examples of limitations of experiments

How to Write Limitations of the Study (with examples)

This blog emphasizes the importance of recognizing and effectively writing about limitations in research. It discusses the types of limitations, their significance, and provides guidelines for writing about them, highlighting their role in advancing scholarly research.

Updated on August 24, 2023

No matter how well thought out, every research endeavor encounters challenges. There is simply no way to predict all possible variances throughout the process.

These uncharted boundaries and abrupt constraints are known as limitations in research . Identifying and acknowledging limitations is crucial for conducting rigorous studies. Limitations provide context and shed light on gaps in the prevailing inquiry and literature.

This article explores the importance of recognizing limitations and discusses how to write them effectively. By interpreting limitations in research and considering prevalent examples, we aim to reframe the perception from shameful mistakes to respectable revelations.

What are limitations in research?

In the clearest terms, research limitations are the practical or theoretical shortcomings of a study that are often outside of the researcher’s control . While these weaknesses limit the generalizability of a study’s conclusions, they also present a foundation for future research.

Sometimes limitations arise from tangible circumstances like time and funding constraints, or equipment and participant availability. Other times the rationale is more obscure and buried within the research design. Common types of limitations and their ramifications include:

• Theoretical: limits the scope, depth, or applicability of a study.
• Methodological: limits the quality, quantity, or diversity of the data.
• Empirical: limits the representativeness, validity, or reliability of the data.
• Analytical: limits the accuracy, completeness, or significance of the findings.
• Ethical: limits the access, consent, or confidentiality of the data.

Regardless of how, when, or why they arise, limitations are a natural part of the research process and should never be ignored . Like all other aspects, they are vital in their own purpose.

Why is identifying limitations important?

Whether to seek acceptance or avoid struggle, humans often instinctively hide flaws and mistakes. Merging this thought process into research by attempting to hide limitations, however, is a bad idea. It has the potential to negate the validity of outcomes and damage the reputation of scholars.

By identifying and addressing limitations throughout a project, researchers strengthen their arguments and curtail the chance of peer censure based on overlooked mistakes. Pointing out these flaws shows an understanding of variable limits and a scrupulous research process.

Showing awareness of and taking responsibility for a project’s boundaries and challenges validates the integrity and transparency of a researcher. It further demonstrates the researchers understand the applicable literature and have thoroughly evaluated their chosen research methods.

Presenting limitations also benefits the readers by providing context for research findings. It guides them to interpret the project’s conclusions only within the scope of very specific conditions. By allowing for an appropriate generalization of the findings that is accurately confined by research boundaries and is not too broad, limitations boost a study’s credibility .

Limitations are true assets to the research process. They highlight opportunities for future research. When researchers identify the limitations of their particular approach to a study question, they enable precise transferability and improve chances for reproducibility. 

Simply stating a project’s limitations is not adequate for spurring further research, though. To spark the interest of other researchers, these acknowledgements must come with thorough explanations regarding how the limitations affected the current study and how they can potentially be overcome with amended methods.

How to write limitations

Typically, the information about a study’s limitations is situated either at the beginning of the discussion section to provide context for readers or at the conclusion of the discussion section to acknowledge the need for further research. However, it varies depending upon the target journal or publication guidelines. 

Don’t hide your limitations

It is also important to not bury a limitation in the body of the paper unless it has a unique connection to a topic in that section. If so, it needs to be reiterated with the other limitations or at the conclusion of the discussion section. Wherever it is included in the manuscript, ensure that the limitations section is prominently positioned and clearly introduced.

While maintaining transparency by disclosing limitations means taking a comprehensive approach, it is not necessary to discuss everything that could have potentially gone wrong during the research study. If there is no commitment to investigation in the introduction, it is unnecessary to consider the issue a limitation to the research. Wholly consider the term ‘limitations’ and ask, “Did it significantly change or limit the possible outcomes?” Then, qualify the occurrence as either a limitation to include in the current manuscript or as an idea to note for other projects. 

Writing limitations

Once the limitations are concretely identified and it is decided where they will be included in the paper, researchers are ready for the writing task. Including only what is pertinent, keeping explanations detailed but concise, and employing the following guidelines is key for crafting valuable limitations:

1) Identify and describe the limitations : Clearly introduce the limitation by classifying its form and specifying its origin. For example:

• An unintentional bias encountered during data collection
• An intentional use of unplanned post-hoc data analysis

2) Explain the implications : Describe how the limitation potentially influences the study’s findings and how the validity and generalizability are subsequently impacted. Provide examples and evidence to support claims of the limitations’ effects without making excuses or exaggerating their impact. Overall, be transparent and objective in presenting the limitations, without undermining the significance of the research. 

3) Provide alternative approaches for future studies : Offer specific suggestions for potential improvements or avenues for further investigation. Demonstrate a proactive approach by encouraging future research that addresses the identified gaps and, therefore, expands the knowledge base.

Whether presenting limitations as an individual section within the manuscript or as a subtopic in the discussion area, authors should use clear headings and straightforward language to facilitate readability. There is no need to complicate limitations with jargon, computations, or complex datasets.

Examples of common limitations

Limitations are generally grouped into two categories , methodology and research process .

Methodology limitations

Methodology may include limitations due to:

• Sample size
• Lack of available or reliable data
• Lack of prior research studies on the topic
• Measure used to collect the data
• Self-reported data

The researcher is addressing how the large sample size requires a reassessment of the measures used to collect and analyze the data.

Research process limitations

Limitations during the research process may arise from:

• Access to information
• Longitudinal effects
• Cultural and other biases
• Language fluency
• Time constraints

The author is pointing out that the model’s estimates are based on potentially biased observational studies.

Final thoughts

Successfully proving theories and touting great achievements are only two very narrow goals of scholarly research. The true passion and greatest efforts of researchers comes more in the form of confronting assumptions and exploring the obscure.

In many ways, recognizing and sharing the limitations of a research study both allows for and encourages this type of discovery that continuously pushes research forward. By using limitations to provide a transparent account of the project's boundaries and to contextualize the findings, researchers pave the way for even more robust and impactful research in the future.

Charla Viera, MS

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21 Research Limitations Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Research limitations refer to the potential weaknesses inherent in a study. All studies have limitations of some sort, meaning declaring limitations doesn’t necessarily need to be a bad thing, so long as your declaration of limitations is well thought-out and explained.

Rarely is a study perfect. Researchers have to make trade-offs when developing their studies, which are often based upon practical considerations such as time and monetary constraints, weighing the breadth of participants against the depth of insight, and choosing one methodology or another.

In research, studies can have limitations such as limited scope, researcher subjectivity, and lack of available research tools.

Acknowledging the limitations of your study should be seen as a strength. It demonstrates your willingness for transparency, humility, and submission to the scientific method and can bolster the integrity of the study. It can also inform future research direction.

Typically, scholars will explore the limitations of their study in either their methodology section, their conclusion section, or both.

Research Limitations Examples

Qualitative and quantitative research offer different perspectives and methods in exploring phenomena, each with its own strengths and limitations. So, I’ve split the limitations examples sections into qualitative and quantitative below.

Qualitative Research Limitations

Qualitative research seeks to understand phenomena in-depth and in context. It focuses on the ‘why’ and ‘how’ questions.

It’s often used to explore new or complex issues, and it provides rich, detailed insights into participants’ experiences, behaviors, and attitudes. However, these strengths also create certain limitations, as explained below.

1. Subjectivity

Qualitative research often requires the researcher to interpret subjective data. One researcher may examine a text and identify different themes or concepts as more dominant than others.

Close qualitative readings of texts are necessarily subjective – and while this may be a limitation, qualitative researchers argue this is the best way to deeply understand everything in context.

Suggested Solution and Response: To minimize subjectivity bias, you could consider cross-checking your own readings of themes and data against other scholars’ readings and interpretations. This may involve giving the raw data to a supervisor or colleague and asking them to code the data separately, then coming together to compare and contrast results.

2. Researcher Bias

The concept of researcher bias is related to, but slightly different from, subjectivity.

Researcher bias refers to the perspectives and opinions you bring with you when doing your research.

For example, a researcher who is explicitly of a certain philosophical or political persuasion may bring that persuasion to bear when interpreting data.

In many scholarly traditions, we will attempt to minimize researcher bias through the utilization of clear procedures that are set out in advance or through the use of statistical analysis tools.

However, in other traditions, such as in postmodern feminist research , declaration of bias is expected, and acknowledgment of bias is seen as a positive because, in those traditions, it is believed that bias cannot be eliminated from research, so instead, it is a matter of integrity to present it upfront.

Suggested Solution and Response: Acknowledge the potential for researcher bias and, depending on your theoretical framework , accept this, or identify procedures you have taken to seek a closer approximation to objectivity in your coding and analysis.

3. Generalizability

If you’re struggling to find a limitation to discuss in your own qualitative research study, then this one is for you: all qualitative research, of all persuasions and perspectives, cannot be generalized.

This is a core feature that sets qualitative data and quantitative data apart.

The point of qualitative data is to select case studies and similarly small corpora and dig deep through in-depth analysis and thick description of data.

Often, this will also mean that you have a non-randomized sample size.

While this is a positive – you’re going to get some really deep, contextualized, interesting insights – it also means that the findings may not be generalizable to a larger population that may not be representative of the small group of people in your study.

Suggested Solution and Response: Suggest future studies that take a quantitative approach to the question.

4. The Hawthorne Effect

The Hawthorne effect refers to the phenomenon where research participants change their ‘observed behavior’ when they’re aware that they are being observed.

This effect was first identified by Elton Mayo who conducted studies of the effects of various factors ton workers’ productivity. He noticed that no matter what he did – turning up the lights, turning down the lights, etc. – there was an increase in worker outputs compared to prior to the study taking place.

Mayo realized that the mere act of observing the workers made them work harder – his observation was what was changing behavior.

So, if you’re looking for a potential limitation to name for your observational research study , highlight the possible impact of the Hawthorne effect (and how you could reduce your footprint or visibility in order to decrease its likelihood).

Suggested Solution and Response: Highlight ways you have attempted to reduce your footprint while in the field, and guarantee anonymity to your research participants.

5. Replicability

Quantitative research has a great benefit in that the studies are replicable – a researcher can get a similar sample size, duplicate the variables, and re-test a study. But you can’t do that in qualitative research.

Qualitative research relies heavily on context – a specific case study or specific variables that make a certain instance worthy of analysis. As a result, it’s often difficult to re-enter the same setting with the same variables and repeat the study.

Furthermore, the individual researcher’s interpretation is more influential in qualitative research, meaning even if a new researcher enters an environment and makes observations, their observations may be different because subjectivity comes into play much more. This doesn’t make the research bad necessarily (great insights can be made in qualitative research), but it certainly does demonstrate a weakness of qualitative research.

6. Limited Scope

“Limited scope” is perhaps one of the most common limitations listed by researchers – and while this is often a catch-all way of saying, “well, I’m not studying that in this study”, it’s also a valid point.

No study can explore everything related to a topic. At some point, we have to make decisions about what’s included in the study and what is excluded from the study.

So, you could say that a limitation of your study is that it doesn’t look at an extra variable or concept that’s certainly worthy of study but will have to be explored in your next project because this project has a clearly and narrowly defined goal.

Suggested Solution and Response: Be clear about what’s in and out of the study when writing your research question.

7. Time Constraints

This is also a catch-all claim you can make about your research project: that you would have included more people in the study, looked at more variables, and so on. But you’ve got to submit this thing by the end of next semester! You’ve got time constraints.

And time constraints are a recognized reality in all research.

But this means you’ll need to explain how time has limited your decisions. As with “limited scope”, this may mean that you had to study a smaller group of subjects, limit the amount of time you spent in the field, and so forth.

Suggested Solution and Response: Suggest future studies that will build on your current work, possibly as a PhD project.

8. Resource Intensiveness

Qualitative research can be expensive due to the cost of transcription, the involvement of trained researchers, and potential travel for interviews or observations.

So, resource intensiveness is similar to the time constraints concept. If you don’t have the funds, you have to make decisions about which tools to use, which statistical software to employ, and how many research assistants you can dedicate to the study.

Suggested Solution and Response: Suggest future studies that will gain more funding on the back of this ‘ exploratory study ‘.

9. Coding Difficulties

Data analysis in qualitative research often involves coding, which can be subjective and complex, especially when dealing with ambiguous or contradicting data.

After naming this as a limitation in your research, it’s important to explain how you’ve attempted to address this. Some ways to ‘limit the limitation’ include:

• Triangulation: Have 2 other researchers code the data as well and cross-check your results with theirs to identify outliers that may need to be re-examined, debated with the other researchers, or removed altogether.
• Procedure: Use a clear coding procedure to demonstrate reliability in your coding process. I personally use the thematic network analysis method outlined in this academic article by Attride-Stirling (2001).

Suggested Solution and Response: Triangulate your coding findings with colleagues, and follow a thematic network analysis procedure.

10. Risk of Non-Responsiveness

There is always a risk in research that research participants will be unwilling or uncomfortable sharing their genuine thoughts and feelings in the study.

This is particularly true when you’re conducting research on sensitive topics, politicized topics, or topics where the participant is expressing vulnerability .

This is similar to the Hawthorne effect (aka participant bias), where participants change their behaviors in your presence; but it goes a step further, where participants actively hide their true thoughts and feelings from you.

Suggested Solution and Response: One way to manage this is to try to include a wider group of people with the expectation that there will be non-responsiveness from some participants.

11. Risk of Attrition

Attrition refers to the process of losing research participants throughout the study.

This occurs most commonly in longitudinal studies , where a researcher must return to conduct their analysis over spaced periods of time, often over a period of years.

Things happen to people over time – they move overseas, their life experiences change, they get sick, change their minds, and even die. The more time that passes, the greater the risk of attrition.

Suggested Solution and Response: One way to manage this is to try to include a wider group of people with the expectation that there will be attrition over time.

12. Difficulty in Maintaining Confidentiality and Anonymity

Given the detailed nature of qualitative data , ensuring participant anonymity can be challenging.

If you have a sensitive topic in a specific case study, even anonymizing research participants sometimes isn’t enough. People might be able to induce who you’re talking about.

Sometimes, this will mean you have to exclude some interesting data that you collected from your final report. Confidentiality and anonymity come before your findings in research ethics – and this is a necessary limiting factor.

Suggested Solution and Response: Highlight the efforts you have taken to anonymize data, and accept that confidentiality and accountability place extremely important constraints on academic research.

13. Difficulty in Finding Research Participants

A study that looks at a very specific phenomenon or even a specific set of cases within a phenomenon means that the pool of potential research participants can be very low.

Compile on top of this the fact that many people you approach may choose not to participate, and you could end up with a very small corpus of subjects to explore. This may limit your ability to make complete findings, even in a quantitative sense.

You may need to therefore limit your research question and objectives to something more realistic.

Suggested Solution and Response: Highlight that this is going to limit the study’s generalizability significantly.

14. Ethical Limitations

Ethical limitations refer to the things you cannot do based on ethical concerns identified either by yourself or your institution’s ethics review board.

This might include threats to the physical or psychological well-being of your research subjects, the potential of releasing data that could harm a person’s reputation, and so on.

Furthermore, even if your study follows all expected standards of ethics, you still, as an ethical researcher, need to allow a research participant to pull out at any point in time, after which you cannot use their data, which demonstrates an overlap between ethical constraints and participant attrition.

Suggested Solution and Response: Highlight that these ethical limitations are inevitable but important to sustain the integrity of the research.

For more on Qualitative Research, Explore my Qualitative Research Guide

Quantitative Research Limitations

Quantitative research focuses on quantifiable data and statistical, mathematical, or computational techniques. It’s often used to test hypotheses, assess relationships and causality, and generalize findings across larger populations.

Quantitative research is widely respected for its ability to provide reliable, measurable, and generalizable data (if done well!). Its structured methodology has strengths over qualitative research, such as the fact it allows for replication of the study, which underpins the validity of the research.

However, this approach is not without it limitations, explained below.

1. Over-Simplification

Quantitative research is powerful because it allows you to measure and analyze data in a systematic and standardized way. However, one of its limitations is that it can sometimes simplify complex phenomena or situations.

In other words, it might miss the subtleties or nuances of the research subject.

For example, if you’re studying why people choose a particular diet, a quantitative study might identify factors like age, income, or health status. But it might miss other aspects, such as cultural influences or personal beliefs, that can also significantly impact dietary choices.

When writing about this limitation, you can say that your quantitative approach, while providing precise measurements and comparisons, may not capture the full complexity of your subjects of study.

Suggested Solution and Response: Suggest a follow-up case study using the same research participants in order to gain additional context and depth.

2. Lack of Context

Another potential issue with quantitative research is that it often focuses on numbers and statistics at the expense of context or qualitative information.

Let’s say you’re studying the effect of classroom size on student performance. You might find that students in smaller classes generally perform better. However, this doesn’t take into account other variables, like teaching style , student motivation, or family support.

When describing this limitation, you might say, “Although our research provides important insights into the relationship between class size and student performance, it does not incorporate the impact of other potentially influential variables. Future research could benefit from a mixed-methods approach that combines quantitative analysis with qualitative insights.”

3. Applicability to Real-World Settings

Oftentimes, experimental research takes place in controlled environments to limit the influence of outside factors.

This control is great for isolation and understanding the specific phenomenon but can limit the applicability or “external validity” of the research to real-world settings.

For example, if you conduct a lab experiment to see how sleep deprivation impacts cognitive performance, the sterile, controlled lab environment might not reflect real-world conditions where people are dealing with multiple stressors.

Therefore, when explaining the limitations of your quantitative study in your methodology section, you could state:

“While our findings provide valuable information about [topic], the controlled conditions of the experiment may not accurately represent real-world scenarios where extraneous variables will exist. As such, the direct applicability of our results to broader contexts may be limited.”

Suggested Solution and Response: Suggest future studies that will engage in real-world observational research, such as ethnographic research.

4. Limited Flexibility

Once a quantitative study is underway, it can be challenging to make changes to it. This is because, unlike in grounded research, you’re putting in place your study in advance, and you can’t make changes part-way through.

Your study design, data collection methods, and analysis techniques need to be decided upon before you start collecting data.

For example, if you are conducting a survey on the impact of social media on teenage mental health, and halfway through, you realize that you should have included a question about their screen time, it’s generally too late to add it.

When discussing this limitation, you could write something like, “The structured nature of our quantitative approach allows for consistent data collection and analysis but also limits our flexibility to adapt and modify the research process in response to emerging insights and ideas.”

Suggested Solution and Response: Suggest future studies that will use mixed-methods or qualitative research methods to gain additional depth of insight.

5. Risk of Survey Error

Surveys are a common tool in quantitative research, but they carry risks of error.

There can be measurement errors (if a question is misunderstood), coverage errors (if some groups aren’t adequately represented), non-response errors (if certain people don’t respond), and sampling errors (if your sample isn’t representative of the population).

For instance, if you’re surveying college students about their study habits , but only daytime students respond because you conduct the survey during the day, your results will be skewed.

In discussing this limitation, you might say, “Despite our best efforts to develop a comprehensive survey, there remains a risk of survey error, including measurement, coverage, non-response, and sampling errors. These could potentially impact the reliability and generalizability of our findings.”

