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How to Write the Dissertation Findings or Results – Steps & Tips

Published by Grace Graffin at August 11th, 2021 , Revised On June 11, 2024

Each  part of the dissertation is unique, and some general and specific rules must be followed. The dissertation’s findings section presents the key results of your research without interpreting their meaning .

Theoretically, this is an exciting section of a dissertation because it involves writing what you have observed and found. However, it can be a little tricky if there is too much information to confuse the readers.

The goal is to include only the essential and relevant findings in this section. The results must be presented in an orderly sequence to provide clarity to the readers.

This section of the dissertation should be easy for the readers to follow, so you should avoid going into a lengthy debate over the interpretation of the results.

It is vitally important to focus only on clear and precise observations. The findings chapter of the  dissertation  is theoretically the easiest to write.

It includes  statistical analysis and a brief write-up about whether or not the results emerging from the analysis are significant. This segment should be written in the past sentence as you describe what you have done in the past.

This article will provide detailed information about  how to   write the findings of a dissertation .

When to Write Dissertation Findings Chapter

As soon as you have gathered and analysed your data, you can start to write up the findings chapter of your dissertation paper. Remember that it is your chance to report the most notable findings of your research work and relate them to the research hypothesis  or  research questions set out in  the introduction chapter of the dissertation .

You will be required to separately report your study’s findings before moving on to the discussion chapter  if your dissertation is based on the  collection of primary data  or experimental work.

However, you may not be required to have an independent findings chapter if your dissertation is purely descriptive and focuses on the analysis of case studies or interpretation of texts.

• Always report the findings of your research in the past tense.
• The dissertation findings chapter varies from one project to another, depending on the data collected and analyzed.
• Avoid reporting results that are not relevant to your research questions or research hypothesis.

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1. Reporting Quantitative Findings

The best way to present your quantitative findings is to structure them around the research  hypothesis or  questions you intend to address as part of your dissertation project.

Report the relevant findings for each research question or hypothesis, focusing on how you analyzed them.

Analysis of your findings will help you determine how they relate to the different research questions and whether they support the hypothesis you formulated.

While you must highlight meaningful relationships, variances, and tendencies, it is important not to guess their interpretations and implications because this is something to save for the discussion  and  conclusion  chapters.

Any findings not directly relevant to your research questions or explanations concerning the data collection process  should be added to the dissertation paper’s appendix section.

Use of Figures and Tables in Dissertation Findings

Suppose your dissertation is based on quantitative research. In that case, it is important to include charts, graphs, tables, and other visual elements to help your readers understand the emerging trends and relationships in your findings.

Repeating information will give the impression that you are short on ideas. Refer to all charts, illustrations, and tables in your writing but avoid recurrence.

The text should be used only to elaborate and summarize certain parts of your results. On the other hand, illustrations and tables are used to present multifaceted data.

It is recommended to give descriptive labels and captions to all illustrations used so the readers can figure out what each refers to.

How to Report Quantitative Findings

Here is an example of how to report quantitative results in your dissertation findings chapter;

Two hundred seventeen participants completed both the pretest and post-test and a Pairwise T-test was used for the analysis. The quantitative data analysis reveals a statistically significant difference between the mean scores of the pretest and posttest scales from the Teachers Discovering Computers course. The pretest mean was 29.00 with a standard deviation of 7.65, while the posttest mean was 26.50 with a standard deviation of 9.74 (Table 1). These results yield a significance level of .000, indicating a strong treatment effect (see Table 3). With the correlation between the scores being .448, the little relationship is seen between the pretest and posttest scores (Table 2). This leads the researcher to conclude that the impact of the course on the educators’ perception and integration of technology into the curriculum is dramatic.

Paired Samples

Paired Samples Correlation

Paired Samples Test

Also Read: How to Write the Abstract for the Dissertation.

2. Reporting Qualitative Findings

A notable issue with reporting qualitative findings is that not all results directly relate to your research questions or hypothesis.

The best way to present the results of qualitative research is to frame your findings around the most critical areas or themes you obtained after you examined the data.

In-depth data analysis will help you observe what the data shows for each theme. Any developments, relationships, patterns, and independent responses directly relevant to your research question or hypothesis should be mentioned to the readers.

Additional information not directly relevant to your research can be included in the appendix .

How to Report Qualitative Findings

Here is an example of how to report qualitative results in your dissertation findings chapter;

The last question of the interview focused on the need for improvement in Thai ready-to-eat products and the industry at large, emphasizing the need for enhancement in the current products being offered in the market. When asked if there was any particular need for Thai ready-to-eat meals to be improved and how to improve them in case of ‘yes,’ the males replied mainly by saying that the current products need improvement in terms of the use of healthier raw materials and preservatives or additives. There was an agreement amongst all males concerning the need to improve the industry for ready-to-eat meals and the use of more healthy items to prepare such meals. The females were also of the opinion that the fast-food items needed to be improved in the sense that more healthy raw materials such as vegetable oil and unsaturated fats, including whole-wheat products, to overcome risks associated with trans fat leading to obesity and hypertension should be used for the production of RTE products. The frozen RTE meals and packaged snacks included many preservatives and chemical-based flavouring enhancers that harmed human health and needed to be reduced. The industry is said to be aware of this fact and should try to produce RTE products that benefit the community in terms of healthy consumption.

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What to Avoid in Dissertation Findings Chapter

• Avoid using interpretive and subjective phrases and terms such as “confirms,” “reveals,” “suggests,” or “validates.” These terms are more suitable for the discussion chapter , where you will be expected to interpret the results in detail.
• Only briefly explain findings in relation to the key themes, hypothesis, and research questions. You don’t want to write a detailed subjective explanation for any research questions at this stage.

The Do’s of Writing the Findings or Results Section

• Ensure you are not presenting results from other research studies in your findings.
• Observe whether or not your hypothesis is tested or research questions answered.
• Illustrations and tables present data and are labelled to help your readers understand what they relate to.
• Use software such as Excel, STATA, and SPSS to analyse results and important trends.

Essential Guidelines on How to Write Dissertation Findings

The dissertation findings chapter should provide the context for understanding the results. The research problem should be repeated, and the research goals should be stated briefly.

This approach helps to gain the reader’s attention toward the research problem. The first step towards writing the findings is identifying which results will be presented in this section.

The results relevant to the questions must be presented, considering whether the results support the hypothesis. You do not need to include every result in the findings section. The next step is ensuring the data can be appropriately organized and accurate.

You will need to have a basic idea about writing the findings of a dissertation because this will provide you with the knowledge to arrange the data chronologically.

Start each paragraph by writing about the most important results and concluding the section with the most negligible actual results.

A short paragraph can conclude the findings section, summarising the findings so readers will remember as they transition to the next chapter. This is essential if findings are unexpected or unfamiliar or impact the study.

Our writers can help you with all parts of your dissertation, including statistical analysis of your results . To obtain free non-binding quotes, please complete our online quote form here .

Be Impartial in your Writing

When crafting your findings, knowing how you will organize the work is important. The findings are the story that needs to be told in response to the research questions that have been answered.

Therefore, the story needs to be organized to make sense to you and the reader. The findings must be compelling and responsive to be linked to the research questions being answered.

Always ensure that the size and direction of any changes, including percentage change, can be mentioned in the section. The details of p values or confidence intervals and limits should be included.

The findings sections only have the relevant parts of the primary evidence mentioned. Still, it is a good practice to include all the primary evidence in an appendix that can be referred to later.

The results should always be written neutrally without speculation or implication. The statement of the results mustn’t have any form of evaluation or interpretation.

Negative results should be added in the findings section because they validate the results and provide high neutrality levels.

The length of the dissertation findings chapter is an important question that must be addressed. It should be noted that the length of the section is directly related to the total word count of your dissertation paper.

The writer should use their discretion in deciding the length of the findings section or refer to the dissertation handbook or structure guidelines.

It should neither belong nor be short nor concise and comprehensive to highlight the reader’s main findings.

Ethically, you should be confident in the findings and provide counter-evidence. Anything that does not have sufficient evidence should be discarded. The findings should respond to the problem presented and provide a solution to those questions.

Structure of the Findings Chapter

The chapter should use appropriate words and phrases to present the results to the readers. Logical sentences should be used, while paragraphs should be linked to produce cohesive work.

You must ensure all the significant results have been added in the section. Recheck after completing the section to ensure no mistakes have been made.

The structure of the findings section is something you may have to be sure of primarily because it will provide the basis for your research work and ensure that the discussions section can be written clearly and proficiently.

One way to arrange the results is to provide a brief synopsis and then explain the essential findings. However, there should be no speculation or explanation of the results, as this will be done in the discussion section.

Another way to arrange the section is to present and explain a result. This can be done for all the results while the section is concluded with an overall synopsis.

This is the preferred method when you are writing more extended dissertations. It can be helpful when multiple results are equally significant. A brief conclusion should be written to link all the results and transition to the discussion section.

Numerous data analysis dissertation examples are available on the Internet, which will help you improve your understanding of writing the dissertation’s findings.

Problems to Avoid When Writing Dissertation Findings

One of the problems to avoid while writing the dissertation findings is reporting background information or explaining the findings. This should be done in the introduction section .

You can always revise the introduction chapter based on the data you have collected if that seems an appropriate thing to do.

Raw data or intermediate calculations should not be added in the findings section. Always ask your professor if raw data needs to be included.

If the data is to be included, then use an appendix or a set of appendices referred to in the text of the findings chapter.

Do not use vague or non-specific phrases in the findings section. It is important to be factual and concise for the reader’s benefit.

The findings section presents the crucial data collected during the research process. It should be presented concisely and clearly to the reader. There should be no interpretation, speculation, or analysis of the data.

The significant results should be categorized systematically with the text used with charts, figures, and tables. Furthermore, avoiding using vague and non-specific words in this section is essential.

It is essential to label the tables and visual material properly. You should also check and proofread the section to avoid mistakes.

The dissertation findings chapter is a critical part of your overall dissertation paper. If you struggle with presenting your results and statistical analysis, our expert dissertation writers can help you get things right. Whether you need help with the entire dissertation paper or individual chapters, our dissertation experts can provide customized dissertation support .

FAQs About Findings of a Dissertation

How do i report quantitative findings.

The best way to present your quantitative findings is to structure them around the research hypothesis or research questions you intended to address as part of your dissertation project. Report the relevant findings for each of the research questions or hypotheses, focusing on how you analyzed them.

How do I report qualitative findings?

The best way to present the qualitative research results is to frame your findings around the most important areas or themes that you obtained after examining the data.

An in-depth analysis of the data will help you observe what the data is showing for each theme. Any developments, relationships, patterns, and independent responses that are directly relevant to your research question or hypothesis should be clearly mentioned for the readers.

Can I use interpretive phrases like ‘it confirms’ in the finding chapter?

No, It is highly advisable to avoid using interpretive and subjective phrases in the finding chapter. These terms are more suitable for the discussion chapter , where you will be expected to provide your interpretation of the results in detail.

Can I report the results from other research papers in my findings chapter?

NO, you must not be presenting results from other research studies in your findings.

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Research Findings – Objectives , Importance and Techniques

Published 16 October, 2023

Findings are basically the key outcome of the investigation. It is basically a key fact which you can discover during an investigation. Research findings are facts and phrases, observations, and experimental data resulting from research.

It’s important to note here that “finding” does not always mean “factual information” because conductive research relies on results and implications rather than measurable facts.

For example, A researcher is conducting research for measuring the extent up to which globalization impacts the business activities of firms. The findings of the research reveal that there has been a great increase in the profitability of companies after globalization. An important fact which researcher has discovered is that it is globalization which has enabled firms to expand their business operations at the international level.

Objectives of finding section in the research paper

• The main objective of the finding section in a research paper is to display or showcase the outcome in a logical manner by utilizing, tables, graphs, and charts.
• The objective of research findings is to provide a holistic view of the latest research findings in related areas.
• Research findings also aim at providing novel concepts and innovative findings that can be utilized for further research, development of new products or services, implementation of better business strategies, etc.

For example, an academic paper on “the use of product life cycle theory with reference to various product categories” will not only discuss different dimensions of the product life cycle but would also present a detailed case study analysis on how the concept was applied using several contemporary case studies from diverse industries.

Importance of findings in the research paper

The finding section in the research paper has great importance as

• It is the section in a research paper or dissertation that will help you in developing an in-depth understanding of the research problems .
• This is the section where the theories where you can accept or reject theories.
• The findings section helps you in demonstrating the significance of the problem on which you are performing research.
• It is through analysis of the finding section you can easily address the correlational research between the different types of variables in the study.

How to Write Research Findings?

Every research project is unique, so it is very much important for the researcher to utilize different strategies for writing different sections of the research paper. 5 steps that you need to follow for writing the research findings section are:

Step 1: Review the guidelines or instructions of the instructor

It is an initial step, where you should review the guidelines.  By reading the guidelines you will be able to address the different requirements for presenting the results. While reviewing the guidelines you should also keep in mind the restrictions related to the interpretations. In the reseal findings sections, you can also make a comparison between your research results with the outcome of the investigation which other researchers have performed.

Step 2: Focus on the results of the experiment and other findings

At this step, you should choose specific focus experimental results and other research discoveries which are relevant to research questions and objectives. You utilizing subheadings can avoid excessive and peripheral details.  Students can present raw data in appendices of a research paper. You should provide a summary of key findings after completion of the section. Before making the decision related to the structure of the findings section, you need to consider the hypothesis in research and research questions . You should match the format of the findings chapter with that of the research methods sections.

Step 3: Design effective visual presentations

Designing effective visual presentations of research results will help you in improving textual reports of findings. Students can use tables of different styles and unique figures such as maps, graphs, photos which are mainly used by researchers for presenting research findings. But it is very much essential for you to review the journal guidelines. As this is the tactics which will help you in analyzing the requirement of labeling and specific type of formatting. You should number tables, figures, and placement in the manuscript. You should provide a clear and detailed explanation of the data in tables and charts.  Tables and figures should also be self-explanatory

Step 4: Write findings section

You should write the findings sections in a factual and objective manner. While writing the research findings section you should keep in mind its aim. The main aim of the specific section is to communicate information. While writing a findings chapter, it is very much important for you to construct sentences by using a simple structure. You should use an active voice for writing research-finding chapters.  It is very much crucial for you to maintain your concentration on grammar, punctuation, and spelling. Students can utilize a special type of terminology for presenting the findings of the study. You can use thematic analysis in research for presenting the findings. In the thematic analysis technique, you need to design themes on the basis of the answers of respondents.

You should use a logical approach for organizing the findings section in a research paper.  it is very much necessary to highlight the main point and provide summary information which is important for readers in order to develop an understanding of the research discussion section.

Step 5: Review draft of findings section

After writing the findings, you should revise and review them. It is the review technique that will enable you to check accuracy and consistency in information. You can read the content aloud. It s the strategy which will help you in addressing the mistakes.  Ensure that the order in which you have presented results is the best order for focusing readers on your research objectives and preparing them for the interpretations, speculations. Students can also provide recommendations in the discussion chapter. They in order to provide good suggestions need to review back such as introduction, background material.

