How to write Results Section of your Research Paper

Research data collection

Introduction

The result section is the third major part of the research paper and it’s probably the most important part because it contains actual outcomes about your experiment. The other sections contain a plan, hope and interpretations but the result section is the actual truth of your study.

In the result section, one should aim to narrate his/her finding without trying to interpret or evaluate them. Basically, the result section explains any issues you faced during your data collection, the main results of the experiment and any other interesting trends in the data.

With the results, we want to convey our data in the most accessible way, so we usually use visual elements like graphs and tables to make it easier to understand. The facts, figures, and findings are to be presented in a logical manner leading to the hypothesis and following the sequence of the method section. Mention must be made for the negative results as it would substantiate the discussion section later on. Interpretation of the meaning of the results section is done in the discussion section.

 

How Results Section is Structured?

When structuring the results section, it is important that your information is presented in a logical
order. 

Now, when it comes to the organization of the result section, as a generic rule

  • Always start with textual content, not a Table or Figure
  • Make sure you show the Tables and Figures after they are mentioned in the text
  • Explain any missing data or problems you had while collecting the data.

 

The results section gives you the opportunity to:

  • Summarize the  Data Preprocessing Steps
  • Report on the Findings 
  • Summarize the Research Findings

Results Section

How to Summarize the Data Preprocessing Steps in the Results Section?

At the beginning of the result section, you can discuss how you have collected, transformed and analyzed your data. This step is usually known as data preprocessing.

The data collection step may involve collecting data from various hardware, software or internet sources.

If your research requires data cleaning, then explain the steps and procedures used for data cleaning. Here, the researchers can describe how they transformed data to facilitate analysis (e.g. converting data from one format to another format). If there was missing data, explain how you have substituted missing values and with what techniques you have substituted your data.

You can mention what software or statistical procedures you have used to analyze and interpret the data.  Demonstrate with the help of charts or tables the cleansed data ready to be used for getting results.   In a few research papers, you may find these steps appearing at the end of the method section. 

 

How to Present your Research Findings in Research Section?

Second, present your findings in a structured way (such as thematically or chronologically), bringing the readers’ attention to any important, interesting, or significant findings.

Be sure to include a combination of text and visuals. Data illustrations should not be used to substitute or replace text, but to enhance the narrative of your findings.  

Resultant data are to be presented either through text, figures, graphs or tables or in a combination of all of the best suited for leading to the hypothesis. Care should be taken to prevent any duplication of the text, figures, graphs, and tables. If any result is presented in figures or graphs, it need not be explained through text. Similarly, any data presented through the graph should not be repeated in the table.

Each table and graph should be clearly labelled and titled. Each different finding should be made in a separate sub-section under the proper sub-heading following the sequence adopted in Method Section.

If you are not comfortable with data analysis then you can take professional services for research data analysis.

 

Research Results Findings

Figures 

 Identify and list the figures which are relevant to your results. For example, if you are working on the problem statement of ” Identifying the pathological issues with pomegranate fruits”, then you can add the figures of pomegranate fruits with good quality and bad quality along with their stage of infection. If you are working on pomegranate cultivar-related issues, put the figures of pomegranate fruits belonging to different cultivars. 

The key takeaway here is not to add any figures which may not directly contribute to results. These diagrams may include generic block diagrams, and images conveying generic information like farm fields, plantations etc.

While putting the figures, as much as possible use grayscale images as many users take the photocopies in black and white mode. In certain scenarios you are 

 In the case of figures, the captions should come below, called Fig. 1, Fig. 2 and so on. 

 

Tables

Tables are good for showing the exact values or showing much different information in one place. Graphs are good for showing overall trends and are much easier to understand quickly. It also depends on your data.

Tables are labelled at the top as Table 1,  Table 2 and so on.  Every table must have a caption. It’s good if one can put independent variable conditions on the left side vertically, and the things you have measured horizontally so one can easily compare the measurements across the categories. But you need to decide for each table you make, what is easiest to understand, and what fits on the paper.

Graphs

You can use various types of graphs in your results like a line graph, bar graph, scatter plot, a line graph with colours, a box with whiskers plot and a histogram.

In general, continuous variables like temperature, growth, age, and time can be better displayed in a line graph on a scatter plot or maybe on histograms.

If you have comparative data that you would like to represent through a chart then a bar chart would be the best option. This type of chart is one of the more familiar options as it is easy to interpret.

These charts are useful for displaying data that is classified into nominal or ordinal categories. In any case, you need to decide which is the best option for each particular example you have,  but never put a graph and a table with the same data in your paper.

In the case of graphs, the captions should come below, called Fig. 1, Fig. 2 and so on. 

A limited number of professional tools provide you the chance to add some life to your graphs, charts, and figures and present your data in a way that will astound your audience as much as your astounding results.

How to Summarize the Research Findings in the Results Section?

 

summarizing the results

Third, the results section should include a closing paragraph that clearly summarizes the key findings of the study. This paves the way for the discussion section of the research paper, wherein the results are interpreted and put in conversation with existing literature.

Any unusual correlation observed between variables should be noted in the result section. But any speculation about the reason for such an unusual correlation should be avoided. Such speculations are the domains of the discussion section.

Comparisons between samples or controls are to be clearly defined by specifically mentioning the common quality and the degree of difference between the comparable samples or controls. Results should always be presented in the past tense.

 

 

What are Common Mistakes Observed in the Results Section?