Suggested Solution and Response: Suggest future studies that will use other survey tools to compare and contrast results.

6. Limited Ability to Probe Answers

With quantitative research, you typically can’t ask follow-up questions or delve deeper into participants’ responses like you could in a qualitative interview.

For instance, imagine you are surveying 500 students about study habits in a questionnaire. A respondent might indicate that they study for two hours each night. You might want to follow up by asking them to elaborate on what those study sessions involve or how effective they feel their habits are.

However, quantitative research generally disallows this in the way a qualitative semi-structured interview could.

When discussing this limitation, you might write, “Given the structured nature of our survey, our ability to probe deeper into individual responses is limited. This means we may not fully understand the context or reasoning behind the responses, potentially limiting the depth of our findings.”

Suggested Solution and Response: Suggest future studies that engage in mixed-method or qualitative methodologies to address the issue from another angle.

7. Reliance on Instruments for Data Collection

In quantitative research, the collection of data heavily relies on instruments like questionnaires, surveys, or machines.

The limitation here is that the data you get is only as good as the instrument you’re using. If the instrument isn’t designed or calibrated well, your data can be flawed.

For instance, if you’re using a questionnaire to study customer satisfaction and the questions are vague, confusing, or biased, the responses may not accurately reflect the customers’ true feelings.

When discussing this limitation, you could say, “Our study depends on the use of questionnaires for data collection. Although we have put significant effort into designing and testing the instrument, it’s possible that inaccuracies or misunderstandings could potentially affect the validity of the data collected.”

Suggested Solution and Response: Suggest future studies that will use different instruments but examine the same variables to triangulate results.

8. Time and Resource Constraints (Specific to Quantitative Research)

Quantitative research can be time-consuming and resource-intensive, especially when dealing with large samples.

It often involves systematic sampling, rigorous design, and sometimes complex statistical analysis.

If resources and time are limited, it can restrict the scale of your research, the techniques you can employ, or the extent of your data analysis.

For example, you may want to conduct a nationwide survey on public opinion about a certain policy. However, due to limited resources, you might only be able to survey people in one city.

When writing about this limitation, you could say, “Given the scope of our research and the resources available, we are limited to conducting our survey within one city, which may not fully represent the nationwide public opinion. Hence, the generalizability of the results may be limited.”

Suggested Solution and Response: Suggest future studies that will have more funding or longer timeframes.

How to Discuss Your Research Limitations

1. in your research proposal and methodology section.

In the research proposal, which will become the methodology section of your dissertation, I would recommend taking the four following steps, in order:

• Be Explicit about your Scope – If you limit the scope of your study in your research question, aims, and objectives, then you can set yourself up well later in the methodology to say that certain questions are “outside the scope of the study.” For example, you may identify the fact that the study doesn’t address a certain variable, but you can follow up by stating that the research question is specifically focused on the variable that you are examining, so this limitation would need to be looked at in future studies.
• Acknowledge the Limitation – Acknowledging the limitations of your study demonstrates reflexivity and humility and can make your research more reliable and valid. It also pre-empts questions the people grading your paper may have, so instead of them down-grading you for your limitations; they will congratulate you on explaining the limitations and how you have addressed them!
• Explain your Decisions – You may have chosen your approach (despite its limitations) for a very specific reason. This might be because your approach remains, on balance, the best one to answer your research question. Or, it might be because of time and monetary constraints that are outside of your control.
• Highlight the Strengths of your Approach – Conclude your limitations section by strongly demonstrating that, despite limitations, you’ve worked hard to minimize the effects of the limitations and that you have chosen your specific approach and methodology because it’s also got some terrific strengths. Name the strengths.

Overall, you’ll want to acknowledge your own limitations but also explain that the limitations don’t detract from the value of your study as it stands.

2. In the Conclusion Section or Chapter

In the conclusion of your study, it is generally expected that you return to a discussion of the study’s limitations. Here, I recommend the following steps:

• Acknowledge issues faced – After completing your study, you will be increasingly aware of issues you may have faced that, if you re-did the study, you may have addressed earlier in order to avoid those issues. Acknowledge these issues as limitations, and frame them as recommendations for subsequent studies.
• Suggest further research – Scholarly research aims to fill gaps in the current literature and knowledge. Having established your expertise through your study, suggest lines of inquiry for future researchers. You could state that your study had certain limitations, and “future studies” can address those limitations.
• Suggest a mixed methods approach – Qualitative and quantitative research each have pros and cons. So, note those ‘cons’ of your approach, then say the next study should approach the topic using the opposite methodology or could approach it using a mixed-methods approach that could achieve the benefits of quantitative studies with the nuanced insights of associated qualitative insights as part of an in-study case-study.

Overall, be clear about both your limitations and how those limitations can inform future studies.

In sum, each type of research method has its own strengths and limitations. Qualitative research excels in exploring depth, context, and complexity, while quantitative research excels in examining breadth, generalizability, and quantifiable measures. Despite their individual limitations, each method contributes unique and valuable insights, and researchers often use them together to provide a more comprehensive understanding of the phenomenon being studied.

Attride-Stirling, J. (2001). Thematic networks: an analytic tool for qualitative research. Qualitative research , 1 (3), 385-405. ( Source )

Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J., & Williams, R. A. (2021).  SAGE research methods foundations . London: Sage Publications.

Clark, T., Foster, L., Bryman, A., & Sloan, L. (2021).  Bryman’s social research methods . Oxford: Oxford University Press.

Köhler, T., Smith, A., & Bhakoo, V. (2022). Templates in qualitative research methods: Origins, limitations, and new directions.  Organizational Research Methods ,  25 (2), 183-210. ( Source )

Lenger, A. (2019). The rejection of qualitative research methods in economics.  Journal of Economic Issues ,  53 (4), 946-965. ( Source )

Taherdoost, H. (2022). What are different research approaches? Comprehensive review of qualitative, quantitative, and mixed method research, their applications, types, and limitations.  Journal of Management Science & Engineering Research ,  5 (1), 53-63. ( Source )

Walliman, N. (2021).  Research methods: The basics . New York: Routledge.

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Home » Limitations in Research – Types, Examples and Writing Guide

Limitations in Research – Types, Examples and Writing Guide

Table of Contents

Limitations in Research

Limitations in research refer to the factors that may affect the results, conclusions , and generalizability of a study. These limitations can arise from various sources, such as the design of the study, the sampling methods used, the measurement tools employed, and the limitations of the data analysis techniques.

Types of Limitations in Research

Types of Limitations in Research are as follows:

Sample Size Limitations

This refers to the size of the group of people or subjects that are being studied. If the sample size is too small, then the results may not be representative of the population being studied. This can lead to a lack of generalizability of the results.

Time Limitations

Time limitations can be a constraint on the research process . This could mean that the study is unable to be conducted for a long enough period of time to observe the long-term effects of an intervention, or to collect enough data to draw accurate conclusions.

Selection Bias

This refers to a type of bias that can occur when the selection of participants in a study is not random. This can lead to a biased sample that is not representative of the population being studied.

Confounding Variables

Confounding variables are factors that can influence the outcome of a study, but are not being measured or controlled for. These can lead to inaccurate conclusions or a lack of clarity in the results.

Measurement Error

This refers to inaccuracies in the measurement of variables, such as using a faulty instrument or scale. This can lead to inaccurate results or a lack of validity in the study.

Ethical Limitations

Ethical limitations refer to the ethical constraints placed on research studies. For example, certain studies may not be allowed to be conducted due to ethical concerns, such as studies that involve harm to participants.

Examples of Limitations in Research

Some Examples of Limitations in Research are as follows:

Research Title: “The Effectiveness of Machine Learning Algorithms in Predicting Customer Behavior”

Limitations:

• The study only considered a limited number of machine learning algorithms and did not explore the effectiveness of other algorithms.
• The study used a specific dataset, which may not be representative of all customer behaviors or demographics.
• The study did not consider the potential ethical implications of using machine learning algorithms in predicting customer behavior.

Research Title: “The Impact of Online Learning on Student Performance in Computer Science Courses”

• The study was conducted during the COVID-19 pandemic, which may have affected the results due to the unique circumstances of remote learning.
• The study only included students from a single university, which may limit the generalizability of the findings to other institutions.
• The study did not consider the impact of individual differences, such as prior knowledge or motivation, on student performance in online learning environments.

Research Title: “The Effect of Gamification on User Engagement in Mobile Health Applications”

• The study only tested a specific gamification strategy and did not explore the effectiveness of other gamification techniques.
• The study relied on self-reported measures of user engagement, which may be subject to social desirability bias or measurement errors.
• The study only included a specific demographic group (e.g., young adults) and may not be generalizable to other populations with different preferences or needs.

How to Write Limitations in Research

When writing about the limitations of a research study, it is important to be honest and clear about the potential weaknesses of your work. Here are some tips for writing about limitations in research:

• Identify the limitations: Start by identifying the potential limitations of your research. These may include sample size, selection bias, measurement error, or other issues that could affect the validity and reliability of your findings.
• Be honest and objective: When describing the limitations of your research, be honest and objective. Do not try to minimize or downplay the limitations, but also do not exaggerate them. Be clear and concise in your description of the limitations.
• Provide context: It is important to provide context for the limitations of your research. For example, if your sample size was small, explain why this was the case and how it may have affected your results. Providing context can help readers understand the limitations in a broader context.
• Discuss implications : Discuss the implications of the limitations for your research findings. For example, if there was a selection bias in your sample, explain how this may have affected the generalizability of your findings. This can help readers understand the limitations in terms of their impact on the overall validity of your research.
• Provide suggestions for future research : Finally, provide suggestions for future research that can address the limitations of your study. This can help readers understand how your research fits into the broader field and can provide a roadmap for future studies.

Purpose of Limitations in Research

There are several purposes of limitations in research. Here are some of the most important ones:

• To acknowledge the boundaries of the study : Limitations help to define the scope of the research project and set realistic expectations for the findings. They can help to clarify what the study is not intended to address.
• To identify potential sources of bias: Limitations can help researchers identify potential sources of bias in their research design, data collection, or analysis. This can help to improve the validity and reliability of the findings.
• To provide opportunities for future research: Limitations can highlight areas for future research and suggest avenues for further exploration. This can help to advance knowledge in a particular field.
• To demonstrate transparency and accountability: By acknowledging the limitations of their research, researchers can demonstrate transparency and accountability to their readers, peers, and funders. This can help to build trust and credibility in the research community.
• To encourage critical thinking: Limitations can encourage readers to critically evaluate the study’s findings and consider alternative explanations or interpretations. This can help to promote a more nuanced and sophisticated understanding of the topic under investigation.

When to Write Limitations in Research

Limitations should be included in research when they help to provide a more complete understanding of the study’s results and implications. A limitation is any factor that could potentially impact the accuracy, reliability, or generalizability of the study’s findings.

It is important to identify and discuss limitations in research because doing so helps to ensure that the results are interpreted appropriately and that any conclusions drawn are supported by the available evidence. Limitations can also suggest areas for future research, highlight potential biases or confounding factors that may have affected the results, and provide context for the study’s findings.

Generally, limitations should be discussed in the conclusion section of a research paper or thesis, although they may also be mentioned in other sections, such as the introduction or methods. The specific limitations that are discussed will depend on the nature of the study, the research question being investigated, and the data that was collected.

Examples of limitations that might be discussed in research include sample size limitations, data collection methods, the validity and reliability of measures used, and potential biases or confounding factors that could have affected the results. It is important to note that limitations should not be used as a justification for poor research design or methodology, but rather as a way to enhance the understanding and interpretation of the study’s findings.

Importance of Limitations in Research

Here are some reasons why limitations are important in research:

• Enhances the credibility of research: Limitations highlight the potential weaknesses and threats to validity, which helps readers to understand the scope and boundaries of the study. This improves the credibility of research by acknowledging its limitations and providing a clear picture of what can and cannot be concluded from the study.
• Facilitates replication: By highlighting the limitations, researchers can provide detailed information about the study’s methodology, data collection, and analysis. This information helps other researchers to replicate the study and test the validity of the findings, which enhances the reliability of research.
• Guides future research : Limitations provide insights into areas for future research by identifying gaps or areas that require further investigation. This can help researchers to design more comprehensive and effective studies that build on existing knowledge.
• Provides a balanced view: Limitations help to provide a balanced view of the research by highlighting both strengths and weaknesses. This ensures that readers have a clear understanding of the study’s limitations and can make informed decisions about the generalizability and applicability of the findings.

Advantages of Limitations in Research

Here are some potential advantages of limitations in research:

• Focus : Limitations can help researchers focus their study on a specific area or population, which can make the research more relevant and useful.
• Realism : Limitations can make a study more realistic by reflecting the practical constraints and challenges of conducting research in the real world.
• Innovation : Limitations can spur researchers to be more innovative and creative in their research design and methodology, as they search for ways to work around the limitations.
• Rigor : Limitations can actually increase the rigor and credibility of a study, as researchers are forced to carefully consider the potential sources of bias and error, and address them to the best of their abilities.
• Generalizability : Limitations can actually improve the generalizability of a study by ensuring that it is not overly focused on a specific sample or situation, and that the results can be applied more broadly.

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Research Limitations 101 📖

A Plain-Language Explainer (With Practical Examples)

By: Derek Jansen (MBA) | Expert Reviewer: Dr. Eunice Rautenbach | May 2024

Research limitations are one of those things that students tend to avoid digging into, and understandably so. No one likes to critique their own study and point out weaknesses. Nevertheless, being able to understand the limitations of your study – and, just as importantly, the implications thereof – a is a critically important skill.

In this post, we’ll unpack some of the most common research limitations you’re likely to encounter, so that you can approach your project with confidence.

Overview: Research Limitations 101

• What are research limitations ?
• Access – based limitations
• Temporal & financial limitations
• Sample & sampling limitations
• Design limitations
• Researcher limitations
• Key takeaways

What (exactly) are “research limitations”?

At the simplest level, research limitations (also referred to as “the limitations of the study”) are the constraints and challenges that will invariably influence your ability to conduct your study and draw reliable conclusions .

Research limitations are inevitable. Absolutely no study is perfect and limitations are an inherent part of any research design. These limitations can stem from a variety of sources , including access to data, methodological choices, and the more mundane constraints of budget and time. So, there’s no use trying to escape them – what matters is that you can recognise them.

Acknowledging and understanding these limitations is crucial, not just for the integrity of your research, but also for your development as a scholar. That probably sounds a bit rich, but realistically, having a strong understanding of the limitations of any given study helps you handle the inevitable obstacles professionally and transparently, which in turn builds trust with your audience and academic peers.

Simply put, recognising and discussing the limitations of your study demonstrates that you know what you’re doing , and that you’ve considered the results of your project within the context of these limitations. In other words, discussing the limitations is a sign of credibility and strength – not weakness. Contrary to the common misconception, highlighting your limitations (or rather, your study’s limitations) will earn you (rather than cost you) marks.

So, with that foundation laid, let’s have a look at some of the most common research limitations you’re likely to encounter – and how to go about managing them as effectively as possible.

Need a helping hand?

Limitation #1: Access To Information

One of the first hurdles you might encounter is limited access to necessary information. For example, you may have trouble getting access to specific literature or niche data sets. This situation can manifest due to several reasons, including paywalls, copyright and licensing issues or language barriers.

To minimise situations like these, it’s useful to try to leverage your university’s resource pool to the greatest extent possible. In practical terms, this means engaging with your university’s librarian and/or potentially utilising interlibrary loans to get access to restricted resources. If this sounds foreign to you, have a chat with your librarian 🙃

In emerging fields or highly specific study areas, you might find that there’s very little existing research (i.e., literature) on your topic. This scenario, while challenging, also offers a unique opportunity to contribute significantly to your field , as it indicates that there’s a significant research gap .

All of that said, be sure to conduct an exhaustive search using a variety of keywords and Boolean operators before assuming that there’s a lack of literature. Also, remember to snowball your literature base . In other words, scan the reference lists of the handful of papers that are directly relevant and then scan those references for more sources. You can also consider using tools like Litmaps and Connected Papers (see video below).

Limitation #2: Time & Money

Almost every researcher will face time and budget constraints at some point. Naturally, these limitations can affect the depth and breadth of your research – but they don’t need to be a death sentence.

Effective planning is crucial to managing both the temporal and financial aspects of your study. In practical terms, utilising tools like Gantt charts can help you visualise and plan your research timeline realistically, thereby reducing the risk of any nasty surprises. Always take a conservative stance when it comes to timelines, especially if you’re new to academic research. As a rule of thumb, things will generally take twice as long as you expect – so, prepare for the worst-case scenario.

If budget is a concern, you might want to consider exploring small research grants or adjusting the scope of your study so that it fits within a realistic budget. Trimming back might sound unattractive, but keep in mind that a smaller, well-planned study can often be more impactful than a larger, poorly planned project.

If you find yourself in a position where you’ve already run out of cash, don’t panic. There’s usually a pivot opportunity hidden somewhere within your project. Engage with your research advisor or faculty to explore potential solutions – don’t make any major changes without first consulting your institution.

Limitation #3: Sample Size & Composition

As we’ve discussed before , the size and representativeness of your sample are crucial , especially in quantitative research where the robustness of your conclusions often depends on these factors. All too often though, students run into issues achieving a sufficient sample size and composition.

To ensure adequacy in terms of your sample size, it’s important to plan for potential dropouts by oversampling from the outset . In other words, if you aim for a final sample size of 100 participants, aim to recruit 120-140 to account for unexpected challenges. If you still find yourself short on participants, consider whether you could complement your dataset with secondary data or data from an adjacent sample – for example, participants from another city or country. That said, be sure to engage with your research advisor before making any changes to your approach.

A related issue that you may run into is sample composition. In other words, you may have trouble securing a random sample that’s representative of your population of interest. In cases like this, you might again want to look at ways to complement your dataset with other sources, but if that’s not possible, it’s not the end of the world. As with all limitations, you’ll just need to recognise this limitation in your final write-up and be sure to interpret your results accordingly. In other words, don’t claim generalisability of your results if your sample isn’t random.

Limitation #4: Methodological Limitations

As we alluded earlier, every methodological choice comes with its own set of limitations . For example, you can’t claim causality if you’re using a descriptive or correlational research design. Similarly, as we saw in the previous example, you can’t claim generalisability if you’re using a non-random sampling approach.

Making good methodological choices is all about understanding (and accepting) the inherent trade-offs . In the vast majority of cases, you won’t be able to adopt the “perfect” methodology – and that’s okay. What’s important is that you select a methodology that aligns with your research aims and research questions , as well as the practical constraints at play (e.g., time, money, equipment access, etc.). Just as importantly, you must recognise and articulate the limitations of your chosen methods, and justify why they were the most suitable, given your specific context.

Limitation #5: Researcher (In)experience 

A discussion about research limitations would not be complete without mentioning the researcher (that’s you!). Whether we like to admit it or not, researcher inexperience and personal biases can subtly (and sometimes not so subtly) influence the interpretation and presentation of data within a study. This is especially true when it comes to dissertations and theses , as these are most commonly undertaken by first-time (or relatively fresh) researchers.