Read Also: Research Paper Conclusion Tips

Techniques of summarizing important findings

There are a few techniques that you can apply for writing your findings section in a systematic manner. Firstly, you should summarize the key findings. For example, you should start your finding a section like this:

• The outcome of research reveals that ……
• The investigation represents the correlation among….
• While writing the finding section in a research paper, you do not include information that is not important.
• You should provide a synopsis of outcomes along with a detailed description of the findings. It is considered to be an effective approach that can be applied to highlighting the key finding.
• You should use graphs, tables, and charts for presenting the finding
• While writing the findings section you need to highlight the negative outcomes. Students also need to provide proper justification and explanation for the same.

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From Data to Discovery: The Findings Section of a Research Paper

Discover the role of the findings section of a research paper here. Explore strategies and techniques to maximize your understanding.

Are you curious about the Findings section of a research paper? Did you know that this is a part where all the juicy results and discoveries are laid out for the world to see? Undoubtedly, the findings section of a research paper plays a critical role in presenting and interpreting the collected data. It serves as a comprehensive account of the study’s results and their implications.

Well, look no further because we’ve got you covered! In this article, we’re diving into the ins and outs of presenting and interpreting data in the findings section. We’ll be sharing tips and tricks on how to effectively present your findings, whether it’s through tables, graphs, or good old descriptive statistics.

Overview of the Findings Section of a Research Paper

The findings section of a research paper presents the results and outcomes of the study or investigation. It is a crucial part of the research paper where researchers interpret and analyze the data collected and draw conclusions based on their findings. This section aims to answer the research questions or hypotheses formulated earlier in the paper and provide evidence to support or refute them.

In the findings section, researchers typically present the data clearly and organized. They may use tables, graphs, charts, or other visual aids to illustrate the patterns, trends, or relationships observed in the data. The findings should be presented objectively, without any bias or personal opinions, and should be accompanied by appropriate statistical analyses or methods to ensure the validity and reliability of the results.

Organizing the Findings Section

The findings section of the research paper organizes and presents the results obtained from the study in a clear and logical manner. Here is a suggested structure for organizing the Findings section:

Introduction to the Findings

Start the section by providing a brief overview of the research objectives and the methodology employed. Recapitulate the research questions or hypotheses addressed in the study.

To learn more about methodology, read this article .

Descriptive Statistics and Data Presentation

Present the collected data using appropriate descriptive statistics. This may involve using tables, graphs, charts, or other visual representations to convey the information effectively. Remember: we can easily help you with that.

Data Analysis and Interpretation

Perform a thorough analysis of the data collected and describe the key findings. Present the results of statistical analyses or any other relevant methods used to analyze the data. 

Discussion of Findings

Analyze and interpret the findings in the context of existing literature or theoretical frameworks . Discuss any patterns, trends, or relationships observed in the data. Compare and contrast the results with prior studies, highlighting similarities and differences. 

Limitations and Constraints

Acknowledge and discuss any limitations or constraints that may have influenced the findings. This could include issues such as sample size, data collection methods, or potential biases. 

Summarize the main findings of the study and emphasize their significance. Revisit the research questions or hypotheses and discuss whether they have been supported or refuted by the findings.

Presenting Data in the Findings Section

There are several ways to present data in the findings section of a research paper. Here are some common methods:

• Tables : Tables are commonly used to present organized and structured data. They are particularly useful when presenting numerical data with multiple variables or categories. Tables allow readers to easily compare and interpret the information presented. Learn how to cite tables in research papers here .
• Graphs and Charts: Graphs and charts are effective visual tools for presenting data, especially when illustrating trends, patterns, or relationships. Common types include bar graphs, line graphs, scatter plots, pie charts, and histograms. Graphs and charts provide a visual representation of the data, making it easier for readers to comprehend and interpret.
• Figures and Images: Figures and images can be used to present data that requires visual representation, such as maps, diagrams, or experimental setups. They can enhance the understanding of complex data or provide visual evidence to support the research findings.
• Descriptive Statistics: Descriptive statistics provide summary measures of central tendency (e.g., mean, median, mode) and dispersion (e.g., standard deviation, range) for numerical data. These statistics can be included in the text or presented in tables or graphs to provide a concise summary of the data distribution.

How to Effectively Interpret Results

Interpreting the results is a crucial aspect of the findings section in a research paper. It involves analyzing the data collected and drawing meaningful conclusions based on the findings. Following are the guidelines on how to effectively interpret the results.

Step 1 – Begin with a Recap

Start by restating the research questions or hypotheses to provide context for the interpretation. Remind readers of the specific objectives of the study to help them understand the relevance of the findings.

Step 2 – Relate Findings to Research Questions

Clearly articulate how the results address the research questions or hypotheses. Discuss each finding in relation to the original objectives and explain how it contributes to answering the research questions or supporting/refuting the hypotheses.

Step 3 – Compare with Existing Literature

Compare and contrast the findings with previous studies or existing literature. Highlight similarities, differences, or discrepancies between your results and those of other researchers. Discuss any consistencies or contradictions and provide possible explanations for the observed variations.

Step 4 – Consider Limitations and Alternative Explanations

Acknowledge the limitations of the study and discuss how they may have influenced the results. Explore alternative explanations or factors that could potentially account for the findings. Evaluate the robustness of the results in light of the limitations and alternative interpretations.

Step 5 – Discuss Implications and Significance

Highlight any potential applications or areas where further research is needed based on the outcomes of the study.

Step 6 – Address Inconsistencies and Contradictions

If there are any inconsistencies or contradictions in the findings, address them directly. Discuss possible reasons for the discrepancies and consider their implications for the overall interpretation. Be transparent about any uncertainties or unresolved issues.

Step 7 – Be Objective and Data-Driven

Present the interpretation objectively, based on the evidence and data collected. Avoid personal biases or subjective opinions. Use logical reasoning and sound arguments to support your interpretations.

Reporting Statistical Significance

When reporting statistical significance in the findings section of a research paper, it is important to accurately convey the results of statistical analyses and their implications. Here are some guidelines on how to report statistical significance effectively:

• Clearly State the Statistical Test: Begin by clearly stating the specific statistical test or analysis used to determine statistical significance. For example, you might mention that a t-test, chi-square test, ANOVA, correlation analysis, or regression analysis was employed.
• Report the Test Statistic: Provide the value of the test statistic obtained from the analysis. This could be the t-value, F-value, chi-square value, correlation coefficient, or any other relevant statistic depending on the test used.
• State the Degrees of Freedom: Indicate the degrees of freedom associated with the statistical test. Degrees of freedom represent the number of independent pieces of information available for estimating a statistic. For example, in a t-test, degrees of freedom would be mentioned as (df = n1 + n2 – 2) for an independent samples test or (df = N – 2) for a paired samples test.
• Report the p-value: The p-value indicates the probability of obtaining results as extreme or more extreme than the observed results, assuming the null hypothesis is true. Report the p-value associated with the statistical test. For example, p < 0.05 denotes statistical significance at the conventional level of α = 0.05.
• Provide the Conclusion: Based on the p-value obtained, state whether the results are statistically significant or not. If the p-value is less than the predetermined threshold (e.g., p < 0.05), state that the results are statistically significant. If the p-value is greater than the threshold, state that the results are not statistically significant.
• Discuss the Interpretation: After reporting statistical significance, discuss the practical or theoretical implications of the finding. Explain what the significant result means in the context of your research questions or hypotheses. Address the effect size and practical significance of the findings, if applicable.
• Consider Effect Size Measures: Along with statistical significance, it is often important to report effect size measures. Effect size quantifies the magnitude of the relationship or difference observed in the data. Common effect size measures include Cohen’s d, eta-squared, or Pearson’s r. Reporting effect size provides additional meaningful information about the strength of the observed effects.
• Be Accurate and Transparent: Ensure that the reported statistical significance and associated values are accurate. Avoid misinterpreting or misrepresenting the results. Be transparent about the statistical tests conducted, any assumptions made, and potential limitations or caveats that may impact the interpretation of the significant results.

Conclusion of the Findings Section

The conclusion of the findings section in a research paper serves as a summary and synthesis of the key findings and their implications. It is an opportunity to tie together the results, discuss their significance, and address the research objectives. Here are some guidelines on how to write the conclusion of the Findings section:

Summarize the Key Findings

Begin by summarizing the main findings of the study. Provide a concise overview of the significant results, patterns, or relationships that emerged from the data analysis. Highlight the most important findings that directly address the research questions or hypotheses.

Revisit the Research Objectives

Remind the reader of the research objectives stated at the beginning of the paper. Discuss how the findings contribute to achieving those objectives and whether they support or challenge the initial research questions or hypotheses.

Suggest Future Directions

Identify areas for further research or future directions based on the findings. Discuss any unanswered questions, unresolved issues, or new avenues of inquiry that emerged during the study. Propose potential research opportunities that can build upon the current findings.

The Best Scientific Figures to Represent Your Findings 

Have you heard of any tool that helps you represent your findings through visuals like graphs, pie charts, and infographics? Well, if you haven’t, then here’s the tool you need to explore – Mind the Graph . It’s the tool that has the best scientific figures to represent your findings. Go, try it now, and make your research findings stand out!

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Home » Research Results Section – Writing Guide and Examples

Research Results Section – Writing Guide and Examples

Table of Contents

Research Results

Research results refer to the findings and conclusions derived from a systematic investigation or study conducted to answer a specific question or hypothesis. These results are typically presented in a written report or paper and can include various forms of data such as numerical data, qualitative data, statistics, charts, graphs, and visual aids.

Results Section in Research

The results section of the research paper presents the findings of the study. It is the part of the paper where the researcher reports the data collected during the study and analyzes it to draw conclusions.

In the results section, the researcher should describe the data that was collected, the statistical analysis performed, and the findings of the study. It is important to be objective and not interpret the data in this section. Instead, the researcher should report the data as accurately and objectively as possible.

Structure of Research Results Section

The structure of the research results section can vary depending on the type of research conducted, but in general, it should contain the following components:

• Introduction: The introduction should provide an overview of the study, its aims, and its research questions. It should also briefly explain the methodology used to conduct the study.
• Data presentation : This section presents the data collected during the study. It may include tables, graphs, or other visual aids to help readers better understand the data. The data presented should be organized in a logical and coherent way, with headings and subheadings used to help guide the reader.
• Data analysis: In this section, the data presented in the previous section are analyzed and interpreted. The statistical tests used to analyze the data should be clearly explained, and the results of the tests should be presented in a way that is easy to understand.
• Discussion of results : This section should provide an interpretation of the results of the study, including a discussion of any unexpected findings. The discussion should also address the study’s research questions and explain how the results contribute to the field of study.
• Limitations: This section should acknowledge any limitations of the study, such as sample size, data collection methods, or other factors that may have influenced the results.
• Conclusions: The conclusions should summarize the main findings of the study and provide a final interpretation of the results. The conclusions should also address the study’s research questions and explain how the results contribute to the field of study.
• Recommendations : This section may provide recommendations for future research based on the study’s findings. It may also suggest practical applications for the study’s results in real-world settings.

Outline of Research Results Section

The following is an outline of the key components typically included in the Results section:

I. Introduction

• A brief overview of the research objectives and hypotheses
• A statement of the research question

II. Descriptive statistics

• Summary statistics (e.g., mean, standard deviation) for each variable analyzed
• Frequencies and percentages for categorical variables

III. Inferential statistics

• Results of statistical analyses, including tests of hypotheses
• Tables or figures to display statistical results

IV. Effect sizes and confidence intervals

• Effect sizes (e.g., Cohen’s d, odds ratio) to quantify the strength of the relationship between variables
• Confidence intervals to estimate the range of plausible values for the effect size

V. Subgroup analyses

• Results of analyses that examined differences between subgroups (e.g., by gender, age, treatment group)

VI. Limitations and assumptions

• Discussion of any limitations of the study and potential sources of bias
• Assumptions made in the statistical analyses

VII. Conclusions

• A summary of the key findings and their implications
• A statement of whether the hypotheses were supported or not
• Suggestions for future research

Example of Research Results Section

An Example of a Research Results Section could be:

• This study sought to examine the relationship between sleep quality and academic performance in college students.
• Hypothesis : College students who report better sleep quality will have higher GPAs than those who report poor sleep quality.
• Methodology : Participants completed a survey about their sleep habits and academic performance.

II. Participants

• Participants were college students (N=200) from a mid-sized public university in the United States.
• The sample was evenly split by gender (50% female, 50% male) and predominantly white (85%).
• Participants were recruited through flyers and online advertisements.

III. Results

• Participants who reported better sleep quality had significantly higher GPAs (M=3.5, SD=0.5) than those who reported poor sleep quality (M=2.9, SD=0.6).
• See Table 1 for a summary of the results.
• Participants who reported consistent sleep schedules had higher GPAs than those with irregular sleep schedules.

IV. Discussion

• The results support the hypothesis that better sleep quality is associated with higher academic performance in college students.
• These findings have implications for college students, as prioritizing sleep could lead to better academic outcomes.
• Limitations of the study include self-reported data and the lack of control for other variables that could impact academic performance.

V. Conclusion

• College students who prioritize sleep may see a positive impact on their academic performance.
• These findings highlight the importance of sleep in academic success.
• Future research could explore interventions to improve sleep quality in college students.

Example of Research Results in Research Paper :

Our study aimed to compare the performance of three different machine learning algorithms (Random Forest, Support Vector Machine, and Neural Network) in predicting customer churn in a telecommunications company. We collected a dataset of 10,000 customer records, with 20 predictor variables and a binary churn outcome variable.

Our analysis revealed that all three algorithms performed well in predicting customer churn, with an overall accuracy of 85%. However, the Random Forest algorithm showed the highest accuracy (88%), followed by the Support Vector Machine (86%) and the Neural Network (84%).

Furthermore, we found that the most important predictor variables for customer churn were monthly charges, contract type, and tenure. Random Forest identified monthly charges as the most important variable, while Support Vector Machine and Neural Network identified contract type as the most important.

Overall, our results suggest that machine learning algorithms can be effective in predicting customer churn in a telecommunications company, and that Random Forest is the most accurate algorithm for this task.

Example 3 :

Title : The Impact of Social Media on Body Image and Self-Esteem

Abstract : This study aimed to investigate the relationship between social media use, body image, and self-esteem among young adults. A total of 200 participants were recruited from a university and completed self-report measures of social media use, body image satisfaction, and self-esteem.

Results: The results showed that social media use was significantly associated with body image dissatisfaction and lower self-esteem. Specifically, participants who reported spending more time on social media platforms had lower levels of body image satisfaction and self-esteem compared to those who reported less social media use. Moreover, the study found that comparing oneself to others on social media was a significant predictor of body image dissatisfaction and lower self-esteem.

Conclusion : These results suggest that social media use can have negative effects on body image satisfaction and self-esteem among young adults. It is important for individuals to be mindful of their social media use and to recognize the potential negative impact it can have on their mental health. Furthermore, interventions aimed at promoting positive body image and self-esteem should take into account the role of social media in shaping these attitudes and behaviors.

Importance of Research Results

Research results are important for several reasons, including:

• Advancing knowledge: Research results can contribute to the advancement of knowledge in a particular field, whether it be in science, technology, medicine, social sciences, or humanities.
• Developing theories: Research results can help to develop or modify existing theories and create new ones.
• Improving practices: Research results can inform and improve practices in various fields, such as education, healthcare, business, and public policy.
• Identifying problems and solutions: Research results can identify problems and provide solutions to complex issues in society, including issues related to health, environment, social justice, and economics.
• Validating claims : Research results can validate or refute claims made by individuals or groups in society, such as politicians, corporations, or activists.
• Providing evidence: Research results can provide evidence to support decision-making, policy-making, and resource allocation in various fields.

