When I first started writing the methodology section of a research paper, it felt like I was lost in a maze. I wasn’t sure which direction to go or how to explain things clearly.
I had so many questions. What was the best way to do my study? How could I make sure my results were reliable? And how could I explain everything without making it too complicated?
During this time, I learned an important lesson: balance is key. I had to find the right balance between giving enough detail and keeping things simple.
I read a lot of books and articles to learn from others’ experiences. Their stories of success and struggles helped me feel less alone. With each hurdle I faced, I gained more confidence in my ability to write a good methodology section.
So, to anyone else feeling lost in the methodology maze, my advice is this: embrace the journey and the challenges. And remember, your story is unique and valuable. In the end, it’s your methodology that will help others understand and appreciate your research.
Crafting a well-written research paper involves much more than just presenting your findings and conclusions. One crucial section that forms the backbone of your study is the method section. In this blog post, I will guide you through the process of writing an effective and comprehensive method section that not only showcases the rigour and validity of your research but also allows others to replicate and build upon your work.
The method section is where you provide a detailed account of the research design, data collection methods, and data analysis techniques employed in your study. It is the roadmap that guides readers through the steps you took to gather and interpret your data. By following a structured approach and addressing key elements, you can ensure clarity, transparency, and credibility in your methodology.
In this comprehensive guide, we will break down the key components of a method section and provide practical tips to help you navigate each step. You will learn how to select and justify your research methods, describe your data collection process, and outline your data analysis techniques. We will also cover important considerations such as ethical considerations, limitations, and tips for maintaining conciseness and clarity.
Whether you are a novice researcher or seeking to improve your academic writing skills, this blog post will equip you with the essential knowledge and strategies to write a robust method section. By mastering this critical aspect of research paper writing, you will not only enhance the credibility and reproducibility of your study but also contribute to the broader scientific community by enabling others to build upon your work.
So, let’s dive in and discover the key principles and practical steps to write an outstanding method section that will elevate the impact and significance of your research.
- Introduction
- What Should Not Be Written in a Method Section of a Research Paper?
- What Next? : After the Method Section
- Common Academic Phrases Used in Methods and Material Section of a Research Paper
- Common Academic Phrases Used in Implementation Details of the Method Section
- Conclusion
- Frequently Asked Questions
Introduction
The method section is the core part of any research paper. The method section highlights the procedure used to obtain the results by following an innovative or modified approach to the existing methods. The method section usually begins with a block diagram.
The method section of a research paper represents the technical steps involved in conducting the research. Details about the methods focus on characterizing and defining them, but also explaining your chosen techniques, and providing a complete account of the procedures used for selecting, collecting and analyzing the data.
The method section of a research paper should fully explain the reasons for choosing a specific methodology or technique. Also, it’s essential that you describe the specific research methods of data collection you are going to use, whether they are primary or secondary data collection. The methods you choose should have a clear connection with the overall research approach and you need to explain the reasons for choosing the research techniques in your study, and how they help you understand your study’s purpose.
In the method section, you need to explain the rationale of your article to other researchers. You should focus on answering the following questions:
- Which research methods did you use?
- Why did you choose these methods and techniques?
- How did you collect the data or how did you generate the data?
- How did you use these methods for analyzing the research question or problem?
Based on the questions the following three sections can be identified for writing the research method in question.
1. Selection of research method and justification
2. Data Collection or generation
3. Experimental setup
1. Selection of Research Method and Justification
When writing the method section of a research paper, it is important to explain why you chose a particular research method and how it will help you achieve the aims of your study. The selection of a research method should be based on the research question, the data that you need to collect, and the type of analysis that you plan to conduct.
For example, if you are conducting a study on the effectiveness of a new algorithm for solving a particular problem, you might choose to use a randomized controlled trial as your research method. This would involve randomly assigning participants to either an experimental group (which uses the new algorithm) or a control group (which uses an existing algorithm). You would then compare the outcomes between the two groups to determine whether the new algorithm is more effective.
