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How to Write a Conclusion for Research Papers (with Examples)

The conclusion of a research paper is a crucial section that plays a significant role in the overall impact and effectiveness of your research paper. However, this is also the section that typically receives less attention compared to the introduction and the body of the paper. The conclusion serves to provide a concise summary of the key findings, their significance, their implications, and a sense of closure to the study. Discussing how can the findings be applied in real-world scenarios or inform policy, practice, or decision-making is especially valuable to practitioners and policymakers. The research paper conclusion also provides researchers with clear insights and valuable information for their own work, which they can then build on and contribute to the advancement of knowledge in the field.

The research paper conclusion should explain the significance of your findings within the broader context of your field. It restates how your results contribute to the existing body of knowledge and whether they confirm or challenge existing theories or hypotheses. Also, by identifying unanswered questions or areas requiring further investigation, your awareness of the broader research landscape can be demonstrated.

Remember to tailor the research paper conclusion to the specific needs and interests of your intended audience, which may include researchers, practitioners, policymakers, or a combination of these.

Table of Contents

What is a conclusion in a research paper, summarizing conclusion, editorial conclusion, externalizing conclusion, importance of a good research paper conclusion, how to write a conclusion for your research paper, research paper conclusion examples.

• How to write a research paper conclusion with Paperpal? 

Frequently Asked Questions

A conclusion in a research paper is the final section where you summarize and wrap up your research, presenting the key findings and insights derived from your study. The research paper conclusion is not the place to introduce new information or data that was not discussed in the main body of the paper. When working on how to conclude a research paper, remember to stick to summarizing and interpreting existing content. The research paper conclusion serves the following purposes: 1

• Warn readers of the possible consequences of not attending to the problem.
• Recommend specific course(s) of action.
• Restate key ideas to drive home the ultimate point of your research paper.
• Provide a “take-home” message that you want the readers to remember about your study.

Types of conclusions for research papers

In research papers, the conclusion provides closure to the reader. The type of research paper conclusion you choose depends on the nature of your study, your goals, and your target audience. I provide you with three common types of conclusions:

A summarizing conclusion is the most common type of conclusion in research papers. It involves summarizing the main points, reiterating the research question, and restating the significance of the findings. This common type of research paper conclusion is used across different disciplines.

An editorial conclusion is less common but can be used in research papers that are focused on proposing or advocating for a particular viewpoint or policy. It involves presenting a strong editorial or opinion based on the research findings and offering recommendations or calls to action.

An externalizing conclusion is a type of conclusion that extends the research beyond the scope of the paper by suggesting potential future research directions or discussing the broader implications of the findings. This type of conclusion is often used in more theoretical or exploratory research papers.

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The conclusion in a research paper serves several important purposes:

• Offers Implications and Recommendations : Your research paper conclusion is an excellent place to discuss the broader implications of your research and suggest potential areas for further study. It’s also an opportunity to offer practical recommendations based on your findings.
• Provides Closure : A good research paper conclusion provides a sense of closure to your paper. It should leave the reader with a feeling that they have reached the end of a well-structured and thought-provoking research project.
• Leaves a Lasting Impression : Writing a well-crafted research paper conclusion leaves a lasting impression on your readers. It’s your final opportunity to leave them with a new idea, a call to action, or a memorable quote.

Writing a strong conclusion for your research paper is essential to leave a lasting impression on your readers. Here’s a step-by-step process to help you create and know what to put in the conclusion of a research paper: 2

• Research Statement : Begin your research paper conclusion by restating your research statement. This reminds the reader of the main point you’ve been trying to prove throughout your paper. Keep it concise and clear.
• Key Points : Summarize the main arguments and key points you’ve made in your paper. Avoid introducing new information in the research paper conclusion. Instead, provide a concise overview of what you’ve discussed in the body of your paper.
• Address the Research Questions : If your research paper is based on specific research questions or hypotheses, briefly address whether you’ve answered them or achieved your research goals. Discuss the significance of your findings in this context.
• Significance : Highlight the importance of your research and its relevance in the broader context. Explain why your findings matter and how they contribute to the existing knowledge in your field.
• Implications : Explore the practical or theoretical implications of your research. How might your findings impact future research, policy, or real-world applications? Consider the “so what?” question.
• Future Research : Offer suggestions for future research in your area. What questions or aspects remain unanswered or warrant further investigation? This shows that your work opens the door for future exploration.
• Closing Thought : Conclude your research paper conclusion with a thought-provoking or memorable statement. This can leave a lasting impression on your readers and wrap up your paper effectively. Avoid introducing new information or arguments here.
• Proofread and Revise : Carefully proofread your conclusion for grammar, spelling, and clarity. Ensure that your ideas flow smoothly and that your conclusion is coherent and well-structured.

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Remember that a well-crafted research paper conclusion is a reflection of the strength of your research and your ability to communicate its significance effectively. It should leave a lasting impression on your readers and tie together all the threads of your paper. Now you know how to start the conclusion of a research paper and what elements to include to make it impactful, let’s look at a research paper conclusion sample.

How to write a research paper conclusion with Paperpal?

A research paper conclusion is not just a summary of your study, but a synthesis of the key findings that ties the research together and places it in a broader context. A research paper conclusion should be concise, typically around one paragraph in length. However, some complex topics may require a longer conclusion to ensure the reader is left with a clear understanding of the study’s significance. Paperpal, an AI writing assistant trusted by over 800,000 academics globally, can help you write a well-structured conclusion for your research paper. 

• Sign Up or Log In: Create a new Paperpal account or login with your details.  
• Navigate to Features : Once logged in, head over to the features’ side navigation pane. Click on Templates and you’ll find a suite of generative AI features to help you write better, faster.  
• Generate an outline: Under Templates, select ‘Outlines’. Choose ‘Research article’ as your document type.  
• Select your section: Since you’re focusing on the conclusion, select this section when prompted.  
• Choose your field of study: Identifying your field of study allows Paperpal to provide more targeted suggestions, ensuring the relevance of your conclusion to your specific area of research. 
• Provide a brief description of your study: Enter details about your research topic and findings. This information helps Paperpal generate a tailored outline that aligns with your paper’s content. 
• Generate the conclusion outline: After entering all necessary details, click on ‘generate’. Paperpal will then create a structured outline for your conclusion, to help you start writing and build upon the outline.  
• Write your conclusion: Use the generated outline to build your conclusion. The outline serves as a guide, ensuring you cover all critical aspects of a strong conclusion, from summarizing key findings to highlighting the research’s implications. 
• Refine and enhance: Paperpal’s ‘Make Academic’ feature can be particularly useful in the final stages. Select any paragraph of your conclusion and use this feature to elevate the academic tone, ensuring your writing is aligned to the academic journal standards. 

By following these steps, Paperpal not only simplifies the process of writing a research paper conclusion but also ensures it is impactful, concise, and aligned with academic standards. Sign up with Paperpal today and write your research paper conclusion 2x faster .  

The research paper conclusion is a crucial part of your paper as it provides the final opportunity to leave a strong impression on your readers. In the research paper conclusion, summarize the main points of your research paper by restating your research statement, highlighting the most important findings, addressing the research questions or objectives, explaining the broader context of the study, discussing the significance of your findings, providing recommendations if applicable, and emphasizing the takeaway message. The main purpose of the conclusion is to remind the reader of the main point or argument of your paper and to provide a clear and concise summary of the key findings and their implications. All these elements should feature on your list of what to put in the conclusion of a research paper to create a strong final statement for your work.

A strong conclusion is a critical component of a research paper, as it provides an opportunity to wrap up your arguments, reiterate your main points, and leave a lasting impression on your readers. Here are the key elements of a strong research paper conclusion: 1. Conciseness : A research paper conclusion should be concise and to the point. It should not introduce new information or ideas that were not discussed in the body of the paper. 2. Summarization : The research paper conclusion should be comprehensive enough to give the reader a clear understanding of the research’s main contributions. 3 . Relevance : Ensure that the information included in the research paper conclusion is directly relevant to the research paper’s main topic and objectives; avoid unnecessary details. 4 . Connection to the Introduction : A well-structured research paper conclusion often revisits the key points made in the introduction and shows how the research has addressed the initial questions or objectives. 5. Emphasis : Highlight the significance and implications of your research. Why is your study important? What are the broader implications or applications of your findings? 6 . Call to Action : Include a call to action or a recommendation for future research or action based on your findings.

The length of a research paper conclusion can vary depending on several factors, including the overall length of the paper, the complexity of the research, and the specific journal requirements. While there is no strict rule for the length of a conclusion, but it’s generally advisable to keep it relatively short. A typical research paper conclusion might be around 5-10% of the paper’s total length. For example, if your paper is 10 pages long, the conclusion might be roughly half a page to one page in length.

In general, you do not need to include citations in the research paper conclusion. Citations are typically reserved for the body of the paper to support your arguments and provide evidence for your claims. However, there may be some exceptions to this rule: 1. If you are drawing a direct quote or paraphrasing a specific source in your research paper conclusion, you should include a citation to give proper credit to the original author. 2. If your conclusion refers to or discusses specific research, data, or sources that are crucial to the overall argument, citations can be included to reinforce your conclusion’s validity.

The conclusion of a research paper serves several important purposes: 1. Summarize the Key Points 2. Reinforce the Main Argument 3. Provide Closure 4. Offer Insights or Implications 5. Engage the Reader. 6. Reflect on Limitations

Remember that the primary purpose of the research paper conclusion is to leave a lasting impression on the reader, reinforcing the key points and providing closure to your research. It’s often the last part of the paper that the reader will see, so it should be strong and well-crafted.

• Makar, G., Foltz, C., Lendner, M., & Vaccaro, A. R. (2018). How to write effective discussion and conclusion sections. Clinical spine surgery, 31(8), 345-346.
• Bunton, D. (2005). The structure of PhD conclusion chapters.  Journal of English for academic purposes ,  4 (3), 207-224.

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• How to Write Discussions and Conclusions

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

What makes an effective discussion?

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

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

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

Tip: Not all journals share the same naming conventions.

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

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

Questions to ask yourself:

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

How to structure a discussion

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

Writing Tips

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

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

Don’t

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

Snippets of Effective Discussions:

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

Identifying reliable indicators of fitness in polar bears

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Research Paper Conclusion – Writing Guide and Examples

Table of Contents

Research Paper Conclusion

Definition:

A research paper conclusion is the final section of a research paper that summarizes the key findings, significance, and implications of the research. It is the writer’s opportunity to synthesize the information presented in the paper, draw conclusions, and make recommendations for future research or actions.

The conclusion should provide a clear and concise summary of the research paper, reiterating the research question or problem, the main results, and the significance of the findings. It should also discuss the limitations of the study and suggest areas for further research.

Parts of Research Paper Conclusion

The parts of a research paper conclusion typically include:

Restatement of the Thesis

The conclusion should begin by restating the thesis statement from the introduction in a different way. This helps to remind the reader of the main argument or purpose of the research.

Summary of Key Findings

The conclusion should summarize the main findings of the research, highlighting the most important results and conclusions. This section should be brief and to the point.

Implications and Significance

In this section, the researcher should explain the implications and significance of the research findings. This may include discussing the potential impact on the field or industry, highlighting new insights or knowledge gained, or pointing out areas for future research.

Limitations and Recommendations

It is important to acknowledge any limitations or weaknesses of the research and to make recommendations for how these could be addressed in future studies. This shows that the researcher is aware of the potential limitations of their work and is committed to improving the quality of research in their field.

Concluding Statement

The conclusion should end with a strong concluding statement that leaves a lasting impression on the reader. This could be a call to action, a recommendation for further research, or a final thought on the topic.

How to Write Research Paper Conclusion

Here are some steps you can follow to write an effective research paper conclusion:

• Restate the research problem or question: Begin by restating the research problem or question that you aimed to answer in your research. This will remind the reader of the purpose of your study.
• Summarize the main points: Summarize the key findings and results of your research. This can be done by highlighting the most important aspects of your research and the evidence that supports them.
• Discuss the implications: Discuss the implications of your findings for the research area and any potential applications of your research. You should also mention any limitations of your research that may affect the interpretation of your findings.
• Provide a conclusion : Provide a concise conclusion that summarizes the main points of your paper and emphasizes the significance of your research. This should be a strong and clear statement that leaves a lasting impression on the reader.
• Offer suggestions for future research: Lastly, offer suggestions for future research that could build on your findings and contribute to further advancements in the field.

Remember that the conclusion should be brief and to the point, while still effectively summarizing the key findings and implications of your research.

Example of Research Paper Conclusion

Here’s an example of a research paper conclusion:

Conclusion :

In conclusion, our study aimed to investigate the relationship between social media use and mental health among college students. Our findings suggest that there is a significant association between social media use and increased levels of anxiety and depression among college students. This highlights the need for increased awareness and education about the potential negative effects of social media use on mental health, particularly among college students.

Despite the limitations of our study, such as the small sample size and self-reported data, our findings have important implications for future research and practice. Future studies should aim to replicate our findings in larger, more diverse samples, and investigate the potential mechanisms underlying the association between social media use and mental health. In addition, interventions should be developed to promote healthy social media use among college students, such as mindfulness-based approaches and social media detox programs.

Overall, our study contributes to the growing body of research on the impact of social media on mental health, and highlights the importance of addressing this issue in the context of higher education. By raising awareness and promoting healthy social media use among college students, we can help to reduce the negative impact of social media on mental health and improve the well-being of young adults.

Purpose of Research Paper Conclusion

The purpose of a research paper conclusion is to provide a summary and synthesis of the key findings, significance, and implications of the research presented in the paper. The conclusion serves as the final opportunity for the writer to convey their message and leave a lasting impression on the reader.

The conclusion should restate the research problem or question, summarize the main results of the research, and explain their significance. It should also acknowledge the limitations of the study and suggest areas for future research or action.

Overall, the purpose of the conclusion is to provide a sense of closure to the research paper and to emphasize the importance of the research and its potential impact. It should leave the reader with a clear understanding of the main findings and why they matter. The conclusion serves as the writer’s opportunity to showcase their contribution to the field and to inspire further research and action.

When to Write Research Paper Conclusion

The conclusion of a research paper should be written after the body of the paper has been completed. It should not be written until the writer has thoroughly analyzed and interpreted their findings and has written a complete and cohesive discussion of the research.

Before writing the conclusion, the writer should review their research paper and consider the key points that they want to convey to the reader. They should also review the research question, hypotheses, and methodology to ensure that they have addressed all of the necessary components of the research.

Once the writer has a clear understanding of the main findings and their significance, they can begin writing the conclusion. The conclusion should be written in a clear and concise manner, and should reiterate the main points of the research while also providing insights and recommendations for future research or action.

Characteristics of Research Paper Conclusion

The characteristics of a research paper conclusion include:

• Clear and concise: The conclusion should be written in a clear and concise manner, summarizing the key findings and their significance.
• Comprehensive: The conclusion should address all of the main points of the research paper, including the research question or problem, the methodology, the main results, and their implications.
• Future-oriented : The conclusion should provide insights and recommendations for future research or action, based on the findings of the research.
• Impressive : The conclusion should leave a lasting impression on the reader, emphasizing the importance of the research and its potential impact.
• Objective : The conclusion should be based on the evidence presented in the research paper, and should avoid personal biases or opinions.
• Unique : The conclusion should be unique to the research paper and should not simply repeat information from the introduction or body of the paper.

Advantages of Research Paper Conclusion

The advantages of a research paper conclusion include:

• Summarizing the key findings : The conclusion provides a summary of the main findings of the research, making it easier for the reader to understand the key points of the study.
• Emphasizing the significance of the research: The conclusion emphasizes the importance of the research and its potential impact, making it more likely that readers will take the research seriously and consider its implications.
• Providing recommendations for future research or action : The conclusion suggests practical recommendations for future research or action, based on the findings of the study.
• Providing closure to the research paper : The conclusion provides a sense of closure to the research paper, tying together the different sections of the paper and leaving a lasting impression on the reader.
• Demonstrating the writer’s contribution to the field : The conclusion provides the writer with an opportunity to showcase their contribution to the field and to inspire further research and action.

Limitations of Research Paper Conclusion

While the conclusion of a research paper has many advantages, it also has some limitations that should be considered, including:

• I nability to address all aspects of the research: Due to the limited space available in the conclusion, it may not be possible to address all aspects of the research in detail.
• Subjectivity : While the conclusion should be objective, it may be influenced by the writer’s personal biases or opinions.
• Lack of new information: The conclusion should not introduce new information that has not been discussed in the body of the research paper.
• Lack of generalizability: The conclusions drawn from the research may not be applicable to other contexts or populations, limiting the generalizability of the study.
• Misinterpretation by the reader: The reader may misinterpret the conclusions drawn from the research, leading to a misunderstanding of the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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The conclusion is intended to help the reader understand why your research should matter to them after they have finished reading the paper. A conclusion is not merely a summary of the main topics covered or a re-statement of your research problem, but a synthesis of key points derived from the findings of your study and, if applicable, where you recommend new areas for future research. For most college-level research papers, two or three well-developed paragraphs is sufficient for a conclusion, although in some cases, more paragraphs may be required in describing the key findings and their significance.