How to Write Results in A Research Paper

Here are some general guidelines on how to write results in a research paper:

• Organize the results section: Start by organizing the results section in a logical and coherent manner. Divide the section into subsections if necessary, based on the research questions or hypotheses.
• Present the findings: Present the findings in a clear and concise manner. Use tables, graphs, and figures to illustrate the data and make the presentation more engaging.
• Describe the data: Describe the data in detail, including the sample size, response rate, and any missing data. Provide relevant descriptive statistics such as means, standard deviations, and ranges.
• Interpret the findings: Interpret the findings in light of the research questions or hypotheses. Discuss the implications of the findings and the extent to which they support or contradict existing theories or previous research.
• Discuss the limitations : Discuss the limitations of the study, including any potential sources of bias or confounding factors that may have affected the results.
• Compare the results : Compare the results with those of previous studies or theoretical predictions. Discuss any similarities, differences, or inconsistencies.
• Avoid redundancy: Avoid repeating information that has already been presented in the introduction or methods sections. Instead, focus on presenting new and relevant information.
• Be objective: Be objective in presenting the results, avoiding any personal biases or interpretations.

When to Write Research Results

Here are situations When to Write Research Results”

• After conducting research on the chosen topic and obtaining relevant data, organize the findings in a structured format that accurately represents the information gathered.
• Once the data has been analyzed and interpreted, and conclusions have been drawn, begin the writing process.
• Before starting to write, ensure that the research results adhere to the guidelines and requirements of the intended audience, such as a scientific journal or academic conference.
• Begin by writing an abstract that briefly summarizes the research question, methodology, findings, and conclusions.
• Follow the abstract with an introduction that provides context for the research, explains its significance, and outlines the research question and objectives.
• The next section should be a literature review that provides an overview of existing research on the topic and highlights the gaps in knowledge that the current research seeks to address.
• The methodology section should provide a detailed explanation of the research design, including the sample size, data collection methods, and analytical techniques used.
• Present the research results in a clear and concise manner, using graphs, tables, and figures to illustrate the findings.
• Discuss the implications of the research results, including how they contribute to the existing body of knowledge on the topic and what further research is needed.
• Conclude the paper by summarizing the main findings, reiterating the significance of the research, and offering suggestions for future research.

Purpose of Research Results

The purposes of Research Results are as follows:

• Informing policy and practice: Research results can provide evidence-based information to inform policy decisions, such as in the fields of healthcare, education, and environmental regulation. They can also inform best practices in fields such as business, engineering, and social work.
• Addressing societal problems : Research results can be used to help address societal problems, such as reducing poverty, improving public health, and promoting social justice.
• Generating economic benefits : Research results can lead to the development of new products, services, and technologies that can create economic value and improve quality of life.
• Supporting academic and professional development : Research results can be used to support academic and professional development by providing opportunities for students, researchers, and practitioners to learn about new findings and methodologies in their field.
• Enhancing public understanding: Research results can help to educate the public about important issues and promote scientific literacy, leading to more informed decision-making and better public policy.
• Evaluating interventions: Research results can be used to evaluate the effectiveness of interventions, such as treatments, educational programs, and social policies. This can help to identify areas where improvements are needed and guide future interventions.
• Contributing to scientific progress: Research results can contribute to the advancement of science by providing new insights and discoveries that can lead to new theories, methods, and techniques.
• Informing decision-making : Research results can provide decision-makers with the information they need to make informed decisions. This can include decision-making at the individual, organizational, or governmental levels.
• Fostering collaboration : Research results can facilitate collaboration between researchers and practitioners, leading to new partnerships, interdisciplinary approaches, and innovative solutions to complex problems.

Advantages of Research Results

Some Advantages of Research Results are as follows:

• Improved decision-making: Research results can help inform decision-making in various fields, including medicine, business, and government. For example, research on the effectiveness of different treatments for a particular disease can help doctors make informed decisions about the best course of treatment for their patients.
• Innovation : Research results can lead to the development of new technologies, products, and services. For example, research on renewable energy sources can lead to the development of new and more efficient ways to harness renewable energy.
• Economic benefits: Research results can stimulate economic growth by providing new opportunities for businesses and entrepreneurs. For example, research on new materials or manufacturing techniques can lead to the development of new products and processes that can create new jobs and boost economic activity.
• Improved quality of life: Research results can contribute to improving the quality of life for individuals and society as a whole. For example, research on the causes of a particular disease can lead to the development of new treatments and cures, improving the health and well-being of millions of people.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• How to Write Discussions and Conclusions

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

• the results of your research,
• a discussion of related research, and
• a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

Questions to ask yourself:

• Was my hypothesis correct?
• If my hypothesis is partially correct or entirely different, what can be learned from the results? 
• How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
• Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
• How can future research build on these observations? What are the key experiments that must be done? 
• What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

• Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
• Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
• Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
• State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
• Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
• If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
• Be concise. Adding unnecessary detail can distract from the main findings. 

Don’t

• Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
• Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
• Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
• Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
• Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

• How to Write a Great Title
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The results section is where you report the findings of your study based upon the methodology [or methodologies] you applied to gather information. The results section should state the findings of the research arranged in a logical sequence without bias or interpretation. A section describing results should be particularly detailed if your paper includes data generated from your own research.

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070.

Importance of a Good Results Section

When formulating the results section, it's important to remember that the results of a study do not prove anything . Findings can only confirm or reject the hypothesis underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.

The page length of this section is set by the amount and types of data to be reported . Be concise. Use non-textual elements appropriately, such as figures and tables, to present findings more effectively. In deciding what data to describe in your results section, you must clearly distinguish information that would normally be included in a research paper from any raw data or other content that could be included as an appendix. In general, raw data that has not been summarized should not be included in the main text of your paper unless requested to do so by your professor.

Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good strategy is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper that follows].

Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Brett, Paul. "A Genre Analysis of the Results Section of Sociology Articles." English for Specific Speakers 13 (1994): 47-59; Go to English for Specific Purposes on ScienceDirect;Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit; "Reporting Findings." In Making Sense of Social Research Malcolm Williams, editor. (London;: SAGE Publications, 2003) pp. 188-207.

Structure and Writing Style

I.  Organization and Approach

For most research papers in the social and behavioral sciences, there are two possible ways of organizing the results . Both approaches are appropriate in how you report your findings, but use only one approach.

• Present a synopsis of the results followed by an explanation of key findings . This approach can be used to highlight important findings. For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is appropriate to highlight this finding in the results section. However, speculating as to why this correlation exists and offering a hypothesis about what may be happening belongs in the discussion section of your paper.
• Present a result and then explain it, before presenting the next result then explaining it, and so on, then end with an overall synopsis . This is the preferred approach if you have multiple results of equal significance. It is more common in longer papers because it helps the reader to better understand each finding. In this model, it is helpful to provide a brief conclusion that ties each of the findings together and provides a narrative bridge to the discussion section of the your paper.

NOTE:   Just as the literature review should be arranged under conceptual categories rather than systematically describing each source, you should also organize your findings under key themes related to addressing the research problem. This can be done under either format noted above [i.e., a thorough explanation of the key results or a sequential, thematic description and explanation of each finding].

II.  Content

In general, the content of your results section should include the following:

• Introductory context for understanding the results by restating the research problem underpinning your study . This is useful in re-orientating the reader's focus back to the research problem after having read a review of the literature and your explanation of the methods used for gathering and analyzing information.
• Inclusion of non-textual elements, such as, figures, charts, photos, maps, tables, etc. to further illustrate key findings, if appropriate . Rather than relying entirely on descriptive text, consider how your findings can be presented visually. This is a helpful way of condensing a lot of data into one place that can then be referred to in the text. Consider referring to appendices if there is a lot of non-textual elements.
• A systematic description of your results, highlighting for the reader observations that are most relevant to the topic under investigation . Not all results that emerge from the methodology used to gather information may be related to answering the " So What? " question. Do not confuse observations with interpretations; observations in this context refers to highlighting important findings you discovered through a process of reviewing prior literature and gathering data.
• The page length of your results section is guided by the amount and types of data to be reported . However, focus on findings that are important and related to addressing the research problem. It is not uncommon to have unanticipated results that are not relevant to answering the research question. This is not to say that you don't acknowledge tangential findings and, in fact, can be referred to as areas for further research in the conclusion of your paper. However, spending time in the results section describing tangential findings clutters your overall results section and distracts the reader.
• A short paragraph that concludes the results section by synthesizing the key findings of the study . Highlight the most important findings you want readers to remember as they transition into the discussion section. This is particularly important if, for example, there are many results to report, the findings are complicated or unanticipated, or they are impactful or actionable in some way [i.e., able to be pursued in a feasible way applied to practice].

NOTE:   Always use the past tense when referring to your study's findings. Reference to findings should always be described as having already happened because the method used to gather the information has been completed.

III.  Problems to Avoid

When writing the results section, avoid doing the following :

• Discussing or interpreting your results . Save this for the discussion section of your paper, although where appropriate, you should compare or contrast specific results to those found in other studies [e.g., "Similar to the work of Smith [1990], one of the findings of this study is the strong correlation between motivation and academic achievement...."].
• Reporting background information or attempting to explain your findings. This should have been done in your introduction section, but don't panic! Often the results of a study point to the need for additional background information or to explain the topic further, so don't think you did something wrong. Writing up research is rarely a linear process. Always revise your introduction as needed.
• Ignoring negative results . A negative result generally refers to a finding that does not support the underlying assumptions of your study. Do not ignore them. Document these findings and then state in your discussion section why you believe a negative result emerged from your study. Note that negative results, and how you handle them, can give you an opportunity to write a more engaging discussion section, therefore, don't be hesitant to highlight them.
• Including raw data or intermediate calculations . Ask your professor if you need to include any raw data generated by your study, such as transcripts from interviews or data files. If raw data is to be included, place it in an appendix or set of appendices that are referred to in the text.
• Be as factual and concise as possible in reporting your findings . Do not use phrases that are vague or non-specific, such as, "appeared to be greater than other variables..." or "demonstrates promising trends that...." Subjective modifiers should be explained in the discussion section of the paper [i.e., why did one variable appear greater? Or, how does the finding demonstrate a promising trend?].
• Presenting the same data or repeating the same information more than once . If you want to highlight a particular finding, it is appropriate to do so in the results section. However, you should emphasize its significance in relation to addressing the research problem in the discussion section. Do not repeat it in your results section because you can do that in the conclusion of your paper.
• Confusing figures with tables . Be sure to properly label any non-textual elements in your paper. Don't call a chart an illustration or a figure a table. If you are not sure, go here .

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070; Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008;  Caprette, David R. Writing Research Papers. Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit ; Ng, K. H. and W. C. Peh. "Writing the Results." Singapore Medical Journal 49 (2008): 967-968; Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results. Thesis Writing in the Sciences. Course Syllabus. University of Florida.

Writing Tip

Why Don't I Just Combine the Results Section with the Discussion Section?

It's not unusual to find articles in scholarly social science journals where the author(s) have combined a description of the findings with a discussion about their significance and implications. You could do this. However, if you are inexperienced writing research papers, consider creating two distinct sections for each section in your paper as a way to better organize your thoughts and, by extension, your paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret the information and answer the "So What?" question. As you become more skilled writing research papers, you can consider melding the results of your study with a discussion of its implications.

Driscoll, Dana Lynn and Aleksandra Kasztalska. Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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How to Write the Results/Findings Section in Research

What is the research paper Results section and what does it do?

The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information. It presents these findings in a logical sequence without bias or interpretation from the author, setting up the reader for later interpretation and evaluation in the Discussion section. A major purpose of the Results section is to break down the data into sentences that show its significance to the research question(s).

The Results section appears third in the section sequence in most scientific papers. It follows the presentation of the Methods and Materials and is presented before the Discussion section —although the Results and Discussion are presented together in many journals. This section answers the basic question “What did you find in your research?”

What is included in the Results section?

The Results section should include the findings of your study and ONLY the findings of your study. The findings include:

• Data presented in tables, charts, graphs, and other figures (may be placed into the text or on separate pages at the end of the manuscript)
• A contextual analysis of this data explaining its meaning in sentence form
• All data that corresponds to the central research question(s)
• All secondary findings (secondary outcomes, subgroup analyses, etc.)

If the scope of the study is broad, or if you studied a variety of variables, or if the methodology used yields a wide range of different results, the author should present only those results that are most relevant to the research question stated in the Introduction section .

As a general rule, any information that does not present the direct findings or outcome of the study should be left out of this section. Unless the journal requests that authors combine the Results and Discussion sections, explanations and interpretations should be omitted from the Results.

How are the results organized?

The best way to organize your Results section is “logically.” One logical and clear method of organizing research results is to provide them alongside the research questions—within each research question, present the type of data that addresses that research question.

Let’s look at an example. Your research question is based on a survey among patients who were treated at a hospital and received postoperative care. Let’s say your first research question is:

“What do hospital patients over age 55 think about postoperative care?”

This can actually be represented as a heading within your Results section, though it might be presented as a statement rather than a question:

Attitudes towards postoperative care in patients over the age of 55

Now present the results that address this specific research question first. In this case, perhaps a table illustrating data from a survey. Likert items can be included in this example. Tables can also present standard deviations, probabilities, correlation matrices, etc.

Following this, present a content analysis, in words, of one end of the spectrum of the survey or data table. In our example case, start with the POSITIVE survey responses regarding postoperative care, using descriptive phrases. For example:

“Sixty-five percent of patients over 55 responded positively to the question “ Are you satisfied with your hospital’s postoperative care ?” (Fig. 2)

Include other results such as subcategory analyses. The amount of textual description used will depend on how much interpretation of tables and figures is necessary and how many examples the reader needs in order to understand the significance of your research findings.

Next, present a content analysis of another part of the spectrum of the same research question, perhaps the NEGATIVE or NEUTRAL responses to the survey. For instance:

  “As Figure 1 shows, 15 out of 60 patients in Group A responded negatively to Question 2.”

After you have assessed the data in one figure and explained it sufficiently, move on to your next research question. For example:

  “How does patient satisfaction correspond to in-hospital improvements made to postoperative care?”

This kind of data may be presented through a figure or set of figures (for instance, a paired T-test table).

Explain the data you present, here in a table, with a concise content analysis:

“The p-value for the comparison between the before and after groups of patients was .03% (Fig. 2), indicating that the greater the dissatisfaction among patients, the more frequent the improvements that were made to postoperative care.”

Let’s examine another example of a Results section from a study on plant tolerance to heavy metal stress . In the Introduction section, the aims of the study are presented as “determining the physiological and morphological responses of Allium cepa L. towards increased cadmium toxicity” and “evaluating its potential to accumulate the metal and its associated environmental consequences.” The Results section presents data showing how these aims are achieved in tables alongside a content analysis, beginning with an overview of the findings:

“Cadmium caused inhibition of root and leave elongation, with increasing effects at higher exposure doses (Fig. 1a-c).”

The figure containing this data is cited in parentheses. Note that this author has combined three graphs into one single figure. Separating the data into separate graphs focusing on specific aspects makes it easier for the reader to assess the findings, and consolidating this information into one figure saves space and makes it easy to locate the most relevant results.

Following this overall summary, the relevant data in the tables is broken down into greater detail in text form in the Results section.

• “Results on the bio-accumulation of cadmium were found to be the highest (17.5 mg kgG1) in the bulb, when the concentration of cadmium in the solution was 1×10G2 M and lowest (0.11 mg kgG1) in the leaves when the concentration was 1×10G3 M.”

