How to write the Method Section of your Research Paper

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 full account on the procedures used for selecting, collecting and analyzing the data. The methods 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 towards understanding your study’s purpose.

  • 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

A common limitation of academic articles found in research papers is that the premises of the methodology are not backed by reasons on how they help achieve the aims of the article. Selection of the Research Method is crucial for any branch of science/Engineering because an unreliable method produces unreliable results and, as a consequence, undermines the value of your interpretations of the findings.

The method section  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.

2. Data Collection or Generation

Readers, academics 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:

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/equipments used for data collection
  • Under what conditions data collected (It may be : Summer, winter, morning, evening, temperature etc)
  • Longitude and Lattitude where data is collected (Exact location)
  • If camera is used then : Camera configuration, resolution ,distance and angle between camera and object under observation
  • If any sensors are used then their configuration and operating environment needs to be specified
  • If  data is collected from living being 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 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

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:

  • From which website data is collected
  • On which date data is collected (can be specified in references)
  • what specific data within the data set is used for experiment

Example: Our predictor algorithm is tested for real life bench mark dataset available at: (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 consists of frames captured at 25 frames/second.

3. Experimental Setup

Here you need to describe and explain your chosen methods, 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.

  •  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 cloud then  cloud setup

Example : Let me take an example of 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 imageprocessing and artificial intelligence techniques on images of grape plant leaf. In the proposed system, grape leaf image with complex background is taken as input. Thresholding is deployed to mask green pixels and 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. Feed forward Back Propagation Neural Network was trained for classification.

Here in the Method Section is divided into several 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 Back propagation Neural Network

You can follow the paper link for more details.

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