Conclusions. The Writing Center. University of North Carolina; Conclusions. The Writing Lab and The OWL. Purdue University.

Importance of a Good Conclusion

A well-written conclusion provides you with important opportunities to demonstrate to the reader your understanding of the research problem. These include:

• Presenting the last word on the issues you raised in your paper . Just as the introduction gives a first impression to your reader, the conclusion offers a chance to leave a lasting impression. Do this, for example, by highlighting key findings in your analysis that advance new understanding about the research problem, that are unusual or unexpected, or that have important implications applied to practice.
• Summarizing your thoughts and conveying the larger significance of your study . The conclusion is an opportunity to succinctly re-emphasize  your answer to the "So What?" question by placing the study within the context of how your research advances past research about the topic.
• Identifying how a gap in the literature has been addressed . The conclusion can be where you describe how a previously identified gap in the literature [first identified in your literature review section] has been addressed by your research and why this contribution is significant.
• Demonstrating the importance of your ideas . Don't be shy. The conclusion offers an opportunity to elaborate on the impact and significance of your findings. This is particularly important if your study approached examining the research problem from an unusual or innovative perspective.
• Introducing possible new or expanded ways of thinking about the research problem . This does not refer to introducing new information [which should be avoided], but to offer new insight and creative approaches for framing or contextualizing the research problem based on the results of your study.

Bunton, David. “The Structure of PhD Conclusion Chapters.” Journal of English for Academic Purposes 4 (July 2005): 207–224; Conclusions. The Writing Center. University of North Carolina; Kretchmer, Paul. Twelve Steps to Writing an Effective Conclusion. San Francisco Edit, 2003-2008; Conclusions. The Writing Lab and The OWL. Purdue University; Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8.

Structure and Writing Style

I.  General Rules

The general function of your paper's conclusion is to restate the main argument . It reminds the reader of the strengths of your main argument(s) and reiterates the most important evidence supporting those argument(s). Do this by clearly summarizing the context, background, and necessity of pursuing the research problem you investigated in relation to an issue, controversy, or a gap found in the literature. However, make sure that your conclusion is not simply a repetitive summary of the findings. This reduces the impact of the argument(s) you have developed in your paper.

When writing the conclusion to your paper, follow these general rules:

• Present your conclusions in clear, concise language. Re-state the purpose of your study, then describe how your findings differ or support those of other studies and why [i.e., what were the unique, new, or crucial contributions your study made to the overall research about your topic?].
• Do not simply reiterate your findings or the discussion of your results. Provide a synthesis of arguments presented in the paper to show how these converge to address the research problem and the overall objectives of your study.
• Indicate opportunities for future research if you haven't already done so in the discussion section of your paper. Highlighting the need for further research provides the reader with evidence that you have an in-depth awareness of the research problem but that further investigations should take place beyond the scope of your investigation.

Consider the following points to help ensure your conclusion is presented well:

• If the argument or purpose of your paper is complex, you may need to summarize the argument for your reader.
• If, prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the end of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration that returns the topic to the context provided by the introduction or within a new context that emerges from the data [this is opposite of the introduction, which begins with general discussion of the context and ends with a detailed description of the research problem]. 

The conclusion also provides a place for you to persuasively and succinctly restate the research problem, given that the reader has now been presented with all the information about the topic . Depending on the discipline you are writing in, the concluding paragraph may contain your reflections on the evidence presented. However, the nature of being introspective about the research you have conducted will depend on the topic and whether your professor wants you to express your observations in this way. If asked to think introspectively about the topics, do not delve into idle speculation. Being introspective means looking within yourself as an author to try and understand an issue more deeply, not to guess at possible outcomes or make up scenarios not supported by the evidence.

II.  Developing a Compelling Conclusion

Although an effective conclusion needs to be clear and succinct, it does not need to be written passively or lack a compelling narrative. Strategies to help you move beyond merely summarizing the key points of your research paper may include any of the following:

• If your essay deals with a critical, contemporary problem, warn readers of the possible consequences of not attending to the problem proactively.
• Recommend a specific course or courses of action that, if adopted, could address a specific problem in practice or in the development of new knowledge leading to positive change.
• Cite a relevant quotation or expert opinion already noted in your paper in order to lend authority and support to the conclusion(s) you have reached [a good source would be from your literature review].
• Explain the consequences of your research in a way that elicits action or demonstrates urgency in seeking change.
• Restate a key statistic, fact, or visual image to emphasize the most important finding of your paper.
• If your discipline encourages personal reflection, illustrate your concluding point by drawing from your own life experiences.
• Return to an anecdote, an example, or a quotation that you presented in your introduction, but add further insight derived from the findings of your study; use your interpretation of results from your study to recast it in new or important ways.
• Provide a "take-home" message in the form of a succinct, declarative statement that you want the reader to remember about your study.

III. Problems to Avoid

Failure to be concise Your conclusion section should be concise and to the point. Conclusions that are too lengthy often have unnecessary information in them. The conclusion is not the place for details about your methodology or results. Although you should give a summary of what was learned from your research, this summary should be relatively brief, since the emphasis in the conclusion is on the implications, evaluations, insights, and other forms of analysis that you make. Strategies for writing concisely can be found here .

Failure to comment on larger, more significant issues In the introduction, your task was to move from the general [the field of study] to the specific [the research problem]. However, in the conclusion, your task is to move from a specific discussion [your research problem] back to a general discussion framed around the implications and significance of your findings [i.e., how your research contributes new understanding or fills an important gap in the literature]. In short, the conclusion is where you should place your research within a larger context [visualize your paper as an hourglass--start with a broad introduction and review of the literature, move to the specific analysis and discussion, conclude with a broad summary of the study's implications and significance].

Failure to reveal problems and negative results Negative aspects of the research process should never be ignored. These are problems, deficiencies, or challenges encountered during your study. They should be summarized as a way of qualifying your overall conclusions. If you encountered negative or unintended results [i.e., findings that are validated outside the research context in which they were generated], you must report them in the results section and discuss their implications in the discussion section of your paper. In the conclusion, use negative results as an opportunity to explain their possible significance and/or how they may form the basis for future research.

Failure to provide a clear summary of what was learned In order to be able to discuss how your research fits within your field of study [and possibly the world at large], you need to summarize briefly and succinctly how it contributes to new knowledge or a new understanding about the research problem. This element of your conclusion may be only a few sentences long.

Failure to match the objectives of your research Often research objectives in the social and behavioral sciences change while the research is being carried out. This is not a problem unless you forget to go back and refine the original objectives in your introduction. As these changes emerge they must be documented so that they accurately reflect what you were trying to accomplish in your research [not what you thought you might accomplish when you began].

Resist the urge to apologize If you've immersed yourself in studying the research problem, you presumably should know a good deal about it [perhaps even more than your professor!]. Nevertheless, by the time you have finished writing, you may be having some doubts about what you have produced. Repress those doubts! Don't undermine your authority as a researcher by saying something like, "This is just one approach to examining this problem; there may be other, much better approaches that...." The overall tone of your conclusion should convey confidence to the reader about the study's validity and realiability.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8; Concluding Paragraphs. College Writing Center at Meramec. St. Louis Community College; Conclusions. The Writing Center. University of North Carolina; Conclusions. The Writing Lab and The OWL. Purdue University; Freedman, Leora  and Jerry Plotnick. Introductions and Conclusions. The Lab Report. University College Writing Centre. University of Toronto; Leibensperger, Summer. Draft Your Conclusion. Academic Center, the University of Houston-Victoria, 2003; Make Your Last Words Count. The Writer’s Handbook. Writing Center. University of Wisconsin Madison; Miquel, Fuster-Marquez and Carmen Gregori-Signes. “Chapter Six: ‘Last but Not Least:’ Writing the Conclusion of Your Paper.” In Writing an Applied Linguistics Thesis or Dissertation: A Guide to Presenting Empirical Research . John Bitchener, editor. (Basingstoke,UK: Palgrave Macmillan, 2010), pp. 93-105; Tips for Writing a Good Conclusion. Writing@CSU. Colorado State University; Kretchmer, Paul. Twelve Steps to Writing an Effective Conclusion. San Francisco Edit, 2003-2008; Writing Conclusions. Writing Tutorial Services, Center for Innovative Teaching and Learning. Indiana University; Writing: Considering Structure and Organization. Institute for Writing Rhetoric. Dartmouth College.

Writing Tip

Don't Belabor the Obvious!

Avoid phrases like "in conclusion...," "in summary...," or "in closing...." These phrases can be useful, even welcome, in oral presentations. But readers can see by the tell-tale section heading and number of pages remaining that they are reaching the end of your paper. You'll irritate your readers if you belabor the obvious.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8.

Another Writing Tip

New Insight, Not New Information!

Don't surprise the reader with new information in your conclusion that was never referenced anywhere else in the paper. This why the conclusion rarely has citations to sources. If you have new information to present, add it to the discussion or other appropriate section of the paper. Note that, although no new information is introduced, the conclusion, along with the discussion section, is where you offer your most "original" contributions in the paper; the conclusion is where you describe the value of your research, demonstrate that you understand the material that you’ve presented, and position your findings within the larger context of scholarship on the topic, including describing how your research contributes new insights to that scholarship.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8; Conclusions. The Writing Center. University of North Carolina.

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Scientific Method: Step 6: CONCLUSION

• Step 1: QUESTION
• Step 2: RESEARCH
• Step 3: HYPOTHESIS
• Step 4: EXPERIMENT
• Step 5: DATA
• Step 6: CONCLUSION

Step 6: Conclusion

Finally, you've reached your conclusion. Now it is time to summarize and explain what happened in your experiment. Your conclusion should answer the question posed in step one. Your conclusion should be based solely on your results.

Think about the following questions:

• Was your hypothesis correct?
• If your hypothesis wasn't correct, what can you conclude from that?
• Do you need to run your experiment again changing a variable?
• Is your data clearly defined so everyone can understand the results and follow your reasoning?

Remember, even a failed experiment can yield a valuable lesson.  

Draw your conclusion

• Conclusion Sections in Scientific Research Reports (The Writing Center at George Mason)
• Sample Conclusions (Science Buddies)
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How to Write a Good Lab Conclusion in Science

Last Updated: March 21, 2024 Fact Checked

This article was co-authored by Bess Ruff, MA . Bess Ruff is a Geography PhD student at Florida State University. She received her MA in Environmental Science and Management from the University of California, Santa Barbara in 2016. She has conducted survey work for marine spatial planning projects in the Caribbean and provided research support as a graduate fellow for the Sustainable Fisheries Group. There are 11 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 1,759,804 times.

A lab report describes an entire experiment from start to finish, outlining the procedures, reporting results, and analyzing data. The report is used to demonstrate what has been learned, and it will provide a way for other people to see your process for the experiment and understand how you arrived at your conclusions. The conclusion is an integral part of the report; this is the section that reiterates the experiment’s main findings and gives the reader an overview of the lab trial. Writing a solid conclusion to your lab report will demonstrate that you’ve effectively learned the objectives of your assignment.

Outlining Your Conclusion

• Restate : Restate the lab experiment by describing the assignment.
• Explain : Explain the purpose of the lab experiment. What were you trying to figure out or discover? Talk briefly about the procedure you followed to complete the lab.
• Results : Explain your results. Confirm whether or not your hypothesis was supported by the results.
• Uncertainties : Account for uncertainties and errors. Explain, for example, if there were other circumstances beyond your control that might have impacted the experiment’s results.
• New : Discuss new questions or discoveries that emerged from the experiment.

• Your assignment may also have specific questions that need to be answered. Make sure you answer these fully and coherently in your conclusion.

Discussing the Experiment and Hypothesis

• If you tried the experiment more than once, describe the reasons for doing so. Discuss changes that you made in your procedures.
• Brainstorm ways to explain your results in more depth. Go back through your lab notes, paying particular attention to the results you observed. [5] X Trustworthy Source University of North Carolina Writing Center UNC's on-campus and online instructional service that provides assistance to students, faculty, and others during the writing process Go to source

• Start this section with wording such as, “The results showed that…”
• You don’t need to give the raw data here. Just summarize the main points, calculate averages, or give a range of data to give an overall picture to the reader.
• Make sure to explain whether or not any statistical analyses were significant, and to what degree, such as 1%, 5%, or 10%.

• Use simple language such as, “The results supported the hypothesis,” or “The results did not support the hypothesis.”

Demonstrating What You Have Learned

• If it’s not clear in your conclusion what you learned from the lab, start off by writing, “In this lab, I learned…” This will give the reader a heads up that you will be describing exactly what you learned.
• Add details about what you learned and how you learned it. Adding dimension to your learning outcomes will convince your reader that you did, in fact, learn from the lab. Give specifics about how you learned that molecules will act in a particular environment, for example.
• Describe how what you learned in the lab could be applied to a future experiment.

• On a new line, write the question in italics. On the next line, write the answer to the question in regular text.

• If your experiment did not achieve the objectives, explain or speculate why not.

Wrapping Up Your Conclusion

• If your experiment raised questions that your collected data can’t answer, discuss this here.

• Describe what is new or innovative about your research.
• This can often set you apart from your classmates, many of whom will just write up the barest of discussion and conclusion.

Finalizing Your Lab Report

Frequently asked questions

What should i include in a research paper conclusion.

The conclusion of a research paper has several key elements you should make sure to include:

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

Frequently asked questions: Writing a research paper

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

The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

Research questions anchor your whole project, so it’s important to spend some time refining them.

In general, they should be:

• Focused and researchable
• Answerable using credible sources
• Complex and arguable
• Feasible and specific
• Relevant and original

All research questions should be:

• Focused on a single problem or issue
• Researchable using primary and/or secondary sources
• Feasible to answer within the timeframe and practical constraints
• Specific enough to answer thoroughly
• Complex enough to develop the answer over the space of a paper or thesis
• Relevant to your field of study and/or society more broadly

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

Your research objectives indicate how you’ll try to address your research problem and should be specific:

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

The main guidelines for formatting a paper in Chicago style are to:

• Use a standard font like 12 pt Times New Roman
• Use 1 inch margins or larger
• Apply double line spacing
• Indent every new paragraph ½ inch
• Include a title page
• Place page numbers in the top right or bottom center
• Cite your sources with author-date citations or Chicago footnotes
• Include a bibliography or reference list

To automatically generate accurate Chicago references, you can use Scribbr’s free Chicago reference generator .

The main guidelines for formatting a paper in MLA style are as follows:

• Use an easily readable font like 12 pt Times New Roman
• Set 1 inch page margins
• Include a four-line MLA heading on the first page
• Center the paper’s title
• Use title case capitalization for headings
• Cite your sources with MLA in-text citations
• List all sources cited on a Works Cited page at the end

To format a paper in APA Style , follow these guidelines:

• Use a standard font like 12 pt Times New Roman or 11 pt Arial
• If submitting for publication, insert a running head on every page
• Apply APA heading styles
• Cite your sources with APA in-text citations
• List all sources cited on a reference page at the end

No, it’s not appropriate to present new arguments or evidence in the conclusion . While you might be tempted to save a striking argument for last, research papers 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 results and discussion sections if you are following a scientific structure). The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

Don’t feel that you have to write the introduction first. The introduction is often one of the last parts of the research paper you’ll write, along with the conclusion.

This is because it can be easier to introduce your paper once you’ve already written the body ; you may not have the clearest idea of your arguments until you’ve written them, and things can change during the writing process .

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

The introduction of a research paper includes several key elements:

• A hook to catch the reader’s interest
• Relevant background on the topic
• Details of your research problem

and your problem statement

• A thesis statement or research question
• Sometimes an overview of the paper

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Undergraduate Research Experiences for STEM Students: Successes, Challenges, and Opportunities (2017)

Chapter: 9 conclusions and recommendations, 9 conclusions and recommendations.

Practitioners designing or improving undergraduate research experiences (UREs) can build on the experiences of colleagues and learn from the increasingly robust literature about UREs and the considerable body of evidence about how students learn. The questions practitioners ask themselves during the design process should include questions about the goals of the campus, program, faculty, and students. Other factors to consider when designing a URE include the issues raised in the conceptual framework for learning and instruction, the available resources, how the program or experience will be evaluated or studied, and how to design the program from the outset to incorporate these considerations, as well as how to build in opportunities to improve the experience over time in light of new evidence. (Some of these topics are addressed in Chapter 8 .)