 

research results mistakes

Let’s look at some of the common mistakes which can be observed in the result section.

  1. One should not include raw data which are not directly related to your objectives. Readers will not be able to interpret your intentions and may unnecessarily collect unwanted data while replicating your experiments.
  2. Do not just tell the readers to look at the Table and Figure and figure it out by themselves, e.g “The results are shown in the following Tables and Graphs”.
  3. Do not give too much explanation about Figures and Tables.

 

 

Case Study 1:

“Sparse-FCM and deep learning for effective classification of land area in multi-spectral satellite images” (Paper Link)

This section discusses the results of the proposed multi-class FHS-DBN classifier that engages in classifying the land use area. The effectiveness of the algorithm is proved through the comparative discussion of the existing methods.

Experimental setup

The experimentation of the proposed multiclass FHS-DBN classifier is performed in PC with 2 GB RAM, Intel core processor, and Windows 10 Operating System and the implementation is performed in MATLAB.

Datasets employed

The analysis is progressed using four datasets, such as Indian Pine [29], Salinas scene [29], Pavia Centre and University [29], and Pavia University scene [29] and the description of the datasets are deliberated below.

Indian Pines dataset

The Indian Pines scene presents the forest area and cultivation area with 3 rd of the dataset with forest area that is collected with the help of AVIRIS sensor. The Indian pines scene carries two-lane highways, a rail line, smaller roads, low-density residences, and other kinds of buildings. The scene consists of 224 spectral bands with a wavelength of 0.4‾² × 10‾³ m along with 145 × 145 pixels (Please refer to the paper for exact values). Table 2 describes the 16 classes involved in Indian Pines dataset.

 

                                                       Fig. 1: Sample experimental results of multi-spectral satellite images 

                                                      Table 2: Classes involved in Indian Pine Data Set

 

The evaluation metrics scale the performance of the comparative metrics using the metrics, such as accuracy, True Positive Rate (TPR), and False Positive Rate (FPR).

 

Results- Comparison Chart

                                                        Fig. 2: Comparative Analysis based on accuracy

 

                                                  Table 3: Comparative Analysis of various Data sets

results section- Comparative Table

Case Study 2:

“An Optimized Fuzzy Based Short Term Object Motion Prediction for Real-Life Robot Navigation Environment”  (Paper Link)

Object motions with different motion patterns are generated by a simulator in different directions to generate the initial rule base. The rules generated are clustered based on the direction of the motion pattern into the directional space clusters. Table 1 shows the number of rules that remained in each directional space after removing inconsistencies and redundancies.

D1 D2 D3 D4 D5 D6 D7 D8
143 178 146 152 141 172 144 183

Our predictor algorithm is tested for a real-life benchmark dataset (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. A typical scenario of the INRIA Lab and the Shop Centre is shown in the Figure below.

 

Human capture Shop Centre

                                                      Fig.1: A typical scenario of the INRIA Lab and the Shop Centre

For each test case, the average response time is calculated to find its suitability for a real-life environment. The prediction algorithm is tested by processing the frame data of moving human patterns stored in the database at intervals of 50 frames (02 Seconds).

                                                       Fig. 2: The navigation environment

The navigation environment is presented in the form of a Prediction graph where the x-axis represents the Range parameter and the y-axis represents the Angle parameter. The predicted Angle and Range values are compared with actual values obtained from the real-life environment.

Relative Error

                             Fig.3: The Performance of the predictor

The performance of the predictor is tested when more than one object is sensed by the sensor. The tests are carried out assuming at most 6-8 objects can be visible and can affect the decisions to be made regarding robot traversal.

 

 

 

 

 

Frequently Asked Questions

 

How long should a results section be of a research paper?

An IMRaD paper format suggests around 35% of the text should be dedicated to the results and discussion section. For a research paper of length 10 pages, the results and discussion section should occupy 3-4 pages.

Should the results of a research paper be given in the introduction or in another section?

The results of a research paper should be given in a separate section. However, the highlights of the results can be discussed in the introduction section.

What is the difference between the “discussion” and the “results” section of a research paper?

The results section only depicts the results obtained by implementing the methodology used. The results will be in the form of figures, tables, charts or graphs. The discussion section elaborates the analysis of the results obtained in the results section.

Does the summary be part of the result section in the research article?

The summary can be part of the results section of a research paper. However, the results obtained can be summarized in the form of a table in results section of a research paper.

Why do some scientific papers not include a ‘methods and results’ section?

Survey papers and papers which are focussed on theoretical proofs do not involve separate methods and results sections.

How do you introduce a results section?

The results section is introduced by the data collection steps and the setting up of equipment in different scenarios for obtaining the results.

Why do researchers need to avoid making speculations in the results section of a research paper?

Making speculations in the results section may lead to wrong interpretations by the researcher who is planning to replicate the methodology used for obtaining the results. This may further lead to wrong comparative analysis.

Can I write a research paper without affiliation to any institute or University?

Yes. One can write a research paper without affiliation to any institute or University. The journals and conference editors are more focussed on the work carried out rather than the University to which the author has affiliations.

Vijay Rajpurohit
Author: Vijay Rajpurohit
Vijay Rajpurohit is a researcher in Computer Science. He loves to educate researchers and research scholars on Research Paper Writing, Thesis Writing, Research Grants, Patenting Research Work and the latest Research-related issues. You can reach him @ [email protected]

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