When it comes to dealing with this specific limitation, it’s important to remember the adage “ We don’t know what we don’t know ”. In other words, recognise and embrace your (relative) ignorance and subjectivity – and interpret your study’s results within that context . Simply put, don’t be overly confident in drawing conclusions from your study – especially when they contradict existing literature.

Cultivating a culture of reflexivity within your research practices can help reduce subjectivity and keep you a bit more “rooted” in the data. In practical terms, this simply means making an effort to become aware of how your perspectives and experiences may have shaped the research process and outcomes.

As with any new endeavour in life, it’s useful to garner as many outsider perspectives as possible. Of course, your university-assigned research advisor will play a large role in this respect, but it’s also a good idea to seek out feedback and critique from other academics. To this end, you might consider approaching other faculty at your institution, joining an online group, or even working with a private coach .

Key Takeaways

Understanding and effectively navigating research limitations is key to conducting credible and reliable academic work. By acknowledging and addressing these limitations upfront, you not only enhance the integrity of your research, but also demonstrate your academic maturity and professionalism.

Whether you’re working on a dissertation, thesis or any other type of formal academic research, remember the five most common research limitations and interpret your data while keeping them in mind.

• Access to Information (literature and data)
• Time and money
• Sample size and composition
• Research design and methodology
• Researcher (in)experience and bias

If you need a hand identifying and mitigating the limitations within your study, check out our 1:1 private coaching service .

Psst… there’s more!

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How to present limitations in research

Last updated

30 January 2024

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Limitations don’t invalidate or diminish your results, but it’s best to acknowledge them. This will enable you to address any questions your study failed to answer because of them.

In this guide, learn how to recognize, present, and overcome limitations in research.

• What is a research limitation?

Research limitations are weaknesses in your research design or execution that may have impacted outcomes and conclusions. Uncovering limitations doesn’t necessarily indicate poor research design—it just means you encountered challenges you couldn’t have anticipated that limited your research efforts.

Does basic research have limitations?

Basic research aims to provide more information about your research topic . It requires the same standard research methodology and data collection efforts as any other research type, and it can also have limitations.

• Common research limitations

Researchers encounter common limitations when embarking on a study. Limitations can occur in relation to the methods you apply or the research process you design. They could also be connected to you as the researcher.

Methodology limitations

Not having access to data or reliable information can impact the methods used to facilitate your research. A lack of data or reliability may limit the parameters of your study area and the extent of your exploration.

Your sample size may also be affected because you won’t have any direction on how big or small it should be and who or what you should include. Having too few participants won’t adequately represent the population or groups of people needed to draw meaningful conclusions.

Research process limitations

The study’s design can impose constraints on the process. For example, as you’re conducting the research, issues may arise that don’t conform to the data collection methodology you developed. You may not realize until well into the process that you should have incorporated more specific questions or comprehensive experiments to generate the data you need to have confidence in your results.

Constraints on resources can also have an impact. Being limited on participants or participation incentives may limit your sample sizes. Insufficient tools, equipment, and materials to conduct a thorough study may also be a factor.

Common researcher limitations

Here are some of the common researcher limitations you may encounter:

Time: some research areas require multi-year longitudinal approaches, but you might not be able to dedicate that much time. Imagine you want to measure how much memory a person loses as they age. This may involve conducting multiple tests on a sample of participants over 20–30 years, which may be impossible.

Bias: researchers can consciously or unconsciously apply bias to their research. Biases can contribute to relying on research sources and methodologies that will only support your beliefs about the research you’re embarking on. You might also omit relevant issues or participants from the scope of your study because of your biases.

Limited access to data : you may need to pay to access specific databases or journals that would be helpful to your research process. You might also need to gain information from certain people or organizations but have limited access to them. These cases require readjusting your process and explaining why your findings are still reliable.

• Why is it important to identify limitations?

Identifying limitations adds credibility to research and provides a deeper understanding of how you arrived at your conclusions.

Constraints may have prevented you from collecting specific data or information you hoped would prove or disprove your hypothesis or provide a more comprehensive understanding of your research topic.

However, identifying the limitations contributing to your conclusions can inspire further research efforts that help gather more substantial information and data.

• Where to put limitations in a research paper

A research paper is broken up into different sections that appear in the following order:

Introduction

Methodology

The discussion portion of your paper explores your findings and puts them in the context of the overall research. Either place research limitations at the beginning of the discussion section before the analysis of your findings or at the end of the section to indicate that further research needs to be pursued.

What not to include in the limitations section

Evidence that doesn’t support your hypothesis is not a limitation, so you shouldn’t include it in the limitation section. Don’t just list limitations and their degree of severity without further explanation.

• How to present limitations

You’ll want to present the limitations of your study in a way that doesn’t diminish the validity of your research and leave the reader wondering if your results and conclusions have been compromised.

Include only the limitations that directly relate to and impact how you addressed your research questions. Following a specific format enables the reader to develop an understanding of the weaknesses within the context of your findings without doubting the quality and integrity of your research.

Identify the limitations specific to your study

You don’t have to identify every possible limitation that might have occurred during your research process. Only identify those that may have influenced the quality of your findings and your ability to answer your research question.

Explain study limitations in detail

This explanation should be the most significant portion of your limitation section.

Link each limitation with an interpretation and appraisal of their impact on the study. You’ll have to evaluate and explain whether the error, method, or validity issues influenced the study’s outcome and how.

Propose a direction for future studies and present alternatives

In this section, suggest how researchers can avoid the pitfalls you experienced during your research process.

If an issue with methodology was a limitation, propose alternate methods that may help with a smoother and more conclusive research project . Discuss the pros and cons of your alternate recommendation.

Describe steps taken to minimize each limitation

You probably took steps to try to address or mitigate limitations when you noticed them throughout the course of your research project. Describe these steps in the limitation section.

• Limitation example

“Approaches like stem cell transplantation and vaccination in AD [Alzheimer’s disease] work on a cellular or molecular level in the laboratory. However, translation into clinical settings will remain a challenge for the next decade.”

The authors are saying that even though these methods showed promise in helping people with memory loss when conducted in the lab (in other words, using animal studies), more studies are needed. These may be controlled clinical trials, for example. 

However, the short life span of stem cells outside the lab and the vaccination’s severe inflammatory side effects are limitations. Researchers won’t be able to conduct clinical trials until these issues are overcome.

• How to overcome limitations in research

You’ve already started on the road to overcoming limitations in research by acknowledging that they exist. However, you need to ensure readers don’t mistake weaknesses for errors within your research design.

To do this, you’ll need to justify and explain your rationale for the methods, research design, and analysis tools you chose and how you noticed they may have presented limitations.

Your readers need to know that even when limitations presented themselves, you followed best practices and the ethical standards of your field. You didn’t violate any rules and regulations during your research process.

You’ll also want to reinforce the validity of your conclusions and results with multiple sources, methods, and perspectives. This prevents readers from assuming your findings were derived from a single or biased source.

• Learning and improving starts with limitations in research

Dealing with limitations with transparency and integrity helps identify areas for future improvements and developments. It’s a learning process, providing valuable insights into how you can improve methodologies, expand sample sizes, or explore alternate approaches to further support the validity of your findings.

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Limitations of the Study – How to Write & Examples

What are the limitations of a study?

The limitations of a study are the elements of methodology or study design that impact the interpretation of your research results. The limitations essentially detail any flaws or shortcomings in your study. Study limitations can exist due to constraints on research design, methodology, materials, etc., and these factors may impact the findings of your study. However, researchers are often reluctant to discuss the limitations of their study in their papers, feeling that bringing up limitations may undermine its research value in the eyes of readers and reviewers.

In spite of the impact it might have (and perhaps because of it) you should clearly acknowledge any limitations in your research paper in order to show readers—whether journal editors, other researchers, or the general public—that you are aware of these limitations and to explain how they affect the conclusions that can be drawn from the research.

In this article, we provide some guidelines for writing about research limitations, show examples of some frequently seen study limitations, and recommend techniques for presenting this information. And after you have finished drafting and have received manuscript editing for your work, you still might want to follow this up with academic editing before submitting your work to your target journal.

Why do I need to include limitations of research in my paper?

Although limitations address the potential weaknesses of a study, writing about them toward the end of your paper actually strengthens your study by identifying any problems before other researchers or reviewers find them.

Furthermore, pointing out study limitations shows that you’ve considered the impact of research weakness thoroughly and have an in-depth understanding of your research topic. Since all studies face limitations, being honest and detailing these limitations will impress researchers and reviewers more than ignoring them.

Where should I put the limitations of the study in my paper?

Some limitations might be evident to researchers before the start of the study, while others might become clear while you are conducting the research. Whether these limitations are anticipated or not, and whether they are due to research design or to methodology, they should be clearly identified and discussed in the discussion section —the final section of your paper. Most journals now require you to include a discussion of potential limitations of your work, and many journals now ask you to place this “limitations section” at the very end of your article. 

Some journals ask you to also discuss the strengths of your work in this section, and some allow you to freely choose where to include that information in your discussion section—make sure to always check the author instructions of your target journal before you finalize a manuscript and submit it for peer review .

Limitations of the Study Examples

There are several reasons why limitations of research might exist. The two main categories of limitations are those that result from the methodology and those that result from issues with the researcher(s).

Common Methodological Limitations of Studies

Limitations of research due to methodological problems can be addressed by clearly and directly identifying the potential problem and suggesting ways in which this could have been addressed—and SHOULD be addressed in future studies. The following are some major potential methodological issues that can impact the conclusions researchers can draw from the research.

Issues with research samples and selection

Sampling errors occur when a probability sampling method is used to select a sample, but that sample does not reflect the general population or appropriate population concerned. This results in limitations of your study known as “sample bias” or “selection bias.”

For example, if you conducted a survey to obtain your research results, your samples (participants) were asked to respond to the survey questions. However, you might have had limited ability to gain access to the appropriate type or geographic scope of participants. In this case, the people who responded to your survey questions may not truly be a random sample.

Insufficient sample size for statistical measurements

When conducting a study, it is important to have a sufficient sample size in order to draw valid conclusions. The larger the sample, the more precise your results will be. If your sample size is too small, it will be difficult to identify significant relationships in the data.

Normally, statistical tests require a larger sample size to ensure that the sample is considered representative of a population and that the statistical result can be generalized to a larger population. It is a good idea to understand how to choose an appropriate sample size before you conduct your research by using scientific calculation tools—in fact, many journals now require such estimation to be included in every manuscript that is sent out for review.

Lack of previous research studies on the topic

Citing and referencing prior research studies constitutes the basis of the literature review for your thesis or study, and these prior studies provide the theoretical foundations for the research question you are investigating. However, depending on the scope of your research topic, prior research studies that are relevant to your thesis might be limited.

When there is very little or no prior research on a specific topic, you may need to develop an entirely new research typology. In this case, discovering a limitation can be considered an important opportunity to identify literature gaps and to present the need for further development in the area of study.

Methods/instruments/techniques used to collect the data

After you complete your analysis of the research findings (in the discussion section), you might realize that the manner in which you have collected the data or the ways in which you have measured variables has limited your ability to conduct a thorough analysis of the results.

For example, you might realize that you should have addressed your survey questions from another viable perspective, or that you were not able to include an important question in the survey. In these cases, you should acknowledge the deficiency or deficiencies by stating a need for future researchers to revise their specific methods for collecting data that includes these missing elements.

Common Limitations of the Researcher(s)

Study limitations that arise from situations relating to the researcher or researchers (whether the direct fault of the individuals or not) should also be addressed and dealt with, and remedies to decrease these limitations—both hypothetically in your study, and practically in future studies—should be proposed.

Limited access to data

If your research involved surveying certain people or organizations, you might have faced the problem of having limited access to these respondents. Due to this limited access, you might need to redesign or restructure your research in a different way. In this case, explain the reasons for limited access and be sure that your finding is still reliable and valid despite this limitation.

Time constraints

Just as students have deadlines to turn in their class papers, academic researchers might also have to meet deadlines for submitting a manuscript to a journal or face other time constraints related to their research (e.g., participants are only available during a certain period; funding runs out; collaborators move to a new institution). The time available to study a research problem and to measure change over time might be constrained by such practical issues. If time constraints negatively impacted your study in any way, acknowledge this impact by mentioning a need for a future study (e.g., a longitudinal study) to answer this research problem.

Conflicts arising from cultural bias and other personal issues

Researchers might hold biased views due to their cultural backgrounds or perspectives of certain phenomena, and this can affect a study’s legitimacy. Also, it is possible that researchers will have biases toward data and results that only support their hypotheses or arguments. In order to avoid these problems, the author(s) of a study should examine whether the way the research problem was stated and the data-gathering process was carried out appropriately.

Steps for Organizing Your Study Limitations Section

When you discuss the limitations of your study, don’t simply list and describe your limitations—explain how these limitations have influenced your research findings. There might be multiple limitations in your study, but you only need to point out and explain those that directly relate to and impact how you address your research questions.

We suggest that you divide your limitations section into three steps: (1) identify the study limitations; (2) explain how they impact your study in detail; and (3) propose a direction for future studies and present alternatives. By following this sequence when discussing your study’s limitations, you will be able to clearly demonstrate your study’s weakness without undermining the quality and integrity of your research.

Step 1. Identify the limitation(s) of the study

• This part should comprise around 10%-20% of your discussion of study limitations.

The first step is to identify the particular limitation(s) that affected your study. There are many possible limitations of research that can affect your study, but you don’t need to write a long review of all possible study limitations. A 200-500 word critique is an appropriate length for a research limitations section. In the beginning of this section, identify what limitations your study has faced and how important these limitations are.

You only need to identify limitations that had the greatest potential impact on: (1) the quality of your findings, and (2) your ability to answer your research question.

Step 2. Explain these study limitations in detail

• This part should comprise around 60-70% of your discussion of limitations.

After identifying your research limitations, it’s time to explain the nature of the limitations and how they potentially impacted your study. For example, when you conduct quantitative research, a lack of probability sampling is an important issue that you should mention. On the other hand, when you conduct qualitative research, the inability to generalize the research findings could be an issue that deserves mention.

Explain the role these limitations played on the results and implications of the research and justify the choice you made in using this “limiting” methodology or other action in your research. Also, make sure that these limitations didn’t undermine the quality of your dissertation .

Step 3. Propose a direction for future studies and present alternatives (optional)

• This part should comprise around 10-20% of your discussion of limitations.

After acknowledging the limitations of the research, you need to discuss some possible ways to overcome these limitations in future studies. One way to do this is to present alternative methodologies and ways to avoid issues with, or “fill in the gaps of” the limitations of this study you have presented.  Discuss both the pros and cons of these alternatives and clearly explain why researchers should choose these approaches.

Make sure you are current on approaches used by prior studies and the impacts they have had on their findings. Cite review articles or scientific bodies that have recommended these approaches and why. This might be evidence in support of the approach you chose, or it might be the reason you consider your choices to be included as limitations. This process can act as a justification for your approach and a defense of your decision to take it while acknowledging the feasibility of other approaches.

P hrases and Tips for Introducing Your Study Limitations in the Discussion Section

The following phrases are frequently used to introduce the limitations of the study:

• “There may be some possible limitations in this study.”
• “The findings of this study have to be seen in light of some limitations.”
•  “The first is the…The second limitation concerns the…”
•  “The empirical results reported herein should be considered in the light of some limitations.”
• “This research, however, is subject to several limitations.”
• “The primary limitation to the generalization of these results is…”
• “Nonetheless, these results must be interpreted with caution and a number of limitations should be borne in mind.”
• “As with the majority of studies, the design of the current study is subject to limitations.”
• “There are two major limitations in this study that could be addressed in future research. First, the study focused on …. Second ….”

For more articles on research writing and the journal submissions and publication process, visit Wordvice’s Academic Resources page.

And be sure to receive professional English editing and proofreading services , including paper editing services , for your journal manuscript before submitting it to journal editors.

Wordvice Resources

Proofreading & Editing Guide

Writing the Results Section for a Research Paper

How to Write a Literature Review

Research Writing Tips: How to Draft a Powerful Discussion Section

How to Captivate Journal Readers with a Strong Introduction

Tips That Will Make Your Abstract a Success!

APA In-Text Citation Guide for Research Writing

Additional Resources

• Diving Deeper into Limitations and Delimitations (PhD student)
• Organizing Your Social Sciences Research Paper: Limitations of the Study (USC Library)
• Research Limitations (Research Methodology)
• How to Present Limitations and Alternatives (UMASS)

Article References

Pearson-Stuttard, J., Kypridemos, C., Collins, B., Mozaffarian, D., Huang, Y., Bandosz, P.,…Micha, R. (2018). Estimating the health and economic effects of the proposed US Food and Drug Administration voluntary sodium reformulation: Microsimulation cost-effectiveness analysis. PLOS. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002551

Xu, W.L, Pedersen, N.L., Keller, L., Kalpouzos, G., Wang, H.X., Graff, C,. Fratiglioni, L. (2015). HHEX_23 AA Genotype Exacerbates Effect of Diabetes on Dementia and Alzheimer Disease: A Population-Based Longitudinal Study. PLOS. Retrieved from https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001853

Training videos   |   Faqs

Limitations in Research – A Simplified Guide with Examples

Limitations are flaws and shortcomings of your study. It is very important that you discuss the limitations of your study in the discussion section of your research paper. In this blog, we provide tips for presenting study limitations in your paper along with some real-world examples.

1. Should I Report the Limitations of My Work?

Most studies will have some form of limitation. So be honest and don’t hide your limitations. You have to tell your readers how your limitations might influence the outcomes and conclusions of your research.  In reality, your readers and reviewers will be impressed with your paper if you are upfront about your limitations. 

2. Examples

Let’s look at some examples. We have selected a variety of examples from different research topics.

2.1. Limitations Example 1

Following example is from a Medical research paper.

✔ The authors talk about the limitations and emphasis the importance of reconfirming the findings in a much larger study Study design and small sample size are important limitations. This could have led to an overestimation of the effect. Future research should reconfirm these findings by conducting larger-scale studies. _   Limitation s  _   How it might affect the results?   _   How to fix the limitation?

The authors are saying that the main limitations of the study are the small sample size and weak study design. Then they explain how this might have affected their results. They are saying that it is possible that they are overestimating the actual effect they are measuring. Then finally they are telling the readers that more studies with larger sample sizes should be conducted to reconfirm the findings.

As you can see, the authors are clearly explaining three things here: (1) What is the limitation? (2) How it might affect the study outcomes? and (3) What should be done to address the limitation?

2.2. Limitations Example 2

Following example is from an Engineering research paper.

✔ The authors are acknowledging the limitations and warning readers against generalizing the research findings However, some study limitations should be acknowledged. The experiments do not fully consider the problems that can appear in real situations. Hence, caution should be taken with generalizing the findings and applying them to real-life situations. _   Acknowledging limitations   _   Explaining the limitation   _   How it might affect the results?

The authors acknowledge that their study has some limitations. Then they explain what the limitations are. They are saying that their experiments do not consider all problems that might occur in real-life situations. Then they explain how this might affect their research outcomes. They are saying that readers should be careful when generalizing the results to practical real-world situations, because there is a possibility that the methods might fail.

