Things we are aware of and understand.
It is possible that authors did not identify, want to identify, or acknowledge potential limitations or were unaware of what limitations existed. Cumulative complexity is the result of the presence of multiple limitations because of the accumulation and interaction of limitations and their components. Just mentioning a limitation category and not the specific parts that are the limitation(s) is not enough. Authors telling readers of their known research limitations is a caution to discount the findings and conclusions. At what point does the caution for each limitation, its ramifications, and consequences become a warning? When does the piling up of mistakes, bad and missing data, biases, small sample size, lack of generalizability, confounding factors, etc., reach a point when the findings become s uninterpretable and meaningless? “Caution” indicates a level of potential hazard; a warning is more dire and consequential. Authors use the word “caution” not “warning” to describe their conclusions. There is a point when the number of limitations and their cumulative effects surpasses the point where a caution statement is no longer applicable, and a warning statement is required. This is the reason for establishing a limitations risk score.
Limitations put medical research articles at risk. The accumulation of limitations (variables having additional limitation components) are gaps and flaws diluting the probability of validity. There is currently no assessment method for evaluating the effect(s) of limitations on research outcomes other than awareness that there is an effect. Authors make statements warning that their results may not be reliable or generalizable, and need more research and larger numbers. Just because the weight effect of any given limitation is not known, explained, or how it discounts findings does not negate a causation effect on data, its analysis, and conclusions. Limitation variables and the ramifications of their effects have consequences. The relationship is not zero effect and accumulates with each added limitation.
As a result of this research, a limitation index score (LIS) system and assessment tool were developed. This limitation risk assessment tool gives a scores assessment of the relative validity of conclusions in a medical article having limitations. The adoption of the LIS scoring assessment tool for authors, researchers, editors, reviewers, and readers is a step toward understanding the effects of limitations and their causal relationships to findings and conclusions. The objective is cleaner, tighter methodologies, and better data assessment, to achieve more reliable findings. Adjustments to research conclusions in the presence of limitations are necessary. The degree of modification depends on context. The cumulative effect of this burden must be acknowledged by a tangible reduction and questioning of the legitimacy of statements made under these circumstances. The description calculating the LIS score is detailed in Appendix 1 .
A limitation word or phrase is not one limitation; it is a group of limitations under the heading of that word or phrase having many additional possible components just as an individual named influence. For instance, when an admission of selection bias is noted, the authors do not explain if it was an exclusion criterion, self-selection, nonresponsiveness, lost to follow-up, recruitment error, how it affects external validity, lack of randomization, etc., or any of the least 263 types of known biases causing systematic distortions of the truth whether unintentional or wanton. 40 , 76 Which forms of selection bias are they identifying? 63 Limitations have branches that introduce additional limitations influencing the study’s ability to reach a useful conclusion. Authors rarely tell you the effect consequences and extent limitations have on their study, findings, and conclusions.
This is a sample of limitations and a few of their component variables under the rubric of a single word or phrase. See Table 3 .
A Limitation Word or Phrase is a Limitation Having Additional Components That Are Additional Limitations. When an Author Uses the Limitation Composite Word or Phrase, They Leave out Which One of Its Components is Contributory to the Research Limitations. Each Limitation Interacts with Other Limitations, Creating a Cluster of Cross Complexities of Data, Findings, and Conclusions That Are Tainted and Negatively Affect Findings and Conclusions
Small Sample Size | Retrospective Study | Selection Bias |
---|---|---|
Low statistical power | Missing information | Affects internal validity |
Estimates not reliable | Recall bias | Nonrandom selection |
Prone to biased samples | Observer bias | Leads to confounding |
Not generalizable | Misclassification bias | Not generalizable |
Prone to false negative error | Observer bias | Inaccurate relation to variables |
Prone to false positive error | Evidence less robust than prospective study | Observer bias |
Sampling error | Missing data | Sampling bias |
Confounding factors | Volunteer bias | |
Selection bias | Survivorship bias |
Limitations rarely occur alone. If you see one there are many you do not see or appreciate. Limitation s components interact with their own and other limitations, leading to complex connections interacting and discounting the reliability of findings. By how much is context dependent: but it is not zero. Limitations are variables influencing outcomes. As the number of limitations increases, the reliability of the conclusions decreases. How many variables (limitations) does it take to nullify the claims of the findings? The weight and influence of each limitation, its aggregate components, and interconnectedness have an unknown magnitude and effect. The result is a disorderly concoction of hearsay explanations. Table 4 is an example of just two single explanation limitations and some of their components illustrating the complex compounding of their effects on each other.