Captioning and Referencing Tables and Figures

Tables and figures are central components of your Results section and you need to carefully think about the most effective way to use graphs and tables to present your findings . Therefore, it is crucial to know how to write strong figure captions and to refer to them within the text of the Results section.

The most important advice one can give here as well as throughout the paper is to check the requirements and standards of the journal to which you are submitting your work. Every journal has its own design and layout standards, which you can find in the author instructions on the target journal’s website. Perusing a journal’s published articles will also give you an idea of the proper number, size, and complexity of your figures.

Regardless of which format you use, the figures should be placed in the order they are referenced in the Results section and be as clear and easy to understand as possible. If there are multiple variables being considered (within one or more research questions), it can be a good idea to split these up into separate figures. Subsequently, these can be referenced and analyzed under separate headings and paragraphs in the text.

To create a caption, consider the research question being asked and change it into a phrase. For instance, if one question is “Which color did participants choose?”, the caption might be “Color choice by participant group.” Or in our last research paper example, where the question was “What is the concentration of cadmium in different parts of the onion after 14 days?” the caption reads:

 “Fig. 1(a-c): Mean concentration of Cd determined in (a) bulbs, (b) leaves, and (c) roots of onions after a 14-day period.”

Steps for Composing the Results Section

Because each study is unique, there is no one-size-fits-all approach when it comes to designing a strategy for structuring and writing the section of a research paper where findings are presented. The content and layout of this section will be determined by the specific area of research, the design of the study and its particular methodologies, and the guidelines of the target journal and its editors. However, the following steps can be used to compose the results of most scientific research studies and are essential for researchers who are new to preparing a manuscript for publication or who need a reminder of how to construct the Results section.

Step 1 : Consult the guidelines or instructions that the target journal or publisher provides authors and read research papers it has published, especially those with similar topics, methods, or results to your study.

• The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches.
• Note length limitations on restrictions on content. For instance, while many journals require the Results and Discussion sections to be separate, others do not—qualitative research papers often include results and interpretations in the same section (“Results and Discussion”).
• Reading the aims and scope in the journal’s “ guide for authors ” section and understanding the interests of its readers will be invaluable in preparing to write the Results section.

Step 2 : Consider your research results in relation to the journal’s requirements and catalogue your results.

• Focus on experimental results and other findings that are especially relevant to your research questions and objectives and include them even if they are unexpected or do not support your ideas and hypotheses.
• Catalogue your findings—use subheadings to streamline and clarify your report. This will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Create appendices that might interest specialists but prove too long or distracting for other readers.
• Decide how you will structure of your results. You might match the order of the research questions and hypotheses to your results, or you could arrange them according to the order presented in the Methods section. A chronological order or even a hierarchy of importance or meaningful grouping of main themes or categories might prove effective. Consider your audience, evidence, and most importantly, the objectives of your research when choosing a structure for presenting your findings.

Step 3 : Design figures and tables to present and illustrate your data.

• Tables and figures should be numbered according to the order in which they are mentioned in the main text of the paper.
• Information in figures should be relatively self-explanatory (with the aid of captions), and their design should include all definitions and other information necessary for readers to understand the findings without reading all of the text.
• Use tables and figures as a focal point to tell a clear and informative story about your research and avoid repeating information. But remember that while figures clarify and enhance the text, they cannot replace it.

Step 4 : Draft your Results section using the findings and figures you have organized.

• The goal is to communicate this complex information as clearly and precisely as possible; precise and compact phrases and sentences are most effective.
• In the opening paragraph of this section, restate your research questions or aims to focus the reader’s attention to what the results are trying to show. It is also a good idea to summarize key findings at the end of this section to create a logical transition to the interpretation and discussion that follows.
• Try to write in the past tense and the active voice to relay the findings since the research has already been done and the agent is usually clear. This will ensure that your explanations are also clear and logical.
• Make sure that any specialized terminology or abbreviation you have used here has been defined and clarified in the  Introduction section .

Step 5 : Review your draft; edit and revise until it reports results exactly as you would like to have them reported to your readers.

• Double-check the accuracy and consistency of all the data, as well as all of the visual elements included.
• Read your draft aloud to catch language errors (grammar, spelling, and mechanics), awkward phrases, and missing transitions.
• Ensure that your results are presented in the best order to focus on objectives and prepare readers for interpretations, valuations, and recommendations in the Discussion section . Look back over the paper’s Introduction and background while anticipating the Discussion and Conclusion sections to ensure that the presentation of your results is consistent and effective.
• Consider seeking additional guidance on your paper. Find additional readers to look over your Results section and see if it can be improved in any way. Peers, professors, or qualified experts can provide valuable insights.

One excellent option is to use a professional English proofreading and editing service  such as Wordvice, including our paper editing service . With hundreds of qualified editors from dozens of scientific fields, Wordvice has helped thousands of authors revise their manuscripts and get accepted into their target journals. Read more about the  proofreading and editing process  before proceeding with getting academic editing services and manuscript editing services for your manuscript.

As the representation of your study’s data output, the Results section presents the core information in your research paper. By writing with clarity and conciseness and by highlighting and explaining the crucial findings of their study, authors increase the impact and effectiveness of their research manuscripts.

For more articles and videos on writing your research manuscript, visit Wordvice’s Resources page.

Wordvice Resources

• How to Write a Research Paper Introduction 
• Which Verb Tenses to Use in a Research Paper
• How to Write an Abstract for a Research Paper
• How to Write a Research Paper Title
• Useful Phrases for Academic Writing
• Common Transition Terms in Academic Papers
• Active and Passive Voice in Research Papers
• 100+ Verbs That Will Make Your Research Writing Amazing
• Tips for Paraphrasing in Research Papers

Careers in Qual

Quick answers, related terms.

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Degree In Sight

Discussing your findings

Your dissertation's discussion should tell a story, say experts. What do your data say?

By Beth Azar

"Many students reach this stage of their careers having been focused for several years on the 'trees,'" says Yale University cognitive psychology professor Brian Scholl, PhD. "This section of the dissertation provides an opportunity to revisit the 'forest.'"

Fellow students, your adviser and your dissertation committee members can help provide that outside perspective, adds Yale clinical psychology professor Susan Nolen-Hoeksema, PhD, who teaches a course on writing in psychology.

And while the discussion should put your research into context and tell a story, say experts, it should not overstate your conclusions. How do you find the balance? Follow these do's and don'ts.

DO: Provide context and explain why people should care. DON'T: Simply rehash your results.

Your discussion should begin with a cogent, one-paragraph summary of the study's key findings, but then go beyond that to put the findings into context, says Stephen Hinshaw, PhD, chair of the psychology department at the University of California, Berkeley.

"The point of a discussion, in my view, is to transcend 'just the facts,' and engage in productive speculation," he says.

That means going back to the literature and grappling with what your findings mean, including how they fit in with previous work. If your results differ from others' findings, you should try to explain why, says Nolen-Hoeksema. Then, launch into "bigger picture" issues. For example, a clinical study might discuss how psychologists might apply the findings in a clinical setting or a social psychology project might talk about political implications.

By exploring those kinds of implications, students address what Scholl considers the most important-and often overlooked-purpose of the discussion: to directly explain why others should care about your findings.

"You can't and shouldn't rely on others to intuitively appreciate the beauty and importance of your work," he says.

Sounds simple, right? In fact, choosing what to include can be overwhelming, warns sixth-year Yale University social psychology graduate student Aaron Sackett.

"It is easy to get caught up in the desire to be extremely comprehensive and to bring up every potential issue, flaw, future direction and tangentially related concept," says Sackett. "However, this will make your dissertation seem like it has raised more questions than it answers."

Limit your discussion to a handful of the most important points, as Sackett did on the advice of his adviser.

"No reader wants to wade through ten pages of suppositional reasoning," says Roddy Roediger, PhD, chair of psychology at Washington University.

DO: Emphasize the positive. DON'T: Exaggerate.

One of the biggest errors students make in their discussion is exaggeration, say experts. Speculation is fine as long as you acknowledge that you're speculating and you don't stray too far from your data, say experts. That includes avoiding language that implies causality when your study can only make relational conclusions.

"If your study was not a true experiment, replace verbs that imply causation with words and phrases such as 'correlated with,' 'was associated with' and 'related to,'" write John Cone, PhD, and Sharon Foster, PhD, in a forthcoming revision of "Dissertations and Theses from Start to Finish" (APA, 2006).

Steven David, PhD, who successfully defended his dissertation in clinical geropsychology at the University of Southern California last May, found this point to be particularly difficult. When he defended his master's thesis, his committee told him his conclusions went too far out on a limb. He used more restraint with his dissertation and his committee thought he wasn't positive enough.

"The moral here is to try to find a balance where you set a tone that indeed celebrates interesting findings without too many leaps, while at the same time reporting limitations without being unnecessarily negative," says David.

Indeed, every discussion should include a "humility" section that addresses the study's limitations, write Cone and Foster. But avoid beginning the discussion with a long list of study limitations, says Nolen-Hoeksema.

"This makes me think 'Then why should I care or believe anything you found,' and want to stop reading right there," she says. "Limitations should be noted, but after you've discussed your positive results."

DO: Look toward the future. DON'T: End with it.

Along with noting your work's limitations, it's helpful to also suggest follow-up studies. But don't dwell on the future at the expense of the present,says Scholl.

"I think that too many discussions make the mistake of ending with 'the future,'" he says. "Too often I am left excited not by what was in the dissertation, but by what was not in the dissertation."

Roediger agrees: "Conclude the general discussion with a strong paragraph stating the main point or points again, in somewhat different terms-if possible-than used before."

Remember, adds Scholl, you want readers to remember you and your work. The discussion section is the place to leave your mark. So instead of simply summarizing your data and suggesting a few obvious follow-up studies, think about presenting your data in a novel way, showing how the work might resolve an existing controversy in the literature or explaining how it connects to an entirely different literature.

By the time readers get to your discussion, they're tired, adds Sackett. Give them something clear, concise and interesting to read, and they're sure to appreciate it.

Beth Azar is a writer in Portland, Ore.

10 most common dissertation discussion mistakes

Starting with limitations instead of implications.

Going overboard on limitations, leading readers to wonder why they should read on.

Failing to acknowledge limitations or dismissing them out of hand. 

Making strong claims about weak results.

Failing to differentiate between strong and weak results as you make conclusions about them.

Lapsing into causal language when your data were correlational.

Repeating the introduction.

Restating the results without interpretation or links to other research.

Presenting new results; such data belong in the results section.

Offering no concluding statements or ending with the limitations.

Source: Susan Nolen-Hoeksema, PhD, Yale University

Letters to the Editor

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World Leaders in Research-Based User Experience

Data vs. findings vs. insights: the differences explained.

April 23, 2023 2023-04-23

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A shared vocabulary and understanding of data, findings, and insights will enable you to communicate where you are relative to where you need to be in your research analysis.

Data are simply a collection of data points which lack significance individually. As soon as researchers start to do any level of analysis on these data points, we have information. The type of information we have depends on the level of analysis completed. The first level of analysis yields findings, which are patterns among a specific set of data points that still lack critical context. The final level of analysis yields insights, which explain observed patterns and identify actionable opportunities. Insights are what researchers should strive to create.

In This Article:

Raw data lacks context, findings = what happened, not why, insights = opportunities to the business, how to mitigate bias in insights.

Definition: Data refers to an unanalyzed collection of observations about users that may include transcripts, notes, metrics, or survey output.

Data is comprised of single-observation points, otherwise known as data points. The data points are anything that gets captured — for example, user quotes or clicks in a user-testing session. There is no analysis or synthesis that happens at this stage, so no conclusions can be drawn.

Take, for instance, capturing data from a survey. The answers selected by participants would be the data points. If one question in the survey asked participants how likely they are to recommend the system to someone else ,  a single data point would represent the single response from a respondent for that question. The data would represent the collective responses from all respondents for all questions in the survey.

Data can be quantitative or qualitative.  User quotes or behaviors are qualitative data. But task time, success , analytics metrics , or responses to certain survey questions like the net- promoter–score (NPS) question above are quantitative.

Definition: Findings describe patterns in collected data or summaries across it. They lack consideration of background, past research, and organizational factors.

To come up with findings, researchers take the many distinct data points they collected and examine them for patterns. For   qualitative data , they rely on thematic-analysis techniques. Quantitative data is analyzed through statistics.

To extract findings, we look across everything captured, but we can look for patterns only across comparable things. In the survey example above, we could look at all the answers to the NPS question and find that the NPS score is 40, with a margin of error of 10. This is a summary of several data points, so it is a finding. However, there is no context that tells us details, such as whether this score is good and the reason behind this score. Thus, findings are not that useful by themselves.

Context is required to be able to interpret a finding. With findings alone, researchers are not able to determine why a pattern was observed or to make recommendations that are right for users and the business.

Definition: Insights are focused explanations of opportunities, based on other user research and business context.

While findings describe what is observed in the scope of a particular study or time frame of a live product, insights tie specific opportunities to specific user needs and they relate to valuable business objectives. Interpreting findings in context yields insights.

In the case of the NPS question above, consider these additional three pieces of context:

• This question was administered to users of a recently redesigned medical-appointment–booking site. The organization redesigned the website to decrease support call costs resulting from users who struggled to book appointments with specialist providers.
• Before the redesign, the NPS score was 35, with a margin of error of 15.
• Subsequent qualitative usability testing of the redesigned interface revealed that users struggled with it due to weak information scent and medical jargon .

Given this context, here is a potential insight:

Even though the NPS score increased, this difference was not statistically significant compared with the NPS for the older design.  (If you were to plot confidence intervals  for the two metrics, you would see that the one for the original NPS includes the one for the NPS of the redesign). Users struggled to understand the terminology used on the site and had a hard time identifying the correct specialist for their condition. The recommendation is to use plain language to align with users’ existent mental models.

This insight marries the finding around the NPS score with a usability finding that adds important context and highlights a clear opportunity connected to one of the organization’s goals.

Researchers should strategically use insights as a tool to connect their research to recommendations and opportunities. Insights are not meant to be prescriptive; rather, they narrow design possibilities, which can then be tested to find the best one. There are an infinitely many number of design possibilities for any problem, so some initial direction is highly beneficial for efficiency.

Given that the researcher designs the study, facilitates it, analyzes it, and interprets the data, there is inevitably some bias inherent to an insight. The threat posed by bias can be mitigated through the process of triangulation, which means relying on multiple sources of data, multiple approaches to analyzing the data, and multiple researchers doing the analysis, to reduce the chance that one particular researcher’s bias results in a faulty assessment.

Concerns around lack of scientific statistical significance and validity are common, but practically speaking, it is wise to make some recommendation that could have a positive business impact, rather than making no recommendation at all.

Data, findings, and insights are the language we use to communicate significantly different degrees of research analysis that your team as completed. For example, if you are currently working with findings, then you need to develop your analysis further to insights, because you can’t make decisions without understanding context.

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• CAREER NEWS
• 19 June 2024

What’s the state of hiring researchers in science? Share your insights with Nature

• Linda Nordling 0

Linda Nordling is a freelance writer in Cape Town, South Africa.

You can also search for this author in PubMed   Google Scholar

Nature launches its first survey on recruiting and hiring researchers. Group and lab leaders, tell us how you find the best candidates and learn how to enter our £100 prize draw. Credit: Sanjeri/Getty

Nature ’s first survey of global hiring practices in science, technology, engineering and mathematics (STEM) is now live. If you are a lab leader, head up a research group or make staffing decisions on your science team, we want to hear from you .

