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How to Write Recommendations in Research | Examples & Tips

Published on September 15, 2022 by Tegan George . Revised on July 18, 2023.

Recommendations in research are a crucial component of your discussion section and the conclusion of your thesis , dissertation , or research paper .

As you conduct your research and analyze the data you collected , perhaps there are ideas or results that don’t quite fit the scope of your research topic. Or, maybe your results suggest that there are further implications of your results or the causal relationships between previously-studied variables than covered in extant research.

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Table of contents

What should recommendations look like, building your research recommendation, how should your recommendations be written, recommendation in research example, other interesting articles, frequently asked questions about recommendations.

Recommendations for future research should be:

• Concrete and specific
• Supported with a clear rationale
• Directly connected to your research

Overall, strive to highlight ways other researchers can reproduce or replicate your results to draw further conclusions, and suggest different directions that future research can take, if applicable.

Relatedly, when making these recommendations, avoid:

• Undermining your own work, but rather offer suggestions on how future studies can build upon it
• Suggesting recommendations actually needed to complete your argument, but rather ensure that your research stands alone on its own merits
• Using recommendations as a place for self-criticism, but rather as a natural extension point for your work

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There are many different ways to frame recommendations, but the easiest is perhaps to follow the formula of research question   conclusion  recommendation. Here’s an example.

Conclusion An important condition for controlling many social skills is mastering language. If children have a better command of language, they can express themselves better and are better able to understand their peers. Opportunities to practice social skills are thus dependent on the development of language skills.

As a rule of thumb, try to limit yourself to only the most relevant future recommendations: ones that stem directly from your work. While you can have multiple recommendations for each research conclusion, it is also acceptable to have one recommendation that is connected to more than one conclusion.

These recommendations should be targeted at your audience, specifically toward peers or colleagues in your field that work on similar subjects to your paper or dissertation topic . They can flow directly from any limitations you found while conducting your work, offering concrete and actionable possibilities for how future research can build on anything that your own work was unable to address at the time of your writing.

See below for a full research recommendation example that you can use as a template to write your own.

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While it may be tempting to present new arguments or evidence in your thesis or disseration conclusion , especially if you have a particularly striking argument you’d like to finish your analysis with, you shouldn’t. Theses and dissertations follow a more formal structure than this.

All your findings and arguments should be presented in the body of the text (more specifically in the discussion section and results section .) The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

The conclusion of your thesis or dissertation should include the following:

• A restatement of your research question
• A summary of your key arguments and/or results
• A short discussion of the implications of your research

For a stronger dissertation conclusion , avoid including:

• Important evidence or analysis that wasn’t mentioned in the discussion section and results section
• Generic concluding phrases (e.g. “In conclusion …”)
• Weak statements that undermine your argument (e.g., “There are good points on both sides of this issue.”)

Your conclusion should leave the reader with a strong, decisive impression of your work.

In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.

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Research Recommendations – Guiding policy-makers for evidence-based decision making

Research recommendations play a crucial role in guiding scholars and researchers toward fruitful avenues of exploration. In an era marked by rapid technological advancements and an ever-expanding knowledge base, refining the process of generating research recommendations becomes imperative.

But, what is a research recommendation?

Research recommendations are suggestions or advice provided to researchers to guide their study on a specific topic . They are typically given by experts in the field. Research recommendations are more action-oriented and provide specific guidance for decision-makers, unlike implications that are broader and focus on the broader significance and consequences of the research findings. However, both are crucial components of a research study.

Difference Between Research Recommendations and Implication

Although research recommendations and implications are distinct components of a research study, they are closely related. The differences between them are as follows:

Types of Research Recommendations

Recommendations in research can take various forms, which are as follows:

These recommendations aim to assist researchers in navigating the vast landscape of academic knowledge.

Let us dive deeper to know about its key components and the steps to write an impactful research recommendation.

Key Components of Research Recommendations

The key components of research recommendations include defining the research question or objective, specifying research methods, outlining data collection and analysis processes, presenting results and conclusions, addressing limitations, and suggesting areas for future research. Here are some characteristics of research recommendations:

Research recommendations offer various advantages and play a crucial role in ensuring that research findings contribute to positive outcomes in various fields. However, they also have few limitations which highlights the significance of a well-crafted research recommendation in offering the promised advantages.

The importance of research recommendations ranges in various fields, influencing policy-making, program development, product development, marketing strategies, medical practice, and scientific research. Their purpose is to transfer knowledge from researchers to practitioners, policymakers, or stakeholders, facilitating informed decision-making and improving outcomes in different domains.

How to Write Research Recommendations?

Research recommendations can be generated through various means, including algorithmic approaches, expert opinions, or collaborative filtering techniques. Here is a step-wise guide to build your understanding on the development of research recommendations.

1. Understand the Research Question:

Understand the research question and objectives before writing recommendations. Also, ensure that your recommendations are relevant and directly address the goals of the study.

2. Review Existing Literature:

Familiarize yourself with relevant existing literature to help you identify gaps , and offer informed recommendations that contribute to the existing body of research.

3. Consider Research Methods:

Evaluate the appropriateness of different research methods in addressing the research question. Also, consider the nature of the data, the study design, and the specific objectives.

4. Identify Data Collection Techniques:

Gather dataset from diverse authentic sources. Include information such as keywords, abstracts, authors, publication dates, and citation metrics to provide a rich foundation for analysis.

5. Propose Data Analysis Methods:

Suggest appropriate data analysis methods based on the type of data collected. Consider whether statistical analysis, qualitative analysis, or a mixed-methods approach is most suitable.

6. Consider Limitations and Ethical Considerations:

Acknowledge any limitations and potential ethical considerations of the study. Furthermore, address these limitations or mitigate ethical concerns to ensure responsible research.

7. Justify Recommendations:

Explain how your recommendation contributes to addressing the research question or objective. Provide a strong rationale to help researchers understand the importance of following your suggestions.

8. Summarize Recommendations:

Provide a concise summary at the end of the report to emphasize how following these recommendations will contribute to the overall success of the research project.

By following these steps, you can create research recommendations that are actionable and contribute meaningfully to the success of the research project.

Download now to unlock some tips to improve your journey of writing research recommendations.

Example of a Research Recommendation

Here is an example of a research recommendation based on a hypothetical research to improve your understanding.

Research Recommendation: Enhancing Student Learning through Integrated Learning Platforms

Background:

The research study investigated the impact of an integrated learning platform on student learning outcomes in high school mathematics classes. The findings revealed a statistically significant improvement in student performance and engagement when compared to traditional teaching methods.

Recommendation:

In light of the research findings, it is recommended that educational institutions consider adopting and integrating the identified learning platform into their mathematics curriculum. The following specific recommendations are provided:

• Implementation of the Integrated Learning Platform:

Schools are encouraged to adopt the integrated learning platform in mathematics classrooms, ensuring proper training for teachers on its effective utilization.

• Professional Development for Educators:

Develop and implement professional programs to train educators in the effective use of the integrated learning platform to address any challenges teachers may face during the transition.

• Monitoring and Evaluation:

Establish a monitoring and evaluation system to track the impact of the integrated learning platform on student performance over time.

• Resource Allocation:

Allocate sufficient resources, both financial and technical, to support the widespread implementation of the integrated learning platform.

By implementing these recommendations, educational institutions can harness the potential of the integrated learning platform and enhance student learning experiences and academic achievements in mathematics.

This example covers the components of a research recommendation, providing specific actions based on the research findings, identifying the target audience, and outlining practical steps for implementation.

Using AI in Research Recommendation Writing

Enhancing research recommendations is an ongoing endeavor that requires the integration of cutting-edge technologies, collaborative efforts, and ethical considerations. By embracing data-driven approaches and leveraging advanced technologies, the research community can create more effective and personalized recommendation systems. However, it is accompanied by several limitations. Therefore, it is essential to approach the use of AI in research with a critical mindset, and complement its capabilities with human expertise and judgment.

Here are some limitations of integrating AI in writing research recommendation and some ways on how to counter them.

1. Data Bias

AI systems rely heavily on data for training. If the training data is biased or incomplete, the AI model may produce biased results or recommendations.

How to tackle: Audit regularly the model’s performance to identify any discrepancies and adjust the training data and algorithms accordingly.

2. Lack of Understanding of Context:

AI models may struggle to understand the nuanced context of a particular research problem. They may misinterpret information, leading to inaccurate recommendations.

How to tackle: Use AI to characterize research articles and topics. Employ them to extract features like keywords, authorship patterns and content-based details.

3. Ethical Considerations:

AI models might stereotype certain concepts or generate recommendations that could have negative consequences for certain individuals or groups.

How to tackle: Incorporate user feedback mechanisms to reduce redundancies. Establish an ethics review process for AI models in research recommendation writing.

4. Lack of Creativity and Intuition:

AI may struggle with tasks that require a deep understanding of the underlying principles or the ability to think outside the box.

How to tackle: Hybrid approaches can be employed by integrating AI in data analysis and identifying patterns for accelerating the data interpretation process.

5. Interpretability:

Many AI models, especially complex deep learning models, lack transparency on how the model arrived at a particular recommendation.

How to tackle: Implement models like decision trees or linear models. Provide clear explanation of the model architecture, training process, and decision-making criteria.

6. Dynamic Nature of Research:

Research fields are dynamic, and new information is constantly emerging. AI models may struggle to keep up with the rapidly changing landscape and may not be able to adapt to new developments.

How to tackle: Establish a feedback loop for continuous improvement. Regularly update the recommendation system based on user feedback and emerging research trends.

The integration of AI in research recommendation writing holds great promise for advancing knowledge and streamlining the research process. However, navigating these concerns is pivotal in ensuring the responsible deployment of these technologies. Researchers need to understand the use of responsible use of AI in research and must be aware of the ethical considerations.

Exploring research recommendations plays a critical role in shaping the trajectory of scientific inquiry. It serves as a compass, guiding researchers toward more robust methodologies, collaborative endeavors, and innovative approaches. Embracing these suggestions not only enhances the quality of individual studies but also contributes to the collective advancement of human understanding.

Frequently Asked Questions

The purpose of recommendations in research is to provide practical and actionable suggestions based on the study's findings, guiding future actions, policies, or interventions in a specific field or context. Recommendations bridges the gap between research outcomes and their real-world application.

To make a research recommendation, analyze your findings, identify key insights, and propose specific, evidence-based actions. Include the relevance of the recommendations to the study's objectives and provide practical steps for implementation.

Begin a recommendation by succinctly summarizing the key findings of the research. Clearly state the purpose of the recommendation and its intended impact. Use a direct and actionable language to convey the suggested course of action.

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Implications or Recommendations in Research: What's the Difference?

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High-quality research articles that get many citations contain both implications and recommendations. Implications are the impact your research makes, whereas recommendations are specific actions that can then be taken based on your findings, such as for more research or for policymaking.

Updated on August 23, 2022

That seems clear enough, but the two are commonly confused.

This confusion is especially true if you come from a so-called high-context culture in which information is often implied based on the situation, as in many Asian cultures. High-context cultures are different from low-context cultures where information is more direct and explicit (as in North America and many European cultures).

Let's set these two straight in a low-context way; i.e., we'll be specific and direct! This is the best way to be in English academic writing because you're writing for the world.

Implications and recommendations in a research article

The standard format of STEM research articles is what's called IMRaD:

• Introduction
• Discussion/conclusions

Some journals call for a separate conclusions section, while others have the conclusions as the last part of the discussion. You'll write these four (or five) sections in the same sequence, though, no matter the journal.

The discussion section is typically where you restate your results and how well they confirmed your hypotheses. Give readers the answer to the questions for which they're looking to you for an answer.

At this point, many researchers assume their paper is finished. After all, aren't the results the most important part? As you might have guessed, no, you're not quite done yet.

The discussion/conclusions section is where to say what happened and what should now happen

The discussion/conclusions section of every good scientific article should contain the implications and recommendations.

The implications, first of all, are the impact your results have on your specific field. A high-impact, highly cited article will also broaden the scope here and provide implications to other fields. This is what makes research cross-disciplinary.

Recommendations, however, are suggestions to improve your field based on your results.

These two aspects help the reader understand your broader content: How and why your work is important to the world. They also tell the reader what can be changed in the future based on your results.

These aspects are what editors are looking for when selecting papers for peer review.

Implications and recommendations are, thus, written at the end of the discussion section, and before the concluding paragraph. They help to “wrap up” your paper. Once your reader understands what you found, the next logical step is what those results mean and what should come next.

Then they can take the baton, in the form of your work, and run with it. That gets you cited and extends your impact!

The order of implications and recommendations also matters. Both are written after you've summarized your main findings in the discussion section. Then, those results are interpreted based on ongoing work in the field. After this, the implications are stated, followed by the recommendations.

Writing an academic research paper is a bit like running a race. Finish strong, with your most important conclusion (recommendation) at the end. Leave readers with an understanding of your work's importance. Avoid generic, obvious phrases like "more research is needed to fully address this issue." Be specific.

