In my role as a journal reviewer, I’ve had the privilege (and sometimes the frustration) of reviewing numerous research papers.
I’ve encountered papers where authors neglect to discuss how their results benefit the problem at hand, the domain they are working in, and society at large. This oversight often fails to make a lasting impression on reviewers
One recurring issue that I’ve noticed, time and again, is the challenge of properly delineating the boundaries of the discussion section in research papers. It’s not uncommon to come across papers where authors blur the lines between various crucial sections in a research paper.
Some authors mistakenly present their results within the discussion section, failing to provide a clear delineation between the findings of their study and their interpretation. Even if the journal allows to combine the results and discussion section, adopting a structured approach, such as dedicating a paragraph for results and a paragraph for discussing the results, can enhance the flow and readability of the paper, ensuring that readers can easily follow the progression of the study and its implications.
I vividly recall one instance where an author proceeded to rehash the entire methodology, complete with block diagrams, within the discussion section—without ever drawing any substantive conclusions. It felt like wading through familiar territory, only to find myself back at square one.
And then there are those authors who seem more interested in speculating about future directions than analyzing the outcomes of their current work in the discussion section. While it’s important to consider the implications of one’s research, it shouldn’t overshadow the critical analysis of the results at hand.
In another instance, a researcher concealed all failures or limitations in their work, presenting only the best-case scenarios, which created doubts about the validity of their findings. As a reviewer, I promptly sent it back for a relook and suggested adding various scenarios to reflect the true behaviour of the experiment.
In this post, I’ll delve into practical strategies for crafting the discussion section of a research paper. Drawing from my experiences as a reviewer and researcher, I’ll explore the nuances of the discussion section and provide insights into how to engage in critical discussion.
- Introduction
- Which are these 07 steps for writing an Effective Discussion Section of a Research Paper?
- How to Validate the Claims I made in the Discussion Section of My Research Paper?
- Phrases that can be used in the Discussion Section of a Research Paper
- Phrases that can be used in the Analysis part of the Discussion Section of a Research Paper
- Your Next Move...
- Conclusion
- Frequently Asked Questions
Introduction
The Discussion section of a research paper is where authors interpret their findings, contextualize their research, and propose future directions. It is a crucial section that provides the reader with insights into the significance and implications of the study.
Writing an effective discussion section is a crucial aspect of any research paper, as it allows researchers to delve into the significance of their findings and explore their implications. A well-crafted discussion section not only summarizes the key observations and limitations of the study but also establishes connections with existing research and opens avenues for future exploration. In this article, we will present a comprehensive guide to help you structure your discussion section in seven simple steps.
By following these steps, you’ll be able to write a compelling Discussion section that enhances the reader’s understanding of your research and contributes to the broader scientific community.
Please note, the discussion section usually follows after the Results Section. I have written a comprehensive article on ” How to Write Results Section of your Research Paper“. Please visit the article to enhance your write-up on the results section.
Which are these 07 steps for writing an Effective Discussion Section of a Research Paper?
Writing an effective discussion section is a crucial aspect of any research paper, as it allows researchers to delve into the significance of their findings and explore their implications. A well-crafted discussion section not only summarizes the key observations and limitations of the study but also establishes connections with existing research and opens avenues for future exploration. In this article, we will present a comprehensive guide to help you structure your discussion section in seven simple steps.
Step 1: Focus on the Relevance: In the first step, we will discuss the importance of emphasizing the relevance of your research findings to the broader scientific context. By clearly articulating the significance of your study, you can help readers understand how your work contributes to the existing body of knowledge and why it matters.
Step 2: Highlight the Limitations: Every research study has its limitations, and it is essential to address them honestly and transparently. We will explore how to identify and describe the limitations of your study, demonstrating a thorough understanding of potential weaknesses and areas for improvement.
Step 3: Highlight the Observations: In this step, we will delve into the core findings of your study. We will discuss the key observations and results, focusing on their relevance to your research objectives. By providing a concise summary of your findings, you can guide readers through the main outcomes of your study.