Colleges and universities that offer or wish to offer UREs to their students should undertake baseline evaluations of their current offerings and create plans to develop a culture of improvement in which faculty are supported in their efforts to continuously refine UREs based on the evidence currently available and evidence that they and others generate in the future. While much of the evidence to date is descriptive, it forms a body of knowledge that can be used to identify research questions about UREs, both those designed around the apprenticeship model and those designed using the more recent course-based undergraduate research experience (CURE) model. Internships and other avenues by which undergraduates do research provide many of the same sorts of experiences but are not well studied. In any case, it is clear that students value these experiences; that many faculty do as well; and that they contribute to broadening participation in science,

technology, engineering, and mathematics (STEM) education and careers. The findings from the research literature reported in Chapter 4 provide guidance to those designing both opportunities to improve practical and academic skills and opportunities for students to “try out” a professional role of interest.

Little research has been done that provides answers to mechanistic questions about how UREs work. Additional studies are needed to know which features of UREs are most important for positive outcomes with which students and to gain information about other questions of this type. This additional research is needed to better understand and compare different strategies for UREs designed for a diversity of students, mentors, and institutions. Therefore, the committee recommends steps that could increase the quantity and quality of evidence available in the future and makes recommendations for how faculty, departments, and institutions might approach decisions about UREs using currently available information. Multiple detailed recommendations about the kinds of research that might be useful are provided in the research agenda in Chapter 7 .

In addition to the specific research recommended in Chapter 7 , in this chapter the committee provides a series of interrelated conclusions and recommendations related to UREs for the STEM disciplines and intended to highlight the issues of primary importance to administrators, URE program designers, mentors to URE students, funders of UREs, those leading the departments and institutions offering UREs, and those conducting research about UREs. These conclusions and recommendations are based on the expert views of the committee and informed by their review of the available research, the papers commissioned for this report, and input from presenters during committee meetings. Table 9-1 defines categories of these URE “actors,” gives examples of specific roles included in each category, specifies key URE actions for which that category is responsible, and lists the conclusions and recommendations the committee views as most relevant to that actor category.

RESEARCH ON URES

Conclusion 1: The current and emerging landscape of what constitutes UREs is diverse and complex. Students can engage in STEM-based undergraduate research in many different ways, across a variety of settings, and along a continuum that extends and expands upon learning opportunities in other educational settings. The following characteristics define UREs. Due to the variation in the types of UREs, not all experiences include all of the following characteristics in the same way; experiences vary in how much a particular characteristic is emphasized.

TABLE 9-1 Audiences for Committee’s Conclusions and Recommendations

• They engage students in research practices including the ability to argue from evidence.
• They aim to generate novel information with an emphasis on discovery and innovation or to determine whether recent preliminary results can be replicated.
• They focus on significant, relevant problems of interest to STEM researchers and, in some cases, a broader community (e.g., civic engagement).
• They emphasize and expect collaboration and teamwork.
• They involve iterative refinement of experimental design, experimental questions, or data obtained.
• They allow students to master specific research techniques.
• They help students engage in reflection about the problems being investigated and the work being undertaken to address those problems.
• They require communication of results, either through publication or presentations in various STEM venues.
• They are structured and guided by a mentor, with students assuming increasing ownership of some aspects of the project over time.

UREs are generally designed to add value to STEM offerings by promoting an understanding of the ways that knowledge is generated in STEM fields and to extend student learning beyond what happens in the small group work of an inquiry-based course. UREs add value by enabling students to understand and contribute to the research questions that are driving the field for one or more STEM topics or to grapple with design challenges of interest to professionals. They help students understand what it means to be a STEM researcher in a way that would be difficult to convey in a lecture course or even in an inquiry-based learning setting. As participants in a URE, students can learn by engaging in planning, experimentation, evaluation, interpretation, and communication of data and other results in light of what is already known about the question of interest. They can pose relevant questions that can be solved only through investigative or design efforts—individually or in teams—and attempt to answer these questions despite the challenges, setbacks, and ambiguity of the process and the results obtained.

The diversity of UREs reflects the reality that different STEM disciplines operate from varying traditions, expectations, and constraints (e.g., lab safety issues) in providing opportunities for undergraduates to engage in research. In addition, individual institutions and departments have cultures that promote research participation to various degrees and at different stages in students’ academic careers. Some programs emphasize design and problem solving in addition to discovery. UREs in different disciplines can

take many forms (e.g., apprentice-style, course-based, internships, project-based), but the definitional characteristics described above are similar across different STEM fields.

Furthermore, students in today’s university landscape may have opportunities to engage with many different types of UREs throughout their education, including involvement in a formal program (which could include mentoring, tutoring, research, and seminars about research), an apprentice-style URE under the guidance of an individual or team of faculty members, an internship, or enrolling in one or more CUREs or in a consortium- or project-based program.

Conclusion 2: Research on the efficacy of UREs is still in the early stages of development compared with other interventions to improve undergraduate STEM education.

• The types of UREs are diverse, and their goals are even more diverse. Questions and methodologies used to investigate the roles and effectiveness of UREs in achieving those goals are similarly diverse.
• Most of the studies of UREs to date are descriptive case studies or use correlational designs. Many of these studies report positive outcomes from engagement in a URE.
• Only a small number of studies have employed research designs that can support inferences about causation. Most of these studies find evidence for a causal relationship between URE participation and subsequent persistence in STEM. More studies are needed to provide evidence that participation in UREs is a causal factor in a range of desired student outcomes.

Taking the entire body of evidence into account, the committee concludes that the published peer-reviewed literature to date suggests that participation in a URE is beneficial for students .

As discussed in the report’s Introduction (see Chapter 1 ) and in the research agenda (see Chapter 7 ), the committee considered descriptive, causal, and mechanistic questions in our reading of the literature on UREs. Scientific approaches to answering descriptive, causal, and mechanistic questions require deciding what to look for, determining how to examine it, and knowing appropriate ways to score or quantify the effect.

Descriptive questions ask what is happening without making claims as to why it is happening—that is, without making claims as to whether the research experience caused these changes. A descriptive statement about UREs only claims that certain changes occurred during or after the time the students were engaged in undergraduate research. Descriptive studies

cannot determine whether any benefits observed were caused by participation in the URE.

Causal questions seek to discover whether a specific intervention leads to a specific outcome, other things being equal. To address such questions, causal evidence can be generated from a comparison of carefully selected groups that do and do not experience UREs. The groups can be made roughly equivalent by random assignment (ensuring that URE and non-URE groups are the same on average as the sample size increases) or by controlling for an exhaustive set of characteristics and experiences that might render the groups different prior to the URE. Other quasi-experimental strategies can also be used. Simply comparing students who enroll in a URE with students who do not is not adequate for determining causality because there may be selection bias. For example, students already interested in STEM are more likely to seek out such opportunities and more likely to be selected for such programs. Instead the investigator would have to compare future enrollment patterns (or other measures) between closely matched students, some of whom enrolled in a URE and some of whom did not. Controlling for selection bias to enable an inference about causation can pose significant challenges.

Questions of mechanism or of process also can be explored to understand why a causal intervention leads to the observed effect. Perhaps the URE enhances a student’s confidence in her ability to succeed in her chosen field or deepens her commitment to the field by exposing her to the joy of discovery. Through these pathways that act on the participant’s purposive behavior, the URE enhances the likelihood that she persists in STEM. The question for the researcher then becomes what research design would provide support for this hypothesis of mechanism over other candidate explanations for why the URE is a causal factor in STEM persistence.

The committee has examined the literature and finds a rich descriptive foundation for testable hypotheses about the effects of UREs on student outcomes. These studies are encouraging; a few of them have generated evidence that a URE can be a positive causal factor in the progression and persistence of STEM students. The weight of the evidence has been descriptive; it relies primarily on self-reports of short-term gains by students who chose to participate in UREs and does not include direct measures of changes in the students’ knowledge, skills, or other measures of success across comparable groups of students who did and did not participate in UREs.

While acknowledging the scarcity of strong causal evidence on the benefits of UREs, the committee takes seriously the weight of the descriptive evidence. Many of the published studies of UREs show that students who participate report a range of benefits, such as increased understanding of the research process, encouragement to persist in STEM, and support that helps them sustain their identity as researchers and continue with their

plans to enroll in a graduate program in STEM (see Chapter 4 ). These are effective starting points for causal studies.

Conclusion 3: Studies focused on students from historically underrepresented groups indicate that participation in UREs improves their persistence in STEM and helps to validate their disciplinary identity.

Various UREs have been specifically designed to increase the number of historically underrepresented students who go on to become STEM majors and ultimately STEM professionals. While many UREs offer one or more supplemental opportunities to support students’ academic or social success, such as mentoring, tutoring, summer bridge programs, career or graduate school workshops, and research-oriented seminars, those designed for underrepresented students appear to emphasize such features as integral and integrated components of the program. In particular, studies of undergraduate research programs targeting underrepresented minority students have begun to document positive outcomes such as degree completion and persistence in interest in STEM careers ( Byars-Winston et al., 2015 ; Chemers et al., 2011 ; Jones et al., 2010 ; Nagda et al., 1998 ; Schultz et al., 2011 ). Most of these studies collected data on apprentice-style UREs, in which the undergraduate becomes a functioning member of a research group along with the graduate students, postdoctoral fellows, and mentor.

Recommendation 1: Researchers with expertise in education research should conduct well-designed studies in collaboration with URE program directors to improve the evidence base about the processes and effects of UREs. This research should address how the various components of UREs may benefit students. It should also include additional causal evidence for the individual and additive effects of outcomes from student participation in different types of UREs. Not all UREs need be designed to undertake this type of research, but it would be very useful to have some UREs that are designed to facilitate these efforts to improve the evidence base .

As the focus on UREs has grown, so have questions about their implementation. Many articles have been published describing specific UREs (see Chapter 2 ). Large amounts of research have also been undertaken to explore more generally how students learn, and the resulting body of evidence has led to the development and adoption of “active learning” strategies and experiences. If a student in a URE has an opportunity to, for example, analyze new data or to reformulate a hypothesis in light of the student’s analysis, this activity fits into the category that is described as active learning. Surveys of student participants and unpublished evaluations pro-

vide additional information about UREs but do not establish causation or determine the mechanism(s). Consequently, little is currently known about the mechanisms of precisely how UREs work and which aspects of UREs are most powerful. Important components that have been reported include student ownership of the URE project, time to tackle a question iteratively, and opportunities to report and defend one’s conclusions ( Hanauer and Dolan, 2014 ; Thiry et al., 2011 ).

There are many unanswered questions and opportunities for further research into the role and mechanism of UREs. Attention to research design as UREs are planned is important; more carefully designed studies are needed to understand the ways that UREs influence a student’s education and to evaluate the outcomes that have been reported for URE participants. Appropriate studies, which include matched samples or similar controls, would facilitate research on the ways that UREs benefit students, enabling both education researchers and implementers of UREs to determine optimal features for program design and giving the community a more robust understanding of how UREs work.

See the research agenda ( Chapter 7 ) for specific recommendations about research topics and approaches.

Recommendation 2: Funders should provide appropriate resources to support the design, implementation, and analysis of some URE programs that are specifically designed to enable detailed research establishing the effects on participant outcomes and on other variables of interest such as the consequences for mentors or institutions.

Not all UREs need to be the subject of extensive study. In many cases, a straightforward evaluation is adequate to determine whether the URE is meeting its goals. However, to achieve more widespread improvement in both the types and quality of the UREs offered in the future, additional evidence about the possible causal effects and mechanisms of action of UREs needs to be systematically collected and disseminated. This includes a better understanding of the implementation differences for a variety of institutions (e.g., community colleges, primarily undergraduate institutions, research universities) to ensure that the desired outcomes can translate across settings. Increasing the evidence about precisely how UREs work and which aspects of UREs are most powerful will require careful attention to study design during planning for the UREs.

Not all UREs need to be designed to achieve this goal; many can provide opportunities to students by relying on pre-existing knowledge and iterative improvement as that knowledge base grows. However, for the knowledge base to grow, funders must provide resources for some URE designers and social science researchers to undertake thoughtful and well-planned studies

on causal and mechanistic issues. This will maximize the chances for the creation and dissemination of information that can lead to the development of sustainable and effective UREs. These studies can result from a partnership formed as the URE is designed and funded, or evaluators and social scientists could identify promising and/or effective existing programs and then raise funds on their own to support the study of those programs to answer the questions of interest. In deciding upon the UREs that are chosen for these extensive studies, it will be important to consider whether, collectively, they are representative of UREs in general. For example, large and small UREs at large and small schools targeted at both introductory and advanced students and topics should be studied.

CONSTRUCTION OF URES

Conclusion 4: The committee was unable to find evidence that URE designers are taking full advantage of the information available in the education literature on strategies for designing, implementing, and evaluating learning experiences. STEM faculty members do not generally receive training in interpreting or conducting education research. Partnerships between those with expertise in education research and those with expertise in implementing UREs are one way to strengthen the application of evidence on what works in planning and implementing UREs.

As discussed in Chapters 3 and 4 , there is an extensive body of literature on pedagogy and how people learn; helping STEM faculty to access the existing literature and incorporate those concepts as they design UREs could improve student experiences. New studies that specifically focus on UREs may provide more targeted information that could be used to design, implement, sustain, or scale up UREs and facilitate iterative improvements. Information about the features of UREs that elicit particular outcomes or best serve certain populations of students should be considered when implementing a new instantiation of an existing model of a URE or improving upon an existing URE model.

Conclusion 5: Evaluations of UREs are often conducted to inform program providers and funders; however, they may not be accessible to others. While these evaluations are not designed to be research studies and often have small sample sizes, they may contain information that could be useful to those initiating new URE programs and those refining UREs. Increasing access to these evaluations and to the accumulated experience of the program providers may enable URE designers and implementers to build upon knowledge gained from earlier UREs.

As discussed in Chapter 1 , the committee searched for evaluations of URE programs in several different ways but was not able to locate many published evaluations to study. Although some evaluations were found in the literature, the committee could not determine a way to systematically examine the program evaluations that have been prepared. The National Science Foundation and other funders generally require grant recipients to submit evaluation data, but that information is not currently aggregated and shared publicly, even for programs that are using a common evaluation tool. 1

Therefore, while program evaluation likely serves a useful role in providing descriptive data about a program for the institutions and funders supporting the program, much of the summative evaluation work that has been done to date adds relatively little to the broader knowledge base and overall conversations around undergraduate research. Some of the challenges of evaluation include budget and sample size constraints.

Similarly, it is difficult for designers of UREs to benefit systematically from the work of others who have designed and run UREs in the past because of the lack of an easy and consistent mechanism for collecting, analyzing, and sharing data. If these evaluations were more accessible they might be beneficial to others designing and evaluating UREs by helping them to gather ideas and inspiration from the experiences of others. A few such stories are provided in this report, and others can be found among the many resources offered by the Council on Undergraduate Research 2 and on other websites such as CUREnet. 3

Recommendation 3: Designers of UREs should base their design decisions on sound evidence. Consultations with education and social science researchers may be helpful as designers analyze the literature and make decisions on the creation or improvement of UREs. Professional development materials should be created and made available to faculty. Educational and disciplinary societies should consider how they can provide resources and connections to those working on UREs.

Faculty and other organizers of UREs can use the expanding body of scholarship as they design or improve the programs and experiences offered to their students. URE designers will need to make decisions about how to adapt approaches reported in the literature to make the programs they develop more suitable to their own expertise, student population(s), and available resources. Disciplinary societies and other national groups, such as those focused on improving pedagogy, can play important roles in

___________________

1 Personal knowledge of Janet Branchaw, member of the Committee on Strengthening Research Experiences for Undergraduate STEM Students.

2 See www.cur.org [November 2016].

3 See ( curenet.cns.utexas.edu ) [November 2016].

bringing these issues to the forefront through events at their national and regional meetings and through publications in their journals and newsletters. They can develop repositories for various kinds of resources appropriate for their members who are designing and implementing UREs. The ability to travel to conferences and to access and discuss resources created by other individuals and groups is a crucial aspect of support (see Recommendations 7 and 8 for further discussion).

See Chapter 8 for specific questions to consider when one is designing or implementing UREs.

CURRENT OFFERINGS

Conclusion 6: Data at the institutional, state, or national levels on the number and type of UREs offered, or who participates in UREs overall or at specific types of institutions, have not been collected systematically. Although the committee found that some individual institutions track at least some of this type of information, we were unable to determine how common it is to do so or what specific information is most often gathered.

There is no one central database or repository that catalogs UREs at institutions of higher education, the nature of the research experiences they provide, or the relevant demographics (student, departmental, and institutional). The lack of comprehensive data makes it difficult to know how many students participate in UREs; where UREs are offered; and if there are gaps in access to UREs across different institutional types, disciplines, or groups of students. One of the challenges of describing the undergraduate research landscape is that students do not have to be enrolled in a formal program to have a research experience. Informal experiences, for example a work-study job, are typically not well documented. Another challenge is that some students participate in CUREs or other research experiences (such as internships) that are not necessarily labeled as such. Institutional administrators may be unaware of CUREs that are already part of their curriculum. (For example, establishment of CUREs may be under the purview of a faculty curriculum committee and may not be recognized as a distinct program.) Student participation in UREs may occur at their home institution or elsewhere during the summer. Therefore, it is very difficult for a science department, and likely any other STEM department, to know what percentage of their graduating majors have had a research experience, let alone to gather such information on students who left the major. 4

4 This point was made by Marco Molinaro, University of California, Davis, in a presentation to the Committee on Strengthening Research Experience for Undergraduate STEM Students, September 16, 2015.