The main differences between implications and recommendations (table)

Now let's dig a bit deeper into actually how to write these parts.

What are implications?

Research implications tell us how and why your results are important for the field at large. They help answer the question of “what does it mean?” Implications tell us how your work contributes to your field and what it adds to it. They're used when you want to tell your peers why your research is important for ongoing theory, practice, policymaking, and for future research.

Crucially, your implications must be evidence-based. This means they must be derived from the results in the paper.

Implications are written after you've summarized your main findings in the discussion section. They come before the recommendations and before the concluding paragraph. There is no specific section dedicated to implications. They must be integrated into your discussion so that the reader understands why the results are meaningful and what they add to the field.

A good strategy is to separate your implications into types. Implications can be social, political, technological, related to policies, or others, depending on your topic. The most frequently used types are theoretical and practical. Theoretical implications relate to how your findings connect to other theories or ideas in your field, while practical implications are related to what we can do with the results.

Key features of implications

• State the impact your research makes
• Helps us understand why your results are important
• Must be evidence-based
• Written in the discussion, before recommendations
• Can be theoretical, practical, or other (social, political, etc.)

Examples of implications

Let's take a look at some examples of research results below with their implications.

The result : one study found that learning items over time improves memory more than cramming material in a bunch of information at once .

The implications : This result suggests memory is better when studying is spread out over time, which could be due to memory consolidation processes.

The result : an intervention study found that mindfulness helps improve mental health if you have anxiety.

The implications : This result has implications for the role of executive functions on anxiety.

The result : a study found that musical learning helps language learning in children .

The implications : these findings suggest that language and music may work together to aid development.

What are recommendations?

As noted above, explaining how your results contribute to the real world is an important part of a successful article.

Likewise, stating how your findings can be used to improve something in future research is equally important. This brings us to the recommendations.

Research recommendations are suggestions and solutions you give for certain situations based on your results. Once the reader understands what your results mean with the implications, the next question they need to know is "what's next?"

Recommendations are calls to action on ways certain things in the field can be improved in the future based on your results. Recommendations are used when you want to convey that something different should be done based on what your analyses revealed.

Similar to implications, recommendations are also evidence-based. This means that your recommendations to the field must be drawn directly from your results.

The goal of the recommendations is to make clear, specific, and realistic suggestions to future researchers before they conduct a similar experiment. No matter what area your research is in, there will always be further research to do. Try to think about what would be helpful for other researchers to know before starting their work.

Recommendations are also written in the discussion section. They come after the implications and before the concluding paragraphs. Similar to the implications, there is usually no specific section dedicated to the recommendations. However, depending on how many solutions you want to suggest to the field, they may be written as a subsection.

Key features of recommendations

• Statements about what can be done differently in the field based on your findings
• Must be realistic and specific
• Written in the discussion, after implications and before conclusions
• Related to both your field and, preferably, a wider context to the research

Examples of recommendations

Here are some research results and their recommendations.

A meta-analysis found that actively recalling material from your memory is better than simply re-reading it .

• The recommendation: Based on these findings, teachers and other educators should encourage students to practice active recall strategies.

A medical intervention found that daily exercise helps prevent cardiovascular disease .

• The recommendation: Based on these results, physicians are recommended to encourage patients to exercise and walk regularly. Also recommended is to encourage more walking through public health offices in communities.

A study found that many research articles do not contain the sample sizes needed to statistically confirm their findings .

The recommendation: To improve the current state of the field, researchers should consider doing power analysis based on their experiment's design.

What else is important about implications and recommendations?

When writing recommendations and implications, be careful not to overstate the impact of your results. It can be tempting for researchers to inflate the importance of their findings and make grandiose statements about what their work means.

Remember that implications and recommendations must be coming directly from your results. Therefore, they must be straightforward, realistic, and plausible.

Another good thing to remember is to make sure the implications and recommendations are stated clearly and separately. Do not attach them to the endings of other paragraphs just to add them in. Use similar example phrases as those listed in the table when starting your sentences to clearly indicate when it's an implication and when it's a recommendation.

When your peers, or brand-new readers, read your paper, they shouldn't have to hunt through your discussion to find the implications and recommendations. They should be clear, visible, and understandable on their own.

That'll get you cited more, and you'll make a greater contribution to your area of science while extending the life and impact of your work.

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• Klara Brunnhuber , clinical editor 1 ,
• Kalipso Chalkidou , associate director, research and development 2 ,
• Iain Chalmers , director 3 ,
• Mike Clarke , director 4 ,
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• 1 BMJ Publishing Group, London WC1H 9JR,
• 2 National Institute for Health and Clinical Excellence, London WC1V 6NA,
• 3 Database of Uncertainties about the Effects of Treatments, James Lind Alliance Secretariat, James Lind Initiative, Oxford OX2 7LG,
• 4 UK Cochrane Centre, Oxford OX2 7LG,
• 5 Centre for Reviews and Dissemination, University of York, York YO10 5DD,
• 6 National Coordinating Centre for Health Technology Assessment, University of Southampton, Southampton SO16 7PX,
• 7 Scottish Intercollegiate Guidelines Network, Edinburgh EH2 1EN,
• 8 Update Software, Oxford OX2 7LG
• Correspondence to: PBrown
• Accepted 22 September 2006

“More research is needed” is a conclusion that fits most systematic reviews. But authors need to be more specific about what exactly is required

Long awaited reports of new research, systematic reviews, and clinical guidelines are too often a disappointing anticlimax for those wishing to use them to direct future research. After many months or years of effort and intellectual energy put into these projects, authors miss the opportunity to identify unanswered questions and outstanding gaps in the evidence. Most reports contain only a less than helpful, general research recommendation. This means that the potential value of these recommendations is lost.

Current recommendations

In 2005, representatives of organisations commissioning and summarising research, including the BMJ Publishing Group, the Centre for Reviews and Dissemination, the National Coordinating Centre for Health Technology Assessment, the National Institute for Health and Clinical Excellence, the Scottish Intercollegiate Guidelines Network, and the UK Cochrane Centre, met as members of the development group for the Database of Uncertainties about the Effects of Treatments (see bmj.com for details on all participating organisations). Our aim was to discuss the state of research recommendations within our organisations and to develop guidelines for improving the presentation of proposals for further research. All organisations had found weaknesses in the way researchers and authors of systematic reviews and clinical guidelines stated the need for further research. As part of the project, a member of the Centre for Reviews and Dissemination under-took a rapid literature search to identify information on research recommendation models, which found some individual methods but no group initiatives to attempt to standardise recommendations.

Suggested format for research recommendations on the effects of treatments

Core elements.

E Evidence (What is the current state of the evidence?)

P Population (What is …

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Quantitative Research

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• First Online: 13 January 2019
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• Leigh A. Wilson 2 , 3  

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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Home » Research Findings – Types Examples and Writing Guide

Research Findings – Types Examples and Writing Guide

Table of Contents

Research Findings

Definition:

Research findings refer to the results obtained from a study or investigation conducted through a systematic and scientific approach. These findings are the outcomes of the data analysis, interpretation, and evaluation carried out during the research process.

Types of Research Findings

There are two main types of research findings:

Qualitative Findings

Qualitative research is an exploratory research method used to understand the complexities of human behavior and experiences. Qualitative findings are non-numerical and descriptive data that describe the meaning and interpretation of the data collected. Examples of qualitative findings include quotes from participants, themes that emerge from the data, and descriptions of experiences and phenomena.

Quantitative Findings

Quantitative research is a research method that uses numerical data and statistical analysis to measure and quantify a phenomenon or behavior. Quantitative findings include numerical data such as mean, median, and mode, as well as statistical analyses such as t-tests, ANOVA, and regression analysis. These findings are often presented in tables, graphs, or charts.

Both qualitative and quantitative findings are important in research and can provide different insights into a research question or problem. Combining both types of findings can provide a more comprehensive understanding of a phenomenon and improve the validity and reliability of research results.

Parts of Research Findings

Research findings typically consist of several parts, including:

• Introduction: This section provides an overview of the research topic and the purpose of the study.
• Literature Review: This section summarizes previous research studies and findings that are relevant to the current study.
• Methodology : This section describes the research design, methods, and procedures used in the study, including details on the sample, data collection, and data analysis.
• Results : This section presents the findings of the study, including statistical analyses and data visualizations.
• Discussion : This section interprets the results and explains what they mean in relation to the research question(s) and hypotheses. It may also compare and contrast the current findings with previous research studies and explore any implications or limitations of the study.
• Conclusion : This section provides a summary of the key findings and the main conclusions of the study.
• Recommendations: This section suggests areas for further research and potential applications or implications of the study’s findings.

How to Write Research Findings

Writing research findings requires careful planning and attention to detail. Here are some general steps to follow when writing research findings:

• Organize your findings: Before you begin writing, it’s essential to organize your findings logically. Consider creating an outline or a flowchart that outlines the main points you want to make and how they relate to one another.
• Use clear and concise language : When presenting your findings, be sure to use clear and concise language that is easy to understand. Avoid using jargon or technical terms unless they are necessary to convey your meaning.
• Use visual aids : Visual aids such as tables, charts, and graphs can be helpful in presenting your findings. Be sure to label and title your visual aids clearly, and make sure they are easy to read.
• Use headings and subheadings: Using headings and subheadings can help organize your findings and make them easier to read. Make sure your headings and subheadings are clear and descriptive.
• Interpret your findings : When presenting your findings, it’s important to provide some interpretation of what the results mean. This can include discussing how your findings relate to the existing literature, identifying any limitations of your study, and suggesting areas for future research.
• Be precise and accurate : When presenting your findings, be sure to use precise and accurate language. Avoid making generalizations or overstatements and be careful not to misrepresent your data.
• Edit and revise: Once you have written your research findings, be sure to edit and revise them carefully. Check for grammar and spelling errors, make sure your formatting is consistent, and ensure that your writing is clear and concise.

Research Findings Example

Following is a Research Findings Example sample for students:

Title: The Effects of Exercise on Mental Health

Sample : 500 participants, both men and women, between the ages of 18-45.

Methodology : Participants were divided into two groups. The first group engaged in 30 minutes of moderate intensity exercise five times a week for eight weeks. The second group did not exercise during the study period. Participants in both groups completed a questionnaire that assessed their mental health before and after the study period.

Findings : The group that engaged in regular exercise reported a significant improvement in mental health compared to the control group. Specifically, they reported lower levels of anxiety and depression, improved mood, and increased self-esteem.

Conclusion : Regular exercise can have a positive impact on mental health and may be an effective intervention for individuals experiencing symptoms of anxiety or depression.

Applications of Research Findings

Research findings can be applied in various fields to improve processes, products, services, and outcomes. Here are some examples:

• Healthcare : Research findings in medicine and healthcare can be applied to improve patient outcomes, reduce morbidity and mortality rates, and develop new treatments for various diseases.
• Education : Research findings in education can be used to develop effective teaching methods, improve learning outcomes, and design new educational programs.
• Technology : Research findings in technology can be applied to develop new products, improve existing products, and enhance user experiences.
• Business : Research findings in business can be applied to develop new strategies, improve operations, and increase profitability.
• Public Policy: Research findings can be used to inform public policy decisions on issues such as environmental protection, social welfare, and economic development.
• Social Sciences: Research findings in social sciences can be used to improve understanding of human behavior and social phenomena, inform public policy decisions, and develop interventions to address social issues.
• Agriculture: Research findings in agriculture can be applied to improve crop yields, develop new farming techniques, and enhance food security.
• Sports : Research findings in sports can be applied to improve athlete performance, reduce injuries, and develop new training programs.

When to use Research Findings

Research findings can be used in a variety of situations, depending on the context and the purpose. Here are some examples of when research findings may be useful:

• Decision-making : Research findings can be used to inform decisions in various fields, such as business, education, healthcare, and public policy. For example, a business may use market research findings to make decisions about new product development or marketing strategies.
• Problem-solving : Research findings can be used to solve problems or challenges in various fields, such as healthcare, engineering, and social sciences. For example, medical researchers may use findings from clinical trials to develop new treatments for diseases.
• Policy development : Research findings can be used to inform the development of policies in various fields, such as environmental protection, social welfare, and economic development. For example, policymakers may use research findings to develop policies aimed at reducing greenhouse gas emissions.
• Program evaluation: Research findings can be used to evaluate the effectiveness of programs or interventions in various fields, such as education, healthcare, and social services. For example, educational researchers may use findings from evaluations of educational programs to improve teaching and learning outcomes.
• Innovation: Research findings can be used to inspire or guide innovation in various fields, such as technology and engineering. For example, engineers may use research findings on materials science to develop new and innovative products.

Purpose of Research Findings

The purpose of research findings is to contribute to the knowledge and understanding of a particular topic or issue. Research findings are the result of a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques.

