dynamics datasets including MD17, revised MD17, and Chignolin, the total potential energy is obtained as the sum of the final scalar embedding of the nodes. As an energy-conserving potential, the forces are then calculated using the negative gradients of the predicted total potential energy with respect to the atomic coordinates:

Statistics and reproducibility

For the QM9 dataset, we randomly split it into 110,000 samples as the train set, 10,000 samples as the validation set, and the rest as the test set by following the previous studies 18 , 19 . For the Molecule3D and OGB-LSC PCQM4Mv2 datasets, the splitting has been provided in their paper 32 , 33 .

To evaluate the effectiveness of ViSNet in simulation data, ViSNet was trained on MD17 and rMD17 with a limited data setting, which consists of only 950 uniformly sampled conformations for model training and 50 conformations for validation for each molecule. For the MD22 dataset, we use the same number of molecules as in ref. 30 for training and validation, and the rest as the test set.

Furthermore, the whole Chignolin dataset was randomly split into 80%, 10%, and 10% as the training, validation, and test datasets. Six representative conformations were picked from the test set for illustration.

Experimental settings

For the QM9 dataset, we adopted a batch size of 32 and a learning rate of 1e−4 for all the properties. For the Molecule3D dataset, we adopted a larger batch size of 512 and a learning rate of 2e−4. For the OGB-LSC PCQM4Mv2 dataset, we trained our model in a mixed 2D/3D mode with a batch size of 256 and a learning rate of 2e−4. The mean squared error (MSE) loss was used for model training. For the molecular dynamic dataset including MD17, rMD17, MD22, and Chignolin, we leveraged a combined MSE loss for energy and force prediction. The weight of energy loss was set to 0.05. The weight of force loss was set to 0.95. The batch size was chosen from 2, 4, 8 due to the GPU memory and the learning rate was chosen from 1e−4 to 4e−4 for different molecules. The cutoff was set to 5 for small molecules in QM9, MD17, rMD17, and Molecule3D, and changed to 4 for Chignolin in order to reduce the number of edges in the molecular graphs. For the MD22 dataset, the cutoff of relatively small molecules was set to 5, that of bigger molecules was set to 4. Cutoff was not used in the OGB-LSC PCQM4Mv2 dataset. We used the learning rate decay if the validation loss stopped decreasing. The patience was set to 5 epochs for Molecule3D, 15 epochs for QM9, and 30 epochs for MD17, rMD17, MD22, and Chignolin. The learning rate decay factor was set to 0.8 for these models. Training is stopped if a maximum number of epochs is reached, or the validation loss does not improve for a maximum number of early stopping patience. The ViSNet model trained on the molecular dynamic datasets and Molecule3D had 9 hidden layers and the embedding dimension was set to 256. We used a larger model for the QM9 dataset, i.e., the embedding dimension changed to 512. For the OGB-LSC PCQM4Mv2 dataset, we use the 12-layer and 768-dimension Transformer-M 37 as the backbone. More details about the hyperparameters of ViSNet can be found in Supplementary Table  S4 . Experiments were conducted on NVIDIA 32G-V100 GPUs.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

All relevant data supporting the key findings of this study are available within the article and its Supplementary Information files. MD17 dataset [ http://www.quantum-machine.org/gdml/data/npz ], MD22 dataset [ http://www.quantum-machine.org/gdml/data/npz ], rMD17 dataset [ https://archive.materialscloud.org/record/file?filename=rmd17.tar.bz2&record_id=466 ], QM9 dataset [ https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/molnet_publish/qm9.zip ], Molecule3D dataset [ https://github.com/divelab/MoleculeX/tree/molx/Molecule3D ], OGB-LSC PCQM4Mv2 dataset [ https://ogb.stanford.edu/docs/lsc/pcqm4mv2 ] and Chignolin dataset [ https://github.com/microsoft/AI2BMD/tree/ViSNet/chignolin_data ].  Source data are provided with this paper.

Code availability

Most experiments were run with Python with version 3.9.15, Pytorch with version 1.11.0, Pytorch Geometric with version 2.1.0, and Pytorch Lightning with version 1.8.0. The code used to reproduce our results is available at https://github.com/microsoft/AI2BMD/tree/ViSNet 57 . Matplotlib and Seaborn were used for plotting figures.

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Acknowledgements

We would like to express our sincere gratitude to S. Chmiela, H.E. Sauceda, K.R. Müller, and A. Tkatchenko, for their invaluable assistance in performing the simulations and analyzing the vibrational spectra. Their extensive expertise and knowledge greatly contributed to the completion of the supplementary experiments, making our manuscript more solid.

Author information

These authors contributed equally: Yusong Wang, Tong Wang, Shaoning Li.

Authors and Affiliations

Microsoft Research AI4Science, 100080, Beijing, China

Yusong Wang, Tong Wang, Shaoning Li, Xinheng He, Mingyu Li, Zun Wang, Bin Shao & Tie-Yan Liu

National Key Laboratory of Human–Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, 710049, Xi’an, China

Yusong Wang & Nanning Zheng

The CAS Key Laboratory of Receptor Research and State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China

University of Chinese Academy of Sciences, 100049, Beijing, China

Medicinal Chemistry and Bioinformatics Center, School of Medicine, Shanghai Jiaotong University, Shanghai, 200025, China

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Contributions

T.W. led, conceived, and designed the study. T.W. is the lead contact. Y.W., S.L., X.H., and M.L. conducted the work when they were visiting Microsoft Research. S.L., Y.W., and T.W. carried out algorithm design. Y.W., S.L., X.H., and T.W. carried out experiments, evaluations, analysis, and visualization. Y.W. and S.L. wrote the original manuscript. T.W., X.H., M.L., Z.W., and B.S. revised the manuscript. N.Z. and T.-Y.L. contributed to the writing. All authors reviewed the final manuscript.

Corresponding authors

Correspondence to Tong Wang or Bin Shao .

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T.W., B.S., and T.-Y.L. have been filing a patent on ViSNet model. The remaining authors declare no competing interests.

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Wang, Y., Wang, T., Li, S. et al. Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing. Nat Commun 15 , 313 (2024). https://doi.org/10.1038/s41467-023-43720-2

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo 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.

There are five common approaches to qualitative research :

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

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

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

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

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

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