Conclusion 7: While data are lacking on the precise number of students engaged in UREs, there is some evidence of a recent growth in course-based undergraduate research experiences (CUREs), which engage a cohort of students in a research project as part of a formal academic experience.

There has been an increase in the number of grants and the dollar amount spent on CUREs over the past decade (see Chapter 3 ). CUREs can be particularly useful in scaling UREs to reach a much larger population of students ( Bangera and Brownell, 2014 ). By using a familiar mechanism—enrollment in a course—a CURE can provide a more comfortable route for students unfamiliar with research to gain their first experience. CUREs also can provide such experiences to students with diverse backgrounds, especially if an institution or department mandates participation sometime during a student’s matriculation. Establishing CUREs may be more cost-effective at schools with little on-site research activity. However, designing a CURE is a new and time-consuming challenge for many faculty members. Connecting to nationally organized research networks can provide faculty with helpful resources for the development of a CURE based around their own research or a local community need, or these networks can link interested faculty to an ongoing collaborative project. Collaborative projects can provide shared curriculum, faculty professional development and community, and other advantages when starting or expanding a URE program. See the discussion in the report from a convocation on Integrating Discovery-based Research into the Undergraduate Curriculum ( National Academies of Sciences, Engineering, and Medicine, 2015 ).

Recommendation 4: Institutions should collect data on student participation in UREs to inform their planning and to look for opportunities to improve quality and access.

Better tracking of student participation could lead to better assessment of outcomes and improved quality of experience. Such metrics could be useful for both prospective students and campus planners. An integrated institutional system for research opportunities could facilitate the creation of tiered research experiences that allow students to progress in skills and responsibility and create support structures for students, providing, for example, seminars in communications, safety, and ethics for undergraduate researchers. Institutions could also use these data to measure the impact of UREs on student outcomes, such as student success rates in introductory courses, retention in STEM degree programs, and completion of STEM degrees.

While individual institutions may choose to collect additional information depending on their goals and resources, relevant student demographics

and the following design elements would provide baseline data. At a minimum, such data should include

• Type of URE;
• Each student’s discipline;
• Duration of the experience;
• Hours spent per week;
• When the student began the URE (e.g., first year, capstone);
• Compensation status (e.g., paid, unpaid, credit); and
• Location and format (e.g., on home campus, on another campus, internship, co-op).

National aggregation of some of the student participation variables collected by various campuses might be considered by funders. The existing Integrated Postsecondary Education Data System database, organized by the National Center for Education Statistics at the U.S. Department of Education, may be a suitable repository for certain aspects of this information.

Recommendation 5: Administrators and faculty at all types of colleges and universities should continually and holistically evaluate the range of UREs that they offer. As part of this process, institutions should:

• Consider how best to leverage available resources (including off-campus experiences available to students and current or potential networks or partnerships that the institution may form) when offering UREs so that they align with their institution’s mission and priorities;
• Consider whether current UREs are both accessible and welcoming to students from various subpopulations across campus (e.g., historically underrepresented students, first generation college students, those with disabilities, non-STEM majors, prospective kindergarten-through-12th-grade teachers); and
• Gather and analyze data on the types of UREs offered and the students who participate, making this information widely available to the campus community and using it to make evidence-based decisions about improving opportunities for URE participation. This may entail devising or implementing systems for tracking relevant data (see Conclusion 4 ).

Resources available for starting, maintaining, and expanding UREs vary from campus to campus. At some campuses, UREs are a central focus and many resources are devoted to them. At other institutions—for example, many community colleges—UREs are seen as extra, and new resources may be required to ensure availability of courses and facilities. Resource-

constrained institutions may need to focus more on ensuring that students are aware of potential UREs that already exist on campus and elsewhere in near proximity to campus. All institutional discussions about UREs must consider both the financial resources and physical resources (e.g., laboratories, field stations, engineering design studios) required, while remembering that faculty time is a crucial resource. The incentives and disincentives for faculty to spend time on UREs are significant. Those institutions with an explicit mission to promote undergraduate research may provide more recognition and rewards to departments and faculty than those with another focus. The culture of the institution with respect to innovation in pedagogy and support for faculty development also can have a major influence on the extent to which UREs are introduced or improved.

Access to UREs may vary across campus and by department, and participation in UREs may vary across student groups. It is important for campuses to consider the factors that may facilitate or discourage students from participation in UREs. Inconsistent procedures or a faculty preference for students with high grades or previous research experience may limit options for some student populations.

UREs often grow based on the initiative of individual faculty members and other personnel, and an institution may not have complete or even rudimentary knowledge of all of the opportunities available or whether there are gaps or inconsistencies in its offerings. A uniform method for tracking the UREs available on a given campus would be useful to students and would provide a starting point for analyzing the options. Tracking might consist of notations in course listings and, where feasible, on student transcripts. Analysis might consider the types of UREs offered, the resources available to each type of URE, and variations within or between various disciplines and programs. Attention to whether all students or groups of students have appropriate access to UREs would foster consideration of how to best allocate resources and programming on individual campuses, in order to focus resources and opportunities where they are most needed.

Conclusion 8: The quality of mentoring can make a substantial difference in a student’s experiences with research. However, professional development in how to be a good mentor is not available to many faculty or other prospective mentors (e.g., graduate students, postdoctoral fellows).

Engagement in quality mentored research experiences has been linked to self-reported gains in research skills and productivity as well as retention in STEM (see Chapter 5 ). Quality mentoring in UREs has been shown

to increase persistence in STEM for historically underrepresented students ( Hernandez et al., 2016 ). In addition, poor mentoring during UREs has been shown to decrease retention of students ( Hernandez et al., 2016 ).

More general research on good mentoring in the STEM environment has been positively associated with self-reported gains in identity as a STEM researcher, a sense of belonging, and confidence to function as a STEM researcher ( Byars-Winston et al., 2015 ; Chemers et al., 2011 ; Pfund et al., 2016 ; Thiry et al., 2011 ). The frequency and quality of mentee-mentor interactions has been associated with students’ reports of persistence in STEM, with mentoring directly or indirectly improving both grades and persistence in college. For students from historically underrepresented ethnic/racial groups, quality mentoring has been associated with self-reported enhanced recruitment into graduate school and research-related career pathways ( Byars-Winston et al., 2015 ). Therefore, it is important to ensure that faculty and mentors receive the proper development of mentoring skills.

Recommendation 6: Administrators and faculty at colleges and universities should ensure that all who mentor undergraduates in research experiences (this includes faculty, instructors, postdoctoral fellows, graduate students, and undergraduates serving as peer mentors) have access to appropriate professional development opportunities to help them grow and succeed in this role.

Although many organizations recognize effective mentors (e.g., the National Science Foundation’s Presidential Awards for Excellence in Science, Mathematics, and Engineering Mentoring), there currently are no standard criteria for selecting, evaluating, or recognizing mentors specifically for UREs. In addition, there are no requirements that mentors meet some minimum level of competency before engaging in mentoring or participate in professional development to obtain a baseline of knowledge and skills in mentoring, including cultural competence in mentoring diverse groups of students. Traditionally, the only experience required for being a mentor is having been mentored, regardless of whether the experience was negative or positive ( Handelsman et al., 2005 ; Pfund et al., 2015 ). Explicit consideration of how the relationships are formed, supported, and evaluated can improve mentor-mentee relationships. To ensure that the mentors associated with a URE are prepared appropriately, thereby increasing the chances of a positive experience for both mentors and mentees, all prospective mentors should prepare for their role. Available resources include the Entering Mentoring course (see Pfund et al., 2015 ) and the book Successful STEM Mentoring Initiative for Underrepresented Students ( Packard, 2016 ).

A person who is an ineffective mentor for one student might be inspiring for another, and the setting in which the mentoring takes place (e.g., a CURE or apprentice-style URE, a laboratory or field-research environment) may also influence mentor effectiveness. Thus, there should be some mechanism for monitoring such relationships during the URE, or there should be opportunity for a student who is unhappy with the relationship to seek other mentors. Indeed, cultivating a team of mentors with different experiences and expertise may be the best strategy for any student. A parallel volume to the Entering Mentoring curriculum mentioned above, Entering Research Facilitator’s Manual ( Branchaw et al., 2010 ), is designed to help students with their research mentor-mentee relationships and to coach them on building teams of mentors to guide them. As mentioned in Chapter 5 , the Entering Research curriculum also contains information designed to support a group of students as they go through their first apprentice-style research experience, each working in separate research groups and also meeting together as a cohort focused on learning about research.

PRIORITIES FOR THE FUTURE

Conclusion 9: The unique assets, resources, priorities, and constraints of the department and institution, in addition to those of individual mentors, impact the goals and structures of UREs. Schools across the country are showing considerable creativity in using unique resources, repurposing current assets, and leveraging student enthusiasm to increase research opportunities for their students.

Given current calls for UREs and the growing conversation about their benefits, an increasing number of two- and four-year colleges and universities are increasing their efforts to support undergraduate research. Departments, institutions, and individual faculty members influence the precise nature of UREs in multiple ways and at multiple levels. The physical resources available, including laboratories, field stations, and engineering design studios and testing facilities, make a difference, as does the ability to access resources in the surrounding community (including other parts of the campus). Institutions with an explicit mission to promote undergraduate research may provide more time, resources (e.g., financial, support personnel, space, equipment), and recognition and rewards to departments and faculty in support of UREs than do institutions without that mission. The culture of the institution with respect to innovation in pedagogy and support for faculty development also affects the extent to which UREs are introduced or improved.

Development of UREs requires significant time and effort. Whether or not faculty attempt to implement UREs can depend on whether departmental

or institutional reward and recognition systems compensate for or even recognize the time required to initiate and implement them. The availability of national consortia can help to alleviate many of the time and logistical problems but not those obstacles associated with recognition and resources.

It will be harder for faculty to find the time to develop UREs at institutions where they are required to teach many courses per semester, although in some circumstances faculty can teach CUREs that also advance their own research ( Shortlidge et al., 2016 ). Faculty at community colleges generally have the heaviest teaching expectations, little or no expectations or incentives to maintain a research program, limited access to lab or design space or to scientific and engineering journals, and few resources to undertake any kind of a research program. These constraints may limit the extent to which UREs can be offered to the approximately 40 percent of U.S. undergraduates who are enrolled in the nation’s community colleges (which collectively also serve the highest percentage of the nation’s underrepresented students). 5

Recommendation 7: Administrators and faculty at all types of colleges and universities should work together within and, where feasible, across institutions to create a culture that supports the development of evidence-based, iterative, and continuous refinement of UREs, in an effort to improve student learning outcomes and overall academic success. This should include the development, evaluation, and revision of policies and practices designed to create a culture supportive of the participation of faculty and other mentors in effective UREs. Policies should consider pedagogy, professional development, cross-cultural awareness, hiring practices, compensation, promotion (incentives, rewards), and the tenure process.

Colleges and universities that would like to expand or improve the UREs offered to their students should consider the campus culture and climate and the incentives that affect faculty choices. Those campuses that cultivate an environment supportive of the iterative and continuous refinement of UREs and that offer incentives for evaluation and evidence-based improvement of UREs seem more likely to sustain successful programs. Faculty and others who develop and implement UREs need support to be able to evaluate their courses or programs and to analyze evidence to make decisions about URE design. This kind of support may be fostered by expanding the mission of on-campus centers for learning and teaching to focus more on UREs or by providing incentives for URE developers from the natural sciences and engineering to collaborate with colleagues in the social sciences or colleges of education with expertise in designing studies

5 See http://nces.ed.gov/programs/coe/indicator_cha.asp [November 2016].

involving human subjects. Supporting closer communication between URE developers and the members of the campus Institutional Review Board may help projects to move forward more seamlessly. Interdepartmental and intercampus connections (especially those between two- and four-year institutions) can be valuable for linking faculty with the appropriate resources, colleagues, and diverse student populations. Faculty who have been active in professional development on how students learn in the classroom may have valuable experiences and expertise to share.

The refinement or expansion of UREs should build on evidence from data on student participation, pedagogy, and outcomes, which are integral components of the original design. As UREs are validated and refined, institutions should make efforts to facilitate connections among different departments and disciplines, including the creation of multidisciplinary UREs. Student engagement in learning in general, and with UREs more specifically, depends largely on the culture of the department and the institution and on whether students see their surroundings as inclusive and energetic places to learn and thrive. A study that examined the relationship between campus missions and the five benchmarks for effective educational practice (measured by the National Survey of Student Engagement) showed that different programs, policies, and approaches may work better, depending on the institution’s mission ( Kezar and Kinzie, 2006 ).

The Council on Undergraduate Research (2012) document Characteristics of Excellence in Undergraduate Research outlines several best practices for UREs based on the apprenticeship model (see Chapter 8 ). That document is not the result of a detailed analysis of the evidence but is based on the extensive experiences and expertise of the council’s members. It suggests that undergraduate research should be a normal part of the undergraduate experience regardless of the type of institution. It also identifies changes necessary to include UREs as part of the curriculum and culture changes necessary to support curricular reform, co-curricular activities, and modifications to the incentives and rewards for faculty to engage with undergraduate research. In addition, professional development opportunities specifically designed to help improve the pedagogical and mentoring skills of instructional staff in using evidence-based practices can be important for a supportive learning culture.

Recommendation 8: Administrators and faculty at all types of colleges and universities should work to develop strong and sustainable partnerships within and between institutions and with educational and professional societies for the purpose of sharing resources to facilitate the creation of sustainable URE programs.

Networks of faculty, institutions, regionally and nationally coordinated URE initiatives, professional societies, and funders should be strengthened

to facilitate the exchange of evidence and experience related to UREs. These networks could build on the existing work of professional societies that assist faculty with pedagogy. They can help provide a venue for considering the policy context and larger implications of increasing the number, size, and scope of UREs. Such networks also can provide a more robust infrastructure, to improve the sustainability and expansion of URE opportunities. The sharing of human, financial, scientific, and technical resources can strengthen the broad implementation of effective, high-quality, and more cost-efficient UREs. It may be especially important for community colleges and minority-serving institutions to engage in partnerships in order to expand the opportunities for undergraduates (both transfer and technical students) to participate in diverse UREs (see discussion in National Academies of Sciences, Engineering, and Medicine, 2015 , and Elgin et al., 2016 ). Consortia can facilitate the sharing of resources across disciplines and departments within the same institution or at different institutions, organizations, and agencies. Consortia that employ research methodologies in common can share curriculum, research data collected, and common assessment tools, lessening the time burden for individual faculty and providing a large pool of students from which to assess the efficacy of individual programs.

Changes in the funding climate can have substantial impacts on the types of programs that exist, iterative refinement of programs, and whether and how programs might be expanded to broaden participation by more undergraduates. For those institutions that have not yet established URE programs or are at the beginning phases of establishing one, mechanisms for achieving success and sustainability may include increased institutional ownership of programs of undergraduate research, development of a broad range of programs of different types and funding structures, formation of undergraduate research offices or repurposing some of the responsibilities and activities of those which already exist, and engagement in community promotion and dissemination of student accomplishments (e.g., student symposia, support for undergraduate student travel to give presentations at professional meetings).

Over time, institutions must develop robust plans for ensuring the long-term sustained funding of high-quality UREs. Those plans should include assuming that more fiscal responsibility for sustaining such efforts will be borne by the home institution as external support for such efforts decreases and ultimately ends. Building UREs into the curriculum and structure of a department’s courses and other programs, and thus its funding model, can help with sustainability. Partnerships with nonprofit organizations and industry, as well as seeking funding from diverse agencies, can also facilitate programmatic sustainability, especially if the UREs they fund can also support the mission and programs of the funders (e.g., through research internships or through CUREs that focus on community-

based research questions and challenges). Partnerships among institutions also may have greater potential to study and evaluate student outcomes from URE participation across broader demographic groups and to reduce overall costs through the sharing of administrative or other resources (such as libraries, microscopes, etc.).

Bangera, G., and Brownell, S.E. (2014). Course-based undergraduate research experiences can make scientific research more inclusive. CBE–Life Sciences Education , 13 (4), 602-606.

Branchaw, J.L., Pfund, C., and Rediske, R. (2010) Entering Research Facilitator’s Manual: Workshops for Students Beginning Research in Science . New York: Freeman & Company.

Byars-Winston, A.M., Branchaw, J., Pfund, C., Leverett, P., and Newton, J. (2015). Culturally diverse undergraduate researchers’ academic outcomes and perceptions of their research mentoring relationships. International Journal of Science Education , 37 (15), 2,533-2,554.