2.3. Limitations Example 3

It is important to remember not to end your paper with limitations. Finish your paper on a positive note by telling your readers about the benefits of your research and possible future directions. In the following example, right after listing the limitations, the authors proceed to talk about the positive aspects of the work.

✔ The authors finish their paper on a positive note by talking about the benefits of their work and possible future work With this limited study, it is not known whether this finding can be applied to all clinical scenarios. Notwithstanding these limitations, this study has proven that Ultrasound can potentially serve as a more efficient alternative to X-rays in diagnosis. Future directions include studying the effects of different ultrasound pulsing schemes on pain relief. Another interesting direction would be to consider applications in nonhuman primates. _   Limitations   _   Benefits of the work   _   Possible future directions

The authors are saying that their experiments were somewhat limited and are not sure if their findings apply to the wider clinical practice. Then the authors highlight the benefits of their research. The authors say that their study has proven that ultrasound can be used instead of X-rays for diagnosis of certain types of diseases. Then they are explaining how future research can extend this work further. The authors are suggesting that it will be interesting to explore if ultrasound can be used for the treatment of chronic pain. And they are also suggesting that future studies can explore treating certain types of animal diseases with ultrasound. This is a very good example of how to finish the discussion section of your paper on a positive note.

Limitations are a vital component of the discussion section of your research paper. Remember, every study has limitations. There is no such thing as a perfect study. One of the major mistakes beginner writers make is hiding the limitations in the paper. Don’t do this, reviewers will reject your paper. Explain clearly how your limitations might have impacted your results, and provide ideas to mitigate them in the future. For further reading, please refer to our blogs on handling negative results and advanced tactics to address study limitations.

If you have any questions, please drop a comment below, and we will answer as soon as possible. We also recommend you to refer to our other blogs on  academic writing tools ,   academic writing resources ,  academic writing phrases  and  research paper examples  which are relevant to the topic discussed in this blog. 

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Limitations of the Study
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

The limitations of the study are those characteristics of design or methodology that impacted or influenced the interpretation of the findings from your research. Study limitations are the constraints placed on the ability to generalize from the results, to further describe applications to practice, and/or related to the utility of findings that are the result of the ways in which you initially chose to design the study or the method used to establish internal and external validity or the result of unanticipated challenges that emerged during the study.

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67; Theofanidis, Dimitrios and Antigoni Fountouki. "Limitations and Delimitations in the Research Process." Perioperative Nursing 7 (September-December 2018): 155-163. .

Importance of...

Always acknowledge a study's limitations. It is far better that you identify and acknowledge your study’s limitations than to have them pointed out by your professor and have your grade lowered because you appeared to have ignored them or didn't realize they existed.

Keep in mind that acknowledgment of a study's limitations is an opportunity to make suggestions for further research. If you do connect your study's limitations to suggestions for further research, be sure to explain the ways in which these unanswered questions may become more focused because of your study.

Acknowledgment of a study's limitations also provides you with opportunities to demonstrate that you have thought critically about the research problem, understood the relevant literature published about it, and correctly assessed the methods chosen for studying the problem. A key objective of the research process is not only discovering new knowledge but also to confront assumptions and explore what we don't know.

Claiming limitations is a subjective process because you must evaluate the impact of those limitations . Don't just list key weaknesses and the magnitude of a study's limitations. To do so diminishes the validity of your research because it leaves the reader wondering whether, or in what ways, limitation(s) in your study may have impacted the results and conclusions. Limitations require a critical, overall appraisal and interpretation of their impact. You should answer the question: do these problems with errors, methods, validity, etc. eventually matter and, if so, to what extent?

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67; Structure: How to Structure the Research Limitations Section of Your Dissertation. Dissertations and Theses: An Online Textbook. Laerd.com.

Descriptions of Possible Limitations

All studies have limitations . However, it is important that you restrict your discussion to limitations related to the research problem under investigation. For example, if a meta-analysis of existing literature is not a stated purpose of your research, it should not be discussed as a limitation. Do not apologize for not addressing issues that you did not promise to investigate in the introduction of your paper.

Here are examples of limitations related to methodology and the research process you may need to describe and discuss how they possibly impacted your results. Note that descriptions of limitations should be stated in the past tense because they were discovered after you completed your research.

Possible Methodological Limitations

• Sample size -- the number of the units of analysis you use in your study is dictated by the type of research problem you are investigating. Note that, if your sample size is too small, it will be difficult to find significant relationships from the data, as statistical tests normally require a larger sample size to ensure a representative distribution of the population and to be considered representative of groups of people to whom results will be generalized or transferred. Note that sample size is generally less relevant in qualitative research if explained in the context of the research problem.
• Lack of available and/or reliable data -- a lack of data or of reliable data will likely require you to limit the scope of your analysis, the size of your sample, or it can be a significant obstacle in finding a trend and a meaningful relationship. You need to not only describe these limitations but provide cogent reasons why you believe data is missing or is unreliable. However, don’t just throw up your hands in frustration; use this as an opportunity to describe a need for future research based on designing a different method for gathering data.
• Lack of prior research studies on the topic -- citing prior research studies forms the basis of your literature review and helps lay a foundation for understanding the research problem you are investigating. Depending on the currency or scope of your research topic, there may be little, if any, prior research on your topic. Before assuming this to be true, though, consult with a librarian! In cases when a librarian has confirmed that there is little or no prior research, you may be required to develop an entirely new research typology [for example, using an exploratory rather than an explanatory research design ]. Note again that discovering a limitation can serve as an important opportunity to identify new gaps in the literature and to describe the need for further research.
• Measure used to collect the data -- sometimes it is the case that, after completing your interpretation of the findings, you discover that the way in which you gathered data inhibited your ability to conduct a thorough analysis of the results. For example, you regret not including a specific question in a survey that, in retrospect, could have helped address a particular issue that emerged later in the study. Acknowledge the deficiency by stating a need for future researchers to revise the specific method for gathering data.
• Self-reported data -- whether you are relying on pre-existing data or you are conducting a qualitative research study and gathering the data yourself, self-reported data is limited by the fact that it rarely can be independently verified. In other words, you have to the accuracy of what people say, whether in interviews, focus groups, or on questionnaires, at face value. However, self-reported data can contain several potential sources of bias that you should be alert to and note as limitations. These biases become apparent if they are incongruent with data from other sources. These are: (1) selective memory [remembering or not remembering experiences or events that occurred at some point in the past]; (2) telescoping [recalling events that occurred at one time as if they occurred at another time]; (3) attribution [the act of attributing positive events and outcomes to one's own agency, but attributing negative events and outcomes to external forces]; and, (4) exaggeration [the act of representing outcomes or embellishing events as more significant than is actually suggested from other data].

Possible Limitations of the Researcher

• Access -- if your study depends on having access to people, organizations, data, or documents and, for whatever reason, access is denied or limited in some way, the reasons for this needs to be described. Also, include an explanation why being denied or limited access did not prevent you from following through on your study.
• Longitudinal effects -- unlike your professor, who can literally devote years [even a lifetime] to studying a single topic, the time available to investigate a research problem and to measure change or stability over time is constrained by the due date of your assignment. Be sure to choose a research problem that does not require an excessive amount of time to complete the literature review, apply the methodology, and gather and interpret the results. If you're unsure whether you can complete your research within the confines of the assignment's due date, talk to your professor.
• Cultural and other type of bias -- we all have biases, whether we are conscience of them or not. Bias is when a person, place, event, or thing is viewed or shown in a consistently inaccurate way. Bias is usually negative, though one can have a positive bias as well, especially if that bias reflects your reliance on research that only support your hypothesis. When proof-reading your paper, be especially critical in reviewing how you have stated a problem, selected the data to be studied, what may have been omitted, the manner in which you have ordered events, people, or places, how you have chosen to represent a person, place, or thing, to name a phenomenon, or to use possible words with a positive or negative connotation. NOTE :   If you detect bias in prior research, it must be acknowledged and you should explain what measures were taken to avoid perpetuating that bias. For example, if a previous study only used boys to examine how music education supports effective math skills, describe how your research expands the study to include girls.
• Fluency in a language -- if your research focuses , for example, on measuring the perceived value of after-school tutoring among Mexican-American ESL [English as a Second Language] students and you are not fluent in Spanish, you are limited in being able to read and interpret Spanish language research studies on the topic or to speak with these students in their primary language. This deficiency should be acknowledged.

Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Senunyeme, Emmanuel K. Business Research Methods. Powerpoint Presentation. Regent University of Science and Technology; ter Riet, Gerben et al. “All That Glitters Isn't Gold: A Survey on Acknowledgment of Limitations in Biomedical Studies.” PLOS One 8 (November 2013): 1-6.

Structure and Writing Style

Information about the limitations of your study are generally placed either at the beginning of the discussion section of your paper so the reader knows and understands the limitations before reading the rest of your analysis of the findings, or, the limitations are outlined at the conclusion of the discussion section as an acknowledgement of the need for further study. Statements about a study's limitations should not be buried in the body [middle] of the discussion section unless a limitation is specific to something covered in that part of the paper. If this is the case, though, the limitation should be reiterated at the conclusion of the section.

If you determine that your study is seriously flawed due to important limitations , such as, an inability to acquire critical data, consider reframing it as an exploratory study intended to lay the groundwork for a more complete research study in the future. Be sure, though, to specifically explain the ways that these flaws can be successfully overcome in a new study.

But, do not use this as an excuse for not developing a thorough research paper! Review the tab in this guide for developing a research topic . If serious limitations exist, it generally indicates a likelihood that your research problem is too narrowly defined or that the issue or event under study is too recent and, thus, very little research has been written about it. If serious limitations do emerge, consult with your professor about possible ways to overcome them or how to revise your study.

When discussing the limitations of your research, be sure to:

• Describe each limitation in detailed but concise terms;
• Explain why each limitation exists;
• Provide the reasons why each limitation could not be overcome using the method(s) chosen to acquire or gather the data [cite to other studies that had similar problems when possible];
• Assess the impact of each limitation in relation to the overall findings and conclusions of your study; and,
• If appropriate, describe how these limitations could point to the need for further research.

Remember that the method you chose may be the source of a significant limitation that has emerged during your interpretation of the results [for example, you didn't interview a group of people that you later wish you had]. If this is the case, don't panic. Acknowledge it, and explain how applying a different or more robust methodology might address the research problem more effectively in a future study. A underlying goal of scholarly research is not only to show what works, but to demonstrate what doesn't work or what needs further clarification.

Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Ioannidis, John P.A. "Limitations are not Properly Acknowledged in the Scientific Literature." Journal of Clinical Epidemiology 60 (2007): 324-329; Pasek, Josh. Writing the Empirical Social Science Research Paper: A Guide for the Perplexed. January 24, 2012. Academia.edu; Structure: How to Structure the Research Limitations Section of Your Dissertation. Dissertations and Theses: An Online Textbook. Laerd.com; What Is an Academic Paper? Institute for Writing Rhetoric. Dartmouth College; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

Writing Tip

Don't Inflate the Importance of Your Findings!

After all the hard work and long hours devoted to writing your research paper, it is easy to get carried away with attributing unwarranted importance to what you’ve done. We all want our academic work to be viewed as excellent and worthy of a good grade, but it is important that you understand and openly acknowledge the limitations of your study. Inflating the importance of your study's findings could be perceived by your readers as an attempt hide its flaws or encourage a biased interpretation of the results. A small measure of humility goes a long way!

Another Writing Tip

Negative Results are Not a Limitation!

Negative evidence refers to findings that unexpectedly challenge rather than support your hypothesis. If you didn't get the results you anticipated, it may mean your hypothesis was incorrect and needs to be reformulated. Or, perhaps you have stumbled onto something unexpected that warrants further study. Moreover, the absence of an effect may be very telling in many situations, particularly in experimental research designs. In any case, your results may very well be of importance to others even though they did not support your hypothesis. Do not fall into the trap of thinking that results contrary to what you expected is a limitation to your study. If you carried out the research well, they are simply your results and only require additional interpretation.

Lewis, George H. and Jonathan F. Lewis. “The Dog in the Night-Time: Negative Evidence in Social Research.” The British Journal of Sociology 31 (December 1980): 544-558.

Yet Another Writing Tip

Sample Size Limitations in Qualitative Research

Sample sizes are typically smaller in qualitative research because, as the study goes on, acquiring more data does not necessarily lead to more information. This is because one occurrence of a piece of data, or a code, is all that is necessary to ensure that it becomes part of the analysis framework. However, it remains true that sample sizes that are too small cannot adequately support claims of having achieved valid conclusions and sample sizes that are too large do not permit the deep, naturalistic, and inductive analysis that defines qualitative inquiry. Determining adequate sample size in qualitative research is ultimately a matter of judgment and experience in evaluating the quality of the information collected against the uses to which it will be applied and the particular research method and purposeful sampling strategy employed. If the sample size is found to be a limitation, it may reflect your judgment about the methodological technique chosen [e.g., single life history study versus focus group interviews] rather than the number of respondents used.

Boddy, Clive Roland. "Sample Size for Qualitative Research." Qualitative Market Research: An International Journal 19 (2016): 426-432; Huberman, A. Michael and Matthew B. Miles. "Data Management and Analysis Methods." In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 428-444; Blaikie, Norman. "Confounding Issues Related to Determining Sample Size in Qualitative Research." International Journal of Social Research Methodology 21 (2018): 635-641; Oppong, Steward Harrison. "The Problem of Sampling in qualitative Research." Asian Journal of Management Sciences and Education 2 (2013): 202-210.

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• The Scientist University

How to Present a Research Study’s Limitations

All studies have imperfections, but how to present them without diminishing the value of the work can be tricky..

Nathan Ni holds a PhD from Queens University. He is a science editor for The Scientist’s Creative Services Team who strives to better understand and communicate the relationships between health and disease.

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Scientists work with many different limitations. First and foremost, they navigate informational limitations, work around knowledge gaps when designing studies, formulating hypotheses, and analyzing data. They also handle technical limitations, making the most of what their hands, equipment, and instruments can achieve. Finally, researchers must also manage logistical limitations. Scientists will often experience sample scarcity, financial issues, or simply be unable to access the technology or materials that they want.

All scientific studies have limitations, and no study is perfect. Researchers should not run from this reality, but engage it directly. It is better to directly address the specific limitations of the work in question, and doing so is actually a way to demonstrate an author’s proficiency and aptitude.

Do: Be Transparent

From a practical perspective, being transparent is the main key to directly addressing the specific limitations of a study. Was there an experiment that the researchers wanted to perform but could not, or a sample that existed that the scientists could not obtain? Was there a piece of knowledge that would explain a question raised by the data presented within the current study? If the answer is yes, the authors should mention this and elaborate upon it within the discussion section.

Asking and addressing these questions demonstrates that the authors have knowledge, understanding, and expertise of the subject area beyond what the study directly investigated. It further demonstrates a solid grasp of the existing literature—which means a solid grasp of what others are doing, what techniques they are using, and what limitations impede their own studies. This information helps the authors contextualize where their study fits within what others have discovered, thereby mitigating the perceived effect of a given limitation on the study’s legitimacy. In essence, this strategy turns limitations, often considered weaknesses, into strengths.

For example, in their 2021 Cell Reports study on macrophage polarization mechanisms, dermatologist Alexander Marneros and colleagues wrote the following. 1

A limitation of studying macrophage polarization in vitro is that this approach only partially captures the tissue microenvironment context in which many different factors affect macrophage polarization. However, it is likely that the identified signaling mechanisms that promote polarization in vitro are also critical for polarization mechanisms that occur in vivo. This is supported by our observation that trametinib and panobinostat inhibited M2-type macrophage polarization not only in vitro but also in skin wounds and laser-induced CNV lesions.

This is a very effective structure. In the first sentence ( yellow ), the authors outlined the limitation. In the next sentence ( green ), they offered a rationalization that mitigates the effect of the limitation. Finally, they provided the evidence ( blue ) for this rationalization, using not just information from the literature, but also data that they obtained in their study specifically for this purpose. 

Don't: Be Defensive

It can feel natural to avoid talking about a study’s limitations. Scientists may believe that mentioning the drawbacks still present in their study will jeopardize their chances of publication. As such, researchers will sometimes skirt around the issue. They will present “boilerplate faults”—generalized concerns about sample size/diversity and time limitations that all researchers face—rather than honestly discussing their own study. Alternatively, they will describe their limitations in a defensive manner, positioning their problems as something that “could not be helped”—as something beyond what science can currently achieve.

However, their audience can see through this, because they are largely peers who understand and have experienced how modern research works. They can tell the difference between global challenges faced by every scientific study and limitations that are specific to a single study. Avoiding these specific limitations can therefore betray a lack of confidence that the study is good enough to withstand problems stemming from legitimate limitations. As such, researchers should actively engage with the greater scientific implications of the limitations that they face. Indeed, doing this is actually a way to demonstrate an author’s proficiency and aptitude.

In an example, neurogeneticist Nancy Bonini and colleagues, in their publication in Nature , discussed a question raised by their data that they have elected not to directly investigate in this study, writing “ Among the intriguing questions raised by these data is how senescent glia promote LDs in other glia. ” To show both the legitimacy of the question and how seriously they have considered it, the authors provided a comprehensive summary of the literature in the following seven sentences, offering two hypotheses backed by a combined eight different sources. 2 Rather than shying away from a limitation, they attacked it as something to be curious about and to discuss. This is not just a very effective way of demonstrating their expertise, but it frames the limitation as something that, when overcome, will build upon the present study rather than something that negatively affects the legitimacy of their current findings.

Striking the Right Balance

Scientists have to navigate the fine line between acknowledging the limitations of their study while also not diminishing the effect and value of their own work. To be aware of legitimate limitations and properly assess and dissect them shows a profound understanding of a field, where the study fits within that field, and what the rest of the scientific community are doing and what challenges they face.

All studies are parts of a greater whole. Pretending otherwise is a disservice to the scientific community.

Looking for more information on scientific writing? Check out  The Scientist’ s  TS SciComm  section. Looking for some help putting together a manuscript, a figure, a poster, or anything else?  The Scientist ’s  Scientific Services  may have the professional help that you need.

• He L, et al. Global characterization of macrophage polarization mechanisms and identification of M2-type polarization inhibitors . Cell Rep . 2021;37(5):109955.
• Byrns CN, et al. Senescent glia link mitochondrial dysfunction and lipid accumulation . Nature . 2024.

Writing Limitations of Research Study — 4 Reasons Why It Is Important!

It is not unusual for researchers to come across the term limitations of research during their academic paper writing. More often this is interpreted as something terrible. However, when it comes to research study, limitations can help structure the research study better. Therefore, do not underestimate significance of limitations of research study.

Allow us to take you through the context of how to evaluate the limits of your research and conclude an impactful relevance to your results.

Table of Contents

What Are the Limitations of a Research Study?

Every research has its limit and these limitations arise due to restrictions in methodology or research design.  This could impact your entire research or the research paper you wish to publish. Unfortunately, most researchers choose not to discuss their limitations of research fearing it will affect the value of their article in the eyes of readers.