Step 4: Compare and Relate with Other Research Works: Research is a collaborative and cumulative process, and it is vital to establish connections between your study and previous research. We will explore strategies to compare and relate your findings to existing literature, highlighting similarities, differences, and gaps in knowledge.
Step 5: Provide Alternate Viewpoints: Science thrives on the diversity of perspectives. Acknowledging different viewpoints and interpretations of your results fosters a more comprehensive understanding of the research topic. We will discuss how to incorporate alternative viewpoints into your discussion, encouraging a balanced and nuanced analysis.
Step 6: Show Future Directions: A well-crafted discussion section not only summarizes the present but also points towards the future. We will explore techniques to suggest future research directions based on the implications of your study, providing a roadmap for further investigations in the field.
Step 7: Concluding Thoughts: In the final step, we will wrap up the discussion section by summarizing the key points and emphasizing the overall implications of your research. We will discuss the significance of your study’s contributions and offer some closing thoughts to leave a lasting impression on your readers.
By following these seven steps, you can craft a comprehensive and insightful discussion section that not only synthesizes your findings but also engages readers in a thought-provoking dialogue about the broader implications and future directions of your research. Let’s delve into each step in detail to enhance the quality and impact of your discussion section.
I. Focus on the Relevance
The purpose of every research is to implement the results for the positive development of the relevant subject. In research, it is crucial to emphasize the relevance of your study to the field and its potential impact. Before delving into the details of how the research was conceived and the sequence of developments that took place, consider highlighting the following factors to establish the relevance of your work:
- Identifying a pressing problem or research gap: Example: “This research addresses the critical problem of network security in wireless communication systems. With the widespread adoption of wireless networks, the vulnerability to security threats has increased significantly. Existing security mechanisms have limitations in effectively mitigating these threats. Therefore, there is a pressing need to develop novel approaches that enhance the security of wireless communication systems.”
- Explaining the significance and potential impact of the research: Example: “By developing an intelligent intrusion detection system using machine learning algorithms, this research aims to significantly enhance the security of wireless networks. The successful implementation of such a system would not only protect sensitive data and communication but also ensure the reliability and integrity of wireless networks in various applications, including Internet of Things (IoT), smart cities, and critical infrastructure.”
- Establishing connections with previous research and advancements in the field: Example: “This study builds upon previous research on intrusion detection systems and machine learning techniques. By leveraging recent advancements in deep learning algorithms and anomaly detection methods, we aim to overcome the limitations of traditional rule-based intrusion detection systems and achieve higher detection accuracy and efficiency.”
By emphasizing the relevance of your research and articulating its potential impact, you set the stage for readers to understand the significance of your work in the broader context. This approach ensures that readers grasp the motivations behind your research and the need for further exploration in the field.
II. Highlight the Limitations
Many times the research is on a subject that might have legal limitations or restrictions. This limitation might have caused certain imperfections in carrying out research or in results. This issue should be acknowledged by the researcher before the work is criticized by others later in his/her discussion section.
In computer science research, it is important to identify and openly acknowledge the limitations of your study. By doing so, you demonstrate transparency and a thorough understanding of potential weaknesses, allowing readers to interpret the findings in a more informed manner. Here’s an example:
Example: “It is crucial to acknowledge certain limitations and constraints that have affected the outcomes of this research. In the context of privacy-sensitive applications such as facial recognition systems, there are legal limitations and ethical concerns that can impact the accuracy and performance of the developed algorithm. These limitations stem from regulations and policies that impose restrictions on data collection, access, and usage to protect individuals’ privacy rights. As a result, the algorithm developed in this study operates under these legal constraints, which may have introduced certain imperfections.”
In this example, the researcher is working on a facial recognition system and acknowledges the legal limitations and ethical concerns associated with privacy-sensitive applications. By openly addressing these limitations, the researcher demonstrates an understanding of the challenges imposed by regulations and policies. This acknowledgement sets the stage for a more nuanced discussion and prevents others from solely criticizing the work based on these limitations without considering the broader legal context.
By highlighting the limitations, researchers can also offer potential solutions or future directions to mitigate the impact of these constraints. For instance, the researcher may suggest exploring advanced privacy-preserving techniques or collaborating with legal experts to find a balance between privacy protection and system performance.