An Example of Interactions between Only Two Limitations and Some of Their Components Causes 16 Interactions
Retrospective Study | Small Sample Size |
---|---|
The novelty of this paper on limitations in medical science is not the identification of research article limitations or their number or frequency; it is the recognition of the multiplier effect(s) limitations and the influence they have on diminishing any conclusion(s) the paper makes. It is possible that limitations contribute to the inability of studies to replicate and why so many are one-time occurrences. Therefore, the generalizability statement that should be given to all readers is BEWARE THERE IS A REDUCTION EFFECT ON THE CONCLUSIONS IN THIS ARTICLE BECAUSE OF ITS LIMITATIONS.
Journals accept studies done with too many limitations, creating forking path situations resulting in an enormous number of possible associations of individual data points as multiple comparisons. 79 The result is confusion, a muddled mess caused by interactions of limitations undermining the ability to make valid inferences. Authors know and acknowledge but rarely explain them or their influence. They also use incomplete and biased databases, biased methods, small sample sizes, and not eliminating confounders, etc., but persist in doing research with these circumstances. Why is that? Is it because when limitations are acknowledged, authors feel justified in their conclusions? It wasn’t my poor research design; it was the limitation(s). How do peer reviewers score and analyze these papers without a method to discount the findings and conclusions in the presence of limitations? What are the calculus editors use to justify papers with multiple limitations, reaching compromised or spurious conclusions? How much caution or warning should a journal say must be taken in interpreting article results? How much? Which results? When? Under what circumstance(s)?
Since a critical component of research is its limitations, the quality and rigor of research are largely defined by, 75 these constraints making it imperative that limitations be exposed and explained. All studies have limitations admitted to or not, and these limitations influence outcomes and conclusions. Unfortunately, they are given insufficient attention, accompanied by feeble excuses, but they all matter. The degrees of freedom of each limitation influence every other limitation, magnifying their ramifications and confusion. Limitations of a scientific article must put the findings in context so the reader can judge the validity and strength of the conclusions. While authors acknowledge the limitations of their study, they influence its legitimacy.
Not only are limitations not properly acknowledged in the scientific literature, 8 but their implications, magnitude, and how they affect a conclusion are not explained or appreciated. Authors work at claiming their work and methods “overcome,” “avoid,” or “circumvent” limitations. Limitations are explained away as “Failure to prove a difference does not prove lack of a difference.” 60 Sample size, bias, confounders, bad data, etc. are not what they seem and do not sully the results. The implication is “trust me.” But that’s not science. Limitations create cognitive distortions and framing (misperception of reality) for the authors and readers. Data in studies with limitations is data having limitations. It was real but tainted.
Limitations are not a trivial aspect of research. It is a tangible something, positive or negative, put into a data set to be analyzed and used to reach a conclusion. How did these extra somethings, known unknowns, not knowns, and unknown knowns, affect the validity of the data set and conclusions? Research presented with the vagaries of explicit limitations is intensified by additional limitations and their component effects on top of the first limitation s , quickly diluting any conclusion making its dependability questionable.
This study’s analysis of limitations in medical articles averaged 3.9% per article for JSLS and 7.4% for Surg Endosc . Authors admit to some and are aware of limitations, but not all of them and discount or leave out others. Limitations were often presented with misleading and hedging language. Authors do not give weight or suggest the percent discount limitations have on the reliance of conclusion(s). Since limitations influence findings, reliability, generalizability, and validity without knowing the magnitude of each and their context, the best that can be said about the conclusions is that they are specific to the study described, context-driven, and suspect.
Limitations mean something is missing, added, incorrect, unseen, unaware of, fabricated, or unknown; circumstances that confuse, confound, and compromise findings and information to the extent that a notice is necessary. All medical articles should have this statement, “Any conclusion drawn from this medical study should be interpreted considering its limitations. Readers should exercise caution, use critical judgement, and consult other sources before accepting these findings. Findings may not be generalizable regardless of sample size, composition, representative data points, and subject groups. Methodologic, analytic, and data collection may have introduced biases or limitations that can affect the accuracy of the results. Controlling for confounding variables, known and unknown, may have influenced the data and/or observations. The accuracy and completeness of the data used to draw a conclusion may not be reliable. The study was specific to time, place, persons, and prevailing circumstances. The weight of each of these factors is unknown to us. Their effect may be limited or compounded and diminish the validity of the proposed conclusions.”
