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
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.
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.
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 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: In the current work, the images of diseased samples of pomegranate leaves are captured using a Nikon Coolpix L20 digital camera having 10 megapixels of resolution and 3.6x optical zoom, maintaining an equal distance of 16 cm to the object. All the images are then saved in the same format i.e., JPEG. For the purpose of image acquisition, authors have visited and
captured images from several pomegranate farms in the places of Hagaribommanahalli(15.0456° N, 76.2074° E), Bellary district and Kaladhagi (16.2050° N, 75.5015° E), Bagalkot district of Karnataka, India.
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: Our predictor algorithm is 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.
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 labeling: using existing data labeling software and outsourcing the labeling process.
With existing data labeling 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 labeling process.
On the other hand, outsourcing the data labeling process to external providers can also be a viable option, especially when dealing with large datasets or complex labeling tasks. Outsourcing can also help researchers save time and resources, while benefiting from the expertise and experience of professional data labeling services. By carefully considering their options and choosing the right approach for their needs, researchers can ensure that their data is labeled accurately and efficiently, enabling them to conduct their research with confidence. Discover the benefits of outsourcing your data labeling 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.”
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 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. 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:
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.
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 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:
|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:
|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.”|
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.