By acknowledging and addressing the limitations, researchers demonstrate their awareness of potential weaknesses in their study, maintaining credibility, and fostering a more constructive discussion of their findings within the context of legal and ethical considerations.
III. Introduce New Discoveries
Begin the discussion section by stating all the major findings in the course of the research. The first paragraph should have the findings mentioned, which is expected to be synoptic, naming and briefly describing the analysis of results.
Example: “In this study, several significant discoveries emerged from the analysis of the collected data. The findings revealed compelling insights into the performance of parallel computing architectures for large-scale data processing. Through comprehensive experimentation and analysis, the following key discoveries were made:
- Discovery 1: The proposed parallel computing architecture demonstrated a 30% improvement in processing speed compared to traditional sequential computing methods. This finding highlights the potential of parallel computing for accelerating data-intensive tasks.
- Discovery 2: A direct relationship between the number of processing cores and the overall system throughput was observed. As the number of cores increased, the system exhibited a near-linear scalability, enabling efficient utilization of available computational resources.
- Discovery 3: The analysis revealed a trade-off between processing speed and energy consumption. While parallel computing achieved faster processing times, it also resulted in higher energy consumption. This finding emphasizes the importance of optimizing energy efficiency in parallel computing systems.
These discoveries shed light on the performance characteristics and trade-offs associated with parallel computing architectures for large-scale data processing tasks. The following sections will delve into the implications of these findings, discussing their significance, limitations, and potential applications.”
In this example, the researcher presents a concise overview of the major discoveries made during the research. Each discovery is briefly described, highlighting the key insights obtained from the analysis. By summarizing the findings in a synoptic manner, the reader gains an immediate understanding of the notable contributions and can anticipate the subsequent detailed discussion.
This approach allows the discussion section to begin with a clear and impactful introduction of the major discoveries, capturing the reader’s interest and setting the stage for a comprehensive exploration of each finding in subsequent paragraphs.
IV. Highlight the Observations
Coming to the major part of the findings, the discussion section should interpret the key observations, the analysis of charts, and the analysis of tables. In the field of computer science, presenting and explaining the results in a clear and accessible manner is essential for readers to grasp the significance of the findings. Here are some examples of how to effectively highlight observations in computer science research:
Begin with explaining the objective of the research, followed by what inspired you as a researcher to study the subject:
In a study on machine learning algorithms for sentiment analysis, start by stating the goal of developing an accurate and efficient sentiment analysis model. Share your motivation for choosing this research topic, such as the increasing importance of sentiment analysis in various domains like social media, customer feedback analysis, and market research.
Example: The objective of this research was to develop a sentiment analysis model using machine learning algorithms. As sentiment analysis plays a vital role in understanding public opinion and customer feedback, we were motivated by the need for an accurate and efficient model that could be applied in various domains such as social media analysis, customer reviews, and market research.
Explain the meaning of the findings, as every reader might not understand the analysis of graphs and charts as easily as people who are in the same field as you:
If your research involves analyzing performance metrics of different algorithms, consider presenting the results in a visually intuitive manner, such as line graphs or bar charts. In the discussion section, explain the significance of the trends observed in the graphs. For instance, if a particular algorithm consistently outperforms others in terms of accuracy, explain why this finding is noteworthy and how it aligns with existing knowledge in the field.
Example: To present the performance evaluation of the algorithms, we analyzed multiple metrics, including precision, recall, and F1 score. The line graph in Figure 1 demonstrates the trends observed. It is noteworthy that Algorithm A consistently outperformed the other algorithms across all metrics. This finding indicates that Algorithm A has a higher ability to accurately classify sentiment in comparison to its counterparts. This aligns with previous studies that have also highlighted the robustness of Algorithm A in sentiment analysis tasks.
Ensure the reader can understand the key observations without being forced to go through the whole paper:
In computer science research, it is crucial to present concise summaries of your key observations to facilitate understanding for readers who may not have the time or expertise to go through the entire paper. For example, if your study compares the runtime performance of two programming languages for a specific task, clearly state the observed differences and their implications. Highlight any unexpected or notable findings that may challenge conventional wisdom or open up new avenues for future exploration.