The main purposes of research findings are:

• To generate new knowledge : Research findings contribute to the body of knowledge on a particular topic, by adding new information, insights, and understanding to the existing knowledge base.
• To test hypotheses or theories : Research findings can be used to test hypotheses or theories that have been proposed in a particular field or discipline. This helps to determine the validity and reliability of the hypotheses or theories, and to refine or develop new ones.
• To inform practice: Research findings can be used to inform practice in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners to make informed decisions and improve outcomes.
• To identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research.
• To contribute to policy development: Research findings can be used to inform policy development in various fields, such as environmental protection, social welfare, and economic development. By providing evidence-based recommendations, research findings can help policymakers to develop effective policies that address societal challenges.

Characteristics of Research Findings

Research findings have several key characteristics that distinguish them from other types of information or knowledge. Here are some of the main characteristics of research findings:

• Objective : Research findings are based on a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques. As such, they are generally considered to be more objective and reliable than other types of information.
• Empirical : Research findings are based on empirical evidence, which means that they are derived from observations or measurements of the real world. This gives them a high degree of credibility and validity.
• Generalizable : Research findings are often intended to be generalizable to a larger population or context beyond the specific study. This means that the findings can be applied to other situations or populations with similar characteristics.
• Transparent : Research findings are typically reported in a transparent manner, with a clear description of the research methods and data analysis techniques used. This allows others to assess the credibility and reliability of the findings.
• Peer-reviewed: Research findings are often subject to a rigorous peer-review process, in which experts in the field review the research methods, data analysis, and conclusions of the study. This helps to ensure the validity and reliability of the findings.
• Reproducible : Research findings are often designed to be reproducible, meaning that other researchers can replicate the study using the same methods and obtain similar results. This helps to ensure the validity and reliability of the findings.

Advantages of Research Findings

Research findings have many advantages, which make them valuable sources of knowledge and information. Here are some of the main advantages of research findings:

• Evidence-based: Research findings are based on empirical evidence, which means that they are grounded in data and observations from the real world. This makes them a reliable and credible source of information.
• Inform decision-making: Research findings can be used to inform decision-making in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners and policymakers to make informed decisions and improve outcomes.
• Identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research. This contributes to the ongoing development of knowledge in various fields.
• Improve outcomes : Research findings can be used to develop and implement evidence-based practices and interventions, which have been shown to improve outcomes in various fields, such as healthcare, education, and social services.
• Foster innovation: Research findings can inspire or guide innovation in various fields, such as technology and engineering. By providing new information and understanding of a particular topic, research findings can stimulate new ideas and approaches to problem-solving.
• Enhance credibility: Research findings are generally considered to be more credible and reliable than other types of information, as they are based on rigorous research methods and are subject to peer-review processes.

Limitations of Research Findings

While research findings have many advantages, they also have some limitations. Here are some of the main limitations of research findings:

• Limited scope: Research findings are typically based on a particular study or set of studies, which may have a limited scope or focus. This means that they may not be applicable to other contexts or populations.
• Potential for bias : Research findings can be influenced by various sources of bias, such as researcher bias, selection bias, or measurement bias. This can affect the validity and reliability of the findings.
• Ethical considerations: Research findings can raise ethical considerations, particularly in studies involving human subjects. Researchers must ensure that their studies are conducted in an ethical and responsible manner, with appropriate measures to protect the welfare and privacy of participants.
• Time and resource constraints : Research studies can be time-consuming and require significant resources, which can limit the number and scope of studies that are conducted. This can lead to gaps in knowledge or a lack of research on certain topics.
• Complexity: Some research findings can be complex and difficult to interpret, particularly in fields such as science or medicine. This can make it challenging for practitioners and policymakers to apply the findings to their work.
• Lack of generalizability : While research findings are intended to be generalizable to larger populations or contexts, there may be factors that limit their generalizability. For example, cultural or environmental factors may influence how a particular intervention or treatment works in different populations or contexts.

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How to Write Recommendations in a Research Paper Correctly and Appropriately

Updated 13 Mar 2024

Completing a research paper can be daunting, but it becomes more manageable if you delve deeper into the process. Academic papers adhere to specific formats that must be followed to ensure high-quality content.

The conclusion and recommendations sections are crucial components of a research paper. They mark the end of your research, leave a lasting impression on your readers, and should be approached with great care. No wonder many students search for information about how to write recommendations in research papers. Explore this comprehensive guide to infuse your content with thoughtfulness and coherence, thereby elevating the impact of your research paper.

Recommendations in a research paper: meaning and goals

Before you start learning how to write recommendations in a research paper, the first thing is to clarify the meaning of this term. It is a significant element in the research paper structure, as it is critical to your discussion section and conclusion. While conducting research and analyzing gathered data, you may come across ideas or results that only partially align with the scope of your research topic. Alternatively, your findings offer possible implications or causal relationships between the aspects not covered in existing research.

This section will provide practical solutions for further research based on your conclusions and findings. The particular goals of this section depend on the research nature and usually include the following:

• Providing strategies to address the issues considered in the paper;
• Delivering suggestions on how the investigation findings can be applied in practice;
• Identifying gaps in the subject area and suggesting ways to extend existing knowledge;
• Enhancing reliability and validity of the research findings. 

Where to put recommendations?

To better understand how to write recommendations in research, you should know where to insert them. These elements are typically added in the conclusion (a short version) and discussion sections. Still, if you’re doing research with a practical or business focus, you can also include your suggestions in an advisory report or separate section. This text part should be completed based on the research findings and evidence. It should be clear, specific, and actionable, targeted to the intended audience, such as researchers, practitioners, or policymakers.

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What should recommendations look like?

When providing your solutions for further research, it’s important to ensure they are specific, fully connected to your investigation, and supported by a comprehensible rationale. The essential goal is to show how other researchers can generate the same results to make conclusions and offer potential directions for future research. 

Recommendations should be clear and include actionable words. While completing this section, the writer should show a solution-oriented approach by highlighting the scope for future investigation. Using bullet points is a better way to ensure clarity instead of writing long paragraphs.

Look at the following recommendation in a research paper example:

It is recommended that company X should create and promote sugar-free biscuits along with their existing product range. The marketing department should focus on creating a positive and healthy image. 

Let’s rewrite this paragraph to make it clear and well-structured:

• The corporation has to introduce and promote sugar-free products;
• The company has to create a new positive image;
• The company has to launch an advertising campaign to show their products’ benefits for health.

When visiting the EduBirdie website, you’ll find many helpful tips on writing a research paper, ranging from completing a research paper conclusion to exploring examples of a well-thought-out recommendations section. Don’t miss your chance to improve your paper with our assistance!

Structure of recommendations

Let’s consider the typical structure of this part. You’ll come across many various ways to organize it. The most common approach uses a simple formula with three elements: research question, conclusion, and recommendation. Now, you’ll see how this structure can be implemented.

Research question:

Which category of people is more prone to social exclusion? 

Conclusion:

The study found that individuals over 65 have a greater risk of being isolated from society.

Recommendation:

It is recommended that the institutions dealing with overcoming social exclusion should focus on this particular group. 

In this example, the author delivers a suggestion based on the research findings (the risk of social isolation grows among people aged 65 and more). The measures to improve this situation are indicated (the organizations dealing with problems of social isolation should pay more attention to people over 65 years old).

How to write recommendations in research papers: essential guidelines

Look at some tips to help you complete a flawless chapter for your papers.

• Be concise in your statements.  Ensure that your suggestions are written in clear and concise language, avoiding jargon or technical terms difficult to understand. Try to limit yourself to one-sentence statements to present your recommendation. Not only it can help with language learning overall, but will also look more professional.
• Organize your ideas logically and coherently . You may use lists or paragraphs depending on your institution's guidelines or field of study. Use headings and subheadings to structure your section for easy navigation.
• Provide specific and concrete suggestions.  Clearly state the issues you explore and offer specific measures and solutions. Your call to action and suggestions should be related to the issues mentioned in the previous sections. Focusing on the most relevant and actionable suggestions directly stemming from your research is crucial.
• Match recommendations to your conclusion.  Ensure that your suggestions logically align with your conclusions. Refrain from suggesting too many solutions. You can create one recommendation addressing several conclusions when you must provide numerous suggestions for every study conclusion.
• Ensure your solutions are achievable.  Your recommendations should be practical and feasible to implement. Suggest specific and actionable steps to effectively address the considered issues or gaps in the research, avoiding vague or impractical suggestions.
• Use a comprehensive approach.  Make sure your solutions cover all relevant areas within your research scope. Consider different contexts, stakeholders, and perspectives affected by the recommendations. Be thorough in identifying potential improvement areas and offering appropriate actions.
• Don’t add new information to this part of your paper.  Avoid introducing new issues or ideas to complete your argument when writing recommendations in a research paper. Your academic paper has to stand on its own merits. 
• Create content tailored to your readers.  Ensure that your recommendations are aimed at your audience, namely your colleagues in the field of study who work on similar topics. The ideas you provide in the paper should be based on limitations identified during research. They should offer concrete possibilities for further study to rely on areas your investigation could not cover when completed.
• Explain how your recommendations can solve the issues you explore.  Go beyond listing suggestions and provide a rationale for each, including why it is essential, how it handles the research problem, and what evidence or theory supports it. Use relevant literature citations to strengthen your content. Explain how the suggested solutions can effectively answer the research question. This can be done by adding the following:
• Ideas for improving the methodology or approach;
• Policy suggestions;
• Perspectives for future research.
• Don’t undermine your research contribution or criticize yourself.   Avoid criticizing yourself in this section. Instead, use it as a perfect opportunity to provide ideas on how future studies can build upon your findings, making them a natural extension point. 
• Acknowledge any limitations or constraints of your research.  Reflect on how these limitations may impact the feasibility or generalizability of your solutions. This demonstrates critical thinking and awareness of the limitations of your study.
• End this section with a summary.  Highlight the key suggestions and their potential impact in a short conclusion. Emphasize the significance of your ideas and their valuable contribution to the field.

Don’t forget to consult and adhere to the requirements and specific guidelines provided by your institution for this section.

How do the discussion and the conclusion sections differ in a research paper? 

The discussion usually entails a comprehensive analysis of the results, delving into the significance of your findings and providing contextualization using citations of relevant sources. On the other hand, the conclusion is typically more concise and general. It briefly considers the main research question and provides suggestions from your findings.

Can the research paper conclusion come with new arguments? 

Although adding fresh evidence or arguments in the conclusion might be tempting, especially if you have a compelling point, we don’t recommend doing it. Research papers, dissertations, or theses typically adhere to a formal structure. Exposing all your arguments and findings in the thesis body is crucial. It’s better to do it in the discussion and results chapters. The conclusion should serve as a summary and reflection of your evidence and arguments rather than a place to introduce new ideas.

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Written by Steven Robinson

Steven Robinson is an academic writing expert with a degree in English literature. His expertise, patient approach, and support empower students to express ideas clearly. On EduBirdie's blog, he provides valuable writing guides on essays, research papers, and other intriguing topics. Enjoys chess in free time.

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Evans D, Coad J, Cottrell K, et al. Public involvement in research: assessing impact through a realist evaluation. Southampton (UK): NIHR Journals Library; 2014 Oct. (Health Services and Delivery Research, No. 2.36.)

Public involvement in research: assessing impact through a realist evaluation.

Chapter 9 conclusions and recommendations for future research.

• How well have we achieved our original aim and objectives?

The initially stated overarching aim of this research was to identify the contextual factors and mechanisms that are regularly associated with effective and cost-effective public involvement in research. While recognising the limitations of our analysis, we believe we have largely achieved this in our revised theory of public involvement in research set out in Chapter 8 . We have developed and tested this theory of public involvement in research in eight diverse case studies; this has highlighted important contextual factors, in particular PI leadership, which had not previously been prominent in the literature. We have identified how this critical contextual factor shapes key mechanisms of public involvement, including the identification of a senior lead for involvement, resource allocation for involvement and facilitation of research partners. These mechanisms then lead to specific outcomes in improving the quality of research, notably recruitment strategies and materials and data collection tools and methods. We have identified a ‘virtuous circle’ of feedback to research partners on their contribution leading to their improved confidence and motivation, which facilitates their continued contribution. Following feedback from the HS&DR Board on our original application we did not seek to assess the cost-effectiveness of different mechanisms of public involvement but we did cost the different types of public involvement as discussed in Chapter 7 . A key finding is that many research projects undercost public involvement.

In our original proposal we emphasised our desire to include case studies involving young people and families with children in the research process. We recruited two studies involving parents of young children aged under 5 years, and two projects involving ‘older’ young people in the 18- to 25-years age group. We recognise that in doing this we missed studies involving children and young people aged under 18 years; in principle we would have liked to have included studies involving such children and young people, but, given the resources at our disposal and the additional resource, ethical and governance issues this would have entailed, we regretfully concluded that this would not be feasible for our study. In terms of the four studies with parental and young persons’ involvement that we did include, we have not done a separate analysis of their data, but the themes emerging from those case studies were consistent with our other case studies and contributed to our overall analysis.