In this case, the justification for using a randomized controlled trial is that it allows you to control for confounding variables that might affect the outcomes of your study. By randomly assigning participants to the groups, you can ensure that any differences in outcomes are due to the algorithm and not other factors.
Another example from computer science is if you are conducting a study on user behaviour in a social networking platform, you might choose to use a survey as your research method. This would involve collecting data from users through an online questionnaire and asking questions about their behaviour and preferences on the platform.
The justification for using a survey, in this case, is that it allows you to collect data from a large number of users in a relatively short amount of time. You can then use statistical analysis to identify patterns and trends in the data, which can help you make informed decisions about the design of the platform.
In addition to explaining why you chose a particular research method, it is important to discuss any potential problems that you anticipated and the steps you took to prevent or minimize them. For example, if you are conducting a survey, you might discuss how you ensured the validity and reliability of the survey questions, how you recruited participants to ensure a representative sample, and how you minimized the risk of non-response bias.
The Table below represents the Research Method and Justification for its selection
| Research Method | Justification |
|---|---|
| Randomized Controlled Trial | – Allows control for confounding variables. ( Ensures any differences in outcomes are due to the algorithm being tested). |
| Survey | – Enables data collection from a large number of users in a short time ( Allows identification of patterns and trends through statistical analysis) |
| Deep Learning Model | – Excels at capturing complex patterns and relationships in large datasets. ( Well-suited for image recognition, natural language processing, and speech recognition tasks.) |
| Penetration Testing | – Identifies vulnerabilities and weaknesses in computer systems or networks. ( Provides insights into potential security threats and helps strengthen defenses) |
| Sentiment Analysis | – Analyzes and categorizes opinions, attitudes, and emotions expressed in text data. (Enables understanding of customer sentiments, brand reputation monitoring, and data-driven decision-making) |
Overall, the selection of a research method and justification is a critical component of the method section in a research paper. It is important to carefully consider the research question, the data you need to collect, and the type of analysis you plan to conduct in order to choose the most appropriate method for your study.
2. Data Collection or Generation
Readers, academicians and other researchers need to know how the data used in your academic article was collected. The research methods used for collecting or generating data will influence the discoveries and, by extension, how you will interpret them and explain their contribution to general knowledge. The most basic methods for data collection are as follows:
Primary Data
Primary data represent data originated for the specific purpose of the study, with its research questions. The methods vary on how Authors and Researchers conduct an experiment, survey or study, but, in general, it uses a particular scientific method to gather data. Here readers need to understand how the information was gathered or generated in a way that is consistent with research practices in a field of study.
For primary data, that involve surveys, experiments or observations, authors should provide information about
- Devices/equipment used for data collection
- Under what conditions were data collected ( Summer, winter, morning, evening, temperature etc)
- Longitude and Lattitude where data is collected (Exact location)
- If the camera is used then: Camera configuration, resolution, distance and angle between the camera and object under observation
- If any sensors are used then their configuration and operating environment need to be specified
- If data is collected from living beings then species name, sex, age etc needs to be provided
- Inclusion or exclusion criteria used for data collection
Example 1:
Here’s an example based on the given criteria:
Example: In this research study, data collection was conducted using a combination of devices and equipment to gather information on air quality parameters in urban areas. The data collection process involved the use of sensors, cameras, and monitoring equipment to capture relevant data.
Devices/Equipment Used:
- Air quality sensors: These sensors were specifically designed to measure particulate matter (PM), carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3) levels in the air.
- Weather station: A weather station was deployed to collect meteorological data such as temperature, humidity, and wind speed.
- Cameras: High-resolution cameras were installed to capture images of the urban environment, focusing on areas with significant air pollution sources.
Conditions of Data Collection:
- Data collection occurred over a period of one year, encompassing all four seasons (spring, summer, fall, and winter).
- Locations were selected in various urban areas known for their diverse air quality profiles and pollution sources.