This study and findings are limited and constrained by the limitations of the articles reviewed. They have known and unknown limitations not accounted for, missing data, small sample size, incongruous populations, internal and external validity concerns, confounders, and more. See Tables 2 and and 3 . 3 . Some of these are correctible by the author’s awareness of the consequences of limitations, making plans to address them in the methodology phase of hypothesis assessment and performance of the research to diminish their effects.
Limitations in research articles are expected, but they can be reduced in their effect so that conclusions are closer to being valid. Limitations introduce elements of ignorance and suspicion. They need to be explained so their influence on the believability of the study and its conclusions is closer to meeting construct, content, face, and criterion validity. As the number of limitations increases, common sense, skepticism, study component acceptability, and understanding the ramifications of each limitation are necessary to accept, discount, or reject the author’s findings. As the number of hedging and weasel words used to explain conclusion(s) increases, believability decreases, and raises suspicion regarding claims. Establishing a systematic limitation scoring index limitations for authors, editors, reviewers, and readers and recognizing their cumulative effects will result in a clearer understanding of research content and legitimacy.
How to calculate the Limitation Index Score (LIS). See Tables 5 – 5 . Each limitation admitted to by authors in the article equals (=) one (1) point. Limitations may be generally stated by the author as a broad category, but can have multiple components, such as a retrospective study with these limitation components: 1. data or recall not accurate, 2. data missing, 3. selection bias not controlled, 4. confounders not controlled, 5. no randomization, 6. no blinding, 7. difficult to establish cause and effect, and 8. cannot draw a conclusion of causation. For each component, no matter how many are not explained and corrected, add an additional one (1) point to the score. See Table 2 .
The Limitation Scoring Index is a Numeric Limitation Risk Assessment Score to Rank Risk Categories and Discounting Probability of Validity and Conclusions. The More Limitations in a Study, the Greater the Risk of Unreliable Findings and Conclusions
Number of limitations | Word description of discounting | Proposed percent discounting of conclusions | Outcome probability | Increasing level of less reliable conclusions |
---|---|---|---|---|
0 | Unknown unknowns | 1–10% | May have valid conclusion(s) | Warning |
1–2 | Some | 15–25% | ↓ | ↓ |
3–4 | Probable | 35–45% | ↓ | Caution |
5–6 | Likely | 70–80% | ↓ | ↓ |
7–8 | Highly likely | 85–95% | ↓ | ↓ |
>8 | Certain | 97–100% | Very questionable conclusion(s) | Danger |
Limitations May Be Generally Stated by the Author but Have Multiple Components, Such as a Retrospective Study Having Disadvantage Components of 1. Data or Recall Not Accurate, 2. Data Missing, 3. Selection Bias Not Controlled, 4. Confounders Not Controlled, 5. No Randomization, 6. No Blinding, 7 Difficult to Establish Cause and Effect, 8. Results Are Hypothesis Generating, and 9. Cannot Draw a Conclusion of Causation. For Each Component, Not Explained and Corrected, Add an Additional One (1) Point Is Added to the Score. Extra Blanks Are for Additional Limitations
One point for each limitation | |
---|---|
One additional point for each component of each limitation | |
Retrospective study | |
Small sample size | |
Not generalizable | |
Selection bias | |
Not controlling for confounders | |
Not controlling for comorbidities | |
Incomplete or missing data | |
No long-term follow-up | |
Reporting errors | |
Measurement problems | |
Study design problems | |
Lack of standardized treatment | |
Subtotal for Table 2 |
An Automatic 2 Points is Added for Meta-Analysis Studies Since They Have All the Retrospective Detrimental Components. 44 Data from Insurance, State, National, Medicare, and Medicaid, Because of Incorrect Coding, Over Reporting, and Under-Reporting, Etc. Each Component of the Limitation Adds One Additional Point. For Surveys and Questionnaires Add One Additional Point for Each Bias. Extra Blanks Are for Additional Limitations
Two points for these limitations | |
---|---|
One additional point for each limitation and one additional point for each limitation component. | |
Meta-analysis | |
Data from Medicare, Medicaid, insurance companies, disease, state, and national databases | |
Surveys and questionnaires | |
Each limitation not admitted to | |
Subtotal for Table 3 |