In terms of the initial objectives, we successfully recruited the sample of eight diverse case studies and collected and analysed data from them (objective 1). As intended, we identified the outcomes of involvement from multiple stakeholders‘ perspectives, although we did not get as many research partners‘ perspectives as we would have liked – see limitations below (objective 2). It was more difficult than expected to track the impact of public involvement from project inception through to completion (objective 3), as all of our projects turned out to have longer time scales than our own. Even to track involvement over a stage of a case study research project proved difficult, as the research usually did not fall into neatly staged time periods and one study had no involvement activity over the study period.

Nevertheless, we were able to track seven of the eight case studies prospectively and in real time over time periods of up to 9 months, giving us an unusual window on involvement processes that have previously mainly been observed retrospectively. We were successful in comparing the contextual factors, mechanisms and outcomes associated with public involvement from different stakeholders‘ perspectives and costing the different mechanisms for public involvement (objective 4). We only partly achieved our final objective of undertaking a consensus exercise among stakeholders to assess the merits of the realist evaluation approach and our approach to the measurement and valuation of economic costs of public involvement in research (objective 5). A final consensus event was held, where very useful discussion and amendment of our theory of public involvement took place, and the economic approach was discussed and helpfully critiqued by participants. However, as our earlier discussions developed more fully than expected, we decided to let them continue rather than interrupt them in order to run the final exercise to assess the merits of the realist evaluation approach. We did, however, test our analysis with all our case study participants by sending a draft of this final report for comment. We received a number of helpful comments and corrections but no disagreement with our overall analysis.

• What were the limitations of our study?

Realist evaluation is a relatively new approach and we recognise that there were a number of limitations to our study. We sought to follow the approach recommended by Pawson, but we acknowledge that we were not always able to do so. In particular, our theory of public involvement in research evolved over time and initially was not as tightly framed in terms of a testable hypothesis as Pawson recommends. In his latest book Pawson strongly recommends that outcomes should be measured with quantitative data, 17 but we did not do so; we were not aware of the existence of quantitative data or tools that would enable us to collect such data to answer our research questions. Even in terms of qualitative data, we did not capture as much information on outcomes as we initially envisaged. There were several reasons for this. The most important was that capturing outcomes in public involvement is easier the more operational the focus of involvement, and more difficult the more strategic the involvement. Thus, it was relatively easy to see the impact of a patient panel on the redesign of a recruitment leaflet but harder to capture the impact of research partners in a multidisciplinary team discussion of research design.

We also found it was sometimes more difficult to engage research partners as participants in our research than researchers or research managers. On reflection this is not surprising. Research partners are generally motivated to take part in research relevant to their lived experience of a health condition or situation, whereas our research was quite detached from their lived experience; in addition people had many constraints on their time, so getting involved in our research as well as their own was likely to be a burden too far for some. Researchers clearly also face significant time pressures but they had a more direct interest in our research, as they are obliged to engage with public involvement to satisfy research funders such as the NIHR. Moreover, researchers were being paid by their employers for their time during interviews with us, while research partners were not paid by us and usually not paid by their research teams. Whatever the reasons, we had less response from research partners than researchers or research managers, particularly for the third round of data collection; thus we have fewer data on outcomes from research partners‘ perspectives and we need to be aware of a possible selection bias towards more engaged research partners. Such a bias could have implications for our findings; for example payment might have been a more important motivating factor for less engaged advisory group members.

There were a number of practical difficulties we encountered. One challenge was when to recruit the case studies. We recruited four of our eight case studies prior to the full application, but this was more than 1 year before our project started and 15 months or more before data collection began. In this intervening period, we found that the time scales of some of the case studies were no longer ideal for our project and we faced the choice of whether to continue with them, although this timing was not ideal, or seek at a late moment to recruit alternative ones. One of our case studies ultimately undertook no involvement activity over the study period, so we obtained fewer data from it, and it contributed relatively little to our analysis. Similarly, one of the four case studies we recruited later experienced some delays itself in beginning and so we had a more limited period for data collection than initially envisaged. Research governance approvals took much longer than expected, particularly as we had to take three of our research partners, who were going to collect data within NHS projects, through the research passport process, which essentially truncated our data collection period from 1 year to 9 months. Even if we had had the full year initially envisaged for data collection, our conclusion with hindsight was that this was insufficiently long. To compare initial plans and intentions for involvement with the reality of what actually happened required a longer time period than a year for most of our case studies.

In the light of the importance we have placed on the commitment of PIs, there is an issue of potential selection bias in the recruitment of our sample. As our sampling strategy explicitly involved a networking approach to PIs of projects where we thought some significant public involvement was taking place, we were likely (as we did) to recruit enthusiasts and, at worst, those non-committed who were at least open to the potential value of public involvement. There were, unsurprisingly, no highly sceptical PIs in our sample. We have no data therefore on how public involvement may work in research where the PI is sceptical but may feel compelled to undertake involvement because of funder requirements or other factors.

• What would we do differently next time?

If we were to design this study again, there are a number of changes we would make. Most importantly we would go for a longer time period to be able to capture involvement through the whole research process from initial design through to dissemination. We would seek to recruit far more potential case studies in principle, so that we had greater choice of which to proceed with once our study began in earnest. We would include case studies from the application stage to capture the important early involvement of research partners in the initial design period. It might be preferable to research a smaller number of case studies, allowing a more in-depth ethnographic approach. Although challenging, it would be very informative to seek to sample sceptical PIs. This might require a brief screening exercise of a larger group of PIs on their attitudes to and experience of public involvement.

The economic evaluation was challenging in a number of ways, particularly in seeking to obtain completed resource logs from case study research partners. Having a 2-week data collection period was also problematic in a field such as public involvement, where activity may be very episodic and infrequent. Thus, collecting economic data alongside other case study data in a more integrated way, and particularly with interviews and more ethnographic observation of case study activities, might be advantageous. The new budgeting tool developed by INVOLVE and the MHRN may provide a useful resource for future economic evaluations. 23

We have learned much from the involvement of research partners in our research team and, although many aspects of our approach worked well, there are some things we would do differently in future. Even though we included substantial resources for research partner involvement in all aspects of our study, we underestimated how time-consuming such full involvement would be. We were perhaps overambitious in trying to ensure such full involvement with the number of research partners and the number and complexity of the case studies. We were also perhaps naive in expecting all the research partners to play the same role in the team; different research partners came with different experiences and skills, and, like most of our case studies, we might have been better to be less prescriptive and allow the roles to develop more organically within the project.

• Implications for research practice and funding

If one of the objectives of R&D policy is to increase the extent and effectiveness of public involvement in research, then a key implication of this research is the importance of influencing PIs to value public involvement in research or to delegate to other senior colleagues in leading on involvement in their research. Training is unlikely to be the key mechanism here; senior researchers are much more likely to be influenced by peers or by their personal experience of the benefits of public involvement. Early career researchers may be shaped by training but again peer learning and culture may be more influential. For those researchers sceptical or agnostic about public involvement, the requirement of funders is a key factor that is likely to make them engage with the involvement agenda. Therefore, funders need to scrutinise the track record of research teams on public involvement to ascertain whether there is any evidence of commitment or leadership on involvement.

One of the findings of the economic analysis was that PIs have consistently underestimated the costs of public involvement in their grant applications. Clearly the field will benefit from the guidance and budgeting tool recently disseminated by MHRN and INVOLVE. It was also notable that there was a degree of variation in the real costs of public involvement and that effective involvement is not necessarily costly. Different models of involvement incur different costs and researchers need to be made aware of the costs and benefits of these different options.

One methodological lesson we learned was the impact that conducting this research had on some participants’ reflection on the impact of public involvement. Particularly for research staff, the questions we asked sometimes made them reflect upon what they were doing and change aspects of their approach to involvement. Thus, the more the NIHR and other funders can build reporting, audit and other forms of evaluation on the impact of public involvement directly into their processes with PIs, the more likely such questioning might stimulate similar reflection.

• Recommendations for further research

There are a number of gaps in our knowledge around public involvement in research that follow from our findings, and would benefit from further research, including realist evaluation to extend and further test the theory we have developed here:

• In-depth exploration of how PIs become committed to public involvement and how to influence agnostic or sceptical PIs would be very helpful. Further research might compare, for example, training with peer-influencing strategies in engendering PI commitment. Research could explore the leadership role of other research team members, including research partners, and how collective leadership might support effective public involvement.
• More methodological work is needed on how to robustly capture the impact and outcomes of public involvement in research (building as well on the PiiAF work of Popay et al. 51 ), including further economic analysis and exploration of impact when research partners are integral to research teams.
• Research to develop approaches and carry out a full cost–benefit analysis of public involvement in research would be beneficial. Although methodologically challenging, it would be very useful to conduct some longer-term studies which sought to quantify the impact of public involvement on such key indicators as participant recruitment and retention in clinical trials.
• It would also be helpful to capture qualitatively the experiences and perspectives of research partners who have had mixed or negative experiences, since they may be less likely than enthusiasts to volunteer to participate in studies of involvement in research such as ours. Similarly, further research might explore the (relatively rare) experiences of marginalised and seldom-heard groups involved in research.
• Payment for public involvement in research remains a contested issue with strongly held positions for and against; it would be helpful to further explore the value research partners and researchers place on payment and its effectiveness for enhancing involvement in and impact on research.
• A final relatively narrow but important question that we identified after data collection had finished is: what is the impact of the long periods of relative non-involvement following initial periods of more intense involvement for research partners in some types of research, particularly clinical trials?

Included under terms of UK Non-commercial Government License .

• Cite this Page Evans D, Coad J, Cottrell K, et al. Public involvement in research: assessing impact through a realist evaluation. Southampton (UK): NIHR Journals Library; 2014 Oct. (Health Services and Delivery Research, No. 2.36.) Chapter 9, Conclusions and recommendations for future research.
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How many participants for quantitative usability studies: a summary of sample-size recommendations.

July 25, 2021 2021-07-25

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In This Article:

Introduction, the intuition behind the 40-participants guideline: why you need 40 participants, the assumptions behind the 40-participant guideline, when you may get away with fewer participants, what if your metric is continuous.

The exact number of participants required for quantitative usability testing can vary. Apparently contradictory recommendations (ranging from 20 to 30 to 40 or more) often confuse new quantitative UX researchers. (In fact, we’ve recommended different numbers over the years.)

Where do these recommendations come from and how many participants do you really need? This is an important question. If you test with too few , your results may not be statistically reliable . If you test with too many, you’re essentially throwing your money away. We want to strike the perfect balance — collecting enough data points to be confident in our results, but not so many that we’re wasting precious research funding.

In most cases, we recommend 40 participants for quantitative studies. If you don’t really care about the reasoning behind that number, you can stop reading here. Read on if you do want to know where that number comes from, when to use a different number, and why you may have seen different recommendations.

Since this is a common confusion, let’s clarify: there are two kinds of studies, qualitative and quantitative. Qual aims at insights, not numbers , so statistical significance doesn’t come into play. In contrast, quant does focus on collecting UX metrics , so we need to ensure that these numbers are correct. And the key point: this article is about quant, not qual . ( Qualitative studies only need a small number of users , but that’s not what we’re discussing here.)

When we conduct quantitative usability studies, we’re collecting UX metrics — numbers that represent some aspect of the user experience.

For example, we might want to know what percentage of our users are able to book a hotel room on Expedia, a travel-booking site. We won’t be able to ask every Expedia user to try to book a hotel room. Instead, we will run a study in which will ask a subset of our target population of Expedia users to make a reservation.

Then, we’ll count how many participants in that study are able to complete the task and we’ll use that percentage to estimate the percentage of our population. Of course, what we get from the study is not going to be exactly the same as our population success rate (there is always going to be some amount of measurement error), but we hope that it will be close enough.

When the number of people we include in the study is small, the percentage from the study will be unlikely to predict the success rate of the whole population — that number will simply be too noisy.

As another example, image you want to figure out the average daily temperature in Berlin, Germany during the summer. You decide to estimate that average by looking only at three random daily temperatures. Those three days probably will not give you a very accurate number, will they? This is the problem with small samples for quantitative studies.

In a quantitative usability study, to get a reasonably trustworthy prediction for the behavior of your overall population, you need around 40 data points. There are nuances depending on how much risk you are willing to take and what exactly you are trying to measure.

The 40-participant recommendation comes from a calculation . That calculation estimates the minimum number of users needed to produce a reasonable prediction of your population behavior based on one study. It has specific assumptions, but it will work for many quantitative usability studies .

If you don’t care about statistics, you can stop reading at this point (or jump directly to the conclusion ). Otherwise, if you’re curious about the nuances behind this recommendation, keep reading.