- Longitude and latitude coordinates were recorded for each data collection site to precisely locate the study areas.
Camera Configuration:
- The cameras used had a resolution of 20 megapixels, ensuring clear and detailed images.
- Distances between the cameras and the objects under observation were kept at approximately 100 meters for optimal image capture.
- Angles of view were adjusted to encompass a wide range of the urban landscape, focusing on areas with notable air pollution sources.
Sensors Configuration:
- The air quality sensors were configured to collect real-time data at regular intervals (e.g., every 5 minutes) throughout the data collection period.
- Operating environments varied based on the sensor type. Some sensors were deployed outdoors, while others were placed indoors in locations of interest.
Inclusion/Exclusion Criteria:
- Data collection included multiple species of trees, plants, and vegetation to assess their impact on air quality. Species names were recorded for each sample.
- The study focused on both male and female trees to account for potential gender-related variations in pollutant absorption.
- Age was not a factor in this particular study.
By employing these devices, following specific conditions of data collection, and considering inclusion/exclusion criteria, this study aimed to gain insights into the spatial and temporal variations in air quality parameters in urban environments.
Example 2 :
- In the current work, the images of diseased samples of pomegranate leaves were captured using a Nikon Coolpix L20 digital camera with a resolution of 10 megapixels and a 3.6x optical zoom capability.
- The camera was positioned at an equal distance of 16 cm from the object to ensure consistent image capture.
- All the captured images were saved in the JPEG format.
- For the purpose of image acquisition, the authors visited several pomegranate farms in the places of Hagaribommanahalli (15.0456° N, 76.2074° E) in Bellary district and Kaladhagi (16.2050° N, 75.5015° E) in Bagalkot district of Karnataka, India.
This format presents the information in a concise and pointwise manner, highlighting the key details of the image acquisition process, camera specifications, image format, and the specific locations where the data collection took place.
Secondary Data
Secondary data are data that have been previously collected or gathered for other purposes than the aim of the academic article’s study. This type of data is already available, in different forms, from a variety of sources. Here Authors should provide information about the following:
- From which website data is collected
- On which date the data is collected (can be specified in references)
- what specific data within the data set is used for the experiment
Example:
Example 1: Secondary data were collected from the official website of the World Health Organization (WHO). The data was accessed on March 15, 2022, and specific information related to global vaccination rates and COVID-19 cases was extracted from the dataset for the experiment.
Example 2: Data for this study was obtained from the National Bureau of Economic Research (NBER) database. The data was collected on January 1, 2021, and specific variables such as GDP growth, inflation rates, and unemployment rates were utilized from the dataset for the experiment.
Example 3: Secondary data were collected from the United States Census Bureau website. The data was gathered on July 1, 2020, and specific demographic information, including age, gender, and income levels, was extracted from the dataset for the experiment.
Example 4: Our predictor algorithm will be tested for real-life benchmark datasets available at CAVIAR Project (EC Funded CAVIAR project/IST 2001 37540) to check for relative error. The data set consists of different human motion patterns observed at INRIA Lab at Grenoble, France and Shop Centre. These motion patterns consist of frames captured at 25 frames/second.
In each of these examples, the authors clearly mention the source from which the secondary data was collected (e.g., WHO website, NBER database, United States Census Bureau website, EC Funded CAVIAR project/IST 2001 37540). The specific date of data collection is also provided, which can be cited in the references. Additionally, the authors specify the particular data or variables that were utilized from the dataset for the experiment, ensuring clarity on the specific information used from the secondary data.
When it comes to data collection or generation, researchers often face the challenge of having to label their data. There are two main approaches to data labelling: using existing data labelling software and outsourcing the labelling process.
With existing data labelling software, researchers can utilize pre-existing tools and platforms to label their data efficiently and accurately. This can save time and resources, as well as ensure consistency in the labelling process.