Example: In this study comparing the runtime performance of Python and Java for a specific computational task, we observed notable differences. Python consistently showed faster execution times, averaging 20% less time than Java across varying input sizes. These results challenge the common perception that Java is the superior choice for computationally intensive tasks. The observed performance advantage of Python in this context suggests the need for further investigation into the underlying factors contributing to this discrepancy, such as differences in language design and optimization strategies.
By employing these strategies, researchers can effectively highlight their observations in the discussion section. This enables readers to gain a clear understanding of the significance of the findings and their implications without having to delve into complex technical details.
V. Compare and Relate with other Research Works
No one is ever the only person researching a particular subject. A researcher always has companions and competitors. The discussion section should have a detailed comparison of the research. It should present the facts that relate the research to studies done on the same subject.
Example: The table below compares some of the well-known prediction techniques with our fuzzy predictor with MOM defuzzification for response time, relative error and Environmental constraints. Based on the results obtained it can be concluded that the Fuzzy predictor with MOM defuzzification has a less relative error and quick response time as compared to other prediction techniques. The proposed predictor is more flexible, simple to implement and deals with noisy and uncertain data from real-life situations. The relative error of 5-10% is acceptable for our system as the predicted fuzzy region and the fuzzy region of the actual position remains the same.
Table 1: Comparison of well-known Robot Motion prediction Techniques
| Short Term Predictor | Environmental constraints if any | Relative Error | Response time in seconds |
| ANN Predictor | A simulated environment with Rectilinear paths | 6-17% | — |
| Bayesian Occupancy filter | Only for small-scale environments | Not specified | 100 x 10-3 |
| Polynomial NN | Simulated environment | 1-10% | Not specified |
| Auto Regressive model | Simulated Environment | Not specified | Computationally intensive |
| Fuzzy Predictor with MOM | Real-life environment | 1-10% | 07×10-3 sec to 09×10-3 sec |
VI. Provide Alternate View Points
Almost every time, it has been noticed that analysis of charts and graphs shows results that tend to have more than one explanation. The researcher must consider every possible explanation and potential enhancement of the study from alternative viewpoints. It is critically important that this is clearly put out to the readers in the discussion section.
In the discussion section of a research paper, it is important to acknowledge that data analysis often yields results that can be interpreted in multiple ways. By considering different viewpoints and potential enhancements, researchers can provide a more comprehensive and nuanced analysis of their findings. Here are some examples:
Example 1: “The analysis of our experimental data showed a decrease in system performance following the implementation of the proposed optimization technique. While our initial interpretation suggested that the optimization failed to achieve the desired outcome, an alternate viewpoint could be that the decrease in performance was influenced by an external factor, such as the configuration of the hardware setup. Further investigation into the hardware settings and benchmarking protocols is necessary to fully understand the observed results and identify potential enhancements.”
Example 2: “The analysis of user feedback revealed a mixed response to the redesigned user interface. While some participants reported improved usability and satisfaction, others expressed confusion and dissatisfaction. An alternate viewpoint could be that the diverse range of user backgrounds and preferences might have influenced these varied responses. Further research should focus on conducting user studies with a larger and more diverse sample to gain a deeper understanding of the underlying factors contributing to the contrasting user experiences.”
Example 3: “Our study found a positive correlation between the implementation of agile methodologies and project success rates. However, an alternate viewpoint suggests that other factors, such as team dynamics and project complexity, could have influenced the observed correlation. Future research should explore the interactions between agile methodologies and these potential confounding factors to gain a more comprehensive understanding of their impact on project success.”
In these examples, researchers present alternative viewpoints that offer different interpretations or explanations for the observed results. By acknowledging these alternate viewpoints, researchers demonstrate a balanced and comprehensive analysis of their findings. It is crucial to clearly communicate these alternative perspectives to readers in the discussion section, as it encourages critical thinking and highlights the complexity and potential limitations of the research.