In statistical terms, the 40-participant guideline comes from a very specific situation, which may or may not apply to your particular scenario. It assumes that you have a considerable user population (over 500 people) and that the following are true:

• You want to estimate a binary metric such as success rate or conversion rate based on a study with a sample of your user population.
• You aim for a 15% margin of error — namely, you want your true score (e.g., the success rate or conversion rate for your whole population) to be within 15% of the observed score (the percentage you obtained from your study).
• You want to take very little risk of being wrong in this prediction (that is, you will use a confidence level of 95% for computing your margin of error).

If all the above are true, it turns out that you can calculate the number of participants you need for your study, and it is 39. We round it up to 40 — hence the above recommendation. (These estimates are often rounded up by a few participants. First, rounding up makes the numbers more memorable. Second, slight overrecruiting helps if something goes wrong with one or two participants and their data has to be removed. For example, you may discover during the study that you accidentally recruited an unrepresentative user or a cheater.)

It is possible to need fewer participants if the last two of the assumptions above are not true. Specifically, if you are:

• Willing to have a margin of error that is bigger than 15% 
• Willing to take a larger risk  

Willing to Have a Margin of Error Bigger than 15% 

The margin of error tells you how much you can expect your overall population rate to vary as a function of the observed score. Any time you collect a metric you should compute a margin of error (or, equivalently, a confidence interval). In other words, if in your Expedia study, 70% of your study participants were able to book a room and your margin of error was 15%, it means that your whole-population completion rate (the true score) is 70% ± 15% — that is, it could be anywhere from 55% to 85%.

That range is 30% wide and it represents the precision of your estimate; it could, however, be the case that in some situations you don’t care if it’s a little wider and your margin of error is bigger (for example, if you want to be able to say that most people can use a certain feature of your UI). We don’t recommend going for margins of error bigger than 20% because your confidence interval for the true score will be quite wide and unlikely to be useful. 

Willing to Take a Larger Risk  

A 95% confidence level means that your margin of error computations will be wrong only 5% of the time. It is the gold standard for published academic research. However, most UX researchers work in applied research, not academic research. For practical purposes, you may be willing to take a little bit more risk.

(Taking more risk is cheaper and is a good idea if the risks of a somewhat unreliable result won’t be catastrophic. However, bear in mind that UX teams often use quantitative usability testing to inform prioritization and resource allocation, so unreliable data may be quite problematic.)

If you are willing to drop the confidence level to 90%, then a margin of error of 15% will require 28 users and a margin of error of 20% will require 15 users . Again, you may consider rounding these up for many good reasons (for example, you may end up having to remove some of your trials when you clean up the data). This is the origin of the 30-user guideline that you may have encountered elsewhere — that recommendation accepts more risk.

This table shows the number of participants needed for different confidence levels and desired margins of error for binary metrics. The lower the confidence level, the riskier the study. The bigger the margin of error, the lower your precision and the less useful the numbers will be.

If your metric is continuous or can be treated as continuous (e.g., task time, satisfaction or other types of rating, SUS score ), the formula for the number of participants will depend on an additional factor: the variability of your target population. (It will also depend, like for binary metrics, on the desired margin of error and the confidence level used). That is something that you could estimate separately for your population by running a pilot study.

Of course, a pilot study to estimate the standard deviation is quite expensive and it will itself involve a fairly large number of participants. On the other hand, in most quantitative usability studies, there are several metrics involved and usually at least one of them is binary. Therefore, we recommend using that binary metric as a constraint in deciding the number of users. In other words, if you are collecting success, task time, and satisfaction, then you can simply say I want a 15% margin of error for success at a 90% or 95% confidence level (and recruit 30 or 40 users respectively). That will usually result in good margins of error for the other metrics involved.

If, however, you collect only continuous metrics (this is unusual) and you cannot afford to estimate the standard deviation of your population, you must first settle on a desired value for your margin of error. Of course, your desired value will depend on what you are measuring and the range for a task. We usually recommend using as a desired value 15% or 20% of the mean — in other words, if your task time is around 1 minute, you would like a margin of error no bigger than 0.15–0.20 minutes (9 to 12 seconds); if your task time is around 10 minutes, your margin of error should be no bigger than 1.5–2 minutes.

Next, you can use Jakob Nielsen’s estimate of variability for website- and intranet-related continuous metrics . That estimate is 52% of the mean .  In other words, if the mean task time is 1 min, your estimated standard deviation is 0.52 x 1 min = 0.52 minutes. If the mean task time is 10 minutes, then your estimated standard deviation will be 0.52 x 10 min = 5.2 minutes. With that supplementary assumption, you would need 47 users for a 15% margin of error at 95% confidence level, 33 users for a 15% margin of error at 90% confidence level, 26 users for a 20% margin of error at 95% confidence level and 19 users for a 20% margin of error at 90% confidence level. (Note that a 15% margin of error of 1 minute translates into 0.15 minutes — that is, 9 seconds.)

This table shows the required number of participants needed for a study involving continuous metrics such as time on task or satisfaction. Different numbers of participants are appropriate for different confidence levels and desired margins of error.

In general, the number of users can be determined using the following formula:

The variables in that formula are:

• K is a constant (1.96 for 95% confidence level or 1.645 for 90% confidence level)
• s is your standard deviation as a proportion of the mean
• m is your desired margin of error, also expressed as a proportion of the mean (0.15 corresponding to 15% or 0.20 corresponding to 20%)

If you estimate your standard deviation as a 52% (or 0.52) of the mean, then you can use the formula below:

Even though there are many different recommendations for sample sizes in quantitative usability testing, they are all consistent with each other — they simply make slightly different assumptions. We think the 40-user guideline is the simplest and the most likely to lead to good results — namely, a relatively small margin of error with a high confidence level.

However, you may settle for a lower number of users (around 30) if you want to take slightly more risk that your findings will not represent the behavior of your user population and thus decrease your confidence level to 90%. Moreover, if you also have tolerance for a larger margin of error, you can drop the number of users to 20 or even fewer, but that is generally a lot riskier.

An acceptable strategy (especially if you are on a tight budget and mostly interested in continuous metrics such as task time and satisfaction) is to start with as many users as you can comfortably afford — say, 20–25 users. Once you’ve collected your data from these users, calculate your margins of error and determine if they are tight enough for your purposes. If they are too wide, then consider adding more users. This approach, however, requires that you work fast: you’ll need to do your analysis in a matter of a few days in order to be able to run the extra participants very soon after the first batch. Otherwise, you risk compromising the validity of your study.

Choose the right sample size for your situation to ensure you’ll optimize your quantitative study: collecting just enough data, but not too much.

Jeff Sauro, James Lewis. 2016. Quantifying the User Experience: Practical Statistics for User Research . Elsevier.

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How to write recommendations in a research paper

Many students put in a lot of effort and write a good report however they are not able to give proper recommendations. Recommendations in the research paper should be included in your research. As a researcher, you display a deep understanding of the topic of research. Therefore you should be able to give recommendations. Here are a few tips that will help you to give appropriate recommendations. 

Recommendations in the research paper should be the objective of the research. Therefore at least one of your objectives of the paper is to provide recommendations to the parties associated or the parties that will benefit from your research. For example, to encourage higher employee engagement HR department should make strategies that invest in the well-being of employees. Additionally, the HR department should also collect regular feedback through online surveys.

Recommendations in the research paper should come from your review and analysis For example It was observed that coaches interviewed were associated with the club were working with the club from the past 2-3 years only. This shows that the attrition rate of coaches is high and therefore clubs should work on reducing the turnover of coaches.

Recommendations in the research paper should also come from the data you have analysed. For example, the research found that people over 65 years of age are at greater risk of social isolation. Therefore, it is recommended that policies that are made for combating social isolation should target this specific group.

Recommendations in the research paper should also come from observation. For example, it is observed that Lenovo’s income is stable and gross revenue has displayed a negative turn. Therefore the company should analyse its marketing and branding strategy.

Recommendations in the research paper should be written in the order of priority. The most important recommendations for decision-makers should come first. However, if the recommendations are of equal importance then it should come in the sequence in which the topic is approached in the research. 

Recommendations in a research paper if associated with different categories then you should categorize them. For example, you have separate recommendations for policymakers, educators, and administrators then you can categorize the recommendations. 

Recommendations in the research paper should come purely from your research. For example, you have written research on the impact on HR strategies on motivation. However, nowhere you have discussed Reward and recognition. Then you should not give recommendations for using rewards and recognition measures to boost employee motivation.

The use of bullet points offers better clarity rather than using long paragraphs. For example this paragraph “ It is recommended  that Britannia Biscuit should launch and promote sugar-free options apart from the existing product range. Promotion efforts should be directed at creating a fresh and healthy image. A campaign that conveys a sense of health and vitality to the consumer while enjoying biscuit  is recommended” can be written as:

• The company should launch and promote sugar-free options
• The company should work towards creating s fresh and healthy image
• The company should run a campaign to convey its healthy image

The inclusion of an action plan along with recommendation adds more weightage to your recommendation. Recommendations should be clear and conscience and written using actionable words. Recommendations should display a solution-oriented approach and in some cases should highlight the scope for further research. 

Thesis Writing: What to Write in Chapter 5

Table of contents, introduction.

This article tells what a budding researcher must include in Chapter 5-the Summary. It also includes the tense of the verb and the semantic markers, which are predominantly used in writing the summary, conclusions, and recommendations.

For others, writing Chapter 5 is the easiest part of thesis writing, but there are groups of students who would like to know more about it. If you are one of them, this article on how to write chapter 5 of your thesis is purposely written for you.

What to Write in Chapter 5

1. write the summary.

First, you must go directly to the point of highlighting the main points. There is no need to explain the details thoroughly. You must avoid copying and pasting what you have written in the previous chapters. Just KISS (keep it short and simple)!

Then, write sentences in  simple past  and always use  passive voice  construction rather than the active voice. You must also be familiar with the different semantic markers.

When I was enrolled in Academic Writing in my master’s degree, I learned that there are semantic markers which can be used in order not to repeat the same words or phrases such as  additionally, also, further, in addition to, moreover, contrary to, with regard to, as regards, however, finally, during the past ___ years, from 1996 to 2006, after 10 years, as shown in, as presented in, consequently, nevertheless, in fact, on the other hand, subsequently and nonetheless.

Next, you may use the following guide questions to check that you have not missed anything in writing the summary:

Finally, organize the summary of the results of your study according to the way the questions are sequenced in the statement of the problem.

2. Write the Conclusion or Conclusions

Once you have written the summary in Chapter 5, draw out a conclusion from each finding or result. It can be done per question, or you may arrange the questions per topic or sub-topic if there is any. But if your research is quantitative, answer the research question directly and tell if the hypothesis is rejected or accepted based on the findings.

3. Write the Recommendations

The recommendations must contain practical suggestions that will improve the situation or solve the problem investigated in the study.

First, it must be logical, specific, attainable, and relevant. Second, it should be addressed to persons, organizations, or agencies directly concerned with the issues or to those who can immediately implement the recommended solutions. Third, present another topic which is very relevant to the present study that can be further investigated by future researchers.

But never recommend anything that is not part of your study or not being mentioned in your findings.

First, it must be logical, specific, attainable, and relevant. Second, it should be addressed to persons, organizations, or agencies directly concerned with the issues or to those who can immediately implement the recommended solutions. Third, present another topic that is very relevant to the present study that can be further investigated by future researchers.

Recommend nothing that is not part of your research or not being mentioned in your findings.

© 2014 July 29 M. G. Alvior | Updated 2024 January 10

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Contingent valuation method example: vehicle owners’ willingness to pay for maintenance costs to improve air quality, writing a research article: how to paraphrase, using mixed methods in research, about the author, mary g. alvior, phd, 100 comments.

This is very helpful especially the grammar part. It really jumped start my writing effort… really want to finish my study with style.

Hello maam my PhD research purely a qualitative study on community based organization of slum ..i used 3 tool case study , participant observation and FGDs to analyse role, impact, challenge and aspiration of CBOs . i used tabular form (matrix to analyse ) did not use any software..

Eric, you are welcome. I wish you are able to finish your work.

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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Qualitative vs. quantitative data in research: what's the difference?

If you're reading this, you likely already know the importance of data analysis. And you already know it can be incredibly complex.

At its simplest, research and it's data can be broken down into two different categories: quantitative and qualitative. But what's the difference between each? And when should you use them? And how can you use them together?

Understanding the differences between qualitative and quantitative data is key to any research project. Knowing both approaches can help you in understanding your data better—and ultimately understand your customers better. Quick takeaways:

Quantitative research uses objective, numerical data to answer questions like "what" and "how often." Conversely, qualitative research seeks to answer questions like "why" and "how," focusing on subjective experiences to understand motivations and reasons.

Quantitative data is collected through methods like surveys and experiments and analyzed statistically to identify patterns. Qualitative data is gathered through interviews or observations and analyzed by categorizing information to understand themes and insights.

Effective data analysis combines quantitative data for measurable insights with qualitative data for contextual depth.