On the other hand, outsourcing the data labelling process to external providers can also be a viable option, especially when dealing with large datasets or complex labelling tasks. Outsourcing can also help researchers save time and resources while benefiting from the expertise and experience of professional data labelling services.
By carefully considering their options and choosing the right approach for their needs, researchers can ensure that their data is labelled accurately and efficiently, enabling them to conduct their research with confidence. Discover the benefits of outsourcing your data labelling needs by checking out our blog post on Outsourcing Research Data Labelling: Risks and Rewards for Researchers and find the right partner to help you unlock the full potential of your research data.”
3. Experimental Setup
Here you need to describe and explain your chosen methods and relate them to your research questions and/or hypotheses. The description of the methods used should include enough details so that the study can be replicated by other Researchers, or at least repeated in a similar situation or framework. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized. Detailed discussion on the following points is essential in the method section.
- Instruments used and their configuration.
- Computing machine (like server/desktop/laptop) configuration, speed, memory size etc.
- The software setup and the algorithms applied in computation.
- If deployed on the Cloud platform then cloud setup.
Example: Let me take an example from one of my research papers ” Diagnosis and Classification of Grape Leaf Diseases using Neural Networks”
The goal of research work is to diagnose the disease using image processing and artificial intelligence techniques on images of the grape plant leaf. In the proposed system, a grape leaf image with a complex background is taken as input. Thresholding is deployed to mask green pixels and the image is processed to remove noise using anisotropic diffusion. Then grape leaf disease segmentation is done using K-means clustering. The diseased portion from segmented images is identified. The Feedforward Back Propagation Neural Network was trained for classification.
Here the Method Section is divided into several sub-sections such as
A. Image Acquisition:
B. Background Removal:
C. Preprocessing:
D. Segmentation: Using K-means Clustering Algorithm
E. Extract Lesion:
F. Feature Extraction:
G. Classification: Using Backpropagation Neural Network
Please follow the link and refer to the paper for complete details of the methods section.
I had the privilege of interviewing two eminent research scholars Barry Menglong Yao, a Ph.D. student at Virginia Tech and Vinay Kabadi, a Research Scholar, at the University of Melbourne regarding their award-winning research papers. There I had a lengthy discussion regarding their approach towards the methodology of research. I have added these interviews to my blog posts. Please take a visit to the blog posts for further details.
What Should Not Be Written in a Method Section of a Research Paper?
When writing a method section for a research paper, it’s important to focus on including relevant and essential information while avoiding unnecessary or inappropriate content. Here are some things that should generally be avoided in a method section:
- Excessive background information: The method section should primarily focus on the specific procedures, techniques, and processes employed in your study. Avoid providing excessive background information or delving into detailed literature reviews.
- Personal anecdotes or stories: The method section should maintain a professional and objective tone. Avoid including personal anecdotes, stories, or unrelated information that does not contribute to the understanding of the research methods.
- Interpretation of results: The method section should not include any discussion or interpretation of the results. Save the interpretation and analysis of data for the results or discussion sections of your paper.
- Citations or references: The method section is not the appropriate place to include citations or references to external sources. Save these for the introduction or discussion sections of your research paper.
- Speculation or assumptions: Stick to describing the actual procedures and steps followed in your research. Avoid including speculative or hypothetical assumptions about your findings or potential outcomes.
- Excessive technical details: While it is important to provide sufficient detail to allow for replication of your study, avoid overwhelming readers with excessively technical jargon or intricate details that are not crucial for understanding the overall research process.
- Repetition: Avoid repeating information that has already been presented in the introduction or other sections of the paper. Keep the method section focused on the unique aspects of your research methodology.
Remember that the method section should be clear, concise, and focused on providing a thorough and replicable account of your research methods. Stick to the essentials and avoid including irrelevant or unnecessary information that does not contribute to the understanding of your study.