However, it is very important to discuss your study limitations and show it to your target audience (other researchers, journal editors, peer reviewers etc.). It is very important that you provide an explanation of how your research limitations may affect the conclusions and opinions drawn from your research. Moreover, when as an author you state the limitations of research, it shows that you have investigated all the weaknesses of your study and have a deep understanding of the subject. Being honest could impress your readers and mark your study as a sincere effort in research.

Why and Where Should You Include the Research Limitations?

The main goal of your research is to address your research objectives. Conduct experiments, get results and explain those results, and finally justify your research question . It is best to mention the limitations of research in the discussion paragraph of your research article.

At the very beginning of this paragraph, immediately after highlighting the strengths of the research methodology, you should write down your limitations. You can discuss specific points from your research limitations as suggestions for further research in the conclusion of your thesis.

1. Common Limitations of the Researchers

Limitations that are related to the researcher must be mentioned. This will help you gain transparency with your readers. Furthermore, you could provide suggestions on decreasing these limitations in you and your future studies.

2. Limited Access to Information

Your work may involve some institutions and individuals in research, and sometimes you may have problems accessing these institutions. Therefore, you need to redesign and rewrite your work. You must explain your readers the reason for limited access.

3. Limited Time

All researchers are bound by their deadlines when it comes to completing their studies. Sometimes, time constraints can affect your research negatively. However, the best practice is to acknowledge it and mention a requirement for future study to solve the research problem in a better way.

4. Conflict over Biased Views and Personal Issues

Biased views can affect the research. In fact, researchers end up choosing only those results and data that support their main argument, keeping aside the other loose ends of the research.

Types of Limitations of Research

Before beginning your research study, know that there are certain limitations to what you are testing or possible research results. There are different types that researchers may encounter, and they all have unique characteristics, such as:

1. Research Design Limitations

Certain restrictions on your research or available procedures may affect your final results or research outputs. You may have formulated research goals and objectives too broadly. However, this can help you understand how you can narrow down the formulation of research goals and objectives, thereby increasing the focus of your study.

2. Impact Limitations

Even if your research has excellent statistics and a strong design, it can suffer from the influence of the following factors:

• Presence of increasing findings as researched
• Being population specific
• A strong regional focus.

3. Data or statistical limitations

In some cases, it is impossible to collect sufficient data for research or very difficult to get access to the data. This could lead to incomplete conclusion to your study. Moreover, this insufficiency in data could be the outcome of your study design. The unclear, shabby research outline could produce more problems in interpreting your findings.

How to Correctly Structure Your Research Limitations?

There are strict guidelines for narrowing down research questions, wherein you could justify and explain potential weaknesses of your academic paper. You could go through these basic steps to get a well-structured clarity of research limitations:

• Declare that you wish to identify your limitations of research and explain their importance,
• Provide the necessary depth, explain their nature, and justify your study choices.
• Write how you are suggesting that it is possible to overcome them in the future.

In this section, your readers will see that you are aware of the potential weaknesses in your business, understand them and offer effective solutions, and it will positively strengthen your article as you clarify all limitations of research to your target audience.

Know that you cannot be perfect and there is no individual without flaws. You could use the limitations of research as a great opportunity to take on a new challenge and improve the future of research. In a typical academic paper, research limitations may relate to:

1. Formulating your goals and objectives

If you formulate goals and objectives too broadly, your work will have some shortcomings. In this case, specify effective methods or ways to narrow down the formula of goals and aim to increase your level of study focus.

2. Application of your data collection methods in research

If you do not have experience in primary data collection, there is a risk that there will be flaws in the implementation of your methods. It is necessary to accept this, and learn and educate yourself to understand data collection methods.

3. Sample sizes

This depends on the nature of problem you choose. Sample size is of a greater importance in quantitative studies as opposed to qualitative ones. If your sample size is too small, statistical tests cannot identify significant relationships or connections within a given data set.

You could point out that other researchers should base the same study on a larger sample size to get more accurate results.

4. The absence of previous studies in the field you have chosen

Writing a literature review is an important step in any scientific study because it helps researchers determine the scope of current work in the chosen field. It is a major foundation for any researcher who must use them to achieve a set of specific goals or objectives.

However, if you are focused on the most current and evolving research problem or a very narrow research problem, there may be very little prior research on your topic. For example, if you chose to explore the role of Bitcoin as the currency of the future, you may not find tons of scientific papers addressing the research problem as Bitcoins are only a new phenomenon.

It is important that you learn to identify research limitations examples at each step. Whatever field you choose, feel free to add the shortcoming of your work. This is mainly because you do not have many years of experience writing scientific papers or completing complex work. Therefore, the depth and scope of your discussions may be compromised at different levels compared to academics with a lot of expertise. Include specific points from limitations of research. Use them as suggestions for the future.

Have you ever faced a challenge of writing the limitations of research study in your paper? How did you overcome it? What ways did you follow? Were they beneficial? Let us know in the comments below!

Frequently Asked Questions

Setting limitations in our study helps to clarify the outcomes drawn from our research and enhance understanding of the subject. Moreover, it shows that the author has investigated all the weaknesses in the study.

Scope is the range and limitations of a research project which are set to define the boundaries of a project. Limitations are the impacts on the overall study due to the constraints on the research design.

Limitation in research is an impact of a constraint on the research design in the overall study. They are the flaws or weaknesses in the study, which may influence the outcome of the research.

1. Limitations in research can be written as follows: Formulate your goals and objectives 2. Analyze the chosen data collection method and the sample sizes 3. Identify your limitations of research and explain their importance 4. Provide the necessary depth, explain their nature, and justify your study choices 5. Write how you are suggesting that it is possible to overcome them in the future

Excellent article ,,,it has helped me big

This is very helpful information. It has given me an insight on how to go about my study limitations.

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the topic is well covered

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What are the limitations in research and how to write them?

Learn about the potential limitations in research and how to appropriately address them in order to deliver honest and ethical research.

It is fairly uncommon for researchers to stumble into the term research limitations when working on their research paper. Limitations in research can arise owing to constraints on design, methods, materials, and so on, and these aspects, unfortunately, may have an influence on your subject’s findings.

In this Mind The Graph’s article, we’ll discuss some recommendations for writing limitations in research , provide examples of various common types of limitations, and suggest how to properly present this information.

What are the limitations in research?

The limitations in research are the constraints in design, methods or even researchers’ limitations that affect and influence the interpretation of your research’s ultimate findings. These are limitations on the generalization and usability of findings that emerge from the design of the research and/or the method employed to ensure validity both internally and externally. 

Researchers are usually cautious to acknowledge the limitations of their research in their publications for fear of undermining the research’s scientific validity. No research is faultless or covers every possible angle. As a result, addressing the constraints of your research exhibits honesty and integrity .

Why should include limitations of research in my paper?

Though limitations tackle potential flaws in research, commenting on them at the conclusion of your paper, by demonstrating that you are aware of these limitations and explaining how they impact the conclusions that may be taken from the research, improves your research by disclosing any issues before other researchers or reviewers do . 

Additionally, emphasizing research constraints implies that you have thoroughly investigated the ramifications of research shortcomings and have a thorough understanding of your research problem. 

Limits exist in any research; being honest about them and explaining them would impress researchers and reviewers more than disregarding them. 

Remember that acknowledging a research’s shortcomings offers a chance to provide ideas for future research, but be careful to describe how your study may help to concentrate on these outstanding problems .

Possible limitations examples

Here are some limitations connected to methodology and the research procedure that you may need to explain and discuss in connection to your findings.

Methodological limitations

Sample size.

The number of units of analysis used in your study is determined by the sort of research issue being investigated. It is important to note that if your sample is too small, finding significant connections in the data will be challenging, as statistical tests typically require a larger sample size to ensure a fair representation and this can be limiting. 

Lack of available or reliable data

A lack of data or trustworthy data will almost certainly necessitate limiting the scope of your research or the size of your sample, or it can be a substantial impediment to identifying a pattern and a relevant connection.

Lack of prior research on the subject

Citing previous research papers forms the basis of your literature review and aids in comprehending the research subject you are researching. Yet there may be little if any, past research on your issue.

The measure used to collect data

After finishing your analysis of the findings, you realize that the method you used to collect data limited your capacity to undertake a comprehensive evaluation of the findings. Recognize the flaw by mentioning that future researchers should change the specific approach for data collection.

Issues with research samples and selection

Sampling inaccuracies arise when a probability sampling method is employed to choose a sample, but that sample does not accurately represent the overall population or the relevant group. As a result, your study suffers from “sampling bias” or “selection bias.”

Limitations of the research

When your research requires polling certain persons or a specific group, you may have encountered the issue of limited access to these interviewees. Because of the limited access, you may need to reorganize or rearrange your research. In this scenario, explain why access is restricted and ensure that your findings are still trustworthy and valid despite the constraint.

Time constraints

Practical difficulties may limit the amount of time available to explore a research issue and monitor changes as they occur. If time restrictions have any detrimental influence on your research, recognize this impact by expressing the necessity for a future investigation.

Due to their cultural origins or opinions on observed events, researchers may carry biased opinions, which can influence the credibility of a research. Furthermore, researchers may exhibit biases toward data and conclusions that only support their hypotheses or arguments.

The structure of the limitations section 

The limitations of your research are usually stated at the beginning of the discussion section of your paper so that the reader is aware of and comprehends the limitations prior to actually reading the rest of your findings, or they are stated at the end of the discussion section as an acknowledgment of the need for further research.

The ideal way is to divide your limitations section into three steps: 

1. Identify the research constraints; 

2. Describe in great detail how they affect your research; 

3. Mention the opportunity for future investigations and give possibilities. 

By following this method while addressing the constraints of your research, you will be able to effectively highlight your research’s shortcomings without jeopardizing the quality and integrity of your research.

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International Science Editing

June 6, 2019 by Lisa Clancy

Discussing your study’s limitations

Why include a limitations section.

Including a section on the limitations of your findings will demonstrate command over your research. A reviewer may look negatively upon your study if they spot a limitation that you failed to acknowledge. If you discuss each limitation in the context of future research—i.e., suggest ways to improve the validity of the research in future studies—your article is more likely to be cited, as it will inform the research questions of other researchers.

How to identify the limitations of your study

You should think about your study from two angles – internal validity and external validity.

Internal validity refers to the strength of the inferences from the study, i.e., how confident you are that the outcome observed was caused by the test variable. Could other factors have affected the outcome? If so, the internal validity of your study may be threatened.

External validity refers to the degree to which the results can be generalised to a more universal population. If you were to re-do the study in a different context, e.g., with different subjects or in a different setting, would you get a similar outcome? If not, the external validity of your study may be questionable.

Limitations should not be feared

It is important to remember that all studies are questionable in one way or another. Therefore, a study does not have to be limitation-free to be deemed acceptable.

In this post…

…we list the most commonly seen limitations in STEM studies and provide real-world examples. However, please be advised that this is not a comprehensive list. In addition, please note that these limitations are not mutually exclusive; many can overlap.

Examples of study limitations

Selection bias

Selection bias occurs when the selection of individuals, groups, or data for analysis is not randomised.

For example, imagine a study in which different surgical procedures are retrospectively compared in relation to mortality risk [e.g., 1]. One of the procedures is newer than the others. Surgeons typically choose the most ideal surgery candidates when testing new procedures. Therefore, the outcome of the study could be affected by surgeons selectively choosing a particular type of individual—ideal candidates for surgery—for only one of the treatment groups.

Confounding

A confounder is another, sometimes hidden, variable that affects the dependent variable. If a confounder is not accounted for, any relationships detected between the test variable and outcome could be inaccurate.

For example, imagine a study in which the use of eye-tracking applications to measure cognitive performance is examined [e.g., 2]. Cognitive performance is known to decrease with age. Therefore, if age is not included as a confounder in the study, the effect size could be under- or overestimated.

In another example, imagine a study in which the association between osteoarthritis and cardiovascular (CV) events is examined [e.g., 3]. CV events have been linked to many factors, including smoking status, abdominal obesity, family history of CV events … etc., all of which could confound the outcome if not controlled for.

Survivorship bias

Survivorship bias occurs if inferences are made on the basis of only those subjects that made it past some selection process and those that did not were overlooked, typically because of their lack of visibility.

For example, imagine a study in which the link between cycling and sexual dysfunction is examined [e.g., 4]. It is possible that a person who experiences sexual dysfunction due to cycling would quit the activity. Therefore, if only active cyclists were recruited in the study, such a person would be overlooked, constituting a bias that could affect the study outcome.

Study scope limitations

Unreliable or unavailable data can limit the scope of a study and thus the overall outcome.

For example, imagine a study in which heat generation in different world regions is examined [e.g., 5]. The researchers do not have data on the use of firewood in households. In some regions, e.g., developing countries, household firewood use contributes greatly to the total heat produced. Therefore, the heat generation for such regions could be underestimated.

Sample size limitations

A small sample size may make it difficult to determine if a particular outcome is a true finding and in some cases a type II error may occur, i.e., the null hypothesis is incorrectly accepted and no difference between the study groups is reported.

For example, imagine a study in which the efficacy of thrombolysis in treating acute myocardial infarction (AMI) is examined. Thrombolysis has an important but very small effect on AMI. Therefore, a study with a relatively small sample size may not have the (statistical) power to expose such a small effect, possibly resulting in a type II error [6].

Experimenter bias

Experimenter bias occurs when the individuals running the experiment inadvertently affect the outcome by unconsciously behaving in different ways to participants in the different treatment groups.

For example, imagine a study in which gamers are tested for their ability to know whether they are playing against a human or an AI avatar [e.g., 7]. The facilitator stands behind the participant and observes gameplay. If the facilitator is aware of the nature of the avatar, there is a chance that they could unintentionally influence the participant.

Referral bias

Referral bias refers to the phenomenon whereby patients that are referred from one clinic to another, often to specialised units, tend to be sicker than non-referred patients. In studies including many referrals, risk factors are likely to be overestimated.

For example, imagine a study in which the clinical characteristics of neuroarcoidosis are evaluated in a specialised referral centre [e.g., 8]. Chronic aseptic meningitis is found to be the most frequently reported pathology—37% of cases. This frequency is relatively higher compared with other studies. The centre is known to have specific expertise on chronic meningitis. Therefore, cases of this kind are more likely to be referred to the centre, constituting a referral bias.

Self-reported data

Self-reported data is subject to various biases, e.g., selective memory, exaggeration … etc., and cannot be independently verified.

For example, imagine a study in which the effectiveness of typing pressure in determining stress in smartphone users is examined [e.g., 9]. Participants are asked to recall a stressful experience and rank their stress on a scale, after which typing pressure is measured. For whatever reason, participants may over- or underestimate their stress levels, affecting the outcome of the study.

Limitations of exploratory studies

If there has been little or no prior research on a topic, researchers may be required to establish a benchmark in relation to the research question and study design. As there is no benchmark for comparison, the validity of the outcome is disputable.

For example, imagine an exploratory study in which TV users are tested for usability of a new type of remote controller [e.g., 10]. Rather than the typical pressing of buttons, actions can be performed by squeezing or puffing on the remote. Findings from this study cannot be deemed conclusive until the results are replicated.

Methodological limitations

This refers to limitations in relation to the methodology used in a study.

For example, imagine a study in which the utility of telomere length as a diagnostic parameter for dyskeratosis congenita (DC) is tested [e.g., 11]. The data of DC patients from two different hospitals are used in the study. Each hospital uses its own method for DNA extraction, one of which has been shown to extract shorter DNA, a limitation which could affect the study outcome.

In another example, imagine a study in which a novel technology is tested for its ability to monitor damage in structures known to be difficult to monitor (e.g., beneath bridges) [e.g., 12]. The study suggests that the new technology is promising; however, its coverage area is only 30 × 30 m, meaning it is only suitable for short-distance applications.

Systematic literature reviews

In a systematic literature review (SLR), researchers use a well-defined search strategy to search for literature relevant to a particular research question. However, depending on the search criteria, there is no guarantee that all relevant literature will be retrieved from the search; Often grey literature – e.g., theses and technical reports – are excluded; and often SLRs only include studies presented in one language, typically English.

Hawthorne effect

This refers to the phenomenon whereby participants behave differently when they are aware that they are being observed.

For example, imagine a study in which fear appeal messages are tested for their ability to promote security behaviour online [e.g., 13]. Participants are shown a fear appeal message detailing the prevalence and effects of cyber-attacks, after which they are surveyed on their behaviour online. Participants are surveyed again 4 weeks later to see if the effect of the fear appeal lasted over time and whether intentions were acted upon. Participants may fraudulently claim to have improved their behaviour in an effort to diminish shame at not having altered their behaviour or in an effort to please the study conductors.

Regression toward the mean

This refers to the phenomenon whereby a variable that is extreme (i.e., far away from the average) the first time it is measured will be less extreme the next time it is measured. This typically happens with asymmetric sampling, e.g., only the very worst or the very best performers are used in a study. However, it can occur by chance as well (see the example given).

For example, imagine a study in which the effects of haematocrit (the ratio of the volume of red blood cells to the total volume of blood) on avian flight performance is examined [e.g., 14]. In the pre-test, i.e., before their haematocrit is manipulated, birds in one of the treatment groups have considerably better flight performance compared to the other groups. Even without manipulation, the flight performance of these birds would likely be reduced if the test was repeated, due to the regression toward the mean effect. Therefore, the results of the post-test, i.e., after manipulation, may be influenced by this effect and may not be reflective of the true effects of the manipulation.

Repeated testing

Repeatedly testing participants may lead to bias. A pre-test may sensitise participants in unanticipated ways, influencing the results of the post-test.

For example, imagine a study in which the anxiety induced from different eye tests used to diagnose glaucoma is measured [e.g., 15]. Almost all of the participants have already experienced one of the tests. This could lead to an underestimation of the magnitude by which anxiety increases with this test.

Population validity

This refers to how representative the sample used in a study is to the target population.

For example, imagine a study in which the target population is all U.S. Internet users. It would not be representative to only use data from Twitter users, as U.S. adult Twitter users are younger and more likely to be Democrats compared to the general public [16].

How to present limitations

Study limitations are generally presented towards the end of the discussion section in the past tense (see our post on Verb Tenses in Scientific Manuscripts ). Start by stating the limitation. Mention if you took any steps to circumvent the issue. Describe any evidence that might lessen the effect of the limitation. Discuss how the limitation could affect the study outcome. Finally, if applicable, discuss the steps that could be taken to overcome the limitation in future studies.