What is quantitative data?

Qualitative and quantitative data differ in their approach and the type of data they collect.

Quantitative data refers to any information that can be quantified — that is, numbers. If it can be counted or measured, and given a numerical value, it's quantitative in nature. Think of it as a measuring stick.

Quantitative variables can tell you "how many," "how much," or "how often."

Some examples of quantitative data :  

How many people attended last week's webinar? 

How much revenue did our company make last year? 

How often does a customer rage click on this app?

To analyze these research questions and make sense of this quantitative data, you’d normally use a form of statistical analysis —collecting, evaluating, and presenting large amounts of data to discover patterns and trends. Quantitative data is conducive to this type of analysis because it’s numeric and easier to analyze mathematically.

Computers now rule statistical analytics, even though traditional methods have been used for years. But today’s data volumes make statistics more valuable and useful than ever. When you think of statistical analysis now, you think of powerful computers and algorithms that fuel many of the software tools you use today.

Popular quantitative data collection methods are surveys, experiments, polls, and more.

Quantitative Data 101: What is quantitative data?

Take a deeper dive into what quantitative data is, how it works, how to analyze it, collect it, use it, and more.

Learn more about quantitative data →

What is qualitative data?

Unlike quantitative data, qualitative data is descriptive, expressed in terms of language rather than numerical values.

Qualitative data analysis describes information and cannot be measured or counted. It refers to the words or labels used to describe certain characteristics or traits.

You would turn to qualitative data to answer the "why?" or "how?" questions. It is often used to investigate open-ended studies, allowing participants (or customers) to show their true feelings and actions without guidance.

Some examples of qualitative data:

Why do people prefer using one product over another?

How do customers feel about their customer service experience?

What do people think about a new feature in the app?

Think of qualitative data as the type of data you'd get if you were to ask someone why they did something. Popular data collection methods are in-depth interviews, focus groups, or observation.

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What are the differences between qualitative vs. quantitative data?

When it comes to conducting data research, you’ll need different collection, hypotheses and analysis methods, so it’s important to understand the key differences between quantitative and qualitative data:

Quantitative data is numbers-based, countable, or measurable. Qualitative data is interpretation-based, descriptive, and relating to language.

Quantitative data tells us how many, how much, or how often in calculations. Qualitative data can help us to understand why, how, or what happened behind certain behaviors .

Quantitative data is fixed and universal. Qualitative data is subjective and unique.

Quantitative research methods are measuring and counting. Qualitative research methods are interviewing and observing.

Quantitative data is analyzed using statistical analysis. Qualitative data is analyzed by grouping the data into categories and themes.

As you can see, both provide immense value for any data collection and are key to truly finding answers and patterns. 

More examples of quantitative and qualitative data

You’ve most likely run into quantitative and qualitative data today, alone. For the visual learner, here are some examples of both quantitative and qualitative data: 

Quantitative data example

The customer has clicked on the button 13 times. 

The engineer has resolved 34 support tickets today. 

The team has completed 7 upgrades this month. 

14 cartons of eggs were purchased this month.

Qualitative data example

My manager has curly brown hair and blue eyes.

My coworker is funny, loud, and a good listener. 

The customer has a very friendly face and a contagious laugh.

The eggs were delicious.

The fundamental difference is that one type of data answers primal basics and one answers descriptively. 

What does this mean for data quality and analysis? If you just analyzed quantitative data, you’d be missing core reasons behind what makes a data collection meaningful. You need both in order to truly learn from data—and truly learn from your customers. 

What are the advantages and disadvantages of each?

Both types of data has their own pros and cons. 

Advantages of quantitative data

It’s relatively quick and easy to collect and it’s easier to draw conclusions from. 

When you collect quantitative data, the type of results will tell you which statistical tests are appropriate to use. 

As a result, interpreting your data and presenting those findings is straightforward and less open to error and subjectivity.

Another advantage is that you can replicate it. Replicating a study is possible because your data collection is measurable and tangible for further applications.

Disadvantages of quantitative data

Quantitative data doesn’t always tell you the full story (no matter what the perspective). 

With choppy information, it can be inconclusive.

Quantitative research can be limited, which can lead to overlooking broader themes and relationships.

By focusing solely on numbers, there is a risk of missing larger focus information that can be beneficial.

Advantages of qualitative data

Qualitative data offers rich, in-depth insights and allows you to explore context.

It’s great for exploratory purposes.

Qualitative research delivers a predictive element for continuous data.

Disadvantages of qualitative data

It’s not a statistically representative form of data collection because it relies upon the experience of the host (who can lose data).

It can also require multiple data sessions, which can lead to misleading conclusions.

The takeaway is that it’s tough to conduct a successful data analysis without both. They both have their advantages and disadvantages and, in a way, they complement each other. 

Now, of course, in order to analyze both types of data, information has to be collected first.

Let's get into the research.

Quantitative and qualitative research

The core difference between qualitative and quantitative research lies in their focus and methods of data collection and analysis. This distinction guides researchers in choosing an appropriate approach based on their specific research needs.

Using mixed methods of both can also help provide insights form combined qualitative and quantitative data.

Best practices of each help to look at the information under a broader lens to get a unique perspective. Using both methods is helpful because they collect rich and reliable data, which can be further tested and replicated.

What is quantitative research?

Quantitative research is based on the collection and interpretation of numeric data. It's all about the numbers and focuses on measuring (using inferential statistics ) and generalizing results. Quantitative research seeks to collect numerical data that can be transformed into usable statistics.

It relies on measurable data to formulate facts and uncover patterns in research. By employing statistical methods to analyze the data, it provides a broad overview that can be generalized to larger populations.

In terms of digital experience data, it puts everything in terms of numbers (or discrete data )—like the number of users clicking a button, bounce rates , time on site, and more. 

Some examples of quantitative research: 

What is the amount of money invested into this service?

What is the average number of times a button was dead clicked ?

How many customers are actually clicking this button?

Essentially, quantitative research is an easy way to see what’s going on at a 20,000-foot view. 

Each data set (or customer action, if we’re still talking digital experience) has a numerical value associated with it and is quantifiable information that can be used for calculating statistical analysis so that decisions can be made. 

You can use statistical operations to discover feedback patterns (with any representative sample size) in the data under examination. The results can be used to make predictions , find averages, test causes and effects, and generalize results to larger measurable data pools. 

Unlike qualitative methodology, quantitative research offers more objective findings as they are based on more reliable numeric data.

Quantitative data collection methods

A survey is one of the most common research methods with quantitative data that involves questioning a large group of people. Questions are usually closed-ended and are the same for all participants. An unclear questionnaire can lead to distorted research outcomes.

Similar to surveys, polls yield quantitative data. That is, you poll a number of people and apply a numeric value to how many people responded with each answer.

Experiments

An experiment is another common method that usually involves a control group and an experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive. 

What is qualitative research?

Qualitative research does not simply help to collect data. It gives a chance to understand the trends and meanings of natural actions. It’s flexible and iterative.

Qualitative research focuses on the qualities of users—the actions that drive the numbers. It's descriptive research. The qualitative approach is subjective, too. 

It focuses on describing an action, rather than measuring it.

Some examples of qualitative research: 

The sunflowers had a fresh smell that filled the office.

All the bagels with bites taken out of them had cream cheese.

The man had blonde hair with a blue hat.

Qualitative research utilizes interviews, focus groups, and observations to gather in-depth insights.

This approach shines when the research objective calls for exploring ideas or uncovering deep insights rather than quantifying elements.

Qualitative data collection methods

An interview is the most common qualitative research method. This method involves personal interaction (either in real life or virtually) with a participant. It’s mostly used for exploring attitudes and opinions regarding certain issues.

Interviews are very popular methods for collecting data in product design .

Focus groups

Data analysis by focus group is another method where participants are guided by a host to collect data. Within a group (either in person or online), each member shares their opinion and experiences on a specific topic, allowing researchers to gather perspectives and deepen their understanding of the subject matter.

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So which type of data is better for data analysis?

So how do you determine which type is better for data analysis ?

Quantitative data is structured and accountable. This type of data is formatted in a way so it can be organized, arranged, and searchable. Think about this data as numbers and values found in spreadsheets—after all, you would trust an Excel formula.

Qualitative data is considered unstructured. This type of data is formatted (and known for) being subjective, individualized, and personalized. Anything goes. Because of this, qualitative data is inferior if it’s the only data in the study. However, it’s still valuable. 

Because quantitative data is more concrete, it’s generally preferred for data analysis. Numbers don’t lie. But for complete statistical analysis, using both qualitative and quantitative yields the best results. 

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A perfect digital customer experience is often the difference between company growth and failure. And the first step toward building that experience is quantifying who your customers are, what they want, and how to provide them what they need.

Access to product analytics is the most efficient and reliable way to collect valuable quantitative data about funnel analysis, customer journey maps , user segments, and more.

But creating a perfect digital experience means you need organized and digestible quantitative data—but also access to qualitative data. Understanding the why is just as important as the what itself.

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Start a free 14-day trial to see how Fullstory can help you combine your most invaluable quantitative and qualitative insights and eliminate blind spots.

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Qualitative vs. Quantitative: Key Differences in Research Types

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Let's say you want to learn how a group will vote in an election. You face a classic decision of gathering qualitative vs. quantitative data.

With one method, you can ask voters open-ended questions that encourage them to share how they feel, what issues matter to them and the reasons they will vote in a specific way. With the other, you can ask closed-ended questions, giving respondents a list of options. You will then turn that information into statistics.

Neither method is more right than the other, but they serve different purposes. Learn more about the key differences between qualitative and quantitative research and how you can use them.

What Is Qualitative Research?

What is quantitative research, qualitative vs. quantitative research: 3 key differences, benefits of combining qualitative and quantitative research.

Qualitative research aims to explore and understand the depth, context and nuances of human experiences, behaviors and phenomena. This methodological approach emphasizes gathering rich, nonnumerical information through methods such as interviews, focus groups , observations and content analysis.

In qualitative research, the emphasis is on uncovering patterns and meanings within a specific social or cultural context. Researchers delve into the subjective aspects of human behavior , opinions and emotions.

This approach is particularly valuable for exploring complex and multifaceted issues, providing a deeper understanding of the intricacies involved.

Common qualitative research methods include open-ended interviews, where participants can express their thoughts freely, and thematic analysis, which involves identifying recurring themes in the data.

Examples of How to Use Qualitative Research

The flexibility of qualitative research allows researchers to adapt their methods based on emerging insights, fostering a more organic and holistic exploration of the research topic. This is a widely used method in social sciences, psychology and market research.

Here are just a few ways you can use qualitative research.

• To understand the people who make up a community : If you want to learn more about a community, you can talk to them or observe them to learn more about their customs, norms and values.
• To examine people's experiences within the healthcare system : While you can certainly look at statistics to gauge if someone feels positively or negatively about their healthcare experiences, you may not gain a deep understanding of why they feel that way. For example, if a nurse went above and beyond for a patient, they might say they are content with the care they received. But if medical professional after medical professional dismissed a person over several years, they will have more negative comments.
• To explore the effectiveness of your marketing campaign : Marketing is a field that typically collects statistical data, but it can also benefit from qualitative research. For example, if you have a successful campaign, you can interview people to learn what resonated with them and why. If you learn they liked the humor because it shows you don't take yourself too seriously, you can try to replicate that feeling in future campaigns.

Types of Qualitative Data Collection

Qualitative data captures the qualities, characteristics or attributes of a subject. It can take various forms, including:

• Audio data : Recordings of interviews, discussions or any other auditory information. This can be useful when dealing with events from the past. Setting up a recording device also allows a researcher to stay in the moment without having to jot down notes.
• Observational data : With this type of qualitative data analysis, you can record behavior, events or interactions.
• Textual data : Use verbal or written information gathered through interviews, open-ended surveys or focus groups to learn more about a topic.
• Visual data : You can learn new information through images, photographs, videos or other visual materials.

Quantitative research is a systematic empirical investigation that involves the collection and analysis of numerical data. This approach seeks to understand, explain or predict phenomena by gathering quantifiable information and applying statistical methods for analysis.

Unlike qualitative research, which focuses on nonnumerical, descriptive data, quantitative research data involves measurements, counts and statistical techniques to draw objective conclusions.

Examples of How to Use Quantitative Research

Quantitative research focuses on statistical analysis. Here are a few ways you can employ quantitative research methods.

• Studying the employment rates of a city : Through this research you can gauge whether any patterns exist over a given time period.
• Seeing how air pollution has affected a neighborhood : If the creation of a highway led to more air pollution in a neighborhood, you can collect data to learn about the health impacts on the area's residents. For example, you can see what percentage of people developed respiratory issues after moving to the neighborhood.

Types of Quantitative Data

Quantitative data refers to numerical information you can measure and count. Here are a few statistics you can use.