Chemers, M.M., Zurbriggen, E.L., Syed, M., Goza, B.K., and Bearman, S. (2011). The role of efficacy and identity in science career commitment among underrepresented minority students. Journal of Social Issues , 67 (3), 469-491.

Council on Undergraduate Research. (2012). Characteristics of Excellence in Undergraduate Research . Washington, DC: Council on Undergraduate Research.

Elgin, S.C.R., Bangera, G., Decatur, S.M., Dolan, E.L., Guertin, L., Newstetter, W.C., San Juan, E.F., Smith, M.A., Weaver, G.C., Wessler, S.R., Brenner, K.A., and Labov, J.B. 2016. Insights from a convocation: Integrating discovery-based research into the undergraduate curriculum. CBE–Life Sciences Education, 15 , 1-7.

Hanauer, D., and Dolan, E. (2014) The Project Ownership Survey: Measuring differences in scientific inquiry experiences, CBE–Life Sciences Education , 13 , 149-158.

Handelsman, J., Pfund, C., Lauffer, S.M., and Pribbenow, C.M. (2005). Entering Mentoring . Madison, WI: The Wisconsin Program for Scientific Teaching.

Hernandez, P.R., Estrada, M., Woodcock, A., and Schultz, P.W. (2016). Protégé perceptions of high mentorship quality depend on shared values more than on demographic match. Journal of Experimental Education. Available: http://www.tandfonline.com/doi/full/10.1080/00220973.2016.1246405 [November 2016].

Jones, P., Selby, D., and Sterling, S.R. (2010). Sustainability Education: Perspectives and Practice Across Higher Education . New York: Earthscan.

Kezar, A.J., and Kinzie, J. (2006). Examining the ways institutions create student engagement: The role of mission. Journal of College Student Development , 47 (2), 149-172.

National Academies of Sciences, Engineering, and Medicine. (2015). Integrating Discovery-Based Research into the Undergraduate Curriculum: Report of a Convocation . Washington, DC: National Academies Press.

Nagda, B.A., Gregerman, S.R., Jonides, J., von Hippel, W., and Lerner, J.S. (1998). Undergraduate student-faculty research partnerships affect student retention. Review of Higher Education, 22 , 55-72. Available: http://scholar.harvard.edu/files/jenniferlerner/files/nagda_1998_paper.pdf [February 2017].

Packard, P. (2016). Successful STEM Mentoring Initiatives for Underrepresented Students: A Research-Based Guide for Faculty and Administrators . Sterling, VA: Stylus.

Pfund, C., Branchaw, J.L., and Handelsman, J. (2015). Entering Mentoring: A Seminar to Train a New Generation of Scientists (2nd ed). New York: Macmillan Learning.

Pfund, C., Byars-Winston, A., Branchaw, J.L., Hurtado, S., and Eagan, M.K. (2016). Defining attributes and metrics of effective research mentoring relationships. AIDS and Behavior, 20 , 238-248.

Schultz, P.W., Hernandez, P.R., Woodcock, A., Estrada, M., Chance, R.C., Aguilar, M., and Serpe, R.T. (2011). Patching the pipeline reducing educational disparities in the sciences through minority training programs. Educational Evaluation and Policy Analysis , 33 (1), 95-114.

Shortlidge, E.E., Bangera, G., and Brownell, S.E. (2016). Faculty perspectives on developing and teaching course-based undergraduate research experiences. BioScience, 66 (1), 54-62.

Thiry, H., Laursen, S.L., and Hunter, A.B. (2011). What experiences help students become scientists? A comparative study of research and other sources of personal and professional gains for STEM undergraduates. Journal of Higher Education, 82 (4), 358-389.

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Undergraduate research has a rich history, and many practicing researchers point to undergraduate research experiences (UREs) as crucial to their own career success. There are many ongoing efforts to improve undergraduate science, technology, engineering, and mathematics (STEM) education that focus on increasing the active engagement of students and decreasing traditional lecture-based teaching, and UREs have been proposed as a solution to these efforts and may be a key strategy for broadening participation in STEM. In light of the proposals questions have been asked about what is known about student participation in UREs, best practices in UREs design, and evidence of beneficial outcomes from UREs.

Undergraduate Research Experiences for STEM Students provides a comprehensive overview of and insights about the current and rapidly evolving types of UREs, in an effort to improve understanding of the complexity of UREs in terms of their content, their surrounding context, the diversity of the student participants, and the opportunities for learning provided by a research experience. This study analyzes UREs by considering them as part of a learning system that is shaped by forces related to national policy, institutional leadership, and departmental culture, as well as by the interactions among faculty, other mentors, and students. The report provides a set of questions to be considered by those implementing UREs as well as an agenda for future research that can help answer questions about how UREs work and which aspects of the experiences are most powerful.

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Experimental Research: A Comprehensive Guide (2024)

 Experimental research is a scientific method used to test a hypothesis or study the cause-and-effect relationship between variables. In this type of research, the researcher manipulates one variable, called the independent variable, to observe the effect on another variable, called the dependent variable. Experimental research aims to establish a causal relationship between variables and eliminate alternative explanations for the observed results.

Experimental research is widely used in various fields, including psychology, medicine, engineering, and social sciences. It is considered the most rigorous research method because it allows the researcher to control the variables and minimize the influence of extraneous factors. However, experimental research can be time-consuming, expensive, and sometimes unethical, especially involving human subjects. Therefore, researchers must carefully design their experiments and follow ethical guidelines to ensure the safety and well-being of their participants.

Overall, experimental research is crucial in advancing scientific knowledge and understanding the world. By following a systematic and rigorous approach, researchers can test their hypotheses and draw valid conclusions about the cause-and-effect relationships between variables. However, it is important to recognize experimental research’s limitations and challenges and use it in conjunction with other research methods to gain a more comprehensive understanding of complex phenomena.

Understanding Experimental Research

Experimental research is a scientific approach that involves manipulating one or more independent variables to observe the effect on a dependent variable. It is a research method widely used in various fields, including psychology, education, and medicine.

The primary goal of experimental research is to establish a cause-and-effect relationship between the independent and dependent variables. This is done by controlling all other variables that may affect the study’s outcome. By manipulating the independent variable, researchers can determine whether it significantly affects the dependent variable.

To conduct experimental research, researchers must first identify a research problem that can be addressed through experimentation. They must then develop a specific hypothesis that can be tested by manipulating the independent variable. The hypothesis should be testable and falsifiable, meaning that it can be proven false if the study’s results do not support it.

Once the hypothesis has been developed, researchers must design the experiment, including the selection of participants, the manipulation of the independent variable, and the measurement of the dependent variable. Researchers must also determine the appropriate research design, such as a pretest-posttest design or a between-subjects design.

During the experiment, researchers must carefully control all other variables that may affect the study’s outcome. This is done to ensure that any observed effects can be attributed to the manipulation of the independent variable and not to other factors.

After the experiment, researchers must analyze the data and draw conclusions based on the results. They must also consider the study’s limitations and the implications of the findings for future research.

Overall, experimental research is a powerful tool for investigating cause-and-effect relationships in various fields. By carefully controlling all variables except the independent variable, researchers can determine whether it significantly affects the dependent variable. However, it is important to recognize the limitations of experimental research and to consider alternative research methods when appropriate.

Types of Experimental Research

Experimental research is a type of research that involves manipulating one or more variables to observe the effect on another variable. Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types: True Experimental Research, Quasi-Experimental Research, and Pre-Experimental Research.

True Experimental Research

True experimental research is the most rigorous type of experimental research design. In this type of design, the researcher randomly assigns participants to either a control or experimental group. The control group receives no treatment, while the experimental group receives the treatment being studied. The researcher then measures the effect of the treatment by comparing the outcomes of the two groups. True experimental research is the gold standard of experimental research designs because it allows the researcher to establish a cause-and-effect relationship between the independent and dependent variables.

Quasi-Experimental Research

Quasi-experimental research is a type of research design that does not involve randomly assigning participants to groups. Instead, the researcher uses an existing group of participants and compares the outcomes of two or more groups. Quasi-experimental research is less rigorous than true experimental research because it does not allow the researcher to establish a cause-and-effect relationship between the independent and dependent variables. However, it is still useful in situations where true experimental research is not possible or ethical.

Pre-Experimental Research

Pre-experimental research design is the most basic type of experimental research design. In this type of design, the researcher observes a group or many groups after implementing the research’s cause and effect factors. Pre-experimental research design is useful when the researcher is interested in studying the effect of a particular intervention but cannot use random assignment of participants to groups. Pre-experimental research design is less rigorous than true experimental research and quasi-experimental research because it does not allow the researcher to establish a cause-and-effect relationship between the independent and dependent variables.

In conclusion, experimental research is a powerful method that allows researchers to establish cause-and-effect relationships between variables. True experimental research is the most rigorous type of experimental research design, while quasi-experimental and pre-experimental research is less rigorous but still useful in certain situations.

Components of Experimental Research

Experimental research is a scientific method of investigation that involves manipulating one or more variables to observe the effect on another variable. The following are the key components of experimental research:

Independent Variables

An independent variable is a variable that the researcher can manipulate. The variable is being tested to determine the effect on the dependent variable. For example, in a study to determine the effect of caffeine on memory, caffeine is the independent variable.

Dependent Variables

A dependent variable is a variable that is being measured in the study. It is the variable that is affected by the independent variable. In the example above, memory is the dependent variable.

Control Group

A control group is a group of participants who are not exposed to the independent variable. The control group is used to compare the experimental group’s results to determine if the independent variable had an effect on the dependent variable.

A hypothesis is a statement that predicts the relationship between the independent and dependent variables. It is a tentative explanation for the observed phenomenon. The hypothesis should be testable and falsifiable.

Experimental research is a powerful tool for investigating cause-and-effect relationships between variables. It allows researchers to manipulate and control extraneous variables to determine the effect on the dependent variable. Using a control group, researchers can determine if the results are due to the independent variable or other factors.

In summary, experimental research involves manipulating one or more variables to observe the effect on another variable. The key components of experimental research include independent variables, dependent variables, control group, and hypothesis. These components are essential for designing a valid and reliable experiment.

Conducting Experimental Research

Experimental research is a scientific method that involves manipulating one or more variables to observe the effects on another variable. It is a powerful tool that allows researchers to establish cause-and-effect relationships between variables. Here are the key steps involved in conducting experimental research:

Data Collection

Data collection is a crucial step in experimental research. Researchers must ensure that data is collected accurately and reliably. Several data collection methods include surveys, interviews, and observations. The choice of data collection method depends on the research question and the data collection type.

Random Assignment

Random assignment is a method used to randomly assign participants to different groups in an experiment. This ensures that each participant has an equal chance of being assigned to any of the groups, reducing the likelihood of bias. Random assignment is essential in experimental research because it helps control individual participant differences.

Research Design

The research design must be carefully planned and executed to ensure the results are valid and reliable. The design should include an experimental group and a control group, each with the same set of variables except for the one being manipulated. The experimental group receives the experimental treatments, while the control group does not. The research design must also include measures to control for extraneous variables that could affect the results.

Experimental research can be conducted using different research designs, including pre-experimental, quasi-experimental, and true experimental designs. The choice of research design depends on the research question and the level of control required.

In summary, conducting experimental research involves:

• Collecting data accurately.
• Randomly assigning participants to groups.
• Carefully planning and executing the research design.

By following these steps, researchers can establish cause-and-effect relationships between variables and make valid conclusions.

Advantages and Disadvantages of Experimental Research

Experimental research offers several advantages, making it a popular research method in various fields. One of the most significant advantages of experimental research is that it provides researchers with a high level of control. By isolating specific variables, it becomes possible to determine if a potential outcome is viable. Each variable can be controlled independently or in different combinations to study what possible outcomes are available for a product, theory, or idea 

Another advantage of experimental research is that it allows researchers to establish a cause-and-effect relationship between variables. This is because experimental research involves manipulating one or more variables to see how they affect the outcome. By controlling all other variables, researchers can determine if a change in one variable causes a change in the outcome.

Experimental research also allows researchers to replicate their findings. By following the same experimental design, other researchers can carry out the same study to see if they obtain similar results. This makes it possible to verify the validity of experimental findings.

Disadvantages

Despite its advantages, experimental research also has some disadvantages that researchers must consider. One of the main disadvantages of experimental research is that it may not apply to real-world situations. This is because experimental research typically takes place in a controlled environment, which may not represent real-world conditions. As a result, experimental research findings may not be generalizable to the real world [4].

Another disadvantage of experimental research is that it may sometimes be unethical. This is because experimental research often involves manipulating variables, which may negatively affect participants. Researchers must ensure that the benefits of the research outweigh the potential risks to participants [5].

Finally, experimental research can be time-consuming and expensive. This is because experimental research requires careful planning and execution to ensure the results are valid and reliable. Additionally, experimental research often requires specialized equipment and facilities, which can be costly [6].

In summary, experimental research provides researchers with a high level of control, allows them to establish cause-and-effect relationships, and enables them to replicate their findings. However, it may not apply to real-world situations, may be unethical sometimes, and can be time-consuming and expensive. Researchers must carefully consider the advantages and disadvantages of experimental research before deciding to use it as a research method.

Applications of Experimental Research

Experimental research is a scientific approach used to evaluate cause-and-effect relationships between variables. It is a rigorous research design used to test hypotheses and establish causal relationships. Experimental research is used in various fields, including education and social sciences.

In Education

Experimental research is an important tool in education research. Researchers use experimental designs to evaluate the effectiveness of teaching methods, interventions, and programs. For example, a group of students can be randomly assigned to different teaching methods, and the outcomes can be compared to evaluate the effectiveness of each method.

Experimental research can also be used to evaluate the effectiveness of educational programs. For instance, a program designed to improve reading skills can be evaluated using an experimental design. A group of students can be assigned to the program while another group is not, and the outcomes can be compared to determine the program’s effectiveness.

In Social Sciences

Experimental research is also widely used in social sciences. Researchers use experimental designs to evaluate the effectiveness of interventions, policies, and programs. For example, experiments are carried out to evaluate the effectiveness of anti-poverty programs, health interventions, and public policies.

Experimental research is also used to evaluate the impact of social interventions. For instance, an experimental design can evaluate a social intervention aimed at reducing prejudice. A group of participants can be randomly assigned to the intervention, while another group is not, and the outcomes can be compared to determine the effectiveness of the intervention.

Overall, experimental research is a powerful tool for evaluating cause-and-effect relationships between variables. It is used in various fields to evaluate the effectiveness of interventions, programs, and policies. Using experimental designs, researchers can establish causal relationships and make evidence-based decisions.

Validity and Reliability in Experimental Research

Experimental research is a scientific method used to establish cause-and-effect relationships between variables. To achieve accurate and meaningful results, experimental research must have high levels of validity and reliability.

Validity refers to the extent to which an experiment measures what it is intended to measure. In experimental research, several types of validity must be considered:

• Internal Validity:  Internal validity refers to the extent to which the experimental results are due to the manipulation of the independent variable and not due to other factors. Internal validity can be threatened by factors such as selection bias, maturation of participants, and history.
• External Validity:  External validity refers to the extent to which the experimental results can be generalized to other populations and settings. External validity can be threatened by factors such as the use of non-representative samples and the artificiality of the experimental setting.
• Construct Validity:  Construct validity refers to the extent to which the experimental results accurately measure the theoretical construct being studied. Construct validity can be threatened by factors such as poor operationalization of variables and inadequate measurement tools.

To ensure high levels of validity, experimental researchers must carefully design their studies, use appropriate measurement tools, and control for potential confounding variables.

Reliability

Reliability refers to the consistency and stability of the experimental results over time and across different observers or measurement tools. In experimental research, several types of reliability must be considered:

• Test-Retest Reliability:  Test-retest reliability refers to the extent to which the same results are obtained when the experiment is repeated later. Test-retest reliability can be threatened by factors such as participant fatigue and practice effects.
• Inter-Rater Reliability:  Inter-rater reliability refers to the extent to which different observers or raters obtain the same results. Differences in observer interpretations and biases can threaten inter-rater reliability.
• Internal Consistency Reliability:  Internal consistency reliability refers to the extent to which the different items or measures within an experiment are consistent. Poorly constructed measurement tools can threaten internal consistency and reliability.

To ensure high levels of reliability, experimental researchers must use standardized procedures, train observers or raters, and use multiple measures to assess the same constructs.

Overall, high levels of validity and reliability are essential for experimental research to produce accurate and meaningful results. By carefully considering and addressing these factors, experimental researchers can increase the credibility and impact of their research.

Statistical Analysis in Experimental Research

Experimental research is a systematic approach to understanding cause-and-effect relationships between variables. It is an essential tool for scientists, businesses, and policymakers who want to test hypotheses and make informed decisions. Statistical analysis is a crucial component of experimental research, as it helps researchers extract meaningful insights from the collected data.