• Stiles ZE, Behrman SW, Glazer ES, Deneve JL, Dong L, Wan JY, Dickson PV. Predictors and implications of unplanned conversion during minimally invasive hepatectomy: an analysis of the ACS-NSQIP database. HPB. 2017 Nov 1;19(11):957–65.
• Rosa PJ, Gamito P, Oliveira J, Morais D, Pavlovic M, Smyth O. Show me your eyes! The combined use of eye tracking and virtual reality applications for cognitive assessment. In Proceedings of the 3rd 2015 workshop on ICTs for Improving Patients Rehabilitation Research Techniques 2015 Oct 1 (pp. 135–138). ACM.
• Kendzerska T, Jüni P, King LK, Croxford R, Stanaitis I, Hawker GA. The longitudinal relationship between hand, hip and knee osteoarthritis and cardiovascular events: a population-based cohort study. Osteoarthr Cartilage. 2017 Nov 1;25(11):1771–80.
• Gaither TW, Awad MA, Murphy GP, Metzler I, Sanford T, Eisenberg ML, Sutcliffe S, Osterberg EC, Breyer BN. Cycling and female sexual and urinary function: results from a large, multinational, cross-sectional study. J Sex Med. 2018 Apr 1;15(4):510–8.
• Mekonnen MM, Gerbens-Leenes PW, Hoekstra AY. The consumptive water footprint of electricity and heat: a global assessment. Environ Sci-Water Res Technol. 2015;1(3):285–97.
• Jones SR, Carley S, Harrison M. An introduction to power and sample size estimation. Emerg Med J. 2003 Sep 1;20(5):453–8.
• Wehbe RR, Lank E, Nacke LE. Left Them 4 Dead: Perception of humans versus non-player character teammates in cooperative gameplay. In Proceedings of the 2017 Conference on Designing Interactive Systems 2017 Jun 10 (pp. 403–415). ACM.
• Leonhard SE, Fritz D, Eftimov F, van der Kooi AJ, van de Beek D, Brouwer MC. Neurosarcoidosis in a tertiary referral center: a cross-sectional cohort study. Medicine. 2016 Apr;95(14).
• Exposito M, Picard RW, Hernandez J. Affective keys: towards unobtrusive stress sensing of smartphone users. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct 2018 Sep 3 (pp. 139–145). ACM.
• Bernhaupt R, Desnos A, Pirker M, Schwaiger D. TV interaction beyond the button press. InIFIP Conference on Human-Computer Interaction 2015 Sep 14 (pp. 412–419). Springer, Cham.
• Gadalla SM, Khincha PP, Katki HA, Giri N, Wong JY, Spellman S, Yanovski JA, Han JC, De Vivo I, Alter BP, Savage SA. The limitations of qPCR telomere length measurement in diagnosing dyskeratosis congenita. Mol Genet Genomic Med. 2016 Jul;4(4):475–9.
• Kang D, Cha YJ. Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo‐tagging. Comput Aided Civ Inf. 2018 Oct;33(10):885–902.
• Jansen J, van Schaik P. The design and evaluation of a theory-based intervention to promote security behaviour against phishing. Int J Hum Comput Stud. 2019 Mar 1;123:40–55.
• Yap KN, Dick MF, Guglielmo CG, Williams TD. Effects of experimental manipulation of hematocrit on avian flight performance in high-and low-altitude conditions. J Exp Biol. 2018 Nov 15;221(22):jeb191056.
• Chew SS, Kerr NM, Wong AB, Craig JP, Chou CY, Danesh-Meyer HV. Anxiety in visual field testing. Br J Ophthalmol. 2016 Aug 1;100(8):1128–33.
• Pew Research Center. Sizing Up Twitter Users. 2019 Apr 24. Available from: https://www.pewinternet.org/2019/04/24/sizing-up-twitter-users/ [Accessed 6 June 2019].

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Diving Deeper into Limitations and Delimitations

If you are working on a thesis, dissertation, or other formal research project, chances are your advisor or committee will ask you to address the delimitations of your study. When faced with this request, many students respond with a puzzled look and then go on to address what are actually the study’s limitations.

In a previous article , we covered what goes into the limitations, delimitations, and assumptions sections of your thesis or dissertation. Here, we will dive a bit deeper into the differences between limitations and delimitations and provide some helpful tips for addressing them in your research project—whether you are working on a quantitative or qualitative study.

Acknowledging Weaknesses vs. Defining Boundaries

These concepts are easy to get confused because both limitations and delimitations restrict (or limit) the questions you’ll be able to answer with your study, most notably in terms of generalizability.

However, the biggest difference between limitations and delimitations is the degree of control you have over them—that is, how much they are based in conscious, intentional choices you made in designing your study.

Limitations occur in all types of research and are, for the most part, outside the researcher’s control (given practical constraints, such as time, funding, and access to populations of interest). They are threats to the study’s internal or external validity.

Limitations may include things such as participant drop-out, a sample that isn’t entirely representative of the desired population, violations to the assumptions of parametric analysis (e.g., normality, homogeneity of variance), the limits of self-report, or the absence of reliability and validity data for some of your survey measures.

Limitations can get in the way of your being able to answer certain questions or draw certain types of inferences from your findings. Therefore, it’s important to acknowledge them upfront and make note of how they restrict the conclusions you’ll be able to draw from your study. Frequently, limitations can get in the way of our ability to generalize our findings to the larger populations or to draw causal conclusions, so be sure to consider these issues when you’re thinking about the potential limitations of your study.

Delimitations are also factors that can restrict the questions you can answer or the inferences you can draw from your findings. However, they are based on intentional choices you make a priori (i.e., as you’re designing the study) about where you’re going to draw the boundaries of your project. In other words, they define the project’s scope.

Like limitations, delimitations are a part of every research project, and this is not a bad thing. In fact, it’s very important! You can’t study everything at once. If you try to do so, your project is bound to get huge and unwieldy, and it will become a lot more difficult to interpret your results or come to meaningful conclusions with so many moving parts. You have to draw the line somewhere, and the delimitations are where you choose to draw these lines.

One of the clearest examples of a delimitation that applies to almost every research project is participant exclusion criteria. In conducting either a quantitative or a qualitative study, you will have to define your population of interest. Defining this population of interest means that you will need to articulate the boundaries of that population (i.e., who is not included). Those boundaries are delimitations.

For example, if you’re interested in understanding the experiences of elementary school teachers who have been implementing a new curriculum into their classrooms, you probably won’t be interviewing or sending a survey to any of the following people: non-teachers, high-school teachers, college professors, principals, parents of elementary school children, or the children themselves. Furthermore, you probably won’t be talking to elementary school teachers who have not yet had the experience of implementing the curriculum in question. You would probably only choose to gather data from elementary school teachers who have had this experience because that is who you’re interested in for the purposes of your study. Perhaps you’ll narrow your focus even more to elementary school teachers in a particular school district who have been teaching for a particular length of time. The possibilities can go on. These are choices you will need to make, both for practical reasons (i.e., the population you have access to) and for the questions you are trying to answer.

Of course, for this particular example, this does not mean that it wouldn’t be interesting to also know what principals think about the new curriculum. Or parents. Or elementary school children. It just means that, for the purposes of your project and your research questions, you’re interested in the experience of the teachers, so you’re excluding anyone who does not meet those criteria. Having delimitations to your population of interest also means that you won’t be able to answer any questions about the experiences of those other populations; this is ok because those populations are outside of the scope of your project . As interesting as their experiences might be, you can save these questions for another study. That is the part of the beauty of research: there will always be more studies to do, more questions to ask. You don’t have to (and can’t) do it all in one project.

Continuing with the previous example, for instance, let’s suppose that the problem you are most interested in addressing is the fact that we know relatively little about elementary school teachers’ experiences of implementing a new curriculum. Perhaps you believe that knowing more about teachers’ experiences could inform their training or help administrators know more about how to support their teachers. If the identified problem is our lack of knowledge about teachers’ experiences, and your research questions focus on better understanding these experiences, that means that you are choosing not to focus on other problems or questions, even those that may seem closely related. For instance, you are not asking how effective the new curriculum is in improving student test scores or graduation rates. You might think that would be a very interesting question, but it will have to wait for another study. In narrowing the focus of your research questions, you limit your ability to answer other questions, and again, that’s ok. These other questions may be interesting and important, but, again, they are beyond the scope of your project .

Common Examples of Limitations

While each study will have its own unique set of limitations, some limitations are more common in quantitative research, and others are more common in qualitative research.

In quantitative research, common limitations include the following:

– Participant dropout

– Small sample size, low power

– Non-representative sample

– Violations of statistical assumptions

– Non-experimental design, lack of manipulation of variables, lack of controls

– Potential confounding variables

– Measures with low (or unknown) reliability or validity

– Limits of an instrument to measure the construct of interest

– Data collection methods (e.g., self-report)

– Anything else that might limit the study’s internal or external validity

In qualitative research, common limitations include the following:

– Lack of generalizability of findings (not the goal of qualitative research, but still worth mentioning as a limitation)

– Inability to draw causal conclusions (again, not the goal of qualitative research, but still worth mentioning)

– Researcher bias/subjectivity (especially if there is only one coder)

– Limitations in participants’ ability/willingness to share or describe their experiences

– Any factors that might limit the rigor of data collection or analysis procedures

Common Examples of Delimitations

As noted above, the two most common sources of delimitations in both quantitative and qualitative research include the following:

– Inclusion/exclusion criteria (or how you define your population of interest)

– Research questions or problems you’ve chosen to examine

Several other common sources of delimitations include the following:

– Theoretical framework or perspective adopted

– Methodological framework or paradigm chosen (e.g., quantitative, qualitative, or mixed-methods)

– In quantitative research, the variables you’ve chosen to measure or manipulate (as opposed to others)

Whether you’re conducting a quantitative or qualitative study, you will (hopefully!) have chosen your research design because it is well suited to the questions you’re hoping to answer. Because these questions define the boundaries or scope of your project and thus point to its delimitations, your research design itself will also be related to these delimitations.

Questions to Ask Yourself

As you are considering the limitations and delimitations of your project, it can be helpful to ask yourself a few different questions.

Questions to help point out your study’s limitations :

1. If I had an unlimited budget, unlimited amounts of time, access to all possible populations, and the ability to manipulate as many variables as I wanted, how would I design my study differently to be better able to answer the questions I want to answer? (The ways in which your study falls short of this will point to its limitations.)

2. Are there design issues that get in the way of my being able to draw causal conclusions?

3. Are there sampling issues that get in the way of my being able to generalize my findings?

4. Are there issues related to the measures I’m using or the methods I’m using to collect data? Do I have concerns about participants telling the truth or being able to provide accurate responses to my questions?

5. Are there any other factors that might limit my study’s internal or external validity?

Questions that help point out your study’s delimitations :

1. What are my exclusion criteria? Who did I not include in my study, and why did I make this choice?

2. What questions did I choose not to address in my study? (Of course, the possibilities are endless here, but consider related questions that you chose not to address.)

3. In what ways did I narrow the scope of my study in order to hone in on a particular issue or question?

4. What other methodologies did I not use that might have allowed me to answer slightly different questions about the same topic?

How to Write About Limitations and Delimitations

Remember, having limitations and delimitations is not a bad thing. They’re present in even the most rigorous research. The important thing is to be aware of them and to acknowledge how they may impact your findings or the conclusions you can draw.

In fact, writing about them and acknowledging them gives you an opportunity to demonstrate that you can think critically about these aspects of your study and how they impact your findings, even if they were out of your control.

Keep in mind that your study’s limitations will likely point to important directions for future research. Therefore, when you’re getting ready to write about your recommendations for future research in your discussion, remember to refer back to your limitations section!

As you write about your delimitations in particular, remember that they are not weaknesses, and you don’t have to apologize for them. Good, strong research projects have clear boundaries. Also, keep in mind that you are the researcher and you can choose whatever delimitations you want for your study. You’re in control of the delimitations. You just have to be prepared—both in your discussion section and in your dissertation defense itself—to justify the choices you make and acknowledge how these choices impact your findings.

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Research Limitations

It is for sure that your research will have some limitations and it is normal. However, it is critically important for you to be striving to minimize the range of scope of limitations throughout the research process.  Also, you need to provide the acknowledgement of your research limitations in conclusions chapter honestly.

It is always better to identify and acknowledge shortcomings of your work, rather than to leave them pointed out to your by your dissertation assessor. While discussing your research limitations, don’t just provide the list and description of shortcomings of your work. It is also important for you to explain how these limitations have impacted your research findings.

Your research may have multiple limitations, but you need to discuss only those limitations that directly relate to your research problems. For example, if conducting a meta-analysis of the secondary data has not been stated as your research objective, no need to mention it as your research limitation.

Research limitations in a typical dissertation may relate to the following points:

1. Formulation of research aims and objectives . You might have formulated research aims and objectives too broadly. You can specify in which ways the formulation of research aims and objectives could be narrowed so that the level of focus of the study could be increased.

2. Implementation of data collection method . Because you do not have an extensive experience in primary data collection (otherwise you would not be reading this book), there is a great chance that the nature of implementation of data collection method is flawed.

3. Sample size. Sample size depends on the nature of the research problem. If sample size is too small, statistical tests would not be able to identify significant relationships within data set. You can state that basing your study in larger sample size could have generated more accurate results. The importance of sample size is greater in quantitative studies compared to qualitative studies.

4. Lack of previous studies in the research area . Literature review is an important part of any research, because it helps to identify the scope of works that have been done so far in research area. Literature review findings are used as the foundation for the researcher to be built upon to achieve her research objectives.

However, there may be little, if any, prior research on your topic if you have focused on the most contemporary and evolving research problem or too narrow research problem. For example, if you have chosen to explore the role of Bitcoins as the future currency, you may not be able to find tons of scholarly paper addressing the research problem, because Bitcoins are only a recent phenomenon.

5. Scope of discussions . You can include this point as a limitation of your research regardless of the choice of the research area. Because (most likely) you don’t have many years of experience of conducing researches and producing academic papers of such a large size individually, the scope and depth of discussions in your paper is compromised in many levels compared to the works of experienced scholars.

You can discuss certain points from your research limitations as the suggestion for further research at conclusions chapter of your dissertation.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline. John Dudovskiy

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Don’t Worry! And Write the LIMITATIONS of Your Research!

Do you know someone who thinks they are simply perfect and has no faults? (Well, I know a few and some even become presidents of extremely important countries).

Well, as shocking and disappointing as it may seem to some people: no one is perfect! Some are too tall, some too short, some enjoy country music (nothing personal), some add water to their fine whiskey (honestly, why?) and some do not drink coffee.

The conclusion is: we all have some negative sides! And research is no different!

And what is considered a limitation of a study?

A limitation is any aspect that hinders a study and its findings.

Does it mean that if my study has limitations it is useless?  NO!!!!!!!!!!!

Very often researchers (students or well established researchers) have concerns about clearly describing the limitations of their studies. Why? Because there is sometimes a misconception that if your research limitations are too clear, readers will undermine the relevance of your work. For example, you might be afraid others will think:

“Why are these findings relevant if there are so many limitations to the study?”

All right, first let us make some things clear here:

• EVERY STUDY HAS LIMITATIONS.
• Clarifying the limitations of a study allows the reader to better understand under which conditions the results should be interpreted.
• Clear descriptions of limitations of a study also show that the researcher has a holistic understanding of his/her study. And this is something very positive!  

In other words, clearly describing the limitations of your study should only strengthen your work!

ALSO CHECK :  Read our “STEP BY STEP Thesis Guide” with Many More Tips!

Video Content: Research Limitations 

In case you are enjoying the article, do not forget to watch the video with further support on how to deal with your research limitations.

Examples of Research Limitations

Ok, you got it so far that no one is perfect, that some weird people become presidents and that research limitations should be included in your work.

I guess the next question would be: which limitations should I mention?

Look, it is extremely difficult to describe all possible types of research limitations. It will vary greatly depending on the type and nature of the study.

However, here are some examples:

• Often studies wish to understand a specific topic (e.g. Brazilian consumers’ perceptions towards a product) but only conduct a study with 50 participants. Considering that the Brazilian population has around 200 million people, can we generalize the results based on only 50 respondents? Clearly NOT! So consider your sample size in relation to the population of your study.
• For example, many academic studies have used student sampling. There are many advantages for this, such as easy access and low costs for data collection. Nonetheless, using purely student sampling is also extremely limiting if the population of the study is comprised of people with varies profiles.
• Very often, a method is accurate for a research aim, but it also includes many limitations. For example: Imagine you wish to understand consumers’ use of toilet paper (weird topic, isn’t it?) and the researcher uses in-depth interviews, as the study has an exploratory nature. Would you, as a respondent, feel comfortable to describe your use of toilet paper to a stranger? Probably not! Thus, your answers might be highly biased according to what is expected from him/her or to what is socially acceptable. So your answers might not exactly resemble the truth, due to the method.
• In the example above, the presence of the researcher influenced the responses, right? But would it be different if the interview had been done over the phone? Perhaps yes. Why? Because the topic is sensitive and private (Literally! ). So the point is: the way in which you collect data can represent a strong limitation. Some researchers collect data in busy areas such as train stations where there are many distractions and respondents are in a rush. Is this a limitation? Certainly! Thus, you must reflect to see if the way in which you collected your data represents a limitation.  
• Imagine you are developing a study involving virtual reality (VR). You can use many different VR devices, ranging from very expensive ones (that have an extraordinary immersion experience) to cheaper ones (that will provide an immersive experience, but not as real). In other words, the type of device used influenced the study results. So if you use an equipment (e.g. devices, products, etc.) you have to consider if the type used represents a limitation or a strength of your work.
• Often students have a deadline to turn in their work. Other academics have conference or journal deadlines. Would we do better work if we had more time? Of course! Do we have unlimited time to do research and collect data? NO! For this reason, “time” is a very common limitation for many studies.
• Are you investigating a phenomenon long after it happened? Did you collect your data in a period that was not exactly suitable for respondents for some specific reason? All of these are examples of how timing might represent a strong limitation for studies.
• Money is always a problem (at least for me. If it is not for you, we should be friends! ). Sometimes we need it to purchase the necessary equipment for a study, to hire people for data collection, to purchase a specific statistical software or to simply reward participants with products or giveaways for having participated in the study. When financial resources are scarce, all of these possibilities are compromised. Consequently, such limitations might be reflected in the results of the study.
• In the majority of cases, studies start when researchers identify gaps in the literature and try to address them. However, the identification or understanding that there is a gap depends on the researchers’ level of access to the existing literature. What may seem as a research gap might be a huge misconception simply because the person did not have access to a larger range of scientific literature. Thus, access to literature can also be a limitation.
• If your study is based on secondary data, pay extra care to the age of the data. Making current assumptions based on old data represents a strong limitation.

Where Should Research Limitations be Included in the Thesis?

Once you are done thinking and considering the limitations of your work, a simple question may arise: Where in my thesis should I include such limitations?

Please note: there is no specific format to this and it may vary from supervisor to supervisor, and sometimes certain universities may have their own guidelines. But USUALLY, the limitations are the VERY LAST section of your thesis, and they appear after the MANAGERIAL RECOMMENDATIONS .

And why? Because as mentioned above, the limitations may be due to any section of your work. For example:

• Access to literature (literature review or theoretical background)
• Method and data collection process (methodology)
• Statistical software (analysis)

For this reason, it doesn’t really make much sense to have it in any other section of your work but the very END .

Got it? Great! Now go ahead and be honest with the limitations of your work! Reviewers will be positively impressed!

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Final Thoughts

Please note: All the suggestions here are personal, according to my own supervision style. Feel absolutely free to discuss them with your supervisor or other academics. Each one tends to have their own style and expectations.

Hope these tips have been useful for you and wish you all the best!