By presenting alternate viewpoints, researchers invite further exploration and discussion, fostering a more comprehensive understanding of the research topic. This approach enriches the scientific discourse and promotes a deeper analysis of the findings, contributing to the overall advancement of knowledge in the field.
VII. Future Directions and Conclusion
A. Future Directions
The section must have suggestions for research that should be done to unanswered questions. These should be suggested at the beginning of the discussion section to avoid questions being asked by critics. Emphasizing the importance of following future directions can lead to new research as well.
Example: ” While this study provides valuable insights into the performance of the proposed algorithm, there are several unanswered questions and avenues for future research that merit attention. By identifying these areas, we aim to stimulate further exploration and contribute to the continuous advancement of the field. The following future directions are suggested:
- Future Direction 1: Investigating the algorithm’s performance under different dataset characteristics and distributions. The current study focused on a specific dataset, but it would be valuable to evaluate the algorithm’s robustness and generalizability across a broader range of datasets, including real-world scenarios and diverse data sources.
- Future Direction 2: Exploring the potential integration of additional machine learning techniques or ensemble methods to further enhance the algorithm’s accuracy and reliability. By combining the strengths of multiple models, it is possible to achieve better performance and handle complex patterns and outliers more effectively.
- Future Direction 3: Extending the evaluation to consider the algorithm’s scalability in large-scale deployment scenarios. As the volume of data continues to grow exponentially, it is crucial to assess the algorithm’s efficiency and scalability in handling big data processing requirements.
By suggesting these future directions, we hope to inspire researchers to explore new avenues and build upon the foundation laid by this study. Addressing these unanswered questions will contribute to a more comprehensive understanding of the algorithm’s capabilities and limitations, paving the way for further advancements in the field.”
In this example, the researcher presents specific future directions that can guide further research. Each future direction is described concisely, highlighting the specific area of investigation and the potential benefits of pursuing those directions. By suggesting these future directions early in the discussion section, the researcher proactively addresses potential questions or criticisms and demonstrates a proactive approach to knowledge expansion.
By emphasizing the importance of following future directions, researchers not only inspire others to continue the research trajectory but also contribute to the collective growth of the field. This approach encourages ongoing exploration, innovation, and collaboration, ensuring the continuous development and improvement of computer science research.
B. Conclusion
In the final step, wrap up the discussion section by summarizing the key points and emphasizing the overall implications of your research. We will discuss the significance of your study’s contributions and offer some closing thoughts to leave a lasting impression on your readers. This section serves as a crucial opportunity to reinforce the main findings and highlight the broader impact of your work. Here are some examples:
Example 1: “In conclusion, this research has made significant contributions to the field of natural language processing. By proposing a novel neural network architecture for language generation, we have demonstrated the effectiveness and versatility of the model in generating coherent and contextually relevant sentences. The experimental results indicate a significant improvement in language generation quality compared to existing approaches. The implications of this research extend beyond traditional applications, opening up new possibilities for automated content creation, chatbot systems, and dialogue generation in artificial intelligence.”
Example 2: “In summary, this study has provided valuable insights into the optimization of network routing protocols for wireless sensor networks. By proposing a novel hybrid routing algorithm that combines the advantages of both reactive and proactive protocols, we have demonstrated enhanced network performance in terms of latency, energy efficiency, and scalability. The experimental results validate the effectiveness of the proposed algorithm in dynamic and resource-constrained environments. These findings have implications for various applications, including environmental monitoring, industrial automation, and smart city infrastructure.”
Example 3: “In closing, this research sheds light on the security vulnerabilities of blockchain-based smart contracts. By conducting an extensive analysis of existing smart contract platforms and identifying potential attack vectors, we have highlighted the need for robust security measures to mitigate risks and protect user assets. The insights gained from this study can guide the development of more secure and reliable smart contract frameworks, ensuring the integrity and trustworthiness of blockchain-based applications across industries such as finance, supply chain, and decentralized applications.”
In these examples, the concluding thoughts summarize the main contributions and findings of the research. They emphasize the significance of the study’s implications and highlight the potential impact on various domains within computer science. By providing a succinct and impactful summary, the researcher leaves a lasting impression on readers, reinforcing the value and relevance of the research in the field.