What Next? : After the Method Section
The method section of a research paper provides a detailed account of the research design and procedures used to collect and analyze data. Once the data is collected and analyzed, the results section is where researchers present their findings to the reader. In the results section, researchers can provide tables, graphs, and statistical analyses to help readers understand the key findings of the study. To learn more about how to effectively present your research findings in the results section, check out our blog post on How to write the Results Section of your Research Paper. By following the guidelines given in the blog post, you can ensure that your research results are presented in a clear, concise, and effective manner, helping to maximize the impact of your study.
Common Academic Phrases Used in Methods and Material Section of a Research Paper
Here are some common academic phrases that can be used in the methods and materials section of a paper or research article. Below here, I’ve included a table with examples to illustrate how these phrases might be used:
| Phrase | Example |
|---|---|
| Research design: This phrase is used to describe the research design or methodology used in the study. | “This study uses a quasi-experimental research design to investigate the effects of a new teaching method on student learning outcomes.” |
| Participants/Sample: This phrase is used to describe the participants or sample of the study. | “The study recruited a sample of 100 undergraduate students majoring in computer science at a large public university in the United States.” |
| Data collection: This phrase is used to describe the methods used to collect data in the study. | “Data was collected through a survey questionnaire administered online to all participants over a period of four weeks.” |
| Data analysis: This phrase is used to describe the methods used to analyze the data collected in the study. | “Data was analyzed using descriptive statistics, including means, standard deviations, and frequencies, as well as inferential statistics, including t-tests and ANOVA.” |
| Instruments/Tools: This phrase is used to describe the instruments or tools used in the study. | “In this study, we used a commercially available eye-tracking device to measure participants’ gaze patterns while they completed a series of programming tasks.” |
| Procedures: This phrase is used to describe the specific procedures followed in the study. | “Participants were randomly assigned to either the treatment or control group, and all participants completed a pre-test and post-test to assess their programming skills.” |
| Ethical considerations: This phrase is used to describe the ethical considerations and procedures followed in the study. | “The study was approved by the Institutional Review Board (IRB) at the university, and all participants provided informed consent before participating in the study.” |
Common Academic Phrases Used in Implementation Details of the Method Section
Here are some common academic phrases that can be used in the Implementation details of the Method Section. Below here, I’ve included a table with examples to illustrate how these phrases might be used:
| Phrase | Example |
|---|---|
| Implementation details: This phrase is used to provide an overview of the implementation details, such as the programming language, frameworks, and libraries used. | “The implementation was done in Python using the TensorFlow and Keras frameworks for deep learning, and the NLTK library for natural language processing tasks.” |
| System architecture: This phrase is used to describe the overall system architecture, including any data flow diagrams, software design patterns, and algorithms used. | “The system is designed as a client-server architecture, with the server running a RESTful API to handle requests from the client and process data using a multilayer perceptron neural network algorithm.” |
| Algorithm details: This phrase is used to describe the specific algorithms used in the implementation. | “The implementation uses the Dijkstra algorithm for shortest path finding in a graph, and the A* algorithm for pathfinding in a 2D grid.” |
| Data preprocessing: This phrase is used to describe the preprocessing steps used to clean and transform raw data. | “The raw data were preprocessed using techniques such as tokenization, stemming, and stop-word removal, to extract the relevant features for training the machine learning models.” |
| Model training: This phrase is used to describe the process of training machine learning models using preprocessed data. | “The machine learning models were trained on a labelled dataset consisting of over 10,000 samples using the stochastic gradient descent optimization algorithm with a learning rate of 0.1.” |
| Evaluation metrics: This phrase is used to describe the evaluation metrics used to assess the performance of the implemented system. | “The performance of the system was evaluated using precision, recall, and F1-score, as well as accuracy and mean squared error for regression tasks.” |
| Experimental setup: This phrase is used to describe the experimental setup used to test the implemented system. | “The experimental setup involved testing the system on a variety of datasets, ranging from small toy datasets to larger real-world datasets with millions of records.” |
Conclusion
Writing a clear and concise method section is crucial for any research paper, as it lays the foundation for the entire study. The method section should include information on the selection of research methods and techniques, the justification for their use, and the approach to data collection or generation. Additionally, it should provide details on the experimental setup, which can help readers to understand the process of analyzing the research question or problem.