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16 Advantages and Disadvantages of Experimental Research

How do you make sure that a new product, theory, or idea has validity? There are multiple ways to test them, with one of the most common being the use of experimental research. When there is complete control over one variable, the other variables can be manipulated to determine the value or validity that has been proposed.

Then, through a process of monitoring and administration, the true effects of what is being studied can be determined. This creates an accurate outcome so conclusions about the final value potential. It is an efficient process, but one that can also be easily manipulated to meet specific metrics if oversight is not properly performed.

Here are the advantages and disadvantages of experimental research to consider.

What Are the Advantages of Experimental Research?

1. It provides researchers with a high level of control. By being able to isolate specific variables, it becomes possible to determine if a potential outcome is viable. Each variable can be controlled on its own or in different combinations to study what possible outcomes are available for a product, theory, or idea as well. This provides a tremendous advantage in an ability to find accurate results.

2. There is no limit to the subject matter or industry involved. Experimental research is not limited to a specific industry or type of idea. It can be used in a wide variety of situations. Teachers might use experimental research to determine if a new method of teaching or a new curriculum is better than an older system. Pharmaceutical companies use experimental research to determine the viability of a new product.

3. Experimental research provides conclusions that are specific. Because experimental research provides such a high level of control, it can produce results that are specific and relevant with consistency. It is possible to determine success or failure, making it possible to understand the validity of a product, theory, or idea in a much shorter amount of time compared to other verification methods. You know the outcome of the research because you bring the variable to its conclusion.

4. The results of experimental research can be duplicated. Experimental research is straightforward, basic form of research that allows for its duplication when the same variables are controlled by others. This helps to promote the validity of a concept for products, ideas, and theories. This allows anyone to be able to check and verify published results, which often allows for better results to be achieved, because the exact steps can produce the exact results.

5. Natural settings can be replicated with faster speeds. When conducting research within a laboratory environment, it becomes possible to replicate conditions that could take a long time so that the variables can be tested appropriately. This allows researchers to have a greater control of the extraneous variables which may exist as well, limiting the unpredictability of nature as each variable is being carefully studied.

6. Experimental research allows cause and effect to be determined. The manipulation of variables allows for researchers to be able to look at various cause-and-effect relationships that a product, theory, or idea can produce. It is a process which allows researchers to dig deeper into what is possible, showing how the various variable relationships can provide specific benefits. In return, a greater understanding of the specifics within the research can be understood, even if an understanding of why that relationship is present isn’t presented to the researcher.

7. It can be combined with other research methods. This allows experimental research to be able to provide the scientific rigor that may be needed for the results to stand on their own. It provides the possibility of determining what may be best for a specific demographic or population while also offering a better transference than anecdotal research can typically provide.

What Are the Disadvantages of Experimental Research?

1. Results are highly subjective due to the possibility of human error. Because experimental research requires specific levels of variable control, it is at a high risk of experiencing human error at some point during the research. Any error, whether it is systemic or random, can reveal information about the other variables and that would eliminate the validity of the experiment and research being conducted.

2. Experimental research can create situations that are not realistic. The variables of a product, theory, or idea are under such tight controls that the data being produced can be corrupted or inaccurate, but still seem like it is authentic. This can work in two negative ways for the researcher. First, the variables can be controlled in such a way that it skews the data toward a favorable or desired result. Secondly, the data can be corrupted to seem like it is positive, but because the real-life environment is so different from the controlled environment, the positive results could never be achieved outside of the experimental research.

3. It is a time-consuming process. For it to be done properly, experimental research must isolate each variable and conduct testing on it. Then combinations of variables must also be considered. This process can be lengthy and require a large amount of financial and personnel resources. Those costs may never be offset by consumer sales if the product or idea never makes it to market. If what is being tested is a theory, it can lead to a false sense of validity that may change how others approach their own research.

4. There may be ethical or practical problems with variable control. It might seem like a good idea to test new pharmaceuticals on animals before humans to see if they will work, but what happens if the animal dies because of the experimental research? Or what about human trials that fail and cause injury or death? Experimental research might be effective, but sometimes the approach has ethical or practical complications that cannot be ignored. Sometimes there are variables that cannot be manipulated as it should be so that results can be obtained.

5. Experimental research does not provide an actual explanation. Experimental research is an opportunity to answer a Yes or No question. It will either show you that it will work or it will not work as intended. One could argue that partial results could be achieved, but that would still fit into the “No” category because the desired results were not fully achieved. The answer is nice to have, but there is no explanation as to how you got to that answer. Experimental research is unable to answer the question of “Why” when looking at outcomes.

6. Extraneous variables cannot always be controlled. Although laboratory settings can control extraneous variables, natural environments provide certain challenges. Some studies need to be completed in a natural setting to be accurate. It may not always be possible to control the extraneous variables because of the unpredictability of Mother Nature. Even if the variables are controlled, the outcome may ensure internal validity, but do so at the expense of external validity. Either way, applying the results to the general population can be quite challenging in either scenario.

7. Participants can be influenced by their current situation. Human error isn’t just confined to the researchers. Participants in an experimental research study can also be influenced by extraneous variables. There could be something in the environment, such an allergy, that creates a distraction. In a conversation with a researcher, there may be a physical attraction that changes the responses of the participant. Even internal triggers, such as a fear of enclosed spaces, could influence the results that are obtained. It is also very common for participants to “go along” with what they think a researcher wants to see instead of providing an honest response.

8. Manipulating variables isn’t necessarily an objective standpoint. For research to be effective, it must be objective. Being able to manipulate variables reduces that objectivity. Although there are benefits to observing the consequences of such manipulation, those benefits may not provide realistic results that can be used in the future. Taking a sample is reflective of that sample and the results may not translate over to the general population.

9. Human responses in experimental research can be difficult to measure. There are many pressures that can be placed on people, from political to personal, and everything in-between. Different life experiences can cause people to react to the same situation in different ways. Not only does this mean that groups may not be comparable in experimental research, but it also makes it difficult to measure the human responses that are obtained or observed.

The advantages and disadvantages of experimental research show that it is a useful system to use, but it must be tightly controlled in order to be beneficial. It produces results that can be replicated, but it can also be easily influenced by internal or external influences that may alter the outcomes being achieved. By taking these key points into account, it will become possible to see if this research process is appropriate for your next product, theory, or idea.

Examples of Limitations of a Study

Damon verial.

Though science has a clear methodology that researchers have virtually perfected over centuries, rarely is an individual study perfect. Studies usually have at least one limitation that makes some aspects of their results less likely to be accurate, such as the hypothesis not being proved though it might be true, the introduction of bias, a necessity to rely on estimates for some data, or limitations on the scope and applicability of the study. Whatever the case, a good scientist knows the potential sources of limitations and mentions those influential ones in her paper.

Explore this article

• Science Doesn’t Tool Around
• Subjects’ Defects
• Acts of Nature
• Confound It!

1 Science Doesn’t Tool Around

A scientist’s toolbox is as strong as its weakest link. Sometimes it’s the tools scientists use that introduce limitations to the study. Contemporary science makes heavy use of tools, which has led to studies that weren’t possible in the past, like the observation of quantum particles in physics. But how well a study reports its results relates to how strong the instruments are. For example, microscopes have limitations on what they measure. While you can use a microscope to view a two-dimensional image of bacteria and record results from that image, you won’t be able to record the height of the bacteria. If one thing you are reporting in your study is the volume of bacteria, you will have to estimate the height and multiply it by the observed area to get a volume. In this way, your microscope limits the accuracy of your study’s results.

2 Subjects’ Defects

From physics to medicine, scientists employ “subjects,” or the objects of observation, in their studies. But for most sciences, the traits of subjects are rarely consistent. The lack of consistence in the subjects can lead to problems drawing conclusions. For example, in medicinal studies, doctors might give one of two chosen drugs to two groups of people. The doctors probably tried to make sure that these two groups are similar in demographics, such as having a similar mix of ages, genders and health statuses. But small differences, such as lifestyle and genetic factors can skew the effects of the drugs on the subjects. In addition, because scientists across the globe have different sources and amounts of funding, not all scientists can use large groups of subjects. A zoologist, for example, might not be able to acquire many chimpanzees for her study. But because small sample sizes make the statistics of a study less dependable, the results of a study that lacks sufficient funding might not be strong or mathematically significant.

3 Acts of Nature

Scientists are still human. Once in a while, a scientist might make a mistake, become ill and pause the study, or run into technical issues. A scientist who suffers such problems will likely report it in his paper, citing them as limitations. Regent University mentions that limitations outside of a scientist’s control are remarkably common. For example, a botanist researching the relationship between the amount of pests on a specific plant and the amount of sunlight that plant receives might have missing data if he finds several plants to be missing next time he reaches his plot of land. He might suspect the plants were eaten by a wild herbivore. In this case, his sample size has been reduced by phenomena he cannot control, which can weaken his study.

4 Confound It!

One thing that can really limit a study and frustrate a scientist, especially in well-designed experiments, is the confounder. A confounder is a quality or variable that affects the results of the study but is not included in the study itself. A scientist often notices a confounder only during or after the experiment. She lists that confounder as a limitation in her report. Confounders include environmental factors, neglected differences between subjects, and unexpected changes during the experiment. For example, a scientist working with lab rats might not concern herself whether the lab rats in her study are kept in room A or room B of the laboratory. So, she keeps some in each room. But at the end of the experiment, she might notice that the lab rats in room A are significantly fatter than those in room B. In this experiment, the room the lab rats are kept in is a confounding factor. She might not have noticed that the temperature in room B is lower or that the lights in room A are dimmer. Regardless, she will likely cite the environment as a confounder, letting other scientists know that future studies should keep the lab rats in the same room.

• 1 Regent University: Guidelines for Writing Research Proposals and Dissertations

About the Author

Having obtained a Master of Science in psychology in East Asia, Damon Verial has been applying his knowledge to related topics since 2010. Having written professionally since 2001, he has been featured in financial publications such as SafeHaven and the McMillian Portfolio. He also runs a financial newsletter at Stock Barometer.

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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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Limitations of the Scientific Method

Clearly, the scientific method is a powerful tool, but it does have its limitations. These limitations are based on the fact that a hypothesis must be testable and falsifiable and that experiments and observations be repeatable. This places certain topics beyond the reach of the scientific method.

Science cannot prove or refute the existence of God or any other supernatural entity. Sometimes, scientific principles are used to try to lend credibility to certain nonscientific ideas, such as intelligent design . Intelligent design is the assertion that certain aspects of the origin of the universe and life can be explained only in the context of an intelligent, divine power. Proponents of intelligent design try to pass this concept off as a scientific theory to make it more palatable to developers of public school curriculums. But intelligent design is not science because the existence of a divine being cannot be tested with an experiment.

Science is also incapable of making value judgments. It cannot say global warming is bad, for example. It can study the causes and effects of global warming and report on those results, but it cannot assert that driving SUVs is wrong or that people who haven't replaced their regular light bulbs with LED bulbs are irresponsible.

Occasionally, certain organizations use scientific data to advance their causes. This blurs the line between science and morality and encourages the creation of "pseudo-science," which tries to legitimize a product or idea with a claim that has not been subjected to rigorous testing.

And yet, used properly, the scientific method is one of the most valuable tools humans have ever created. It helps us solve everyday problems around the house and, at the same time, helps us understand profound questions about the world and universe in which we live.

Most of the time, two competing theories can't exist to describe one phenomenon. But in the case of light , one theory is not enough. Many experiments support the notion that light behaves like a longitudinal wave. Taken collectively, these experiments have given rise to the wave theory of light. Other experiments, however, support the notion that light behaves as a particle. Instead of throwing out one theory and keeping the other, physicists maintain a wave/particle duality to describe the behavior of light.

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• D'Agnese, Joseph. "Scientific Method Man." Wired, September 2004. http://www.wired.com/wired/archive/12.09/rugg.html.
• Introduction to the Scientific Method on Web Site of Frank Wolfs, Department of Physics and Astronomy, University of Rochester. http://teacher.pas.rochester.edu/phy_labs/AppendixE/AppendixE.html
• Keeton, William T. "Biological Science, Third Edition." W.W. Norton & Company, New York, 1980.
• The New Oxford American Dictionary. Oxford University Press, Oxford, United Kingdom. 2001.
• Understanding and Using the Scientific Method on Fact Monster. http://www.factmonster.com/cig/science-fair-projects/understanding-using-scientific-method.html
• Vecchione, Glen. "100 Amazing Award-Winning Science Fair Projects." Sterling Publishing Co., New York, 2001.
• Vecchione, Glen. "100 Amazing First Prize Science Fair Projects." Sterling Publishing Co., New York, 1998.

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A Synthetic Benchmark to Explore Limitations of Localized Drift Detections

Concept drift is a common phenomenon in data streams where the statistical properties of the target variable change over time. Traditionally, drift is assumed to occur globally, affecting the entire dataset uniformly. However, this assumption does not always hold true in real-world scenarios where only specific subpopulations within the data may experience drift. This paper explores the concept of localized drift and evaluates the performance of several drift detection techniques in identifying such localized changes. We introduce a synthetic dataset based on the Agrawal generator, where drift is induced in a randomly chosen subgroup. Our experiments demonstrate that commonly adopted drift detection methods may fail to detect drift when it is confined to a small subpopulation. We propose and test various drift detection approaches to quantify their effectiveness in this localized drift scenario. We make the source code for the generation of the synthetic benchmark available at https://github.com/fgiobergia/subgroup-agrawal-drift .

1 Introduction

In the realm of data stream mining, the detection of concept drift is of fundamental importance to maintain the accuracy and reliability of predictive models. Concept drift refers to the change in the statistical properties of the target variable that the model is trying to predict. Traditionally, drift detection techniques make the (often implicit) assumption that the drift occurs globally, i.e., the change is uniformly distributed across the entire dataset. This assumption, however, may not always hold in real-world situations where drift can occur in a localized manner, affecting only certain subpopulations within the data (e.g., only young women employed in the IT sector).

Localized drift poses a significant challenge for traditional drift detection methods. These methods are designed to identify global changes and may overlook drifts that are confined to a small subset of the data. As a result, models may fail to adapt to these local changes, leading to degraded performance and inaccurate predictions. For instance, a subgroup covering 2% of the population may start behaving in a significantly different way than previously known. It is desirable that this change in behavior be detected by drift detectors. However, the drift goes unnoticed when we observe the overall performance of the model (i.e., the performance on the entire population). Figure  1 shows how the accuracy varies under subgroup drift for the entire population and for the specific subgroup. While the overall performance degrades by approximately 2% and may go unnoticed, the accuracy within the subgroup drops to 0.

To investigate the limitations of existing drift detection methods in the context of localized drift, we introduce a synthetic dataset inspired by the Agrawal generator  [ 1 ] . In this dataset, drift is intentionally induced in a randomly chosen subgroup of a specific size, while the rest of the data remains stable. This setup allows us to simulate a scenario where only a specific subpopulation is subject to drift, thereby providing a controlled environment to evaluate the effectiveness of various drift detection techniques.

The primary contributions of this paper are as follows:

We highlight the importance of recognizing localized drift in data streams and its implications for drift detection methodologies.

We introduce a synthetic dataset based on the Agrawal generator with induced localized drift, providing a benchmark for evaluating drift detection methods.

We conduct a comprehensive evaluation of several drift detection techniques, quantifying their performance in detecting localized drift.

The rest of the paper is organized as follows. Section  2 describes the synthetic dataset and experimental setup. Section  3 presents the results of our experiments and discusses the findings. Finally, Section  4 concludes the paper and suggests directions for future research.

2 Proposed dataset

We introduce a novel dataset based on the synthetic one proposed in  [ 1 ] . In particular, we propose (i) identifying a randomly selected subgroup of the population, defined as a slice of the dataset’s attributes and of a user-specified size, and (ii) only injecting this target subgroup with noise to simulate a situation where the drift occurs locally, instead of globally. The code is available at https://github.com/fgiobergia/subgroup-agrawal-drift .

2.1 Subgroup Agrawal Drift Dataset

To explore the concept of localized drift, we define a synthetic dataset based on the Agrawal generator  [ 1 ] . The Agrawal generator is commonly used for simulating data streams and generates samples x 𝑥 x italic_x in a domain 𝒟 𝒟 \mathcal{D} caligraphic_D with six numerical attributes and three categorical attributes, producing binary classification tasks. The attributes are as follows:

salary , uniformly distributed from $20,000 to $150,000

commission , 0 if salary has a value below $75,000, otherwise it is uniformly distributed from $10,000 to $75,000

age , uniformly distributed from 20 to 80

elevel (education level), uniformly chosen from 0 to 4

car (car maker), uniformly chosen from 1 to 20

zipcode (zip code of the town), uniformly chosen from 0 to 8

hvalue (house value), uniformly distributed from $ 50 , 000 ⋅ \$50,000\,\cdot\, $ 50 , 000 ⋅ zipcode to $ 100 , 000 ⋅ \$100,000\,\cdot\, $ 100 , 000 ⋅ zipcode . Different zip codes, as such, are associated with different average house prices

hyears (years the house has been owned), 1 to 30 uniformly distributed

loan (total loan amount requested), uniformly distributed from $0 to $500,000

A common technique to introduce concept drift [ 5 ] consists of adopting a classification function f i subscript 𝑓 𝑖 f_{i} italic_f start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT for the original concept and a different one f j subscript 𝑓 𝑗 f_{j} italic_f start_POSTSUBSCRIPT italic_j end_POSTSUBSCRIPT ( i ≠ j 𝑖 𝑗 i\neq j italic_i ≠ italic_j ) for the drift concept. At step t 𝑡 t italic_t , the function is defined as a random variable F 𝐹 F italic_F :

1 superscript 𝑒 4 𝑡 𝑘 𝑤 1 p_{t}=(1+e^{-4(t-k)/w})^{-1} italic_p start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT = ( 1 + italic_e start_POSTSUPERSCRIPT - 4 ( italic_t - italic_k ) / italic_w end_POSTSUPERSCRIPT ) start_POSTSUPERSCRIPT - 1 end_POSTSUPERSCRIPT . k 𝑘 k italic_k represents the center of the sigmoid function, and w 𝑤 w italic_w is its width.

This drift, however, is applied uniformly to all samples. Instead, we aim to create a drift that is localized in nature, i.e., that only affects one subpopulation of the dataset. We consider a selector function s : 𝒟 → { 0 , 1 } : 𝑠 → 𝒟 0 1 s:\mathcal{D}\rightarrow\{0,1\} italic_s : caligraphic_D → { 0 , 1 } , having value 1 for samples belonging to the target subgroup, 0 otherwise. We outline the definition of s ⁢ ( ⋅ ) 𝑠 ⋅ s(\cdot) italic_s ( ⋅ ) in more detail in Subsection 2.2 . We define F 𝐹 F italic_F as follows:

In other words, the gradual drift is applied only to samples belonging to the target subgroup defined by s ⁢ ( ⋅ ) 𝑠 ⋅ s(\cdot) italic_s ( ⋅ ) . All other samples will retain the original concept. We note that this definition can be extended to multiple subpopulations, which can be subject to different drifts.