• Heights, yards, volume and more : You can use different measurements to gain insight on different types of research, such as learning the average distance workers are willing to travel for work or figuring out the average height of a ballerina.
• Temperature : Measure in either degrees Celsius or Fahrenheit. Or, if you're looking for the coldest place in the universe , you may measure in Kelvins.
• Sales figures : With this information, you can look at a store's performance over time, compare one company to another or learn what the average amount of sales is in a specific industry.

Quantitative and qualitative research methods are both valid and useful ways to collect data. Here are a few ways that they differ.

• Data collection method : Quantitative research uses standardized instruments, such as surveys, experiments or structured observations, to gather numerical data. Qualitative research uses open-ended methods like interviews, focus groups or content analysis.
• Nature of data : Quantitative research involves numerical data that you can measure and analyze statistically, whereas qualitative research involves exploring the depth and richness of experiences through nonnumerical, descriptive data.
• Sampling : Quantitative research involves larger sample sizes to ensure statistical validity and generalizability of findings to a population. With qualitative research, it's better to work with a smaller sample size to gain in-depth insights into specific contexts or experiences.

You can simultaneously study qualitative and quantitative data. This method , known as mixed methods research, offers several benefits, including:

• A comprehensive understanding : Integration of qualitative and quantitative data provides a more comprehensive understanding of the research problem. Qualitative data helps explain the context and nuances, while quantitative data offers statistical generalizability.
• Contextualization : Qualitative data helps contextualize quantitative findings by providing explanations into the why and how behind statistical patterns. This deeper understanding contributes to more informed interpretations of quantitative results.
• Triangulation : Triangulation involves using multiple methods to validate or corroborate findings. Combining qualitative and quantitative data allows researchers to cross-verify results, enhancing the overall validity and reliability of the study.

This article was created in conjunction with AI technology, then fact-checked and edited by a HowStuffWorks editor.

Please copy/paste the following text to properly cite this HowStuffWorks.com article:

Research: quantifying GitHub Copilot’s impact on developer productivity and happiness

When the GitHub Copilot Technical Preview launched just over one year ago, we wanted to know one thing: Is this tool helping developers? The GitHub Next team conducted research using a combination of surveys and experiments, which led us to expected and unexpected answers.

Everyday, we use tools and form habits to achieve more with less. Software development produces such a high number of tools and technologies to make work efficient, to the point of inducing decision fatigue. When we first launched a technical preview of GitHub Copilot in 2021, our hypothesis was that it would improve developer productivity and, in fact, early users shared reports that it did. In the months following its release, we wanted to better understand and measure its effects with quantitative and qualitative research. To do that, we first had to grapple with the question: what does it mean to be productive?

Why is developer productivity so difficult to measure?

When it comes to measuring developer productivity, there is little consensus and there are far more questions than answers. For example:

• What are the “right” productivity metrics? [ 1 , 2 ]
• How valuable are self-reports of productivity? [ 3 ]
• Is the traditional view of productivity—outputs over inputs—a good fit for the complex problem solving and creativity involved in development work? [ 4 ].

In a 2021 study, we found that developers’ own view of productivity has a twist–it’s more akin to having a good day . The ability to stay focused on the task at hand, make meaningful progress, and feel good at the end of a day’s work make a real difference in developers’ satisfaction and productivity.

This isn’t a one-off finding, either. Other academic research shows that these outcomes are important for developers [ 5 ] and that satisfied developers perform better [ 6 , 7 ]. Clearly, there’s more to developer productivity than inputs and outputs.

How do we think about developer productivity at GitHub?

Because AI-assisted development is a relatively new field, as researchers we have little prior research to draw upon. We wanted to measure GitHub Copilot’s effects, but what are they? After early observations and interviews with users, we surveyed more than 2,000 developers to learn at scale about their experience using GitHub Copilot. We designed our research approach with three points in mind:

• Look at productivity holistically. At GitHub we like to think broadly and sustainably about developer productivity and the many factors that influence it. We used the SPACE productivity framework to pick which aspects to investigate.
• Include developers’ first-hand perspective. We conducted multiple rounds of research including qualitative (perceptual) and quantitative (observed) data to assemble the full picture. We wanted to verify: (a) Do users’ actual experiences confirm what we infer from telemetry? (b) Does our qualitative feedback generalize to our large user base?
• Assess GitHub Copilot’s effects in everyday development scenarios. When setting up our studies, we took extra care to recruit professional developers, and to design tests around typical tasks a developer might work through in a given day.

Let’s dig in and see what we found!

Finding 1: Developer productivity goes beyond speed

Through a large-scale survey, we wanted to see if developers using GitHub Copilot see benefits in other areas beyond speeding up tasks. Here’s what stood out:

• Improving developer satisfaction. Between 60–75% of users reported they feel more fulfilled with their job, feel less frustrated when coding, and are able to focus on more satisfying work when using GitHub Copilot. That’s a win for developers feeling good about what they do!
• Conserving mental energy. Developers reported that GitHub Copilot helped them stay in the flow (73%) and preserve mental effort during repetitive tasks (87%). That’s developer happiness right there, since we know from previous research that context switches and interruptions can ruin a developer’s day, and that certain types of work are draining [ 8 , 9 ].

Table: Survey responses measuring dimensions of developer productivity when using GitHub Copilot

Developers see GitHub Copilot as a productivity aid, but there’s more to it than that. One user described the overall experience:

(With Copilot) I have to think less, and when I have to think it’s the fun stuff. It sets off a little spark that makes coding more fun and more efficient.

The takeaway from our qualitative investigation was that letting GitHub Copilot shoulder the boring and repetitive work of development reduced cognitive load . This makes room for developers to enjoy the more meaningful work that requires complex, critical thinking and problem solving, leading to greater happiness and satisfaction.

Finding 2: … but speed is important, too

In the survey, we saw that developers reported they complete tasks faster when using GitHub Copilot, especially repetitive ones. That was an expected finding (GitHub Copilot writes faster than a human, after all), but >90% agreement was still a pleasant surprise. Developers overwhelmingly perceive that GitHub Copilot is helping them complete tasks faster—can we observe and measure that effect in practice? For that we conducted a controlled experiment.

Figure: Summary of the experiment process and results

In the experiment, we measured—on average—how successful each group was in completing the task and how long each group took to finish.

• The group that used GitHub Copilot had a higher rate of completing the task (78%, compared to 70% in the group without Copilot).
• The striking difference was that developers who used GitHub Copilot completed the task significantly faster–55% faster than the developers who didn’t use GitHub Copilot . Specifically, the developers using GitHub Copilot took on average 1 hour and 11 minutes to complete the task, while the developers who didn’t use GitHub Copilot took on average 2 hours and 41 minutes. These results are statistically significant ( P=.0017 ) and the 95% confidence interval for the percentage speed gain is [21%, 89%].

There’s more to uncover! We’re conducting more experiments and a more thorough analysis of the experiment data we already collected—looking into heterogeneous effects, or potential effects on the quality of code—and we are planning further academic publications to share our findings.

What do these findings mean for developers?

We’re here to support developers while they build software—that includes working more efficiently and finding more satisfaction in their work. In our research, we saw that GitHub Copilot supports faster completion times, conserves developers’ mental energy, helps them focus on more satisfying work, and ultimately find more fun in the coding they do.

We’re also hearing that these benefits are becoming material to engineering leaders in companies that ran early trials with GitHub Copilot. When they consider how to keep their engineers healthy and productive, they are thinking through the same lens of holistic developer wellbeing and promoting the use of tools that bring delight.

The engineers’ satisfaction with doing edgy things and us giving them edgy tools is a factor for me. Copilot makes things more exciting.

With the advent of GitHub Copilot, we’re not alone in exploring the impact of AI-powered code completion tools! In the realm of productivity, we recently saw an evaluation with 24 students , and Google’s internal assessment of ML-enhanced code completion . More broadly, the research community is trying to understand GitHub Copilot’s implications in a number of contexts: education , security , labor market , as well as developer practices and behaviors . We are all currently learning by trying GitHub Copilot in a variety of settings. This is an evolving field, and we’re excited for the findings that the research community — including us — will uncover in the months to come.

Acknowledgements

We are very grateful to all the developers who participated in the survey and experiments–we would be in the dark without your input! GitHub Next conducted the experiment in partnership with the Microsoft Office of the Chief Economist, and specifically in collaboration with Sida Peng and Aadharsh Kannan .

• GitHub Copilot , 

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Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning .

Companies employ predictive analytics to find patterns in this data to identify risks and opportunities. Predictive analytics is often associated with big data and data science .

Today, companies today are inundated with data from log files to images and video, and all of this data resides in disparate data repositories across an organization. To gain insights from this data, data scientists use deep learning and machine learning algorithms to find patterns and make predictions about future events. Some of these statistical techniques include logistic and linear regression models, neural networks and decision trees. Some of these modeling techniques use initial predictive learnings to make additional predictive insights.

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Predictive analytics models are designed to assess historical data, discover patterns, observe trends, and use that information to predict future trends. Popular predictive analytics models include classification, clustering, and time series models.

Classification models

Classification models fall under the branch of supervised machine learning models. These models categorize data based on historical data, describing relationships within a given dataset. For example, this model can be used to classify customers or prospects into groups for segmentation purposes. Alternatively, it can also be used to answer questions with binary outputs, such answering yes or no or true and false; popular use cases for this are fraud detection and credit risk evaluation. Types of classification models include logistic regression , decision trees, random forest, neural networks, and Naïve Bayes.

Clustering models

Clustering models fall under unsupervised learning . They group data based on similar attributes. For example, an e-commerce site can use the model to separate customers into similar groups based on common features and develop marketing strategies for each group. Common clustering algorithms include k-means clustering, mean-shift clustering, density-based spatial clustering of applications with noise (DBSCAN), expectation-maximization (EM) clustering using Gaussian Mixture Models (GMM), and hierarchical clustering.

Time series models

Time series models use various data inputs at a specific time frequency, such as daily, weekly, monthly, et cetera. It is common to plot the dependent variable over time to assess the data for seasonality, trends, and cyclical behavior, which may indicate the need for specific transformations and model types. Autoregressive (AR), moving average (MA), ARMA, and ARIMA models are all frequently used time series models. As an example, a call center can use a time series model to forecast how many calls it will receive per hour at different times of day.

Predictive analytics can be deployed in across various industries for different business problems. Below are a few industry use cases to illustrate how predictive analytics can inform decision-making within real-world situations.

• Banking: Financial services use machine learning and quantitative tools to make predictions about their prospects and customers. With this information, banks can answer questions like who is likely to default on a loan, which customers pose high or low risks, which customers are the most lucrative to target resources and marketing spend and what spending is fraudulent in nature.
• Healthcare: Predictive analytics in health care is used to detect and manage the care of chronically ill patients, as well as to track specific infections such as sepsis. Geisinger Health used predictive analytics to mine health records to learn more about how sepsis is diagnosed and treated.  Geisinger created a predictive model based on health records for more than 10,000 patients who had been diagnosed with sepsis in the past. The model yielded impressive results, correctly predicting patients with a high rate of survival.
• Human resources (HR): HR teams use predictive analytics and employee survey metrics to match prospective job applicants, reduce employee turnover and increase employee engagement. This combination of quantitative and qualitative data allows businesses to reduce their recruiting costs and increase employee satisfaction, which is particularly useful when labor markets are volatile.
• Marketing and sales: While marketing and sales teams are very familiar with business intelligence reports to understand historical sales performance, predictive analytics enables companies to be more proactive in the way that they engage with their clients across the customer lifecycle. For example, churn predictions can enable sales teams to identify dissatisfied clients sooner, enabling them to initiate conversations to promote retention. Marketing teams can leverage predictive data analysis for cross-sell strategies, and this commonly manifests itself through a recommendation engine on a brand’s website.
• Supply chain: Businesses commonly use predictive analytics to manage product inventory and set pricing strategies. This type of predictive analysis helps companies meet customer demand without overstocking warehouses. It also enables companies to assess the cost and return on their products over time. If one part of a given product becomes more expensive to import, companies can project the long-term impact on revenue if they do or do not pass on additional costs to their customer base. For a deeper look at a case study, you can read more about how FleetPride used this type of data analytics to inform their decision making on their inventory of parts for excavators and tractor trailers. Past shipping orders enabled them to plan more precisely to set appropriate supply thresholds based on demand.

An organization that knows what to expect based on past patterns has a business advantage in managing inventories, workforce, marketing campaigns, and most other facets of operation.

• Security: Every modern organization must be concerned with keeping data secure. A combination of automation and predictive analytics improves security. Specific patterns associated with suspicious and unusual end user behavior can trigger specific security procedures.
• Risk reduction: In addition to keeping data secure, most businesses are working to reduce their risk profiles. For example, a company that extends credit can use data analytics to better understand if a customer poses a higher-than-average risk of defaulting. Other companies may use predictive analytics to better understand whether their insurance coverage is adequate. 
• Operational efficiency : More efficient workflows translate to improved profit margins. For example, understanding when a vehicle in a fleet used for delivery is going to need maintenance before it’s broken down on the side of the road means deliveries are made on time, without the additional costs of having the vehicle towed and bringing in another employee to complete the delivery.
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What is personalization?