How to Validate the Claims I made in the Discussion Section of My Research Paper?
Validating claims in the discussion section of a research paper is essential to ensure the credibility and reliability of your findings. Here are some strategies to validate the claims made in the discussion section:
- Referencing supporting evidence: Cite relevant sources from the existing literature that provide evidence or support for your claims. These sources can include peer-reviewed studies, research articles, and authoritative sources in your field. By referencing credible and reputable sources, you establish the validity of your claims and demonstrate that your interpretations are grounded in existing knowledge.
- Relating to the results: Connect your claims to the results presented in the earlier sections of your research paper. Clearly demonstrate how the findings support your claims and provide evidence for your interpretations. Refer to specific data, measurements, statistical analyses, or other evidence from your results section to substantiate your claims.
- Comparing with previous research: Discuss how your findings align with or diverge from previous research in the field. Reference relevant studies and explain how your results compare to or build upon existing knowledge. By contextualizing your claims within the broader research landscape, you provide further validation for your interpretations.
- Addressing limitations and alternative explanations: Acknowledge the limitations of your study and consider alternative explanations for your findings. By addressing potential counterarguments and alternative viewpoints, you demonstrate a thorough evaluation of your claims and increase the robustness of your conclusions.
- Seeking peer feedback: Prior to submitting your research paper, consider seeking feedback from colleagues or experts in your field. They can provide valuable insights and suggestions for further validating your claims or improving the clarity of your arguments.
- Inviting replication and further research: Encourage other researchers to replicate your study or conduct further investigations. By promoting replication and future research, you contribute to the ongoing validation and refinement of your claims.
Remember, the validation of claims in the discussion section is a critical aspect of scientific research. By employing rigorous methods and logical reasoning, you can strengthen the credibility and impact of your findings and contribute to the advancement of knowledge in your field.
Phrases that can be used in the Discussion Section of a Research Paper
Here are some common phrases that can be used in the discussion section of a paper or research article. I’ve included a table with examples to illustrate how these phrases might be used:
| Phrase | Example |
|---|---|
| Interpretation: This phrase is used to explain the meaning and significance of the results. | “The results suggest that increasing the number of hidden layers in a neural network can lead to higher accuracy on certain types of datasets, but may lead to overfitting on others.” |
| Comparison to previous research: This phrase is used to compare the results to previous research in the field. | “Our findings are consistent with previous research on the effectiveness of ensemble methods for classification tasks (Smith et al., 2019; Jones et al., 2020).” |
| Limitations: This phrase is used to describe limitations of the study or potential sources of error or bias. | “One limitation of our study is that we only evaluated the models on a single dataset, which may not generalize to other domains or applications.” |
| Implications: This phrase is used to discuss the practical or theoretical implications of the results. | “The findings of this study could inform the development of more accurate and reliable systems for detecting fraud in financial transactions.” |
| Future work: This phrase is used to suggest directions for future research or improvements to the current system or approach. | “Future work could explore the use of more complex feature engineering techniques to improve the performance of the machine learning models on imbalanced datasets.” |
| Contributions: This phrase is used to describe the original contributions of the study or the novelty of the approach or methodology. | “To the best of our knowledge, this is the first study to evaluate the performance of a hybrid approach combining deep learning and reinforcement learning for autonomous driving in complex environments.” |
| Strengths: This phrase is used to highlight the strengths or advantages of the current approach or methodology. | “One of the strengths of our approach is its ability to handle noisy and incomplete data, which is common in real-world applications.” |
Phrases that can be used in the Analysis part of the Discussion Section of a Research Paper
Here are some common academic phrases that can be used in the analysis section of a paper or research article. I have included a table with examples to illustrate how these phrases might be used:
| Phrase | Example |
|---|---|
| Descriptive statistics: This phrase is used to describe the basic characteristics of the data, such as mean, standard deviation, and range. | “The average processing time for the proposed algorithm was 3.2 seconds, with a standard deviation of 0.5 seconds.” |
| Inferential statistics: This phrase is used to describe the statistical tests used to draw conclusions from the data. | “We used a two-tailed t-test to compare the performance of the two algorithms, with a significance level of 0.05.” |