2.2 Subgroup definition

To simulate a localized drift, we need to define a target subgroup within the dataset. We produce meaningful subgroups by identifying slices of the domain, e.g., { age ∈ \in ∈ [25, 30], salary ∈ \in ∈ [$75,000, $100,000] }. We fully automate the synthetic dataset generation phase by introducing a random subgroup definition policy. This policy produces, for a desired subgroup size (i.e., subgroup support), a slice of the population that approximately encompasses it.

We adopt a greedy policy to identify a subset of slices that, combined, well approximate the target subgroup size. We do this by identifying random ranges of values (e.g., [ c , d ] 𝑐 𝑑 [c,d] [ italic_c , italic_d ] ) for randomly chosen attributes (e.g., attr ∼ U ⁢ ( a , b ) similar-to attr 𝑈 𝑎 𝑏 \texttt{attr}\sim U(a,b) attr ∼ italic_U ( italic_a , italic_b ) , a ≤ c < d ≤ b 𝑎 𝑐 𝑑 𝑏 a\leq c<d\leq b italic_a ≤ italic_c < italic_d ≤ italic_b ). The uniform distribution makes it trivial to compute the probability of belonging to the random range of values, as P ⁢ ( attr ∈ [ c , d ] ) = P ⁢ ( attr ) = d − c b − a 𝑃 attr 𝑐 𝑑 𝑃 attr 𝑑 𝑐 𝑏 𝑎 P(\texttt{attr}\in[c,d])=P(\texttt{attr})=\frac{d-c}{b-a} italic_P ( attr ∈ [ italic_c , italic_d ] ) = italic_P ( attr ) = divide start_ARG italic_d - italic_c end_ARG start_ARG italic_b - italic_a end_ARG . Additionally, the attributes are independent from one another 1 1 1 As discussed in Subsection 2.1 , all but two attributes ( commission and hvalue ) are independently sampled from uniform distributions with known ranges. We only consider independent attributes for the definition of the target subgroup for simplicity. . As such, their combined probability can be computed as the product of the separate probabilities, P ⁢ ( attr.1 ) ⋅ P ⁢ ( attr.2 ) ⋅ … ⋅ P ⁢ ( attr.n ) ⋅ ⋅ 𝑃 attr.1 𝑃 attr.2 … 𝑃 attr.n P(\texttt{attr.1})\cdot P(\texttt{attr.2})\cdot\ldots\cdot P(\texttt{attr.n}) italic_P ( attr.1 ) ⋅ italic_P ( attr.2 ) ⋅ … ⋅ italic_P ( attr.n ) . We either include or discard a candidate slice based on whether it gets the current probability closer to the target one. Figure 2 provides an example where a subgroup of approximately the target size (10%) is iteratively defined by identifying a first slice on age , followed by a second one on salary . Because of the greedy nature of the algorithm, slices that do not provide an immediate improvement in terms of support are discarded. The algorithm terminates when either the subgroup size is within a tolerance threshold of the target one, or a maximum number of iterations is reached.

2.3 Examples of generated subgroups

In this subsection, we provide some instances of generated subgroups for various target subgroup sizes.

To guarantee variety in the generated subgroups, the proposed generator produces random subgroups that approximate the desired target size. As detailed above, the algorithm refines the generated subgroup until either the desired tolerance is reached or a maximum number of iterations has been executed. Figure  3 shows the distribution of gaps between target and obtained subgroup sizes, for a tolerance of 0.01 on the target size. In 83% of cases, the desired tolerance is reached, whereas in the remaining 17% of cases the maximum number of iterations (1,000) is reached.

We report four examples of generated subgroups in Table 1 . For each, we report the target (desired) size (5%, 10%, 25% and 50% of the population, respectively), the one computed according to the greedy policy adopted for subgroup generation, and the actual (empirical) size, as observed over a generated sample of 10,000 points. Both computed and actual sizes are close to the target one. If needed, the gap between computed and target subgroup sizes can be lowered by changing the maximum number of allowed iterations and/or the desired tolerance threshold.

Generated subgroup	Target size	Computed size	Actual size
{ elevel zipcode age } { car salary zipcode } { zipcode salary age car } { elevel age salary hyears } 3 Experimental results In this section, we show the performance of various drift detection techniques that are commonly adopted in literature on the proposed Subgroup Agrawal Drift dataset. We are mainly interested in the change in performance of these techniques as the size of the drifting subgroup changes. Drift detection techniques. We considered the following drift detectors. DDM (Drift Detection Method)   [ 4 ] is a statistical technique that monitors the error rate of a model over time. When the error rate increases significantly, it indicates a possible change in the data distribution. If this increase surpasses a pre-defined drift threshold, DDM triggers the detection of a drift. EDDM (Early Drift Detection Method)   [ 2 ] improves upon DDM by focusing on the distance between errors instead of just the error rate. This method aims to detect gradual changes more effectively. It calculates the average distance between errors and monitors the standard deviation of these distances. Significant changes in these metrics can indicate a drift, allowing the model to adapt more quickly to evolving data streams. HDDM (Hoeffding Drift Detection Method)   [ 3 ] is based on Hoeffding’s inequality, which provides a way to determine the bounds of an estimator with high probability. HDDM uses this statistical method to detect changes in the distribution of incoming data compared to older data. By comparing the distributions of recent data to older data, HDDM can identify when a significant change has occurred, suggesting that the underlying data distribution has drifted. FHDDM (Fast Hoeffding Drift Detection Method)   [ 6 ] is an enhanced version of HDDM, designed to provide faster and more accurate detection. It applies Hoeffding’s bounds to smaller windows of data, allowing it to detect drifts more quickly and with fewer false alarms. FHDDM is particularly useful in scenarios requiring rapid adaptation to changing data. For each method, we identify the best-performing configuration of hyperparameters through a grid search on the dataset. We adopt the proposed synthetic dataset for benchmarking drift detection techniques as the drifting subgroup sizes vary. In particular, we are interested in the performance when the drifting subgroups are small, as these are the drifts that are intuitively more likely to go undetected. We sample subgroup sizes from 1% to 100% (i.e., the full population) logarithmically. For each subgroup size, we conduct 100 experiments. For half of them, we inject drift to a random subgroup of the desired size (positive experiments). The other half is instead not injected with any drift (negative experiments). For positive experiments, we randomly choose one out of the 10 classification functions for the original concept, and a different one for the drift concept. For negative experiments, we instead use a single concept throughout the entire experiment. For all experiments, we build a training set comprised of 10,000 points sampled from the underlying distribution and associated with the original concept. We train a simple decision tree classification model with a depth of up to 5 nodes on this training set. Subsequently, we sample 200 batches of data (1,000 points each). For positive experiments, the concept drift is injected gradually, as detailed in Subsection 2.1 . The injection is centered around the 100 t ⁢ h superscript 100 𝑡 ℎ 100^{th} 100 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT batch, with a width of 100 batches. The subgroup accuracy in Figure 1 provides a visual intuition of the setting. We introduce a perturbation of 25% of the input attributes to make the classification problem non-trivial. For each experiment, the various drift detection techniques are used to monitor and potentially detect drifts. Since each algorithm can potentially produce multiple drift detections, we count the number of detections. We determine the threshold on the minimum number of detections to trigger a drift alert using a ROC curve computed on 30% of the experiments. We use the rest of the experiments to compute the performance in terms of accuracy, F 1 subscript 𝐹 1 F_{1} italic_F start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT score, False Positive Rate (FPR) and False Negative Rate (FNR), of various drift detection techniques. Figure  4 summarizes the main results. Both accuracy and F 1 subscript 𝐹 1 F_{1} italic_F start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT highlight how all considered techniques achieve near-perfect performance in detecting drifts when the drifting subgroup is large enough (approximately 10% of the dataset or more). Instead, none of the approaches achieved satisfactory results for lower support sizes. To better understand the cause of this drop in performance, we additionally computed the FPR and FNR for each technique for various sizes of drifting subgroups. Interestingly, the FPR is largely unaffected by the size of the drifting subgroup. In other words, none of the considered approaches produces an excess of false positive predictions when smaller subgroups are drifting. This is in accordance with what was expected: drifts of smaller subgroups go unnoticed, meaning that fewer positive predictions are produced overall. Instead, the FNR plot presents a different perspective. In this case, it is clear that there exists an abundance of false negatives when the drifting subgroups are smaller in size. These false negatives are drifts that are not being detected: as expected, the various drift detection techniques cannot handle properly drifts of smaller subpopulations. As the drifting subpopulations grow, the number of false positives produced decreases. Some approaches, such as DDM, have an earlier and sharper reduction in FNR, whereas other approaches (more significantly, EDDM) have a delayed response, meaning that they struggle to detect drifts even when the target subgroups are larger. 4 Conclusions In this work, we highlighted a problem that affects commonly adopted drift detection techniques: drifts are only detected if they affect a large fraction of the original data. This implies that drifts affecting smaller subpopulations (e.g., minorities) may go undetected. This is problematic, since it implies that models may be silently drifting and underperforming for certain populations. To benchmark the performance of various detectors under subgroup drifts, we introduce the Subgroup Agrawal Drift Dataset, a synthetic data generator that injects a specific subgroup of a desired size with noise. The experimental results show indeed that commonly adopted techniques only detect subgroup drifts when these cover a large fraction of the dataset, producing a large number of false negatives in the case of smaller diverging subgroups. As a natural next step, we plan on addressing this shortcoming of current drift detection techniques. Acknowledgements This work is partially supported by the FAIR - Future Artificial Intelligence Research (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 – D.D. 1555 11/10/2022, PE00000013) and the spoke “FutureHPC & BigData” of the ICSC - Centro Nazionale di Ricerca in High-Performance Computing, Big Data and Quantum Computing, both funded by the European Union - NextGenerationEU. This manuscript reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them. [1] Agrawal, R., Imielinski, T., Swami, A.: Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering 5 (6), 914–925 (1993) [2] Baena-Garcıa, M., del Campo-Ávila, J., Fidalgo, R., Bifet, A., Gavalda, R., Morales-Bueno, R.: Early drift detection method. In: Fourth International Workshop on Knowledge Discovery from Data Streams. vol. 6, pp. 77–86 (2006) [3] Frias-Blanco, I., del Campo-Ávila, J., Ramos-Jimenez, G., Morales-Bueno, R., Ortiz-Diaz, A., Caballero-Mota, Y.: Online and non-parametric drift detection methods based on hoeffding’s bounds. IEEE Transactions on Knowledge and Data Engineering 27 (3), 810–823 (2014) [4] Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Advances in Artificial Intelligence–SBIA 2004. pp. 286–295 (2004) [5] Montiel, J., Halford, M., Mastelini, S.M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H.M., Read, J., Abdessalem, T., et al.: River: machine learning for streaming data in python (2021) [6] Pesaranghader, A., Viktor, H.L.: Fast hoeffding drift detection method for evolving data streams. In: ECML PKDD 2016. pp. 96–111 (2016) Reaction Chemistry & Engineering Closed-loop identification of enzyme kinetics applying model-based design of experiments †. * Corresponding authors a Institute of Technical Biocatalysis, Hamburg University of Technology, Denickestr. 15, D-21073 Hamburg, Germany E-mail: [email protected] b Institute of Process Systems Engineering, Hamburg University of Technology, Schwarzenberg-Campus 4, D-21073 Hamburg, Germany Accurate kinetic models for enzyme catalysed reactions are integral to process development and optimisation. However, the collection of useful kinetic data is heavily dependent on the experimental design and execution. In order to reduce the limitations associated with traditional statistical design and manual experiments, this study introduces an integrated, automated approach to identifying kinetic models based on model-based optimal experimental design. The immobilised formate dehydrogenase of Candida boidinii catalyses the enzymatic reduction of NAD + to NADH and is used as a model system. Continuous collection of UV/Vis data under steady-state conditions is employed to determine the kinetic parameters in a packed bed reactor. Automation of the experimental work was utilised in Python to compensate for the need for more time-consuming data collection. A completely automated closed-loop system was created and appropriate kinetic models for anticipating process dynamics were identified. The automated platform was able to identify the correct kinetic model out of eight candidate models with only 15 experiments. Further extension of the design space improved model discrimination and led to a properly parameterized kinetic model with sufficeintly high parameter precision for the conditions under examination. Supplementary files Supplementary information PDF (1636K) Article information Download citation, permissions. Closed-loop identification of enzyme kinetics applying model-based design of experiments L. Hennecke, L. Schaare, M. Skiborowski and A. Liese, React. Chem. Eng. , 2024, Advance Article , DOI: 10.1039/D4RE00127C To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page . If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given. 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Advertisements 455 IMAGES 21 Research Limitations Examples (2024) Overview of limitations of the four experiments PPT PPT PPT Limitations+of+Experiments VIDEO Plastic Transport in Rivers: Experimental Laboratory Investigations Why AI Will NEVER Work 😱 Science के चौंका देना वाले EXPERIMENTS. #short #experiment ये जादू नहीं SCIENCE हैं।🧠 #experiment #short Mistakes in limit evaluation Application COMMENTS How to Write Limitations of the Study (with examples) Common types of limitations and their ramifications include: Theoretical: limits the scope, depth, or applicability of a study. Methodological: limits the quality, quantity, or diversity of the data. Empirical: limits the representativeness, validity, or reliability of the data. Analytical: limits the accuracy, completeness, or significance of ... 21 Research Limitations Examples (2024) In research, studies can have limitations such as limited scope, researcher subjectivity, and lack of available research tools. Acknowledging the limitations of your study should be seen as a strength. It demonstrates your willingness for transparency, humility, and submission to the scientific method and can bolster the integrity of the study. Limitations in Research Examples of limitations that might be discussed in research include sample size limitations, data collection methods, the validity and reliability of measures used, and potential biases or confounding factors that could have affected the results. It is important to note that limitations should not be used as a justification for poor research ... Research Limitations: Simple Explainer With Examples Whether you're working on a dissertation, thesis or any other type of formal academic research, remember the five most common research limitations and interpret your data while keeping them in mind. Access to Information (literature and data) Time and money. Sample size and composition. Research design and methodology. Understanding Limitations in Research Here's an example of a limitation explained in a research paper about the different options and emerging solutions for delaying memory decline. These statements appeared in the first two sentences of the discussion section: "Approaches like stem cell transplantation and vaccination in AD [Alzheimer's disease] work on a cellular or molecular level in the laboratory. Limitations of the Study Step 1. Identify the limitation (s) of the study. This part should comprise around 10%-20% of your discussion of study limitations. The first step is to identify the particular limitation (s) that affected your study. There are many possible limitations of research that can affect your study, but you don't need to write a long review of all ... Limitations in Research Following example is from an Engineering research paper. The authors are acknowledging the limitations and warning readers against generalizing the research findings. However, some study limitations should be acknowledged. The experiments do not fully consider the problems that can appear in real situations. PDF How to discuss your study's limitations effectively Start this "limitations" paragraph with a simple topic sentence that signals what you're about to discuss. For example: "Our study had some limitations.". Then, provide a concise sentence or two identifying each limitation and explaining how the limitation may have affected the quality of the study's findings and/or their ... Limitations of the Study Possible Limitations of the Researcher. Access-- if your study depends on having access to people, organizations, data, or documents and, for whatever reason, access is denied or limited in some way, the reasons for this needs to be described.Also, include an explanation why being denied or limited access did not prevent you from following through on your study. How to Present a Research Study's Limitations How scientists present them can make a big difference. iStock, Jacob Wackerhausen. Scientists work with many different limitations. First and foremost, they navigate informational limitations, work around knowledge gaps when designing studies, formulating hypotheses, and analyzing data. They also handle technical limitations, making the most of ... Limitations of a Research Study 3. Identify your limitations of research and explain their importance. 4. Provide the necessary depth, explain their nature, and justify your study choices. 5. Write how you are suggesting that it is possible to overcome them in the future. Limitations can help structure the research study better. What are the limitations in research and how to write them? The ideal way is to divide your limitations section into three steps: 1. Identify the research constraints; 2. Describe in great detail how they affect your research; 3. Mention the opportunity for future investigations and give possibilities. By following this method while addressing the constraints of your research, you will be able to ... Q: What are the limitations of a study and how to write them? Answer: The limitations of a study are its flaws or shortcomings which could be the result of unavailability of resources, small sample size, flawed methodology, etc. No study is completely flawless or inclusive of all possible aspects. Therefore, listing the limitations of your study reflects honesty and transparency and also shows that you ... Discussing your study's limitations Examples of study limitations. Selection bias. Selection bias occurs when the selection of individuals, groups, or data for analysis is not randomised. For example, imagine a study in which different surgical procedures are retrospectively compared in relation to mortality risk [e.g., 1]. Diving Deeper into Limitations and Delimitations While each study will have its own unique set of limitations, some limitations are more common in quantitative research, and others are more common in qualitative research. In quantitative research, common limitations include the following: - Participant dropout. - Small sample size, low power. - Non-representative sample. Limitations of the Study Here are examples of limitations related to methodology and the research process you may need to describe and to discuss how they possibly impacted your results. ... Institute for Writing Rhetoric. Dartmouth College; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University. Research Limitations Research limitations in a typical dissertation may relate to the following points: 1. Formulation of research aims and objectives. You might have formulated research aims and objectives too broadly. You can specify in which ways the formulation of research aims and objectives could be narrowed so that the level of focus of the study could be ... Don't Worry! And Write the LIMITATIONS of Your Research! Clarifying the limitations of a study allows the reader to better understand under which conditions the results should be interpreted. Clear descriptions of limitations of a study also show that the researcher has a holistic understanding of his/her study. And this is something very positive! 16 Advantages and Disadvantages of Experimental Research 6. Experimental research allows cause and effect to be determined. The manipulation of variables allows for researchers to be able to look at various cause-and-effect relationships that a product, theory, or idea can produce. It is a process which allows researchers to dig deeper into what is possible, showing how the various variable ... Examples of Limitations of a Study Though science has a clear methodology that researchers have virtually perfected over centuries, rarely is an individual study perfect. Studies usually have at least one limitation that makes some aspects of their results less likely to be accurate, such as the hypothesis not being proved though it might be true, the ... Guide to Experimental Design Table of contents. Step 1: Define your variables. Step 2: Write your hypothesis. Step 3: Design your experimental treatments. Step 4: Assign your subjects to treatment groups. Step 5: Measure your dependent variable. Other interesting articles. Frequently asked questions about experiments. Limitations of the Scientific Method Limitations of the Scientific Method. Clearly, the scientific method is a powerful tool, but it does have its limitations. These limitations are based on the fact that a hypothesis must be testable and falsifiable and that experiments and observations be repeatable. This places certain topics beyond the reach of the scientific method. A Synthetic Benchmark to Explore Limitations of Localized Drift Detections For negative experiments, we instead use a single concept throughout the entire experiment. For all experiments, we build a training set comprised of 10,000 points sampled from the underlying distribution and associated with the original concept. We train a simple decision tree classification model with a depth of up to 5 nodes on this training ... Closed-loop identification of enzyme kinetics applying model-based However, the collection of useful kinetic data is heavily dependent on the experimental design and execution. In order to reduce the limitations associated with traditional statistical design and manual experiments, this study introduces an integrated, automated approach to identifying kinetic models based on model-based optimal experimental ... essay homework paper phd project resume review speech thesis