The selection of research methods and techniques should be based on a thorough review of the literature and the research question or problem. It is essential to justify why the chosen methods are the most appropriate for the research question and to explain any limitations of the selected methods. Providing this information can help readers to understand the reliability and validity of the study.
Data collection or generation should also be described in detail, including the sample size, population, and data collection or generation methods. This can help readers to understand the generalizability of the study results.
Finally, the experimental setup should be explained in detail, including any relevant variables or parameters, and any procedures or protocols used in the study. This information can help readers to understand the internal validity of the study and to replicate the study if necessary.
Frequently Asked Questions
How should I write the methods section of my research paper if I conducted a survey?
When writing the methods section of your research paper based on a survey, you should include the following key components:
Survey Design: Explain the design and structure of your survey. Mention the survey type (e.g., online, paper-based), the survey instrument used (e.g., questionnaire), and any specific survey software or platforms employed.
Participant Selection: Describe how you selected your survey participants, including any inclusion or exclusion criteria. Specify the target population, the sampling method (e.g., random sampling, convenience sampling), and the sample size.
Survey Administration: Outline how you administered the survey. Explain the process of contacting participants, distributing the survey, and collecting responses. If any incentives were offered to encourage participation, mention them here.
Survey Content: Provide an overview of the survey questions or topics covered. Mention the number of questions, the types of questions (e.g., multiple-choice, Likert scale), and any scales or response formats used. If you used pre-existing survey instruments, provide references or indicate their source.
Data Analysis: Describe the analytical methods employed to analyze the survey data. Mention any statistical techniques, software, or coding processes used for data processing and analysis. If you calculated any descriptive statistics or performed inferential analysis, provide details.
Ethical Considerations: Discuss the ethical considerations you addressed in your survey. Explain how you obtained informed consent from participants and ensured their privacy and confidentiality. If you obtained ethical approval from an institutional review board, mention it here.
Limitations: Highlight any limitations or challenges encountered during the survey process. This could include issues related to sample representativeness, response bias, or any other factors that may have influenced the validity or generalizability of your findings.
Data Validation: If relevant, briefly discuss how you ensured the validity and reliability of the survey data. This could include pilot testing, pre-testing the survey instrument, or assessing the internal consistency of the survey items.
What should be the length of the Method Section in a Research Paper?
The length of the methods section in a research paper can vary depending on factors such as the complexity of the study, the research design, the amount of data collected, and the specific requirements of the journal or conference where the paper will be submitted. However, as a general guideline:
For a research paper of 5-10 pages: The methods section may typically span around 1-1.5 pages.
For a research paper of 10-20 pages: The methods section could range from approximately 1.5-3 pages.
For a research paper of 20-30 pages: The methods section might extend from approximately 3-4 pages.
These estimates are only approximate, and the actual length of the methods section may vary based on the specific details of your study and the extent of information you need to provide to ensure clarity and replicability. It’s essential to prioritize conciseness and clarity while including all the necessary information for readers to understand and reproduce your research.
Can I add any statistical analyses and graphs in the method section of a research paper?
Typically, statistical analyses and graphs are not included in the method section of a research paper. The method section primarily focuses on describing the research design, data collection methods, and data analysis techniques used in your study. It serves as a detailed account of the procedures undertaken to gather and analyze the data.
Statistical analyses and graphs are usually presented in the results section, where you discuss and interpret the findings derived from your data analysis. The results section is where you present the statistical tests, summarize the outcomes, and visualize the data through graphs, charts, or figures.
However, there might be instances where you may need to include a minimal amount of statistical information or basic graphical representation in the method section. For example, you might mention the statistical software used for data analysis or briefly describe the type of graphs that were employed to visually represent the data during data collection (e.g., using bar graphs or scatter plots). But these instances are generally limited and concise.