Quantitative Data

Quantitative data is numerical data that can be measured and analyzed statistically. It is often collected through surveys, experiments, or other objective methods. Quantitative data can be analyzed using statistical methods such as regression analysis, hypothesis testing, and ANOVA (analysis of variance).

Statistical Analysis

Statistical analysis analyzes quantitative data to identify patterns, relationships, and trends. It involves using statistical methods to summarize and interpret the data. Statistical analysis can be used to test hypotheses, make predictions, and identify important variables.

In experimental research, statistical analysis is used to determine whether the results of an experiment are statistically significant. Statistical significance measures the probability that the results are due to chance. If the results are statistically significant, the results are unlikely to be due to chance and are likely to be a real effect.

Statistical analysis is also used to identify important variables in an experiment. Variables are factors that can influence the outcome of an experiment. By identifying important variables, researchers can better understand the underlying mechanisms of an experiment and make more informed decisions.

In conclusion, statistical analysis is a critical component of experimental research. It helps researchers extract meaningful insights from quantitative data and make informed decisions. Using statistical methods to analyze data, researchers can identify patterns, relationships, and trends and determine whether the results of an experiment are statistically significant.

Experimental Research: Qualitative or Quantitative?

Experimental research can be both qualitative and quantitative. However, it is more commonly associated with quantitative research. Quantitative experimental research involves testing theories and hypotheses by measuring and analyzing numerical data, such as statistics and graphs. This type of research is often used in fields such as psychology, medicine, and engineering, where researchers aim to identify cause-and-effect relationships between variables.

In contrast, qualitative experimental research focuses on exploring and understanding the meaning behind human experiences and behaviors. This type of research involves collecting and analyzing non-numerical data, such as interviews, observations, and case studies. Qualitative experimental research is often used in fields such as anthropology, sociology, and education, where researchers aim to gain a deeper understanding of human behavior and culture.

It is important to note that experimental research can also combine quantitative and qualitative approaches. For example, a researcher may use a quantitative experimental design to test a theory or hypothesis and collect qualitative data through interviews or observations to gain a deeper understanding of the participants’ experiences and perspectives.

Overall, the choice between qualitative and quantitative experimental research depends on the research question and the type of data that needs to be collected and analyzed. Researchers should carefully consider the strengths and limitations of each approach before deciding which one to use.

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• Experimental Research Designs: Types, Examples & Methods

Experimental research is the most familiar type of research design for individuals in the physical sciences and a host of other fields. This is mainly because experimental research is a classical scientific experiment, similar to those performed in high school science classes.

Imagine taking 2 samples of the same plant and exposing one of them to sunlight, while the other is kept away from sunlight. Let the plant exposed to sunlight be called sample A, while the latter is called sample B.

If after the duration of the research, we find out that sample A grows and sample B dies, even though they are both regularly wetted and given the same treatment. Therefore, we can conclude that sunlight will aid growth in all similar plants.

What is Experimental Research?

Experimental research is a scientific approach to research, where one or more independent variables are manipulated and applied to one or more dependent variables to measure their effect on the latter. The effect of the independent variables on the dependent variables is usually observed and recorded over some time, to aid researchers in drawing a reasonable conclusion regarding the relationship between these 2 variable types.

The experimental research method is widely used in physical and social sciences, psychology, and education. It is based on the comparison between two or more groups with a straightforward logic, which may, however, be difficult to execute.

Mostly related to a laboratory test procedure, experimental research designs involve collecting quantitative data and performing statistical analysis on them during research. Therefore, making it an example of quantitative research method .

What are The Types of Experimental Research Design?

The types of experimental research design are determined by the way the researcher assigns subjects to different conditions and groups. They are of 3 types, namely; pre-experimental, quasi-experimental, and true experimental research.

Pre-experimental Research Design

In pre-experimental research design, either a group or various dependent groups are observed for the effect of the application of an independent variable which is presumed to cause change. It is the simplest form of experimental research design and is treated with no control group.

Although very practical, experimental research is lacking in several areas of the true-experimental criteria. The pre-experimental research design is further divided into three types

• One-shot Case Study Research Design

In this type of experimental study, only one dependent group or variable is considered. The study is carried out after some treatment which was presumed to cause change, making it a posttest study.

• One-group Pretest-posttest Research Design: 

This research design combines both posttest and pretest study by carrying out a test on a single group before the treatment is administered and after the treatment is administered. With the former being administered at the beginning of treatment and later at the end.

• Static-group Comparison: 

In a static-group comparison study, 2 or more groups are placed under observation, where only one of the groups is subjected to some treatment while the other groups are held static. All the groups are post-tested, and the observed differences between the groups are assumed to be a result of the treatment.

Quasi-experimental Research Design

  The word “quasi” means partial, half, or pseudo. Therefore, the quasi-experimental research bearing a resemblance to the true experimental research, but not the same.  In quasi-experiments, the participants are not randomly assigned, and as such, they are used in settings where randomization is difficult or impossible.

 This is very common in educational research, where administrators are unwilling to allow the random selection of students for experimental samples.

Some examples of quasi-experimental research design include; the time series, no equivalent control group design, and the counterbalanced design.

True Experimental Research Design

The true experimental research design relies on statistical analysis to approve or disprove a hypothesis. It is the most accurate type of experimental design and may be carried out with or without a pretest on at least 2 randomly assigned dependent subjects.

The true experimental research design must contain a control group, a variable that can be manipulated by the researcher, and the distribution must be random. The classification of true experimental design include:

• The posttest-only Control Group Design: In this design, subjects are randomly selected and assigned to the 2 groups (control and experimental), and only the experimental group is treated. After close observation, both groups are post-tested, and a conclusion is drawn from the difference between these groups.
• The pretest-posttest Control Group Design: For this control group design, subjects are randomly assigned to the 2 groups, both are presented, but only the experimental group is treated. After close observation, both groups are post-tested to measure the degree of change in each group.
• Solomon four-group Design: This is the combination of the pretest-only and the pretest-posttest control groups. In this case, the randomly selected subjects are placed into 4 groups.

The first two of these groups are tested using the posttest-only method, while the other two are tested using the pretest-posttest method.

Examples of Experimental Research

Experimental research examples are different, depending on the type of experimental research design that is being considered. The most basic example of experimental research is laboratory experiments, which may differ in nature depending on the subject of research.

Administering Exams After The End of Semester

During the semester, students in a class are lectured on particular courses and an exam is administered at the end of the semester. In this case, the students are the subjects or dependent variables while the lectures are the independent variables treated on the subjects.

Only one group of carefully selected subjects are considered in this research, making it a pre-experimental research design example. We will also notice that tests are only carried out at the end of the semester, and not at the beginning.

Further making it easy for us to conclude that it is a one-shot case study research. 

Employee Skill Evaluation

Before employing a job seeker, organizations conduct tests that are used to screen out less qualified candidates from the pool of qualified applicants. This way, organizations can determine an employee’s skill set at the point of employment.

In the course of employment, organizations also carry out employee training to improve employee productivity and generally grow the organization. Further evaluation is carried out at the end of each training to test the impact of the training on employee skills, and test for improvement.

Here, the subject is the employee, while the treatment is the training conducted. This is a pretest-posttest control group experimental research example.

Evaluation of Teaching Method

Let us consider an academic institution that wants to evaluate the teaching method of 2 teachers to determine which is best. Imagine a case whereby the students assigned to each teacher is carefully selected probably due to personal request by parents or due to stubbornness and smartness.

This is a no equivalent group design example because the samples are not equal. By evaluating the effectiveness of each teacher’s teaching method this way, we may conclude after a post-test has been carried out.

However, this may be influenced by factors like the natural sweetness of a student. For example, a very smart student will grab more easily than his or her peers irrespective of the method of teaching.

What are the Characteristics of Experimental Research?  

Experimental research contains dependent, independent and extraneous variables. The dependent variables are the variables being treated or manipulated and are sometimes called the subject of the research.

The independent variables are the experimental treatment being exerted on the dependent variables. Extraneous variables, on the other hand, are other factors affecting the experiment that may also contribute to the change.

The setting is where the experiment is carried out. Many experiments are carried out in the laboratory, where control can be exerted on the extraneous variables, thereby eliminating them. 

Other experiments are carried out in a less controllable setting. The choice of setting used in research depends on the nature of the experiment being carried out.

• Multivariable

Experimental research may include multiple independent variables, e.g. time, skills, test scores, etc.

Why Use Experimental Research Design?  

Experimental research design can be majorly used in physical sciences, social sciences, education, and psychology. It is used to make predictions and draw conclusions on a subject matter. 

Some uses of experimental research design are highlighted below.

• Medicine: Experimental research is used to provide the proper treatment for diseases. In most cases, rather than directly using patients as the research subject, researchers take a sample of the bacteria from the patient’s body and are treated with the developed antibacterial

The changes observed during this period are recorded and evaluated to determine its effectiveness. This process can be carried out using different experimental research methods.

• Education: Asides from science subjects like Chemistry and Physics which involves teaching students how to perform experimental research, it can also be used in improving the standard of an academic institution. This includes testing students’ knowledge on different topics, coming up with better teaching methods, and the implementation of other programs that will aid student learning.
• Human Behavior: Social scientists are the ones who mostly use experimental research to test human behaviour. For example, consider 2 people randomly chosen to be the subject of the social interaction research where one person is placed in a room without human interaction for 1 year.

The other person is placed in a room with a few other people, enjoying human interaction. There will be a difference in their behaviour at the end of the experiment.

• UI/UX: During the product development phase, one of the major aims of the product team is to create a great user experience with the product. Therefore, before launching the final product design, potential are brought in to interact with the product.

For example, when finding it difficult to choose how to position a button or feature on the app interface, a random sample of product testers are allowed to test the 2 samples and how the button positioning influences the user interaction is recorded.

What are the Disadvantages of Experimental Research?  

• It is highly prone to human error due to its dependency on variable control which may not be properly implemented. These errors could eliminate the validity of the experiment and the research being conducted.
• Exerting control of extraneous variables may create unrealistic situations. Eliminating real-life variables will result in inaccurate conclusions. This may also result in researchers controlling the variables to suit his or her personal preferences.
• It is a time-consuming process. So much time is spent on testing dependent variables and waiting for the effect of the manipulation of dependent variables to manifest.
• It is expensive. 
• It is very risky and may have ethical complications that cannot be ignored. This is common in medical research, where failed trials may lead to a patient’s death or a deteriorating health condition.
• Experimental research results are not descriptive.
• Response bias can also be supplied by the subject of the conversation.
• Human responses in experimental research can be difficult to measure. 

What are the Data Collection Methods in Experimental Research?  

Data collection methods in experimental research are the different ways in which data can be collected for experimental research. They are used in different cases, depending on the type of research being carried out.

1. Observational Study

This type of study is carried out over a long period. It measures and observes the variables of interest without changing existing conditions.

When researching the effect of social interaction on human behavior, the subjects who are placed in 2 different environments are observed throughout the research. No matter the kind of absurd behavior that is exhibited by the subject during this period, its condition will not be changed.

This may be a very risky thing to do in medical cases because it may lead to death or worse medical conditions.

2. Simulations

This procedure uses mathematical, physical, or computer models to replicate a real-life process or situation. It is frequently used when the actual situation is too expensive, dangerous, or impractical to replicate in real life.

This method is commonly used in engineering and operational research for learning purposes and sometimes as a tool to estimate possible outcomes of real research. Some common situation software are Simulink, MATLAB, and Simul8.

Not all kinds of experimental research can be carried out using simulation as a data collection tool . It is very impractical for a lot of laboratory-based research that involves chemical processes.

A survey is a tool used to gather relevant data about the characteristics of a population and is one of the most common data collection tools. A survey consists of a group of questions prepared by the researcher, to be answered by the research subject.

Surveys can be shared with the respondents both physically and electronically. When collecting data through surveys, the kind of data collected depends on the respondent, and researchers have limited control over it.

Formplus is the best tool for collecting experimental data using survey s. It has relevant features that will aid the data collection process and can also be used in other aspects of experimental research.

Differences between Experimental and Non-Experimental Research 

1. In experimental research, the researcher can control and manipulate the environment of the research, including the predictor variable which can be changed. On the other hand, non-experimental research cannot be controlled or manipulated by the researcher at will.

This is because it takes place in a real-life setting, where extraneous variables cannot be eliminated. Therefore, it is more difficult to conclude non-experimental studies, even though they are much more flexible and allow for a greater range of study fields.

2. The relationship between cause and effect cannot be established in non-experimental research, while it can be established in experimental research. This may be because many extraneous variables also influence the changes in the research subject, making it difficult to point at a particular variable as the cause of a particular change

3. Independent variables are not introduced, withdrawn, or manipulated in non-experimental designs, but the same may not be said about experimental research.

Conclusion  

Experimental research designs are often considered to be the standard in research designs. This is partly due to the common misconception that research is equivalent to scientific experiments—a component of experimental research design.

In this research design, one or more subjects or dependent variables are randomly assigned to different treatments (i.e. independent variables manipulated by the researcher) and the results are observed to conclude. One of the uniqueness of experimental research is in its ability to control the effect of extraneous variables.

Experimental research is suitable for research whose goal is to examine cause-effect relationships, e.g. explanatory research. It can be conducted in the laboratory or field settings, depending on the aim of the research that is being carried out. 

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Experimental Psychology pp 153–159 Cite as

Conclusion: From Experimental to Experiential Psychology

• Jaan Valsiner 4 &
• Davood Gozli 5  
• First Online: 29 November 2022

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Part of the book series: Theory and History in the Human and Social Sciences ((THHSS))

The experimental method is the cornerstone of psychology as a science. So we are told—over the past century in various disguises—by various experts and deep believers in the promise that psychology will one day become a “real” science. The label method is supposed to add credibility to what psychologists do, and the constant parallels made with the dependence of physics on experiments set the stage for playing the game of experimenter being in control of all the “variables” selected for inspection in a given study.

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Note the historical changes in the labeling of these actors in the experimental situation (Bibace et al., 2009 ). First, they were called observers —as the experiments used introspective techniques. Then, they were called Versuchsperson in the German areas and subjects in English. Finally, by the twenty-first century, they are research participants who sign forms of giving up their rights of ownership of the data they produce for the anonymization of their person and the place. Note that the organizer of the study—the experimenter—is not considered to belong to the category of participants —even as her role in setting up an experiment is clear key participation. By that exclusion it becomes possible to remain uninformed of what actually happens in the experiment.

The audience here is the readership of the published experimental results that judge these results through the culturally set prisms of societal relevance or through the sieve of moral responsibility.

In psychology, several other genres of comparable structures are used: “interview,” “testing,” “therapy,” etc. These all have their own theatrical setup that differs in some details from that given in Fig. 9.2 but remains similar in the focus on scientific encounter as a form of performance art.

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Valsiner, J., Gozli, D. (2022). Conclusion: From Experimental to Experiential Psychology. In: Gozli, D., Valsiner, J. (eds) Experimental Psychology. Theory and History in the Human and Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-17053-9_9

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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Using Experimental Research Designs to Explore the Scope of Cumulative Culture in Humans and Other Animals

Christine a. caldwell.

1 Division of Psychology, University of Stirling

In humans, cultural evolutionary processes are capable of shaping our cognition, because the conceptual tools we learn from others enable mental feats which otherwise would be beyond our capabilities. This is possible because human culture supports the intergenerational accumulation of skills and knowledge, such that later generations can benefit from the experience and exploration efforts of their predecessors. However, it remains unclear how exactly human social transmission supports the accumulation of advantageous traits, and why we see little evidence of this in the natural behavior of other species. Thus, it is difficult to know whether the cognitive abilities of other animals might be similarly scaffolded by processes of cultural evolution. In this article, I discuss how experimental studies of cultural evolution have contributed to our understanding of human cumulative culture, as well as some of the limitations of these approaches. I also discuss how similar research designs can be used to evaluate the potential for cumulative culture in other species. Such research may be able to clarify what distinguishes human cumulative culture from related phenomena in nonhumans, shedding light on the issue of whether other species also have the potential to develop cognitive capacities that are outcomes of cultural evolution.

Short abstract

Culture drives cognitive evolution by supporting the transmission and intergenerational accumulation of skills and knowledge, based on social learning and teaching: Later generations benefit from what their predecessors acquired. Taking a metaperspective on those experimental studies that explore the mechanisms underlying cultural transmission, Caldwell discusses their potential for generating valuable insights, their possible limitations, and their generalizability to other species.

1. Introduction

1.1. the cultural evolution of cognition: a uniquely human phenomenon.

In considering the role of cultural evolution in shaping cognition, it is important to consider the scope and constraints of any such effects, including whether these are restricted to humans alone. There are now many widely accepted examples of cultural transmission in nonhumans, so does it follow that some of the cognitive capacities of these animals might also be influenced in nontrivial ways by cultural inputs? Or is human cultural evolution fundamentally different, and potentially capable of supporting the transmission of cognitive tools in ways that nonhuman cultural evolution simply cannot? In the current article, I examine how key features of human cultural evolution can be captured in experimental research designs, and I describe how such experimental methods can be used to shed light on what might distinguish human cultural evolution from similar phenomena in nonhumans.