Personal greeting, personal touch, personal best. Close, personal friends. All good things, right? Right, according to consumers in a recent McKinsey survey . When asked to define personalization, consumers associated it with positive experiences that made them feel special. Personalization marketing represents an opportunity for companies to benefit from those warm, fuzzy feelings.

Personalization marketing has real advantages  for companies: it can reduce customer acquisition costs by as much as 50 percent, lift revenues by 5 to 15 percent, and increase marketing ROI by 10 to 30 percent. Personalization has also been shown to improve performance and provide better customer outcomes . Companies with faster growth rates derive 40 percent more of their revenue from personalization than their slower-growing counterparts. McKinsey research also shows that personalized experiences drive up  both customer loyalty and a company’s gross sales. And the COVID-19 pandemic has only made personalization more urgent for brands: three-quarters of customers  switched to a new store, product, or buying method during the pandemic, proving that store and product loyalty is increasingly a thing of the past.

But it’s a tricky needle to thread. Getting it wrong can have lasting consequences for brands. You probably already know the uncanny feeling of being served a too-relevant ad. Sometimes it’s just a little unsettling; other times, it can put us off the product altogether.

Even so, the modern customer does expect a personalized experience—even if they take it for granted. McKinsey research shows that 71 percent of consumers expect companies to deliver personalized interactions . And the story doesn’t end there: 76 percent get frustrated when this doesn’t happen.

How can companies get started with personalization and scale up? What trends can we predict for personalization in the future? And—critically—how can companies toe the line between creepy and helpful? Read on to learn more.

Learn more about McKinsey’s Retail  and Growth, Marketing & Sales  Practices, and McKinsey Digital .

What do customers value in personalized marketing?

McKinsey asked 60 shoppers to create mobile diaries of their personalized interactions with various brands over two weeks. They made over 2,000 entries, which helped us see what works for customers and what doesn’t . Here are four things customers said they wanted from brand interactions:

• “Give me relevant recommendations I wouldn’t have thought of myself.” One common personalization practice is to remind shoppers of items they looked at but didn’t buy. This can be annoying or intrusive if not executed well. Instead, customers appreciate being recommended products or services that complement what they’ve already browsed or bought. Brands should keep track of impressions and stop serving ads to customers who haven’t responded.
• “Talk to me when I’m in shopping mode.” A message’s timing is just as important as its content. Perfecting the timing requires a close look at customer behaviors, patterns, and habits. One clothing retailer found that shoppers who visited a physical or online store were more likely to open and respond to messages delivered either on that same day or exactly a week later.
• “Remind me of things I want to know but might not be keeping track of.” Brands can become relevant to shoppers by tracking events and circumstances. These can include letting a customer know when a desired item is back in stock or when a new style is launched for a product the shopper has previously bought.
• “Know me no matter where I interact with you.” Customers expect communications that seamlessly straddle offline and online experiences. This is challenging for retailers because it requires collaboration between disparate areas of the organization—from store operations to analytics.

How can brands avoid being perceived as creepy?

Customers see value as what they get from a message relative to how much it costs—meaning how much personal information they have to share to get it. To understand how to deliver value while retaining trust , the following questions are helpful:

• Are you infusing empathy into your customer analytics and communications? Create segmentation based on customer attitudes and prioritize customer satisfaction based on the overall journey rather than individual touchpoints.
• Are you listening carefully for feedback on customer acceptance? Test and learn constantly to improve engagement. Do this by digging into upstream (likes, clicks, opens) and downstream (conversions, unsubscribes, ROI) engagement metrics. Engage with qualitative listening tools, like an ongoing shopper panel and ethnographic research and observation.

Learn more about McKinsey’s Growth, Marketing & Sales  Practice.

How might brands use personalization to achieve their goals?

Most marketers know that personalization is important. But we anticipate that, in coming years, personalization will transform the way companies approach marketing. Here’s what brands should focus on to prepare for the future :

• Invest in customer data and analytics foundations. These include systems to pool and analyze data, algorithms to identify behavior patterns and customer propensity, and analytical capabilities to feed that information into simple dashboards. This foundation will allow marketers to understand what high-value customers are looking for on an ongoing basis. A recent survey indicates that nearly one in five organizations are already investing in customer service analytics and customer segmentation AI  use cases.
• Find and train translators and advanced tech talent. This technological leap requires a close partnership between marketing and IT. In addition to data scientists and engineers, product management teams will need analytics translators  who can communicate business goals to tech stakeholders and data-driven outcomes to the business. The ability to recruit and develop this type of translator will provide a significant competitive advantage for organizations.
• Build agile capabilities . A successful personalization program requires cross-disciplinary project teams—and hence, a commitment to agile management. Teams should be organized around specific customer segments or journeys and should excel in creative, collaborative problem solving.
• Protect customer privacy. Data privacy is a big deal to customers: according to a 2022 survey, 85 percent  of customers say that knowing a company’s data privacy policies is important before making a purchase. Companies working on personalization are likely to trigger privacy concerns, so proactively managing these will be important. That means showing customers that they take data privacy seriously.

OK, but I’m starting from scratch. Can you be more specific?

As with most things, the hardest part of personalization marketing is getting started. Here are four steps companies can take to establish and scale digital personalization , without investing millions in IT:

• Use behavioral data to analyze customer journeys. Organize behavioral data by grouping customers, like say, mothers who shop for their children, or fashion-conscious young women who buy new private-label styles. Then understand the customer journey—that’s the series of interactions a customer makes with a brand, from initial consideration to repeat purchases. Combining segments and customer journeys creates microsegments, and that’s a step toward personalization.
• Listen to customer signals—and respond quickly. When customers provide signals about their intentions, marketers should be prepared to respond right away with a relevant message known as a “trigger.” Trigger messages can be any combination of images, copy, titles, or offers to match the situation. Developing the right trigger involves combining creative problem solving with analytics. For example, when a mother clicks on a product but hasn’t bought it, a next-product-to-buy algorithm based on machine learning could send a message suggesting a set of related products.
• Build a small, dedicated team. Empower a small group of the right people to transition the marketing department to a focus on personalized triggers. The team should be staffed with a fully dedicated campaign manager and creative, digital media, analytics, operations, and IT staff—and should have executive sponsorship to remove roadblocks. The team’s goal should go beyond page views and clicks to actual business results.

Focus on processes and technology that help teams work faster. Agile  processes are key here—they enable teams to quickly mix and match copy, creative content, and templates to find out what works and what doesn’t. Mistakes will happen, and that’s OK. Learn lessons and move on.

The right automation technology is also needed to work at this pace. Too often, automation software spits out messages that customers perceive as spam. It’s the tech team’s responsibility to guide the tech stack to find signals and efficiently deliver triggers that work.

How about an example of personalization marketing in action?

Here’s how a personalization journey  might work.

Mary is a mother with two children in primary school. Early last August, she visited a store to buy items for her kids, including several she’d previously viewed online. That’s signal one . The items she purchased were logged and attached to Mary’s profile in the store’s database.

This summer, almost a year later, Mary browses children’s clothes on the same retailer’s website but doesn’t buy anything. This interest combined with her purchases last summer together comprise signal two : Mary might be open to making her first back-to-school online purchase for her children this year.

Within 24 hours of browsing the clothes, Mary receives a trigger message : a personalized email offering a 10 percent discount on some of the items she’s been reviewing if she purchases them online. The message explains how to make the online purchase and suggests additional items she might consider based on her history with the retailer.

Learn more about McKinsey Digital .

I meant a real example.

Sephora is a great example of a real-world brand that has excelled at personalization. The beauty retailer has used personalized experiences  that are truly omnichannel , encouraging shoppers to book in-store makeovers and fashion consultations via its online channels, particularly its mobile app. The app lets makeup artists log each product used in makeovers into each customer’s profile, and lets customers virtually try on products and receive personalized recommendations.

Sephora’s personalization program also demonstrates the effectiveness of focusing on the most loyal customers. The retailer’s loyalty program offers its highest-level members perks such as early access to new products, invitations to exclusive events, free custom beauty services, and more. And all of Sephora’s customer communications, no matter the platform, display the customer’s loyalty points.

The results speak for themselves . In 2018, members accounted for 80 percent of Sephora’s total transactions. As of 2020, the loyalty program had about 25 million members. And in 2022, for the fifth year in a row, Sephora ranked first in Sailthru’s Retail Personalization Index.

Learn more about McKinsey’s Retail  and Growth, Marketing & Sales  Practices.

How can brands scale personalization?

According to McKinsey’s research, four factors—or four “Ds”—drive personalization at scale . These four factors can be further broken down into eight core elements .

Data foundation

Data should be centralized and made available  so activity in one channel can immediately support engagement in another—in real time or close to it.

• Data management. Brands should develop a multidimensional view of the customer to serve as the backbone of analytics. Quality should take precedence over quantity; having the right data is more important than having lots of it.

Introducing McKinsey Explainers : Direct answers to complex questions

Decision making.

Marketers should create an integrated decision-making engine that uses machine learning and AI models to score various propensities for each customer.

• Customer segmentation and analytics. Segment customers, identify value triggers, and score customers accordingly.
• Playbook. Create a library of campaigns and content that can be matched with customers.
• Decision-making engine (campaign coordination). Develop a multichannel decision-making engine to prevent conflicting messages and drive maximum value per touchpoint.

Marketers should break content into small pieces that can be mixed and matched for maximum flexibility.

• Cross-functional team. Assemble a cross-functional, co-located team to manage weekly deployment in a test-and-learn culture for faster results.
• Talents, capabilities, and culture. Secure the right capabilities and talent, often starting out by setting the right ambition in leadership.

Distribution

Integrate channels to coordinate communications and react to customer actions.

• Technology enablement. An optimized technology platform can be complex; start with existing technology and make the most of its potential.
• Test and learn. Don’t let the perfect be the enemy of the good; get started and iterate over time.

How will personalization shift in the near future?

Advances in AI , analytics, and data over the past few years have created new frontiers for marketers. But to capture the opportunities, marketers need to understand the three main shifts in personalization  and build the skills to respond to them.

• Physical spaces will be “digitized.” Deploying personalization beyond digital channels is a huge zone of opportunity, especially as physical stores continue to build back business in the wake of the COVID-19 pandemic. Offline interactions such as store visits could be the new horizon for personalization. Store employees can use insights from advanced analytics to provide customers with personalized offerings, and personal shoppers can use AI-enabled tools to improve service. Finally, facial recognition, location recognition, and biometric sensors will likely become more widely used.
• Empathy will scale. Empathy is the basis of all strong relationships. Understanding social cues and adapting to them builds trust. And it’s not easy to do digitally or at scale. Machine learning is changing that. Sophisticated algorithms are allowing programs to extrapolate emotions from data more easily. Ultimately, these advances can help marketers respond to customers’ specific moods.
• Brands will use ecosystems to personalize journeys. At present, various players contribute to a customer’s in-person experience—for instance, a shopping mall, a retail store, and a brand. Creating connections among these points is a big opportunity for organizations in the retail space to provide customers with more seamless decision journeys.

Learn more about McKinsey’s Retail  and Growth, Marketing & Sales  Practices and McKinsey Digital .  Also check out personalization-related job opportunities if you’re interested in working at McKinsey.

Articles referenced:

• “ The state of AI in 2022—and a half decade in review ,” December 6, 2022, Michael Chui , Bryce Hall , Helen Mayhew , Alex Singla , and Alex Sukharevsky
• “ The value of getting personalization right—or wrong—is multiplying ,” November 12, 2021, Nidhi Arora, Daniel Ensslen, Lars Fiedler , Wei Wei Liu, Kelsey Robinson , Eli Stein, and Gustavo Schüler
• “ Personalizing the customer experience: Driving differentiation in retail ,” April 28, 2020, Erik Lindecrantz , Madeleine Tjon Pian Gi , and Stefano Zerbi
• “ The end of shopping’s boundaries: Omnichannel personalization ,” February 10, 2020, Gal Gitter, Meg Raymond, Kelsey Robinson , and Jamie Wilkie
• “ The future of personalization—and how to get ready for it ,” June 18, 2019, Julien Boudet , Brian Gregg , Kathryn Rathje, Eli Stein, and Kai Vollhardt
• “ A technology blueprint for personalization at scale ,” May 20, 2019, Sean Flavin and Jason Heller
• “ What shoppers really want from personalized marketing ,” October 23, 2017, Julien Boudet , Brian Gregg , Jane Wong , and Gustavo Schüler
• “ Marketing’s Holy Grail: Digital personalization at scale ,” November 18, 2016, Brian Gregg , Hussein Kalaoui, Joel Maynes, and Gustavo Schüler

Want to know more about personalization?

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