The survey, created in partnership with Thinks Insight & Strategy, a research consultancy based in London, covers all parts of the recruitment process from posting positions and screening applications, to the interview stage and later negotiations. Reaching out to employers across academia, industry and other sectors, it asks them about the biggest challenges they face and their top tips for identifying great candidates.

Spiralling living costs and cuts to research funding have meant that some research teams are struggling to fill vacancies; others complain about being flooded with low-quality applications. “With so much going on in the STEM career space, we want to find out which factors make or break an application and the qualities and skills that recruiters look for in job candidates,” says Kendall Powell, senior careers editor at Nature .

Lab leaders wrestle with paucity of postdocs

In common with Nature ’s previous workforce surveys, which have been running since 2010, the results of this 15-minute survey will be published later this year alongside a series of articles. The survey will run until 12 July and can be found here , together with instructions for how to opt in to a cash prize draw that will see four survey-takers each win £100, or your local currency, as a thank you for participating.

Previous surveys have delved into the challenges faced by early-career researchers such as postdocs and graduate students, as well as investigating scientists’ overall satisfaction with their jobs and salaries. “Gathering data on recruitment trends and hearing first-hand from employers about their experiences of attracting high-quality talent will provide valuable information that we can distil and share with our readers,” says Powell. “As early-career researchers draft CVs, craft cover letters and learn new skills, we hope the survey results will prepare them to make their best possible entry into the job market,” says Powell.

doi: https://doi.org/10.1038/d41586-024-02062-9

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Globally, Biden Receives Higher Ratings Than Trump

Still, most disapprove of how biden has dealt with the israel-hamas war, table of contents.

• Views of the U.S.
• Confidence in Biden, Trump and other world leaders
• Differences by ideology, age and gender
• 1. Views of the U.S.
• 2. Confidence in Joe Biden
• 3. Confidence in Donald Trump
• 4. Comparing confidence in Macron, Putin and Xi to ratings of Biden and Trump
• Biden’s handling of global economic problems
• Biden’s handling of climate change
• Biden’s handling of China
• Biden’s handling of the Russia-Ukraine war
• Biden’s handling of the Israel-Hamas war
• 6. Is U.S. democracy a good example to follow?
• Appendix A: Favorability of the United States since 2000
• Appendix B: Confidence in the U.S. president since 2001
• Acknowledgments
• About Pew Research Center’s Spring 2024 Global Attitudes Survey

This Pew Research Center analysis focuses on public opinion of the United States, President Joe Biden and other world leaders. It also explores what people think about Biden’s handling of international issues and their perceptions of American democracy. The study includes publics in 34 countries across the Asia-Pacific region, Europe, Latin America, the Middle East, North America and sub-Saharan Africa.

This analysis draws on nationally representative surveys of 40,566 adults conducted from Jan. 5 to May 21, 2024. All surveys were conducted over the phone with adults in Canada, France, Germany, Greece, Italy, Japan, Malaysia, the Netherlands, Singapore, South Korea, Spain, Sweden and the United Kingdom. Surveys were conducted face to face in Argentina, Bangladesh, Brazil, Chile, Colombia, Ghana, Hungary, India, Israel, Kenya, Mexico, Nigeria, Peru, the Philippines, Poland, South Africa, Sri Lanka, Thailand, Tunisia and Turkey. In Australia, we used a mixed-mode probability-based online panel.

Throughout the report, we analyze respondents’ attitudes based on where they place themselves on an ideological scale. We asked about political ideology using several slightly different scales and categorized people as being on the ideological left, center or right.

• In most countries, we asked people to place themselves on a scale ranging from “Extreme left” to “Extreme right.” The question was asked this way in Argentina, Bangladesh, Brazil, Canada, Chile, Colombia, France, Germany, Greece, Hungary, Israel, Italy, Mexico, the Netherlands, Nigeria, Peru, the Philippines, Poland, South Africa, Spain, Sweden, Turkey and the United Kingdom.
• In Australia, the scale ranged from “Left” to “Right”.
• In Japan, Singapore, South Korea and Thailand, ideology was measured on a scale from “Extremely progressive” to “Extremely conservative.”
• Ideology was not asked about in Ghana, India, Kenya, Malaysia, Sri Lanka or Tunisia. 

Prior to 2024, combined totals were based on rounded topline figures. For all reports beginning in 2024, totals are based on unrounded topline figures, so combined totals might be different than in previous years. Refer to the 2024 topline to see our new rounding procedures applied to past years’ data.

Here are the questions used for the report, along with responses, and the survey methodology .

With many around the world closely following the fiercely contested rematch between U.S. President Joe Biden and former President Donald Trump, a new Pew Research Center survey finds that, internationally, Biden is viewed more positively than his rival.

Across the 34 nations polled, a median of 43% have confidence in Biden to do the right thing regarding world affairs, while just 28% have confidence in Trump. The gap between ratings is quite wide in many countries, especially in Europe. Biden’s confidence rating is at least 40 percentage points higher than Trump’s in Germany, the Netherlands, Poland and Sweden.

However, there are exceptions. There is no statistically significant difference in ratings of Biden and Trump in eight nations we surveyed. And people in Hungary and Tunisia give Trump more positive reviews than Biden, although neither leader gets especially high marks there. (The survey was conducted before Trump’s conviction in a state criminal trial in New York.)

Even though Biden gets better assessments than Trump globally, ratings for the current U.S. president are down since last year in 14 of 21 countries where trends are available, including by double digits in Australia, Israel, Japan, Poland, South Africa, Spain, Sweden and the United Kingdom.

The survey included a series of questions about how Biden is handling major international issues. Overall, opinions are divided on how he is dealing with climate change and global economic problems.

Across the 34 countries polled, a median of around four-in-ten approve of how Biden is dealing with China and with the war between Russia and Ukraine (39% each).

The president gets his most negative reviews on his handling of the Israel-Hamas war: A median of just 31% approve of the way he is handling the conflict, while 57% disapprove. (The survey was conducted prior to Biden announcing a proposal to end the conflict .)

Research in the West Bank and Gaza

Pew Research Center has polled the Palestinian territories in previous years, but we were unable to conduct fieldwork in Gaza or the West Bank for our Spring 2024 survey due to security concerns. We are actively investigating possibilities for both qualitative and quantitative research on public opinion in the region and hope to be able to share data from the region in the coming months.

Six-in-ten Israelis disapprove of how Biden is handling the war, including 53% of Jewish Israelis and 86% of Arab Israelis. (For more on how Israelis rate Biden, read “Israeli Views of the Israel-Hamas War.” )

Of the predominantly Muslim nations surveyed, large majorities in Malaysia, Tunisia and Turkey also disapprove of Biden’s handling of the Israel-Hamas war. Opinion is divided on this issue in Bangladesh.

The new survey finds that overall attitudes toward the United States are generally positive: A median of 54% across the nations polled have a favorable view of the U.S., while 31% have a negative opinion.

However, criticisms of American democracy are common in many nations. We asked respondents whether U.S. democracy is a good example for other countries to follow, used to be a good example but has not been in recent years, or has never been a good example.

The predominant view in most countries is that the U.S. used to be a good model but has not been recently. Overall, a median of 21% believe it is currently a good example, while 22% say it has never been a good model for other countries.

In eight of the 13 countries where trends are available, fewer people say American democracy is a good example than said so in spring 2021, when we last asked this question.

For this report, we surveyed 40,566 people in 34 countries – not including the U.S. – from Jan. 5 to May 21, 2024. In addition to this overview, the report includes chapters on:

• Attitudes toward the United States
• Ratings of Biden
• Ratings of Trump
• Views of Biden and Trump compared with other world leaders
• Views about Biden’s handling of major foreign policy issues
• Is the U.S. a good example of democracy?

Read some of the report’s key findings below.

At least half of those in most countries surveyed express a favorable opinion of the U.S. Poles are the most positive, at 86% favorable. Of the European nations surveyed, ratings also lean positive in Italy, Hungary and the UK. Elsewhere in Europe, however, opinions tend to be closely divided.

Attitudes toward the U.S. are largely favorable in the Asia-Pacific nations polled, especially Japan, the Philippines, South Korea and Thailand. However, most Australians and Malaysians give the U.S. poor marks.

In the Middle East-North Africa region, a 77% majority of Israelis view the U.S. favorably, although this is down from 87% last year. Large majorities in Tunisia and Turkey offer an unfavorable opinion.

The U.S. gets mostly positive ratings in the sub-Saharan African and Latin American nations surveyed. Two-thirds or more see the U.S. favorably in Colombia, Ghana, Kenya, Nigeria and Peru.

Refer to Appendix A  for long-term trends on views of the U.S.

Pew Research Center has explored attitudes toward American presidents for over two decades, finding significant shifts in opinions over the years. Data from four Western European nations that we have surveyed consistently – France, Germany, Spain and the UK – shows long-term trends in views of recent presidents.

George W. Bush received low and declining ratings during his time in the White House, while Barack Obama got mostly high marks. Attitudes toward Donald Trump were overwhelmingly negative throughout his presidency. Biden has consistently received more positive reviews than his predecessor, but his ratings have declined in these four countries during his time in office.

There are nine nations in this year’s survey where six-in-ten adults or more express confidence in Biden. Four are in Europe (Germany, the Netherlands, Poland and Sweden), two are in the Asia-Pacific region (the Philippines and Thailand) and three are in sub-Saharan Africa (Ghana, Kenya and Nigeria).

Since last year, confidence in Biden has dropped significantly in 14 nations: Seven in Europe, plus Australia, Canada, Israel, Japan, Mexico, South Africa and South Korea. Biden gets his lowest ratings in Turkey and Tunisia, where only about one-in-ten express confidence in him.

The two countries where at least six-in-ten adults have confidence in Trump are Nigeria and the Philippines. Like Biden, Trump gets one of his lowest ratings in Turkey, where just 10% view him favorably.

Confidence in Trump has increased slightly in a few European countries since we last asked about him in 2020, although his ratings remain quite low in Europe.

In contrast, Trump’s ratings have become more negative in Poland since 2019, which was the last year we asked about him there. Israeli views toward the former president have also become more negative over the past five years.

Refer to Appendix B  for long-term trends in confidence in U.S. presidents.  

In addition to exploring confidence in Biden and Trump, the survey asked about trust in French President Emmanuel Macron, Russian President Vladimir Putin and Chinese President Xi Jinping.

Overall, Macron receives the most positive ratings across the countries in the study, followed closely by Biden. The French president gets higher ratings than his U.S. counterpart in many of the European nations surveyed. Both Xi and Putin receive mostly poor marks across the countries in the study.

• In 17 of the 28 countries where political ideology is measured, people on the right are more likely to have a positive opinion of the U.S. than those on the left. For example, 65% of people on the right in Spain view the U.S. favorably, compared with 26% of people on the left.
• In 18 countries, people on the right are more likely to express confidence in Trump than those on the left. The gap is especially large in Israel, where 75% of those on the right have confidence in him, compared with just 23% of Israelis on the left.
• There are also some sizable ideological differences on views about Biden’s handling of the Israel-Hamas war. In several countries – including about half of the European countries surveyed – people on the right are more likely than those on the left to approve of how Biden is handling the conflict.

• In several countries – including Canada, all Latin American countries surveyed and several countries in the Asia-Pacific region – adults under 35 are more likely to have a positive opinion of the U.S. when compared with adults ages 50 and older. Australia, Israel and Sweden are the only countries where younger adults have a less favorable view of the U.S.
• In Canada, Australia and seven of the 10 European countries surveyed, young adults are less likely than older adults to approve of how Biden is dealing with the Israel-Hamas war .
• Men have more confidence in Trump than women do in many of the countries surveyed. The largest difference is in the UK, where men are about twice as likely as women to trust the former U.S. president. In many of the countries surveyed, women are less likely than men to answer this question at all.

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How Americans and Israelis view one another and the U.S. role in the Israel-Hamas war

In east asia, many people see china’s power and influence as a major threat, how views of the u.s., china and their leaders have changed over time, comparing views of the u.s. and china in 24 countries, poles and hungarians differ over views of russia and the u.s., most popular, report materials.

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Your chances of a migraine increase with hotter temperatures

Research by dr. vincent martin is featured in national and international media.

A change in weather is one of the most common trigger factors for migraine. Hotter temperatures increase the chance of a migraine attack, according to research led by Vincent Martin, MD, director of the Headache and Facial Pain Center at UC’s Gardner Neuroscience Institute.

Martin, also a professor in the UC College of Medicine, spoke with Cincinnati’s Fox 19 News about his latest study findings which looked at use of Fremanezumab and whether it could prevent headaches caused by temperature increases. Produced by Teva Pharmaceuticals USA, Fremanezumab is sold under the brand name AJOVY®, administered by injection under the skin to treat migraines.

A research team led by Martin cross-referenced 71,030 daily diary records of 660 migraine patients with regional weather data and found that for every temperature increase of 10 degrees Fahrenheit daily, there was a 6% increase in occurrence of any headache. However, during the time periods of Fremanezumab treatment the association completely disappeared.

Record high temperatures across much of the Midwest and Northeast regions of the country has increased dialogue about health ailments associated with the heat. Martin’s findings were reported on more than 200 media outlets including CBS News , The Atlanta Journal Constitution ,  The Miami Herald and internationally in publications such as Greece’s Tempo 24 , Argentina’s Infobae , Italy’s Agenzia Italia ,  Mexico’s El Cuarto Poder , Venezuela’s Noticias Venevision and Indonesia’s Suara Merdeka .

Read about the research findings from Vincent Martin, MD, online .

Learn more about migraines and hotter temperatures on CBS News

Featured top image is courtesy of Istock.

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What every researcher should know about searching – clarified concepts, search advice, and an agenda to improve finding in academia

Michael gusenbauer.

1 Department of Strategic Management, Marketing and Tourism, University of Innsbruck, Innsbruck Austria

2 Chair for Strategy and Organization, Technical University of Munich, Munich Germany

Neal R. Haddaway

3 Mercator Research Institute on Global Commons and Climate Change, Berlin Germany

4 Stockholm Environmental Institute, Stockholm Sweden

5 Africa Centre for Evidence, University of Johannesburg, Johannesburg South Africa

Associated Data

Data sharing is not applicable to this article as no new data were created or analysed.

We researchers have taken searching for information for granted for far too long. The COVID‐19 pandemic shows us the boundaries of academic searching capabilities, both in terms of our know‐how and of the systems we have. With hundreds of studies published daily on COVID‐19, for example, we struggle to find, stay up‐to‐date, and synthesize information—all hampering evidence‐informed decision making. This COVID‐19 information crisis is indicative of the broader problem of information overloaded academic research. To improve our finding capabilities, we urgently need to improve how we search and the systems we use.

We respond to Klopfenstein and Dampier ( Res Syn Meth . 2020) who commented on our 2020 paper and proposed a way of improving PubMed's and Google Scholar's search functionalities. Our response puts their commentary in a larger frame and suggests how we can improve academic searching altogether. We urge that researchers need to understand that search skills require dedicated education and training. Better and more efficient searching requires an initial understanding of the different goals that define the way searching needs to be conducted. We explain the main types of searching that we academics routinely engage in; distinguishing lookup, exploratory, and systematic searching. These three types must be conducted using different search methods (heuristics) and using search systems with specific capabilities. To improve academic searching, we introduce the “Search Triangle” model emphasizing the importance of matching goals, heuristics, and systems. Further, we suggest an urgently needed agenda toward search literacy as the norm in academic research and fit‐for‐purpose search systems.