It is now relatively uncontroversial to claim that at least some aspects of the cognition of modern humans are largely a consequence of cultural evolution, built up over generations of social transmission, rather than biological predispositions shaped primarily by genetic control. Admittedly, there are some striking differences in opinion regarding the range of attributes to which this might apply. For example, some controversy remains over the extent to which cultural evolution might account for human capacities for theory of mind (e.g., compare Heyes & Frith's, 2014 , cultural evolutionary account, with claims of false belief attribution in infancy, for example, Onishi & Baillargeon, 2005 ). But for other examples, such as number systems and notations, algorithms for calculation, or graphical codes for record‐keeping of any kind, few would question the importance of cultural evolution as an explanation for the use of such conceptual tools. There is historical evidence documenting the development of these techniques over many generations (Grattan‐Guinness, 1997 ), they show cultural variability (e.g., Bender & Beller, 2014 ), and mastery of the skills typically requires conscious effort on the part of the learner (and indeed these may need to be explicitly taught).

However, if some of our human cognitive abilities are not a direct outcome of specialized human biology, but are instead learned (e.g., Heyes, 2018a ), then this poses the important question of whether such abilities may, therefore, be possible in other species. We are by no means the only species that exhibits cultural traditions (e.g., Whiten et al., 1999 ), so does this learning build and bolster cognitive capabilities in these animals too, as well as influencing their (more readily observable) behavioral traits?

1.2. Human cultural evolution in comparative perspective

The answer to the above question depends on how similar human cultural evolution is to related processes observed in other species. However, questions remain over the precise nature of the differences between human cultural evolution and cultural transmission in other species, as well as the degree of similarity (e.g., Dean, Vale, Laland, Flynn, & Kendal, 2014 ). Experimental research designs are likely to prove critical in resolving these issues. Although naturalistic observations of both human and nonhuman cultural traditions can be extremely enlightening, experimental research offers important advantages. First, in humans, such designs make it possible to manipulate the conditions under which learning can occur, permitting conclusions about prerequisites and constraints associated with particular population‐level outcomes. Second, in nonhumans, it is possible to actively probe the limits of a species’ capabilities, the extent of which may not be apparent from the available naturalistic evidence. In the following sections, I consider the existing and potential contributions of both of these experimental approaches, with regard to similarities and differences between human and nonhuman cultural processes.

2. Laboratory studies of cultural evolution in human participants

2.1. operationalizing human cultural evolution in experimental research designs, 2.1.1. capturing significant aspects of cultural evolutionary change.

As noted above, experimental research offers potential for manipulation of variables of interest in order to determine critical prerequisites for particular human cultural processes. This is a key step in understanding whether certain cultural processes might also be present in other animals, since it is possible to establish whether the effects are underpinned by mechanisms that are unique to humans (e.g., language) or shared with other species. However, to do this one must first address the issue of precisely which features of human culture we wish to capture experimentally, and how to go about operationalizing these features for measurement. The phenomenon of cumulative culture , or cumulative cultural evolution , has frequently been cited as exemplifying what is particularly special about human culture (Boyd & Richerson, 1996 ; Heyes, 2012 ; Hill, Barton, & Hurtado, 2009 ; Laland & Hoppitt, 2003 ; Tennie, Call, & Tomasello, 2009 ). This is partly because it is believed to be either absent or extraordinarily rare in the natural behavior of nonhumans. However, it is primarily due to the number of other peculiarly human traits it appears to support (e.g., Tomasello, 1999 ). This latter point is of course particularly relevant from the perspective of the issue at hand, that is, the potential for shaping of cognitive abilities.

Cumulative cultural evolution can be conceived as a distinct subcategory of cultural evolution, distinguished by its creation of traits in later generations that would also have been preferred by members of earlier generations. In operationalizing this phenomenon for experimental research, we are, therefore, looking for cases where the traits expressed in later generations are demonstrably more effective or appealing in ways that are not purely dependent on current conditions. This criterion helps to distinguish cumulative culture from otherwise potentially confusable cases in which cultural evolution generates change that is cumulative only in the general sense of being incremental and historically dependent, but without producing traits that are increasingly advantageous. Tomasello ( 1990 , 1999 ) famously applied the analogy of the ratchet to human culture as a means to capture this same notion, describing human cumulative culture as exhibiting a “ratchet effect.” This apt analogy highlighted this particularly interesting and powerful property of cultural evolution, differentiating it from change that is more arbitrary and/or cyclical.

It should be noted that the notion of contextually independent trait value may be considerably more problematic for some behaviors compared with others. Communicative signals, for example, are only effective if they can be interpreted by receivers. The effectiveness of signal form is, therefore, necessarily tied to context. As such, the specifics of communicative conventions are likely to be subject to change that is less ratchet like. Nonetheless, increases in expressive power, independent of the nature of the signals used (e.g., allowing for communication of novel concepts, or more efficient communication of existing ones), remains understandable as cumulative in the sense of the cultural ratchet.

Therefore, in attempting to capture the phenomenon of cumulative culture under laboratory conditions, a key feature which needs to be reflected is the potential to deliver tangible benefits to learners in later generations. Other features of cumulative culture which have been identified in the existing literature have tended to arise from descriptions of exemplar cases, rather than specifications of minimal defining criteria. But if our motivating interests are, first, identifying significant critical preconditions, and second, establishing the extent of similar effects in other species, then it is important to focus on features we consider to be core constructs, as opposed to attempting to simulate phenomena which correspond closely to portrayals of the most striking examples of human cumulative culture.

To illustrate this point, consider the nature of the changes proposed to arise from cumulative culture. In some of the literature, cumulative culture has been characterized as being best illustrated by increases in the complexity of cultural traits (Dean et al., 2014 ), with increases in efficiency sometimes being cited alongside, as another possible outcome that is somehow inferior or less interesting (see Section V.(2) in Dean et al., 2014 ). In contrast, other authors have emphasized the advantages associated with increasing efficiency and compressibility, with outcomes relating to complexity proposed to be less predictable (Kirby, Tamariz, Cornish, & Smith, 2015 ).

However, conceptualizing cumulative culture more broadly as allowing the development of increasingly preferred traits over learner generations helps to clarify the apparent dissent over the expected outcomes of cumulative culture. In some instances, increased complexity is associated with increased functionality (e.g., modular technological innovations), whereas in others (such as refinements of existing technologies) efficiency is desirable. Therefore, although cumulative culture often can be associated with increasing complexity, this will not necessarily be the case. Likewise, it can be associated with increasing efficiency, but this is also by no means guaranteed. Indeed, other benefits may be associated with cumulative cultural traits which are not easily captured as either complexities or efficiencies. For example, Schofield, McGrew, Takahashi, and Hirata ( 2018 ) proposed adding “security” and “convenience” to this list, along with complexity and efficiency. Talking of “preferred” traits may seem altogether more vague and difficult to evaluate objectively, but it does at least allow us to capture what it is about cumulative culture that we believe to be so valuable and compelling, that is, its capacity to deliver advantageous traits, without placing restrictions on the nature of those advantages (they would not, for example, need to be associated with demonstrable fitness benefits). This inclusive concept of preference also effectively encompasses many noteworthy aspects of human culture, which most would agree is broad in scope, extending over a range of diverse domains including useful technologies, highly organized rule‐governed societies, and suites of technical knowledge and skills that permit survival in otherwise inhospitable natural habitats.

In practice, it might turn out to be difficult to distinguish traits that would be preferred over their precursors in an absolute sense, from those which are favored only due to current environmental conditions. Nonetheless, regardless of the difficulties of evaluating particular real‐world cultural traits according to this criterion, it is relatively straightforward to implement within the context of experimental research with human participants, since explicit task goals can be specified against which performance can be objectively evaluated, making quantifiably better solutions easy to identify. For examples, see the next section, describing studies using flight distances of paper planes and heights of spaghetti towers (Caldwell & Millen, 2008 ), measures of similarity to a target goal state (Muthukrishna, Shulman, Vasilescu, & Henrich, 2014 ), and load mass of baskets (Zwirner & Thornton, 2015 ).

2.1.2. Capturing the trans‐generational cumulative potential of social learning

Experimental designs aiming to capture cumulative culture must also effectively capture the trans‐generational nature of the accumulated learning benefits. Simply demonstrating that social information can be beneficial is not quite sufficient to conclude that there is potential for cumulative culture. It is important to be able to show that the benefits of social learning stack up over multiple generations of transmission.

Experimental designs intended to capture cumulative culture must, therefore, incorporate multiple learner generations. However, taking into consideration the ultimate goal of drawing comparisons with nonhuman populations, we are once again faced with the question of specifying minimum criteria. Assuming that the first learner generation of any experimental design is comprised of individuals who approach a task or problem in the absence of any social information (thus providing a baseline of the likely success of naïve task exploration), over how many subsequent generations must any such measures of success be seen to accumulate?

In order to conclude that the experimental results suggest potential for cumulative culture, transmission to only one generation of social learners (exposed to information from the asocial learners’ baseline attempts) would seem insufficient. It is difficult to compare the relative costs of learning from social demonstrations versus individual exploration, and this limits what can be concluded from comparing performances of participants who complete a task using individual exploration alone, with those of participants who have an opportunity to learn from such baseline exploration attempts before making their own. In this scenario, even if the social learners performed better, it is not necessarily possible to conclude that this could equate to a benefit to later learner generations in the real world, since finding an appropriate source of social information, or the time consumed observing them, could easily be at least as costly as simply expending additional individual exploration effort to reach an equivalent level of performance. However, if it is possible to show that comparable social learning opportunities are more valuable in later generations, then it is much more reasonable to conclude that later generation learners are liable to be in a more advantageous position regarding benefits of social information, compared with their predecessors. So, for example, if a further sample of participants were given the opportunity to learn from individuals who had observed the attempts of the asocial learners, and if this second generation of social learners outperformed their socially learning predecessors, then it is possible to conclude that the social information itself was increasingly valuable.

If this is the case, then we can make the argument that benefits can, in principle, accrue with repeated transmission. Such a demonstration, therefore, identifies the potential for cumulative culture, regardless of whether it results in an outcome that is identifiably beyond what a single individual could achieve in principle by themselves. Thus, although cumulative culture is sometimes described as resulting in “behaviours that no individual could invent on their own” (Boyd & Richerson, 1996 , p. 770), as Tennie, Caldwell, and Dean ( 2018 ) have previously argued, this should not be used as a criterion for guiding classification of ambiguous cases, as it will tend to rule out examples which represent incipient cumulative culture. Also, and more pragmatically, from the perspective of experimental research design, feasibility issues dictate that measured behaviors must be relatively achievable within a contracted timeframe. Thus, it may be more helpful to think of the core defining feature of cumulative culture in terms of the potential for increasingly valuable shortcuts to learning available to social learners over generations of transmission.

The studies reported in Caldwell and Millen ( 2008 ) provide examples of how benefits of experience can be shown to accumulate over learner generations in an experimental context. Successive learner “generations” of participants (who could observe and interact with their immediate predecessors) were each required to complete the same task, all scored according to a prespecified goal measure. All participants were given the same time limit in which to complete the task, as well as the same materials. Furthermore, they each had the same amount of time available for observing their predecessors. The time periods were short (5 min of observation time, followed by 5 min of building time) so the tasks were necessarily relatively simple. In one experiment, participants were required to build a paper plane (the goal measure being flight distance), and in another, the task was to build a tower from raw spaghetti and modeling clay (the goal measure being tower height). In both cases, participants in later generations scored higher in relation to these goal measures compared with those in earlier generations. Therefore, these participants were able to achieve better outcomes in an equivalent time period, including the time available for observational learning. Presumably, the performances to which they were exposed were valuable sources of information about how to maximize their own performance, and importantly, more so than the performances of members of earlier generations would have been.

Similar research designs have since been used by other authors to demonstrate similar effects of accumulating benefits of otherwise equivalent social learning opportunities. For example, Muthukrishna et al. ( 2014 ) asked participants to try to match a target image using an unfamiliar image editing software package. In one of their experimental conditions (see Experiment 1, five‐model condition), they found that those in later generations produced images that were quantifiably more similar to the target. Zwirner and Thornton ( 2015 ) presented participants with a basket‐making task, providing them with everyday materials such as paper, string, and rubber bands, and a goal of producing a basket capable of transporting rice. They found that participants in later generations were able to perform better according to the goal measure of the mass of rice successfully transported.

In these studies, it has, therefore, been possible to show that the benefits of social learning could accrue with repeated transmission, such that learning from individuals who had themselves been exposed to social information was typically even more valuable than learning from individuals who had engaged in naïve exploration. These laboratory models capture this powerful property of human cultural transmission, which allows social learners to benefit not just from the experience of others, but the accumulated experience of multiple others, even when opportunities for learning are otherwise held constant.

It should perhaps be noted at this point that the benefits of experience can accumulate over multiple generations even when the “social” information is experienced in a decidedly nonsocial context. Muthukrishna et al. ( 2014 ) passed information between participants in the form of written instructions, so the individuals in question never actually encountered one another directly. In Zwirner and Thornton's ( 2015 ) study, learning from others’ completed attempts was found to be sufficient to generate improvements in performance. This was also the case in the follow‐up to Caldwell and Millen's ( 2008 ) study (Caldwell & Millen, 2009 ), described in the next section. Thus, it remains an empirical question whether or not the “social” aspect of some forms of social learning actively facilitates learning, and this does not necessarily have any bearing on the potential for the accumulation of benefits of experience.

2.2. Value and limitations of experimental research design for understanding prerequisites of cumulative culture

One of the benefits of developing laboratory models, such as those described, is that this allows manipulation of particular variables of interest, to compare group‐level outcomes when members complete the tasks under different conditions. From the perspective of understanding the potential scope of cumulative culture, including the extent to which such effects are likely to be possible in other species, a key question concerns the cognitive and behavioral prerequisites of this phenomenon. This question can, therefore, be addressed experimentally, through manipulation of the availability of sources of information, or constraints placed on participants’ strategies or resources.

However, it should be emphasized that results from any such simplified laboratory simulation must be treated with due caution. Most readers of this article will likely be familiar with the aphorism that “all models are wrong but some are useful,” attributed to Box (e.g., 1979 ), so I will not waste time elaborating on its meaning here. The important point from the perspective of the current article is that the same could equally well be said of cultural evolution experiments. While all experimental research necessitates simplification of the noise and complications of real‐world phenomena, with consequent impact on external validity, cultural evolution experiments arguably represent a particularly extreme case. To preserve their usefulness, we must be mindful of the simplifications imposed.

The need to control factors such as population size and structure render contexts inevitably unrealistic, but the most serious threats to generalizability arise from the necessary differences in scale between experimental and real‐world contexts. This applies most obviously to the timescales involved, with individual participants’ involvement in laboratory experiments typically lasting under an hour. In contrast, the behaviors that these studies are intended to help explain are generally thought to have developed over multiple human lifespans. This scaling down of timeframes places inherent constraints on the tasks that can be presented, with these correspondingly scaled down in their complexity due to the time available for completion. The reliance on simple tasks presents an obvious limitation in that it could be argued that findings from cultural evolution experiments might not extend to any behavior believed to depend on cumulative culture in the real world. In studies investigating the conditions necessary for cumulative culture in humans, these inevitable simplifications impose significant limitations on the types of conclusions that can be drawn, and the nature of the evidence that would be required to draw conclusions of any kind.

So, for example, it is important to note that failure to find an effect of cumulative improvement over multiple learner generations within laboratory transmission chains is not necessarily an indication that some missing element is, therefore, a prerequisite for cumulative culture, since there are likely to be a multitude of alternative reasons why an effect might not have been found. In contrast, finding a positive effect of improved performance over multiple generations of social transmission is more informative. This can allow a researcher to establish that some variable of interest which has been excluded is not a strict prerequisite for cumulative culture. However, the important caveat arising from the necessary simplifications and reductions of scale is that this might well be context dependent. The excluded variable of interest may, therefore, turn out to be necessary for cumulative improvements to be observed in other tasks, or within different population structures.

A further type of result might involve multiple experimental conditions across which a variable of interest had been manipulated. Finding a positive effect of cumulative improvement in one condition, and a significantly reduced effect in at least one other, allows a researcher to conclude that this manipulated variable may be important for cumulative culture. The caveat once again, however, is that this is likely to be context specific. As a consequence, any individual experiment can only ever contribute a small part of the explanation of how human minds generate cumulative culture, and why those of other species appear not to. Ultimately, this question must be approached from a variety of different angles in order to form a more complete picture.