| Correlation analysis: This phrase is used to describe the relationships between variables in the data. | “We found a strong positive correlation between the number of training samples and the accuracy of the classification model.” |
| Regression analysis: This phrase is used to describe the relationships between one or more independent variables and a dependent variable. | “We used a multiple linear regression model to predict the processing time of the algorithm based on the number of input parameters and the complexity of the data.” |
| Classification analysis: This phrase is used to describe the process of assigning observations to predefined categories. | “We evaluated the performance of the classification model using metrics such as precision, recall, and F1-score.” |
| Clustering analysis: This phrase is used to describe the process of grouping similar observations together based on their characteristics. | “We used a k-means clustering algorithm to group the customers into four distinct segments based on their purchasing behavior.” |
| Visualization: This phrase is used to describe the use of graphs or charts to illustrate patterns or relationships in the data. | “The scatter plot showed a clear positive correlation between the size of the training set and the accuracy of the classification model.” |
Your Next Move…
I believe you will proceed to write conclusion section of your research paper. Conclusion section is the most neglected part of the research paper as many authors feel it is unnecessary but write in a hurry to submit the article to some reputed journal.
Please note, once your paper gets published , the readers decide to read your full paper based only on abstract and conclusion. They decide the relevance of the paper based on only these two sections. If they don’t read then they don’t cite and this in turn affects your citation score. So my sincere advice to you is not to neglect this section.
Visit my article on “How to Write Conclusion Section of Research Paper” for further details.
Please visit my article on “Importance and Improving of Citation Score for Your Research Paper” for increasing your visibility in research community and on Google Scholar Citation Score.
Conclusion
The Discussion section of a research paper is an essential part of any study, as it allows the author to interpret their results and contextualize their findings. To write an effective Discussion section, authors should focus on the relevance of their research, highlight the limitations, introduce new discoveries, highlight their observations, compare and relate their findings to other research works, provide alternate viewpoints, and show future directions.
By following these 7 steps, authors can ensure that their Discussion section is comprehensive, informative, and thought-provoking. A well-written Discussion section not only helps the author interpret their results but also provides insights into the implications and applications of their research.
In conclusion, the Discussion section is an integral part of any research paper, and by following these 7 steps, authors can write a compelling and informative discussion section that contributes to the broader scientific community.
Frequently Asked Questions
Whether charts and graphs are allowed in discussion section of my Research Paper?
Yes, charts and graphs are generally allowed in the discussion section of a research paper. While the discussion section is primarily focused on interpreting and discussing the findings, incorporating visual aids such as charts and graphs can be helpful in presenting and supporting the analysis.
Can I add citations in Discussion section of my Research Paper?
Yes, you can add citations in the discussion section of your research paper. In fact, it is highly recommended to support your statements, interpretations, and claims with relevant and credible sources. Citations in the discussion section help to strengthen the validity and reliability of your arguments and demonstrate that your findings are grounded in existing literature.
Can I combine results and discussion section in my Research Paper?
Combining the results and discussion sections in a research paper is a common practice in certain disciplines, particularly in shorter research papers or those with specific formatting requirements. This approach can help streamline the presentation of your findings and provide a more cohesive narrative. However, it is important to note that the decision to combine these sections should be based on the guidelines of the target journal or publication and the specific requirements of your field.
What is the weightage of discussion section in a research paper in terms of selection to a journal?
The weightage of the discussion section in terms of the selection of a research paper for publication in a journal can vary depending on the specific requirements and criteria of the journal. However, it is important to note that the discussion section is a critical component of a research paper as it allows researchers to interpret their findings, contextualize them within the existing literature, and discuss their implications.
Whether literature survey paper has a discussion section?
In general, literature survey papers typically do not have a separate section explicitly labeled as “Discussion.” However, the content of a literature survey paper often incorporates elements of discussion throughout the paper. The focus of a literature survey paper is to review and summarize existing literature on a specific topic or research question, rather than presenting original research findings.