What is already known?

• To stay up‐to‐date, we researchers would need to read hundreds of research papers a day(!). Particularly, the avalanche of COVID‐19 papers exemplifies how we are chronically information overloaded.
• Evidence synthesis is more important than ever, yet we lack the knowledge and systems to effectively and efficiently identify the evidence bases for systematic reviews.

What is new?

• We claim that research discovery needs an urgent overhaul. Only with awareness of the basic concepts of academic searching, we can know how to make our search routines and systems fit‐for‐purpose.
• Our commentary clarifies these search concepts to point out the particularities of lookup, exploratory, and systematic searching. The “Search Triangle” model emphasizes that efficient and effective search only works when goals, systems, and heuristics are well matched.

Potential impact for RSM readers outside the authors' field

• Awareness for the importance of search literacy and search education is needed across disciplines.
• Better search skills not only help in research, but anywhere online.

We thank Klopfenstein and Dampier 1 for their comment on our paper and for acknowledging the need to improve both PubMed and Google Scholar with functionalities that each is currently missing. We welcome increased scrutiny of the functionality of search systems and assessing whether these are truly fit‐for‐purpose as we struggle with information overload, particularly in times of crises like the current COVID‐19 pandemic. We are also very happy to see increased research attention on the systems that we use on a day‐to‐day basis for research discovery: functionalities that have remained unquestioned by those of us who are not information specialists for too long.

Indeed, we were overwhelmed by the substantial attention given to our paper 2 (it currently has an Altmetric score of well above 300) and the positive comments we have received. This demonstrates the need for further scrutiny and improvement to academic search. It shows that researchers want to know more about the limitations of the systems they use to discover research, which limitations they must account for, and how to match their search strategies with each system. These decisions concerning the design of search strategies profoundly affect the resultant evidence that researchers identify, what they (often unknowingly) fail to identify, and what conclusions they draw based on the emergent evidence. 3

In this article, we go beyond our original article and put the work of Klopfenstein and Dampier 1 in a larger frame to discuss the kind of agenda setting needed to overhaul academic searching, and how this might be achieved by the research community.

1. SEARCHING AND BIAS IN TIME OF A GLOBAL CRISIS

The importance of effective and efficient identification of academic publications (hereafter referred to as searching ) has become particularly evident in the current COVID‐19 pandemic: This pandemic is not only a medical crisis, but also an information crisis—not because there is no information on COVID‐19, but because there is more than we can handle. Recently, a Lancet editorial called this an “infodemic” and a “major threat to public health.” 4 According to Semantic Scholar, more than 211 000 scientific articles exist to date on COVID‐19 across all disciplines a —almost all published in 2020. The National Institute of Health (NIH)'s isearch COVID‐19 Portfolio , an expert‐curated data collection, lists 60 297 medical COVID‐19 publications, whereas 79% were listed between May and August 2020 b —amounting to an average daily(!) publication rate of almost 400 publications for medicine alone. This incredible avalanche of evidence is more than any individual can process. For any particular intervention (eg, mask‐wearing), one can find a confusing and conflicting set of studies purportedly demonstrating evidence for and against (eg, face masks for the public during the COVID‐19 crisis 5 ). Thus, the way we can process and make sense of this overabundance of evidence is one of our greatest challenges the current infodemic shows us.

Currently there is overwhelming research attention trying to solve these information challenges in a diverse suite of innovative ways, each aiming to make COVID‐19‐related information readily discoverable and analyzable. On the one hand, there are dozens of new AI‐ or expert‐curated repositories: for example, NIH LitCovid , NIH isearch , OPENICPSR COVID‐19 data repository , WHO COVID‐19 database (also linking to many other repositories) , and the Center for Disease Control and Prevention (CDC) giving an overview of various repositories. On the other hand, there are new tools for visualization, access, categorization, and analysis of COVID‐19 information (eg, SciSight or CoVis ), some of them via crowdsourced idea contests (eg, Kaggle ) or hackathons organized by institutions around the globe. This host of new initiatives is important means to fight the COVID‐19 infodemic with improved information access and analysis. However, we argue that the information overload problem is exacerbated by the insufficient nature of the search systems we must use to find relevant information. If the systems and practices we have in place—to discover, analyze, and evaluate evidence—were fit‐for‐purpose, we would not need to battle COVID‐19 with context‐specific fixes that do only little in battling infodemics in all the other contexts. We advocate that fixing existing search systems and practices is at least as important as building new resources on top. This means raising researchers' awareness and understanding about the objectives of searching, along with improving search heuristics and the search systems that make the avalanche of evidence accessible. Klopfenstein and Dampier 1 provide a good example of how best practices can be adopted across platforms and how researchers across disciplines can influence search system providers in how their systems should be improved.

One of the most critical factors that can easily limit the quality of our work is the belief that how we search academically is perfectly fine. 6 , 7 It is the belief that the systems we use on a daily basis and the habits we have developed throughout our careers are adequate to find effectively and efficiently. However, searching—one of the central elements of research work—needs trained skills, careful thought, and planning. We need to understand that where and how we search greatly impacts what we find and miss, what we conclude, and what we suggest for evidence‐informed decision making. Improving academic searching helps to improve the quality of science and helps fighting so‐called infodemics. Thus, much can be gained if we improve day‐to‐day academic searching for the millions of researchers worldwide.

We argue that the COVID‐19 pandemic is an important time to consider how to improve academic searching altogether. In this text, we clarify some important concepts of academic searching that are the subject of frequent misunderstanding, we introduce the “Search Triangle”—a user‐centric search model to understand the key characteristics of academic searching, and we explore why and how we need to overhaul academic searching to better inform decision making (Box 1 ).

How we expend much effort to get around a terrible searching environment

COVID‐19 exemplifies an information crisis, with researchers building workarounds to cope with the insufficiencies of established search systems.

In theory, research on COVID‐19 could be readily identified by any user searching a database for “COVID‐19” and finding all relevant studies. However, several problems make this difficult, for example: (a) authors describe the concept using different terms; (b) many databases typically index records (and allow searches) based only on titles, abstracts, and keywords, missing potentially relevant terms in the full texts; (c) no single database catalogues all research; (d) poor search literacy in the research community means that errors or inefficiencies in searching are common; (e) paywalls restrict users' access to search facilities and the underlying research articles.

A suite of systems has been built to identify and assemble COVID‐19 relevant research to overcome these problems, making use of artificial intelligence (including machine learning), expert curation and screening for relevant information, and temporarily making resources Open Access.

These are admirable, but necessary only because accurate and efficient identification of (free‐to‐access) relevant research across comprehensive free‐to‐use databases does not exist.

2. UNDERSTANDING ACADEMIC SEARCHING—THE DIFFERENT SEARCH TYPES: LOOKUP, EXPLORATORY, SYSTEMATIC

As Klopfenstein and Dampier 1 point out, Google Scholar is by far the most commonly used resource by researchers. 8 This is not a coincidence—it allows straightforward, user‐friendly access to its vast database of research records. 9 However, Google Scholar also shows us beautifully how a system can be perfectly suited for one type of search, while failing miserably for another. On the one hand it is very capable for targeted searches aimed at finding specific research articles, 10 but has severe limitations in systematic searches (eg, a lack of transparency and reproducibility). 2 , 11 Most academics are unaware of the different types of searching that they use on a day‐to‐day basis. 12 They use the systems they know and to which they are accustomed in ways for which they were never designed. The result is substantially biased, nontransparent, and irreproducible research studies. As researchers, we must start understanding the basic types of searching we engage in and how the objectives behind each search type (why we search) should determine the search methods—that is, system choice (where we search) and search heuristics (how we search).

There is much we can learn about searching from the information retrieval and information science literature: substantial efforts have been made to determine the types of searching at various level of granularity and the capabilities required by search systems. This discipline broadly distinguishes lookup and exploratory searching as the two key search types. 13 , 14 Lookup searches—also called “known item searches” or “navigational searches”—are conducted with a clear goal in mind and “yield precise results with minimal need for result set examination and item comparison.” 14 (p. 42) Here, the search process should be swift and efficient so as not to disturb the user's workflow. However, lookup searches can also be used by researchers or decision‐makers for cherry picking. From the avalanche of studies, it is relatively easy to select evidence that supports a pre‐held belief or dogma that portrays a biased picture of reality. Sometimes, this cherry picking is deliberate; selecting whichever study provides support for an argument or decision that has already been made (ie, post hoc evidence use). And sometimes it is unintentional: when the first evidence encountered is assumed to be representative. In general, users want efficient and convenient information retrieval, particularly in lookup searches 15 , 16 —the first result that fits typically satisfies the information need. 17 However, as researchers or decision‐makers we should explore the available evidence in the least biased way or, better still, to additionally search systematically to have all available evidence for a specific topic (including the counter‐evidence to one cherry‐picked paper). Only then, we can be sure that our conclusions and decisions are sufficiently evidence‐informed.

As many topics are complex and require in‐depth understanding, and we cannot always trust anecdotal evidence (see lookup searches), we need exploratory searches to enrich our understanding. In exploratory searches, the search goal is somewhat abstract. 18 It is a desire to better understand the nature of a topic, and the path to reaching this goal is not always apparent. Exploratory searching is a process characterized by learning 19 where users aim to be exposed to a multitude of different, sometimes contradicting knowledge sources to build their mental models on a topic. Users “submit a tentative query to navigate proximal to relevant documents in the collection, then explore the environment to better understand how to exploit it, selectively seeking and passively obtaining cues about their next steps.” 20 (p. 38) The heuristics that users employ and their ultimate goals change throughout the session as they make sense of the information, linking it to and adapting their mental models iteratively. 21 A single search session might exclusively consist of lookup or exploratory searches, or might alter the two with mixed episodes of lookup (eg, fact checking, navigation) and exploratory searches (eg, discovery and learning). In exploratory searches, the search process often spans multiple sessions (ie, days, weeks, months) or media (eg, search, videos, offline conversations) where users engage with one or more systems, take notes, and save results to knowledge management systems. Users will often stop searching when they believe they have reached their goal (the information need is met) or when they conclude it cannot be reached with the resources available. 17

While both lookup and exploratory searches are established concepts in information retrieval, they do not cover systematic searches —which we claimed in our paper 2 is a distinct third search type with unique heuristics and requirements. Evidence synthesis, in the form of systematic reviews (including meta‐analyses) and systematic maps, has introduced many disciplines to the concept of systematic searches , with the goal to (a) identify all relevant records (within the resource constraints) in a (b) transparent and (c) reproducible manner. 2 None of these three systematic search goals is shared by lookup or exploratory searches. Systematic searching is similar to lookup searching in that the search goal is known, yet the level of rigor in planning and reporting and the sophistication in the search scope are unmatched making it a distinct type of search activity. One key aspect of systematic searching is that the methods used to search should be a priori and developed through careful planning, ideally involving information retrieval experts. 22

There are presently significant misunderstandings within the research community regarding what systematic searches should and should not entail. These misunderstandings have led to criticism of the systematic review method (compared to narrative reviews) which we find are unfounded—at least in view of the literature search phase that identifies the corpus of evidence for subsequent synthesis. A major criticism is that systematic reviews would not entail “hermeneutic circles” of iterative learning about a research concept, so that researchers would not include and reflect upon findings throughout the search process. 19 , 23 In practice, however, systematic searches should always be preceded by a thorough exploratory search phase, which in systematic reviews is called “scoping.” In this initial phase, the researchers use exploratory searches to familiarize themselves with the review topic: they extend their knowledge of concepts and language and define inclusion/exclusion criteria. 24 Only then do they compose a systematic search strategy that aims to identify all available, relevant records on the topic in a transparent and reproducible manner (ie, well reported in the final manuscript). We agree that, when an initial scoping phase is missing, this may limit the validity of a systematic review greatly, since key terms and concepts may have been omitted or misunderstood, even by experts. Thus, for systematic reviews it is essential that systematic searches are preceded by a thorough exploratory search phase.

It is important to note that systematic searches do not themselves entail a learning process. They should be predefined, protocol‐driven, structured means of systematically searching, and extracting all potentially relevant bibliographic records. The search area is specified by these search steps (mostly through the use of building blocks and snowballing heuristics—see Table ​ Table1) 1 ) and lays out all records for subsequent review of relevance/eligibility. In systematic searching, the “hermeneutic circle” of understanding should be well advanced (though it probably will never be finished). Thus, in systematic reviews using the building blocks heuristic (connecting concepts via Boolean operators) only the final iteration of the search string is truly systematic and must be transparently documented in detail. It is typically at this point that the researchers stop exploring for the purpose of improving the search area. While exploratory searches ( scoping ) might use the same heuristics (see Table ​ Table1), 1 ), these initial searches are iterative and incrementally improve the search area used for the systematic review. Hence, one of the main advantages of systematic reviews is that they include both an exploratory and a systematic search, upon which the subsequent synthesis is based. Unlike in narrative reviews that often rely on exploratory searching alone, the systematic search phase in systematic reviews aims to maximize comprehensiveness and full transparency and reproducibility.

Academic search types: Their goals, use cases, dominant heuristics, and key requirements to search systems

To date, systematic searching and its unique requirements have not been described by the information science literature. The influential work of Marchionini 14 that distinguishes between lookup and exploratory searching lists synthesis work as part of exploratory search and fails to capture the nature of systematic searches (as employed in systematic reviews). To help distinguishing the three search types, we define and summarize them and add associated use cases and heuristics in Table ​ Table1 1 .

3. CONDUCTING ACADEMIC SEARCHING—THE “SEARCH TRIANGLE”

We contend that good academic searching starts with users thoughtfully establishing what their search goals are: that is, what they want to know/find. Given their search goals, search‐literate users know which type of search they need to engage in and can thus then select appropriate heuristics and search systems . Whether users are search literate, that is, are able to optimally match heuristics and search systems to their (evolving) search goals, determines the effectiveness and efficiency of finding and learning. We maintain that researchers—and indeed all information seekers—should understand the following three points that span a “Search Triangle” (see Figure ​ Figure1 1 ):

• The users ' goals : what needs to be accomplished with the search task? For lookup searches, the goal is rapid and efficient identification of an artifact where the search area is already well known to users; for exploratory searches, the goal is learning about one or multiple concepts or about an evidence base; for systematic searches, the goal is the identification and extraction of all available records on an already well understood (scoped) topic.
• The appropriate heuristics : how can the search be best conducted? The user must ask which (set of) heuristics best attain the search goal. While simple lookup searches come relatively intuitively with user‐friendly search systems like Google Scholar, 17 the users' considerations of appropriate heuristics become important for effective explorative searches and particularly for systematic searches. Some of the most popular search heuristics described in information science literature (see Table ​ Table1) 1 ) are most specific first , wayfinding , snowballing (or citation chasing/chaining , pearl growing) , (post‐query) filtering , successive fraction , building blocks ( via Boolean operators) , or handsearching . 2 , 17 , 25 , 26 , 32 It is important to note that no single heuristic is associated with a single search type. Rather, the choice of appropriate heuristics depends on the particular nature of the search goal and the options at hand, given a particular search system. For example, while building blocks are primarily used in systematic searching, they might also be used in particular types or phases of exploratory searching. Snowballing, for example, is used both in exploratory and systematic searching—yet with a different level of attention to rigor, transparency, and reproducibility.
• The appropriate systems : which (set of) search system(s) best supports the required search type and the suitable search heuristics? It is important to know what can and cannot be accomplished, given the functional capabilities of a particular search system: eg, of the 28 systems analyzed in our paper 2 only half can be recommended as stand‐alone systems in systematic searches. The selection of search systems, among the dozens available, defines what users will find. The search and retrieval capabilities are defined by the implicit characteristics of the search system in terms of functionality and coverage. It cannot be emphasized enough that no single search system is like the other and that each system is more or less adequate for specific search types (lookup/exploratory/systematic) in terms of coverage and supported heuristics.