It is perhaps helpful to illustrate these points about what can and cannot be concluded from such experimental research designs by considering Caldwell and Millen's ( 2009 ) study, which was the follow‐up to the Caldwell and Millen ( 2008 ) publication described earlier. Caldwell and Millen ( 2009 ) used the paper aeroplane task paradigm established in the earlier (2008) publication in order to compare conditions in which participants had access to different sources of social information (through observation of others’ task completion, inspection of their completed artifacts, and opportunities for exchanging information through spoken communication). The results indicated that cumulative improvement was possible on the basis of any of these sources of information. From a theoretical perspective, the most interesting conclusion was that cumulative culture appeared to be possible even in the absence of opportunities for imitation or teaching. In line with the caveats noted above, the conclusion that these are not strict prerequisites can be justified, whereas a blanket claim that they are unimportant, or unnecessary in any context, cannot.

2.3. Investigating task‐specific effects in human cultural evolution

Even though conclusions about the prerequisites for cumulative culture will be determined in part by the properties of the task in question, this should not be regarded as condemnatory for cultural evolution experiments. Far from closing doors, this perspective illuminates previously underexplored avenues of investigation, which offer potentially rich insights to a more comprehensive and nuanced understanding of the distinctiveness of human culture. Indeed, this is particularly advantageous from the perspective of establishing the potential scope of cumulative cultural effects under different conditions, including whether this might extend to more abstract contexts such as cognitive skills.

Turning the inevitable reality of task‐specific effects into an advantage, rather than a limitation, simply entails direct manipulation of task variables themselves, to investigate how these affect transmission requirements. For example, Caldwell, Renner, and Atkinson ( 2017 ) investigated the transmission of knot‐tying skills for knots of differing complexity. Participants had access to either finished products alone, or had additional pictorial instructions about the process of completion, or were also paired with another participant who had mastered the skill and could, therefore, engage in active demonstration and interactive instruction and feedback. While all transmission conditions were equally effective for the knots classified as simple, the interactive teaching condition was much more effective for those knots classified as complex.

It should be emphasized that this particular example was not a study illustrating cultural evolution, since it considered only transmission to a single generation of learners. Furthermore, since it concerned retention of an experimentally introduced complex skill, only loss can be measured, and therefore there is no way to assess the kind of improvements in performance that would be indicative of potential for cumulative culture. Naturally, such approaches are likely to generate very different conclusions about prerequisites for effective transmission, with studies of loss possibly identifying mechanisms supporting high‐fidelity copying as more important than they would be for cumulative improvement, and likely also underestimating the importance of mechanisms generating effective novel inventions and modifications. Nonetheless, the logic behind the experimental design in Caldwell et al.'s ( 2017 ) study can also be applied to cultural evolution experiments, in order to investigate the differing requirements for cumulative improvement within different behavioral contexts.

3. Laboratory studies of cultural evolution in nonhuman participants

Of course, studying only humans can only provide one part of the picture of the potential scope of cumulative culture. If a key question of interest concerns the extent of such effects in other species, then studies of nonhuman animals also provide a critical source of evidence. Although cumulative culture is widely believed to be either very rare or completely absent in the natural behavior of other species (as noted previously), it stands to reason that it should be possible to document some degree of continuity between ourselves and our closest evolutionary relatives, regarding the tendencies and capacities that support such effects, even if the phenomenon itself does not manifest in quite the same form.

Even within the spontaneously occurring behavior of wild populations, there are putative examples of cumulative culture from nonhumans. For example, see Schofield et al.'s ( 2018 ) analysis of historical data on Japanese macaque foraging techniques, and Sanz, Call, and Morgan's ( 2009 ) report of chimpanzees’ tool modification. However, these examples remain controversial due to the difficulty of ruling out potential alternative interpretations (such as modifications reflecting asocially learned refinements introduced as a consequence of individuals’ increasing personal experience of the particular foraging technique or tool, shaped by environmental feedback alone).

Experimental methods make it possible to systematically investigate the capabilities of other species, using designs specifically devised to elicit key evidence, allowing clearer conclusions regarding whether criteria for cumulative culture have been fulfilled. In addition, from the perspective of the question in hand (i.e., that of the potential scope of cumulative culture in nonhumans, in terms of the traits it might support), experimentation can also establish the kinds of skills that could in principle be supported by cumulative culture in a given species.

In terms of the details of the designs which can be used to establish the potential for cumulative culture in nonhumans, the same principles of design apply as already discussed for humans. If we are primarily interested in the accumulation of benefits of collective experience, then once again we should be looking for evidence of objective benefit to later generations, and social information becoming increasingly valuable.

In fact, currently there are very few studies with nonhumans that follow the principles of design set out in Section  2 , although I will describe one which does. However, before doing so, it is important to note that even when using experimental designs which are in principle capable of demonstrating the accumulation of benefits of experience, we must still be wary of what we can, and cannot, conclude. The very same limitations that apply to human cultural evolution experiments (described in Section  2.2 ) also apply to attempts to use similar research designs with nonhumans.

So, once again there are limits on what we can conclude from a null result. If we find no evidence of later generations of learners being able to benefit from the accumulated expertise and exploration effort of their predecessors, this could occur for any one or more of a number of reasons that may not preclude potential for cumulative culture in other contexts. It might be that the performances that would be required for later generations to demonstrate improvements upon those of their predecessors are simply beyond the capabilities of that species. The individuals might never be able to learn the necessary behaviors under any circumstances. Alternatively, it could be the case that although the task can in principle be mastered to the required degree, the opportunities afforded for learning from others in the context of the research design are not adequate for expertise to be effectively transferred (e.g., reliant on learners paying close attention to a relatively brief performance by an experienced individual—who may be unfamiliar, uninteresting, or potentially antagonistic).

Therefore, just as a failure to identify trans‐generational improvements in task performance in humans does not allow us to draw firm conclusions about necessary preconditions, similar failures in studies of nonhumans likewise do not allow us to make sweeping generalizations about the potential for members of that species to accumulate benefits of experience via social transmission. Also, and again corresponding to the limitations identified in relation to studies of humans, even positive results must be treated with due caution. Positive results, while demonstrating that cumulative improvements are possible, should generally be assumed to be context specific, at least until shown to generalize across a range of varying contexts.

However, echoing the optimistic message in relation to studies involving human participants, the fact that a positive result is likely to be dependent on the details of the task, as well as other contextual aspects, should be viewed as an opportunity rather than a threat. This is in fact particularly true for studies of nonhuman species, because this may allow researchers to design experiments in which later learner generations can benefit from the accumulated experience of their predecessors, through careful consideration of the propensities of the species in question.

Sasaki and Biro's ( 2017 ) study of the efficiency of routes taken by homing pigeons represents an excellent example of such an approach. The researchers took into account the birds’ tendency to fly alongside a partner, or as part of a flock, when released together. This flocking naturally influenced choice of route, smoothing out idiosyncratic deviations. The researchers reasoned that this process could result in the birds learning more efficient routes, and that a replacement design involving multiple learner generations might, therefore, produce later generations who flew more efficient routes compared with earlier ones. Thus, the researchers took advantage of the flocking tendency, which generated social heritability of the trait in question (i.e., route choice). They also permitted the birds within any given generation (i.e., particular pairs) multiple flights together, so there were opportunities for each pair to further refine route choice based on direct experience. Thus, across 10 chains, each composed of five pigeons (each of which flew 12 times with their predecessor in the chain, then 12 times with their successor), routes flown by pairs in the final generations were more efficient than routes flown by pairs in earlier generations.

This result certainly fulfils the criterion of demonstrating tangible benefit to individuals in later generations as a consequence of the accumulated experience of members of earlier generations. It is reasonable to assume that the “goal” of the homing pigeons is to complete their journey as quickly and efficiently as possible, so it follows that the birds from earlier generations would—in principle—also have preferred the shorter, faster routes of their successors. In studies of nonhumans, establishing the unambiguous superiority of certain traits over others is likely to pose particular challenges. As previously noted in relation to experiments involving human participants, the ability to present an explicit task with clearly defined goal measures (e.g., tower height, maximum load mass, or similarity to a target) renders this requirement fairly trivial. But it is of course impossible to simply induce task‐appropriate motivations in nonhumans in quite the same way. Thus, researchers must rely upon their subjects’ preexisting natural motivations. In some cases (such as Sasaki & Biro's study) it may be possible to design an experimental paradigm which embeds this motivation within its original functional context. However, task motivation is more likely to be induced through pretraining, as a means to ensure that subjects associate aspects of experimental task performance with primary reinforcers (such as food) of varying quantity or quality.

A further important detail about Sasaki and Biro's study is that the birds had equivalent opportunities for direct personal experience of flying the routes (i.e., same number of flights completed, in the respective roles as the experienced, and inexperienced, member of a pair). This means that it was possible to compare like with like, in considering the performances of birds from different generations. Thus, we can also be confident that the later generation birds are able to profit from accumulated expertise and exploration effort even in the context of learning opportunities that were otherwise matched to those of their predecessors. The later generation birds performed better because the social information to which they were exposed was apparently more valuable.

It is important to note, however, that simplified task designs might demonstrate effects which in practice do not have that effect in real‐world populations. In real human cultures, we know that later generations (this time also in the “real” sense of population turnover) genuinely learn from, and build on, the accumulated expertise of their predecessors, with systems and technologies being continually refined over many lifetimes. And as such, we do routinely make use of techniques and inventions that were unavailable to our ancestors. So in that respect we know that cumulative culture is a real phenomenon in human societies. In contrast, it remains to be seen whether the effects identified in Sasaki and Biro's research, for example, illustrate effects which would actually bring tangible benefits to later (real) generations of birds, compared with their predecessors. So, do migrating species develop increasingly efficient routes over many years, in spite of complete population turnover? Do contemporary populations of migrating birds use routes which would have been used by their conspecific counterparts in decades gone by, had they only had exposure to these possibilities? Sasaki and Biro's ( 2017 ) findings cannot answer these questions, but they do hint at their plausibility.

Regardless of whether this effect reflects any real‐world intergenerational learning effects or not, it is likely that many readers will in any case find Sasaki and Biro's claims of cumulative culture questionable. The route efficiencies of homing pigeons may not represent an example of cumulative culture as most would ordinarily conceive of it. Such concerns are valid and should not be dismissed. In fact, such observations are essential for understanding how we can evaluate the extent to which nonhuman capacities for cumulative culture approach our own.

This is because task simplification, and careful tailoring of research designs to the competencies of the species in question, may well be our most effective means of approaching the question of how close other animals can come to human‐like culture. As noted previously, drawing informative conclusions depends on being able to identify positive effects, whether this might be a standalone positive result, or a result that demonstrates a significant difference between a positive effect of cumulative performance in one condition, and a reduced effect in another. However, subscribing to this view, that positive effects are far more informative than null results, could potentially result in abandonment of studies of nonhumans as an exercise in futility. If ultimately our endeavor is to evaluate claims about cumulative culture being a uniquely human trait (especially if we regard those claims to be well‐founded), then we might conclude that there is little to be gained from nonhuman experimental research if its success depends on finding positive effects.

However, this is precisely the reason why the likely existence (and indeed virtual inevitability) of task‐specific effects should be viewed not as a threat to the validity of our research conclusions, but rather as the key to progress in this field. Simplified task designs, which nonetheless preserve the integrity of the fundamental value of cumulative culture, may be our only means of escape from this logical quandary of identifying positive evidence of a property which so far has only been identified in human cultural traditions. And regardless of the specifics of the task in question, if it is possible to demonstrate that learners are able to benefit from limited exposure to others’ performance such that they can take advantage of the accumulated expertise of multiple learner generations, then we have captured an unarguably powerful property of social learning.

Such research designs can also then provide a starting point for identifying the constraints on cumulative culture‐like effects in other species. Therefore, somewhat similar to the approach taken in the study of teaching of knot‐tying described previously (Section  2.3 ), critical task variables can be manipulated according to researchers’ expectations about the conditions under which a cumulative culture‐like effect may be possible in their study species. Researchers may, therefore, be able to identify limiting factors which restrict the extent to which we see such effects occurring in natural behaviors.

4. Cumulative culture and the cultural evolution of cognition

Returning to the focus of this special issue, we must also consider the implications of the arguments presented here for our understanding of the cultural evolution of cognition , specifically. Following on from the point above, suggesting that it might be possible to document cumulative cultural phenomena in other species, if in very restricted set contexts, I would be inclined to speculate that such contexts are unlikely to extend to the kinds of conceptual tools that actively facilitate and shape thinking.

Cumulative culture is necessarily dependent on agents being influenced by social information in how they approach their goals. In humans, this can occur across a wide variety of contexts, due to an explicit recognition of the potential value of social information (which Heyes might describe as explicitly metacognitive social learning, for example, Heyes, 2016 ). In contrast, it is reasonable to assume that nonhuman animals lack such explicit understanding, given the contentiousness of claims of metacognitive awareness in nonhumans, even as these relate only to the individual's own knowledge state, (Hampton, 2009 ). Therefore, it is likely that there are major constraints on the contexts within which nonhumans attend and respond to social information. This is probably restricted to two broad categories of circumstance. The animal could either possess a specialized mechanism that generates behavioral heritability, shaped by natural section (similar to the flocking tendency underpinning Sasaki & Biro, 2017 , findings). Alternatively, the animal's own experience could have resulted in the formation of associations such that conspecific behavior becomes a cue indicating that certain behavioral responses are likely to have positive consequences.

In either such circumstance however, although faithful social transmission can occur, it is likely to do so only in very restricted contexts. In the case of naturally selected tendencies, these will likely only operate within the particular domain which created the selective pressure for those mechanisms to exist (e.g., route choice for the pigeons). To illustrate this point, consider the fact that although it might be possible to train pigeons to have a “conversation” (Epstein, Lanza & Skinner, 1980 ), we would not expect naïve untrained pigeons to be able to spontaneously take over one of the roles in this performance, on the basis of simply observing their trained counterparts’ interaction. Although high‐fidelity transmission may be possible for route choices, it is not likely to operate in this novel context.

Associative learning mechanisms provide another possible source of transmission fidelity which may have the power to support cumulative culture‐like effects in nonhumans. Through experience, animals may learn to use cues from conspecifics as predictors of likely reinforcement (e.g., Leadbeater & Chittka, 2009 ). However, although an associatively learned tendency to copy could have the potential to generalize to a degree that permitted reproduction of novel variants (e.g., Custance, Whiten, & Bard, 1995 ), which would be necessary for cumulative enhancements to accrue at all, this would also be unlikely to extend much beyond the domain within which it was originally learned.

If it is true that cumulative culture‐like effects in nonhumans are restricted to cases implicating one or other (or possibly some combination) of these two routes to transmission fidelity, we are unlikely to observe these supporting the development of cognitive skill. This would require extending the reach of social learning mechanisms, developed or selected for within particular behavioral domains, into a completely novel domain involving abstract rules with opaque benefits and functions.

In contrast, if cumulative culture in humans relies heavily on explicitly metacognitive social learning (Heyes, 2018b ), with learners actively seeking out relevant information based on their inferences and assumptions about others’ knowledge, this considerably broadens out the behavioral contexts for which increases in functionality could be observed over generations of social transmission, potentially opening up the possibility of the cultural evolution of more abstract cognitive functions.

5. Conclusions

In conclusion, experimental approaches can, therefore, contribute a great deal to understanding the potential scope of cumulative culture, including the extent to which such effects might also occur in other species. The potential for manipulation of variables of interest is key to the value of experimental research, and I have argued that in the context of cultural evolution experiments, two approaches in particular are useful from the perspective of understanding the scope of, and limits on, cumulative culture. The first of these approaches, now well represented in the existing literature, involves manipulation of the conditions under which participants complete a particular task. This allows researchers to determine (within the context of that task) the constraints on, and prerequisites for, effective cumulative improvement over learner generations. This helps to identify key requirements for cumulative culture in studies involving samples with recognized capacities for cumulative culture (i.e., adult humans).

The second approach, which has yet to be exploited to its full potential, involves manipulation of task features themselves. This would allow researchers to identify whether there are circumstances under which cumulative improvement is theoretically possible in studies involving samples (such as nonhuman species) for which cumulative culture has not been convincingly documented in natural populations. This helps to pin down the limiting factors that prevent such cumulative improvements from being observed in practice under more realistic conditions, or in different contexts or domains.

Acknowledgments

I thank Takao Sasaki and Andrew Whiten for valuable discussions following a seminar by Sasaki hosted by Whiten at the University of St Andrews in October 2017. I am also grateful to my colleagues Rachel Crockett, Paul Dudchenko, Eva Rafetseder, and Craig Roberts, for their thoughtful feedback on an earlier iteration of the ideas presented here. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under grant agreement No. 648841 RATCHETCOG ERC‐2014‐CoG.

This article is part of the topic “The Cultural Evolution of Cognition,” Andrea Bender, Sieghard Beller and Fiona Jordan (Topic Editors). For a full listing of topic papers, see http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1756-8765/earlyview

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