The “Search Triangle”: efficient and effective search only works when all three (search goals, search systems, and search heuristics) are matched well [Colour figure can be viewed at wileyonlinelibrary.com ]

4. IMPROVING ACADEMIC SEARCHING—SETTING AN AGENDA AND CALLS TO ACTION

To improve academic searching, we suggest an agenda that is rooted in three areas: (a) more awareness for the intricacies of academic searching; (b) better search education; and (c) pressure on search system providers to ensure their services are fit‐for‐purpose. We suggest key points that we believe the scholarly community must tackle, also jointly with institutions, publishing bodies, and search system providers.

4.1. More awareness for the intricacies of academic searching

Improving our search practice starts by creating awareness that search literacy is a crucial skill that does not come naturally through extensive computer and internet use, but needs to be trained in search education as part of research training. 33 , 34 Particularly, in the context of systematic reviews we must understand the two consecutive, yet distinct phases: exploratory searching and systematic searching. Too often, researchers skip the exploratory scoping phase and jump straight into systematic searching, while they still are (un/consciously) unsure about the meaning and language of central concepts.

Search literacy becomes increasingly needed as the number of search systems increases and the functionality they offer is diversified and continually updated, making them more or less (or not at all) suitable for specific search types. In recent years, we have seen the introduction of numerous new systems (eg, Microsoft Academic, Dimensions.ai, Meta, The Lens, Semantic Scholar) and techniques (eg, personalized or AI‐based search results) in academic search. Researchers must understand that these systems are all different and that system choice will heavily affect (or bias) what they will find. At the moment, the algorithms of so‐called semantic search systems (eg, Google Scholar or Semantic Scholar) and the precise methods of how they select and rank what is shown on the results page are unknown. However, there is evidence 6 , 35 that these opaque algorithmic decisions influence how we researchers conduct science—what we find, what we cite, how we argue, what we conclude. The academic community needs to be aware of these biases, and equip itself with the know‐how to avoid basing entire research projects (particularly systematic reviews) on potentially biased evidence bases (eg, Burivalova et al 36 ).

We currently see an alarming absence of awareness for search system choice. This is evident in the many publications that confuse search system types 37 : foremost platforms used to access databases (such as Web of Science) and the databases themselves (such as Science Citations Index Expanded). These types are confused not only by research users more generally, but also by experts in the field of Scientometrics and others, where researchers specifically research these systems. This lack of awareness illustrates how urgently we need to start understanding academic search: the search types, the heuristics, and the search systems—to find more, faster, and with less bias.

Call to action : We must raise awareness across research communities—among students, educators, journal editors, university teaching boards, and interest organizations—of the intricacies of academic searching and how it can be improved. Organizations like the Collaboration for Environmental Evidence, 38 Campbell Collaboration, 28 and Cochrane 39 can play important roles in creating awareness for the intricacies of academic search by updating their guidance to include more nuanced academic search advice. Additionally, academic journals must ensure that editors and peer‐reviewers are aware of the importance of robust search methods to encourage more rigor in academic searching (even more so as evidence synthesis become increasingly valued and prevalent). Only with this awareness, we can adequately link search goals to appropriate heuristics and systems to perform “good science”:

• It starts with the users ' goals : Raising awareness so users understand what goals they want to reach with their searching and with which (implicit) scientific standards the specific search types (lookup/exploratory/systematic) are associated.
• Search types : Raising awareness that searching is not always a quick “just Google (Scholar) it,” but in fact can be described by a “Search Triangle” that needs a matching of search goals/types with heuristics and systems (see Figure ​ Figure1 1 ).
• Search heuristics : Raising awareness that we could use better methods in searching databases and should be designing our searches around suitable heuristics that allow us meeting our diverse search goals.
• Search systems : Raising awareness that search systems are all different, not only in coverage, but also in the functions they offer and (equally important) they do not offer. It is also vital to understand that searches can be biased through the use of algorithms to adjust the order of records in search results. 40 In the context of systematic reviews, ensuring transparent and adequate reporting of which systems are searched must be a key responsibility of research authors, editors, and peer‐reviewers. Systems to support reporting of this level of detail are available (eg, PRISMA‐S 41 ) and should be adapted to all forms of research involving searching, not just systematic reviews.

4.2. Better search education: Toward search literacy as the norm

To build search literacy that enables quick choices of both heuristics and systems given an imminent information need involves more than the day‐to‐day search experience we researchers have at hand. Instead, it requires targeted search education . Such education has been shown to significantly improve search quality. 32 , 42 Without anchoring search education in research curricula, much scholarly search effort will remain wasted. 43 , 44

Call to action : we must make search literacy a priority in research education:

• What needs to be taught? Since many researchers think their current search practices and systems suffice, we need to raise awareness about problems associated with search illiteracy 45 in combination with showing better ways of searching. The teaching objective should be to improve knowledge and skills on how to effectively and efficiently find, evaluate, manage, and use information. Taught concepts should include matching: (a) user goals/search types, (b) search heuristics, and (c) search systems. Among others, this includes awareness for the importance of adequate language to describe concepts, the ability to formulate comprehensive, yet precise search strings and the skills to search the most suitable systems.
• Who teaches it? University libraries can play a key role in making emergent and established researchers and professionals search literate. 46 In times where fewer people visit physical libraries, more advice is required in the online realm. The freed‐up resources of librarians and information specialists might be used to teach new formats to students and scholars about search.
• How can it be taught? Search literacy can be taught as stand‐alone course or extend existing teaching concepts on digital literacy or information literacy, particularly also in courses on evidence‐based research. 47 , 48 , 49 As many institutions lack libraries—particularly the ones from resource‐constrained environments—education should also be freely and easily accessible to all (ie, Open Education). Perhaps this could be organized most impactfully as self‐paced online training or freely licensed teaching materials that can be used and adapted by trainers across the world.

4.3. Toward fit‐for‐purpose search systems

No two‐search systems are identical, and none is perfect. The reason for the great popularity of some systems is not because of their adequacy for each of the three search types we describe, 2 but rather because of their ease of use in day‐to‐day research practices. In the last decade, the tremendous success of Google Scholar has shown that users generally want to search intuitively, with as little effort as possible. 17

In terms of functionality, two broad types of search systems exist at present: the traditional “comprehensive‐transparent” (eg, ProQuest, PubMed, Web of Science) and the newer “efficient‐slick” (eg, Google Scholar, Semantic Scholar). The first type allows users to specify their search to the greatest detail, while the second identifies relevant results quickly. The most popular systems are efficient‐slick, while it seems the traditional systems have focused on new features rather than low latency and accessibility. The mission statements of some popular and newly created semantic systems—including Microsoft Academic, Semantic Scholar, and Meta—can be summarized with: simpler and more efficient searching , faster results . Their aim is the fast satisfaction of researchers' information needs, without detours.

While this increase in search efficiency is generally positive, it comes at a cost. We see two fundamental problems: first, in these semantic search systems it is opaque algorithms that decide about the “right” information that is shown (either absolutely or by order). We currently have neither insight, nor control over these decisions. This is particularly problematic for systematic searching, where our study has shown that all semantic search systems in our sample failed to meet the requirements. 2 Second, we must stay alert as these efficient‐slick systems aim at transforming ‘inefficient’ exploratory searching into ‘efficient’ lookup searching (eg, through presentation of pre‐selected cues). This means exploratory searching (and thus learning) might be more and more crippled toward quick, unconsciously biased lookup searching (cherry picking) that users more and more expect when engaging with online systems. 50 To be innovative as an academic it is essential to build own mental models, to connect disconnected threads that have not been connected before—by neither machine nor human. If we reduce these “hermeneutic circles” for the sake of efficiency, we must be aware of the drawbacks. It clearly makes a difference if users are efficient in finding information on for example “the capital of Kiribati” or to which president to vote in the next election. While the first should be efficient (lookup), the latter should largely remain exploratory where users are presented with a balanced information diet. We must be careful and stay alert with systems that give us readymade answers. We must question the algorithms (AI, machine learning) and behavioral data that are used to create relevance rankings and thereby determine what researchers get to see and what not. 6 Unfortunately, it seems as if the greatest level of effort of many search systems does not go into what researchers need to accomplish in all their search tasks, but rather in making users satisfied (and not smarter) sooner.

We researchers need the best of both worlds to ensure the best research outcomes: we need efficient‐slick and comprehensive‐transparent. We claim that, at present, systems could do much more in different areas than fine‐tuning for the sake of efficient lookup searching—particularly in the realm of evidence synthesis.

Call to action :

• Greater transparency : Search functionalities – that is, what can(not) be done with a search system (see our paper 2 for details of how this can be quantified) need to become transparent. This can only be done through an independent assessment of the claims of search system providers—our study, for example, has shown that one out of four systems promoted the functioning of search options (ie, Boolean search) that we found was flawed. 2 Additionally, we need clarity in the algorithms that semantic search systems use to fine‐tune their search results to reflect on how this impacts research work. With transparency, users can make informed choices on which systems to choose and systems can benchmark to compete for users, all driving a healthy competition toward better options of search facilities.
• Toward fit‐for‐purpose—matching requirements with technical possibilities : Some of the limitations we academics are confronted with when using search systems exist because of a lack of communication between the technical (what is possible) and the applied (what is needed). We believe the tools and features of search systems would greatly benefit from effective guidance and feedback from the research community (besides the user testing, etc. they are already doing). By establishing clear rules (similar to what systematic search needs to fulfill), we can help to direct the improvement of search systems, and thereby improving access to future‐proof search functionalities. Here we need to involve information technology research methods that have a long history in investigating the performance of particular search features or technologies (eg, reinforcement learning, 51 interactive intent modeling, 52 query expansion 53 ). We do not need to reinvent the wheel, yet we need to improve communication between library science/evidence‐based research methodologists (the applied) and information technology research, and importantly: the search systems we use on a daily basis (the technical). Klopfenstein and Dampier 1 demonstrate that: first, there is much room for improvement of search system workflows, features, and supported heuristics. Second, cross‐database integration might make sense to combine strengths of different databases (the coverage of Google Scholar and the specialized features of PubMed). Third, transparent comparison of features across search systems can be key to improve the systems we have. To improve our systems, we need an understanding of the exact requirements systems need to have for specific search types. The academic community should rally around these definitions and search types and demand clarity on which systems are best suited for which type of searching.
• Organize change : To see real improvements in academic searching, we must coordinate around the issue of fit‐for‐purpose research discovery. Without organized pressure this will remain a top‐down decision process, where search organizations continue deciding on what systems we use without hearing the requirements of the academic community. The popular example is Google Scholar that has refrained from improving transparency despite the many calls from, for example, the Scientometrics community in recent years. 9 , 11 , 54 , 55 COVID‐19 has shown us that positive change is possible if the pressure and a sense of urgency is great enough: for example, search systems and publishing houses have met criticism of impeding efficient, Open Science by temporarily making COVID‐19 literature Open Access. 56 Thus, we need to decide how to organize the academic community to put pressure on search system providers to design their systems in such a way that supports the three different types of searching. Such demands for improvements are warranted and should be heard particularly by the systems we are (collectively) paying for through subscription fees. As a consequence, a great amount of effort (and thereby public money) could be saved if deliberately imposed barriers (such as view and download limits, paywall barriers, or data access restrictions) were to be removed and search functionalities improved.

5. CONCLUSION

The tremendous thirst for information on COVID‐19 by policy makers, managers, and the general public has triggered an avalanche of research. While this ever‐growing evidence base shows the academic system's capabilities to produce evidence rapidly and on tremendous scale, it has also triggered a COVID‐19 infodemic. The information overloaded researchers found across subjects and disciplines highlight the vital need to improve research discovery. Newly developed COVID‐19‐specific tools and repositories are certainly helpful, yet we also must carefully evaluate what these new technologies promise and why current systems are not already adequate. To fight the COVID‐19 infodemic—and in fact all infodemics—we argue it is essential to foremost fix how we search for scholarly evidence on a daily basis. This not only has the potential to improve search literacy across academic disciplines, but may also have spillover effects to a broader audience by educating students, organizations, and institutions.

Currently, we are at an exciting point in the development of informatics: an avalanche of research publications is being catalogued more comprehensively by an expanding suite of different bibliographic databases and research platforms (interesting developments include Dimensions.ai and The Lens). Intelligent research discovery systems make it easier than ever to identify research that is relevant to us. 9 However, it has been shown how relevance rankings direct science, a phenomenon that is aggravated with new the technologies of artificial intelligence and machine learning that introduce black‐box relevance rankings and auto‐suggestions to the daily scientific enterprise of millions of scholars. Before we have fully understood the cost of such efficient systems, we need to be cautious for how we use them. Without full understanding of the different types of searching and their requirements, users of search systems are increasingly at risk of identifying a biased or unrepresentative set of search results. 6 We must improve our understanding of the intricacies of searching and ensure search systems are specifically designed to tackle all modes of searching: only then can we conduct research with a more balanced information diet and make sure the evidence bases on which decisions are based are fit‐for‐purpose.

We currently see the greatest search issues in systematic searching : both in terms of the inadequate systems we have at hand and the uneducated researchers that use them. If the available search systems were specifically tailored to the needs of search‐literate researchers, the evidence we could produce would be of significantly greater validity and at significantly lower cost. Facilitating and thus accelerating the creation of systematic reviews could particularly help in times of crises—such as we experience today with COVID‐19.

We hope the clarification of academic search concepts, the advice in form of the “Search Triangle” model and our calls to action will help improving academic search. We hope our work informs decision making in academic searching and might prove useful in structuring and conducting search education toward search literacy as a methodical skill every academic exhibits and cherishes.

CONFLICT OF INTEREST

The author reported no conflict of interest.

a Searching for “COVID‐19,” a suggested keyword by Semantic Scholar ( https://www.semanticscholar.org/search?q=COVID-19&sort=year ), accessed on 1 September 2020.

b Isearch was accessed with a blank query to access all records on the database. ( https://icite.od.nih.gov/covid19/search/#search:searchId=5f4dff240e329a34eac4e89f ), 60 297 records as of 3 August 2020, 47 514 between 1 May and 31 August 2020, accessed on 1 September 2020.

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Role of nuclear weapons grows as geopolitical relations deteriorate—new SIPRI Yearbook out now

(Stockholm, 17 June 2024) The Stockholm International Peace Research Institute (SIPRI) today launches its annual assessment of the state of armaments, disarmament and international security. Key findings of  SIPRI Yearbook 2024 are that the number and types of nuclear weapons in development have increased as states deepen their reliance on nuclear deterrence.

Read this press release in Catalan ( PDF ), French ( PDF ), Spanish ( PDF ) or Swedish ( PDF ).

Click here to download the sample chapter of SIPRI Yearbook 2024 on world nuclear forces.

Click here to download the sample chapter of SIPRI Yearbook 2024 on international stability and human security.

Nuclear arsenals being strengthened around the world

The nine nuclear-armed states—the United States, Russia, the United Kingdom, France, China, India, Pakistan, the Democratic People’s Republic of Korea (North Korea) and Israel—continued to modernize their nuclear arsenals and several deployed new nuclear-armed or nuclear-capable weapon systems in 2023.

Of the total global inventory of an estimated 12 121 warheads in January 2024, about 9585 were in military stockpiles for potential use (see the table below). An estimated 3904 of those warheads were deployed with missiles and aircraft— 60 more than in January 2023 —and the rest were in central storage. Around 2100 of the deployed warheads were kept in a state of high operational alert on ballistic missiles. Nearly all of these warheads belonged to Russia or the USA, but for the first time China is believed to have some warheads on high operational alert. 

‘While the global total of nuclear warheads continues to fall as cold war-era weapons are gradually dismantled, regrettably we continue to see year-on-year increases in the number of operational nuclear warheads,’ said SIPRI Director Dan Smith. ‘This trend seems likely to continue and probably accelerate in the coming years and is extremely concerning.’

India, Pakistan and North Korea are all pursuing the capability to deploy multiple warheads on ballistic missiles, something Russia, France, the UK, the USA and—more recently—China already have. This would enable a rapid potential increase in deployed warheads, as well as the possibility for nuclear-armed countries to threaten the destruction of significantly more targets.

Russia and the USA together possess almost  90 per cent of all nuclear weapons . The sizes of their respective military stockpiles (i.e. useable warheads) seem to have remained relatively stable in 2023, although Russia is estimated to have deployed around 36 more warheads with operational forces than in January 2023. Transparency regarding nuclear forces has declined in both countries in the wake of Russia’s full-scale invasion of Ukraine in February 2022, and debates around nuclear-sharing arrangements have increased in saliency. 

Notably, there were several public claims made in 2023 that Russia had deployed nuclear weapons on Belarusian territory, although there is no conclusive visual evidence that the actual deployment of warheads has taken place. 

In addition to their military stockpiles, Russia and the USA each hold more than 1200 warheads previously retired from military service, which they are gradually dismantling. 

SIPRI’s estimate of the size of China ’s nuclear arsenal increased from 410 warheads in January 2023 to 500 in January 2024, and it is expected to keep growing. For the first time, China may also now be deploying a small number of warheads on missiles during peacetime. Depending on how it decides to structure its forces, China could potentially have at least as many intercontinental ballistic missiles (ICBMs) as either Russia or the USA by the turn of the decade, although its stockpile of nuclear warheads is still expected to remain much smaller than the stockpiles of either of those two countries.

‘China is expanding its nuclear arsenal faster than any other country,’ said Hans M. Kristensen, Associate Senior Fellow with SIPRI’s Weapons of Mass Destruction Programme and Director of the Nuclear Information Project at the Federation of American Scientists (FAS). ‘But in nearly all of the nuclear-armed states there are either plans or a significant push to increase nuclear forces.’ 

Although the UK  is not thought to have increased its nuclear weapon arsenal in 2023, its warhead stockpile is expected to grow in the future as a result of the British government’s announcement in 2021 that it was raising its limit from 225 to 260 warheads. The government also said it would no longer publicly disclose its quantities of nuclear weapons, deployed warheads or deployed missiles.

In 2023 France continued its programmes to develop a third-generation nuclear-powered ballistic missile submarine (SSBN) and a new air-launched cruise missile, as well as to refurbish and upgrade existing systems.

India  slightly expanded its nuclear arsenal in 2023. Both India and Pakistan continued to develop new types of nuclear delivery system in 2023. While Pakistan remains the main focus of India’s nuclear deterrent, India appears to be placing growing emphasis on longer-range weapons, including those capable of reaching targets throughout China.

North Korea continues to prioritize its military nuclear programme as a central element of its national security strategy. SIPRI estimates that the country has now assembled around 50 warheads and possesses enough fissile material to reach a total of up to 90 warheads, both significant increases over the estimates for January 2023. While North Korea conducted no nuclear test explosions in 2023, it appears to have carried out its first test of a short-range ballistic missile from a rudimentary silo. It also completed the development of at least two types of land-attack cruise missile (LACM) designed to deliver nuclear weapons. 

‘Like several other nuclear-armed states, North Korea is putting new emphasis on developing its arsenal of tactical nuclear weapons,’ said Matt Korda, Associate Researcher with SIPRI’s Weapons of Mass Destruction Programme and Senior Research Fellow for the Nuclear Information Project at the Federation of American Scientists. ‘Accordingly, there is a growing concern that North Korea might intend to use these weapons very early in a conflict.’

Israel —which does not publicly acknowledge possessing nuclear weapons—is also believed to be modernizing its nuclear arsenal and appears to be upgrading its plutonium production reactor site at Dimona.

Tensions over Ukraine and Gaza wars further weaken nuclear diplomacy

Nuclear arms control and disarmament diplomacy suffered more major setbacks in 2023. In February 2023 Russia announced it was suspending its participation in the 2010 Treaty on Measures for the Further Reduction and Limitation of Strategic Offensive Arms (New START)—the last remaining nuclear arms control treaty limiting Russian and US strategic nuclear forces. As a countermeasure, the USA has also suspended sharing and publication of treaty data.

In November Russia withdrew its ratification of the Comprehensive Nuclear-Test-Ban Treaty (CTBT), citing ‘an imbalance’ with the USA, which has failed to ratify the treaty since it opened for signature in 1996. However, Russia confirmed that it would remain a signatory and would continue to participate in the work of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO). Meanwhile, Russia has continued to make threats regarding the use of nuclear weapons in the context of Western support for Ukraine. In May 2024 Russia carried out tactical nuclear weapon drills close to the Ukrainian border. 

‘We have not seen nuclear weapons playing such a prominent role in international relations since the cold war,’ said Wilfred Wan, Director of SIPRI’s Weapons of Mass Destruction Programme. ‘It is hard to believe that barely two years have passed since the leaders of the five largest nuclear-armed states jointly reaffirmed that “a nuclear war cannot be won and must never be fought”.’

An informal agreement reached between Iran and the USA in June 2023 seemed to temporarily  de-escalate tensions between the two countries, which had intensified over Iran’s military support to Russian forces in Ukraine. However, the start of the Israel–Hamas war in October upended the agreement, with proxy attacks by Iran-backed groups on US forces in Iraq and Syria apparently ending Iranian–US diplomatic efforts. The war also undermined efforts to engage Israel in the Conference on the Establishment of a Middle East Zone Free of Nuclear Weapons and Other Weapons of Mass Destruction.

More positively, the June 2023 visit to Beijing by the US secretary of state, Antony Blinken, seems to have increased space for dialogue between China and the USA on a range of issues, potentially including arms control. Later in the year the two sides agreed to resume military-to-military communication.

Global security and stability in increasing peril 

The 55th edition of the SIPRI Yearbook analyses the continuing deterioration of global security over the past year. The impacts of the wars in Ukraine and Gaza are visible in almost every aspect of the issues connected to armaments, disarmament and international security examined in the Yearbook. Beyond these two wars—which took centre stage in global news reporting, diplomatic energy and discussion of international politics alike—armed conflicts were active in another 50 states in 2023. Fighting in the Democratic Republic of the Congo and Sudan saw millions of people displaced, and conflict flared up again in Myanmar in the final months of 2023. Armed criminal gangs were a major security concern in some Central and South American states, notably leading to the effective collapse of the state in Haiti during 2023 and into 2024. 

‘We are now in one of the most dangerous periods in human history,’ said Dan Smith, SIPRI Director. ‘There are numerous sources of instability—political rivalries, economic inequalities, ecological disruption, an accelerating arms race. The abyss is beckoning and it is time for the great powers to step back and reflect. Preferably together.’

In addition to the usual detailed coverage of nuclear arms control, disarmament and non-proliferation issues, the SIPRI Yearbook presents data and analysis on developments in world military expenditure, international arms transfers, arms production, multilateral peace operations, armed conflicts and more. Special sections in SIPRI Yearbook 2024  explore the role of Russian private military and security companies in conflicts; efforts to reduce the peace and security risks related to artificial intelligence, outer space and cyberspace; and issues around the protection of civilians in the wars in Gaza and Ukraine.

For editors

The SIPRI Yearbook is a compendium of cutting-edge information and analysis on developments in armaments, disarmament and international security. Three major  SIPRI Yearbook 2024 data sets were pre-launched in  2023–24: total arms sales by the top 100 arms-producing companies (December 2023), international arms transfers (March 2024) and world military expenditure (April 2024). The SIPRI Yearbook is published by Oxford University Press. Learn more at www.sipriyearbook.org .

For information or interview requests contact Mimmi Shen ( [email protected] , +46 76 628 61 33) or Stephanie Blenckner ( [email protected] , +46 8 655 97 47).

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How to Thrive as You Age

Got tinnitus a device that tickles the tongue helps this musician find relief.

Allison Aubrey

After using the Lenire device for an hour each day for 12 weeks, Victoria Banks says her tinnitus is "barely noticeable." David Petrelli/Victoria Banks hide caption

After using the Lenire device for an hour each day for 12 weeks, Victoria Banks says her tinnitus is "barely noticeable."

Imagine if every moment is filled with a high-pitched buzz or ring that you can't turn off.

More than 25 million adults in the U.S., have a condition called tinnitus, according to the American Tinnitus Association. It can be stressful, even panic-inducing and difficult to manage. Dozens of factors can contribute to the onset of tinnitus, including hearing loss, exposure to loud noise or a viral illness.

There's no cure, but there are a range of strategies to reduce the symptoms and make it less bothersome, including hearing aids, mindfulness therapy , and one newer option – a device approved by the FDA to treat tinnitus using electrical stimulation of the tongue.

The device has helped Victoria Banks, a singer and songwriter in Nashville, Tenn., who developed tinnitus about three years ago.

"The noise in my head felt like a bunch of cicadas," Banks says. "It was terrifying." The buzz made it difficult for her to sing and listen to music. "It can be absolutely debilitating," she says.

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Banks tried taking dietary supplements , but those didn't help. She also stepped up exercise, but that didn't bring relief either. Then she read about a device called Lenire, which was approved by the FDA in March 2023. It includes a plastic mouthpiece with stainless steel electrodes that electrically stimulate the tongue. It is the first device of its kind to be approved for tinnitus.

"This had worked for other people, and I thought I'm willing to try anything at this point," Banks recalls.

She sought out audiologist Brian Fligor, who treats severe cases of tinnitus in the Boston area. Fligor was impressed by the results of a clinical trial that found 84% of participants who tried Lenire experienced a significant reduction in symptoms. He became one of the first providers in the U.S. to use the device with his patients. Fligor also served on an advisory panel assembled by the company who developed it.

"A good candidate for this device is somebody who's had tinnitus for at least three months," Fligor says, emphasizing that people should be evaluated first to make sure there's not an underlying medical issue.

Tinnitus often accompanies hearing loss, but Victoria Banks' hearing was fine and she had no other medical issue, so she was a good candidate.

Banks used the device for an hour each day for 12 weeks. During the hour-long sessions, the electrical stimulation "tickles" the tongue, she says. In addition, the device includes a set of headphones that play a series of tones and ocean-wave sounds.

The device works, in part, by shifting the brain's attention away from the buzz. We're wired to focus on important information coming into our brains, Fligor says. Think of it as a spotlight at a show pointed at the most important thing on the stage. "When you have tinnitus and you're frustrated or angry or scared by it, that spotlight gets really strong and focused on the tinnitus," Fligor says.

"It's the combination of what you're feeling through the nerves in your tongue and what you're hearing through your ears happening in synchrony that causes the spotlight in your brain to not be so stuck on the tinnitus," Fligor explains.

A clinical trial found 84% of people who used the device experienced a significant reduction in symptoms. Brian Fligor hide caption

A clinical trial found 84% of people who used the device experienced a significant reduction in symptoms.

"It unsticks your spotlight" and helps desensitize people to the perceived noise that their tinnitus creates, he says.

Banks says the ringing in her ears did not completely disappear, but now it's barely noticeable on most days.

"It's kind of like if I lived near a waterfall and the waterfall was constantly going," she says. Over time, the waterfall sound fades out of consciousness.

"My brain is now focusing on other things," and the buzz is no longer so distracting. She's back to listening to music, writing music, and performing music." I'm doing all of those things," she says.

When the buzz comes back into focus, Banks says a refresher session with the device helps.

A clinical trial found that 84% of people who tried Lenire , saw significant improvements in their condition. To measure changes, the participants took a questionnaire that asked them to rate how much tinnitus was impacting their sleep, sense of control, feelings of well-being and quality of life. After 12 weeks of using the device, participants improved by an average of 14 points.

"Where this device fits into the big picture, is that it's not a cure-all, but it's quickly become my go-to," for people who do not respond to other ways of managing tinnitus, Fligor says.

One down-side is the cost. Banks paid about $4,000 for the Lenire device, and insurance doesn't cover it. She put the expense on her credit card and paid it off gradually.

Fligor hopes that as the evidence of its effectiveness accumulates, insurers will begin to cover it. Despite the cost, more than 80% of participants in the clinical trial said they would recommend the device to a friend with tinnitus.

But, it's unclear how long the benefits last. Clinical trials have only evaluated Lenire over a 1-year period. "How durable are the effects? We don't really know yet," says audiologist Marc Fagelson, the scientific advisory committee chair of the American Tinnitus Association. He says research is promising but there's still more to learn.

Fagelson says the first step he takes with his patients is an evaluation for hearing loss. Research shows that hearing aids can be an effective treatment for tinnitus among people who have both tinnitus and hearing loss, which is much more common among older adults. An estimated one-third of adults 65 years of age and older who have hearing loss, also have tinnitus.

"We do see a lot of patients, even with very mild loss, who benefit from hearing aids," Fagelson says, but in his experience it's about 50-50 in terms of improving tinnitus. Often, he says people with tinnitus need to explore options beyond hearing aids.

Bruce Freeman , a scientist at the University of Pittsburgh Medical Center, says he's benefitted from both hearing aids and Lenire. He was fitted for the device in Ireland where it was developed, before it was available in the U.S.

Freeman agrees that the ringing never truly disappears, but the device has helped him manage the condition. He describes the sounds that play through the device headphones as very calming and "almost hypnotic" and combined with the tongue vibration, it's helped desensitize him to the ring.

Freeman – who is a research scientist – says he's impressed with the results of research, including a study published in Nature, Scientific Reports that points to significant improvements among clinical trial participants with tinnitus.

Freeman experienced a return of his symptoms when he stopped using the device. "Without it the tinnitus got worse," he says. Then, when he resumed use, it improved.

Freeman believes his long-term exposure to noisy instruments in his research laboratory may have played a role in his condition, and also a neck injury from a bicycle accident that fractured his vertebra. "All of those things converged," he says.

Freeman has developed several habits that help keep the high-pitched ring out of his consciousness and maintain good health. "One thing that does wonders is swimming," he says, pointing to the swooshing sound of water in his ears. "That's a form of mindfulness," he explains.

When it comes to the ring of tinnitus, "it comes and goes," Freeman says. For now, it has subsided into the background, he told me with a sense of relief. "The last two years have been great," he says – a combination of the device, hearing aids and the mindfulness that comes from a swim.

This story was edited by Jane Greenhalgh

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