Automatic Five (5) Points for Manufacturer and User Facility Device Experience (MAUDE) Database Articles. The FDA Access Data Site Says Submissions Can Be “Incomplete, Inaccurate, Untimely, Unverified, or Biased” and “the Incidence or Prevalence of an Event Cannot Be Determined from This Reporting System Alone Due to Under-Reporting of Events, Inaccuracies in Reports, Lack of Verification That the Device Caused the Reported Event, and Lack of Information” and “DR Data Alone Cannot Be Used to Establish Rates of Events, Evaluate a Change in Event Rates over Time or Compare Event Rates between Devices. The Number of Reports Cannot Be Interpreted or Used in Isolation to Reach Conclusions” 80
Five points for MAUDE based articles | |
---|---|
One additional point for each additional limitation and one point for each of its components. | |
Subtotal for Table 4 |
Total Limitation Index Score
Limitations | Calculation |
---|---|
Subtotal for Table 2 | |
Subtotal for Table 3 | |
Subtotal for Table 4 | |
Total Limitation Index Score |
Each limitation not admitted to = two (2) points. A meta-analysis study gets an automatic 2 points since they are retrospective and have detrimental components that should be added to the 2 points. Data from insurance, state, national, Medicare, and Medicaid, because of incorrect coding, over-reporting, and underreporting, etc., score 2 points, and each component adds one additional point. Surveys and questionnaires get 2 points, and add one additional point for each bias. See Table 3 .
Manufacturer and User Facility Device Experience (MAUDE) database articles receive an automatic five (5) points. The FDA access data site says, submissions can be “incomplete, inaccurate, untimely, unverified, or biased” and “the incidence or prevalence of an event cannot be determined from this reporting system alone due to underreporting of events, inaccuracies in reports, lack of verification that the device caused the reported event, and lack of information” and “MDR data alone cannot be used to establish rates of events, evaluate a change in event rates over time or compare event rates between devices. The number of reports cannot be interpreted or used in isolation to reach conclusions.” 80 See Table 4 . Add one additional point for each additional limitation noted in the article.
Add one additional point for each additional limitation and one point for each of its components. Extra blanks are for additional
limitations and their component scores.
Funding sources: none.
Disclosure: none.
Conflict of interests: none.
Acknowledgments: Author would like to thank Lynda Davis for her help with data collection.
All references have been archived at https://archive.org/web/
4-minute read
Whether you’re a veteran researcher with years of experience under your belt or a novice to the field that’s feeling overwhelmed with where to start, you must understand that every study has its limitations. These are restrictions that arise from the study’s design, or the methodology implemented during the testing phase. Unfortunately, research limitations will always exist due to the subjective nature of testing a hypothesis. We’ve compiled some helpful information below on how to identify and accept research limitations and use them to your advantage. Essentially, we’ll show you how to make lemonade (a brilliant piece of academic work ) from the lemons you receive (the constraints your study reveals).
So, let’s dive straight in, shall we? It’s always beneficial (and good practice) to disclose your research limitations . A common thought is that divulging these shortcomings will undermine the credibility and quality of your research. However, this is certainly not the case— stating the facts upfront not only reinforces your reputation as a researcher but also lets the assessor or reader know that you’re confident and transparent about the results and relevance of your study, despite these constraints.
Additionally, it creates a gap for more research opportunities, where you can analyze these limitations and determine how to incorporate or address them in a new batch of tests or create a new hypothesis altogether. Another bonus is that it helps readers to understand the optimum conditions for how to apply the results of your testing. This is a win-win, making for a far more persuasive research paper .
Now that you know why you should clarify your research limitations, let’s focus on which ones take precedence and should be disclosed. Any given research project can be vulnerable to various hindrances, so how do you identify them and single out the most significant ones to discuss? Well, that depends entirely on the nature of your study. You’ll need to comb through your research approach, methodology, testing processes, and expected results to identify the type of limitations your study may be exposed to. It’s worth noting that this understanding can only offer a broad idea of the possible restrictions you’ll face and may potentially change throughout the study.
We’ve compiled a list of the most common types of research limitations that you may encounter so you can adequately prepare for them and remain vigilant during each stage of your study.
It’s critical that you choose a sample size that accurately represents the population you wish to test your theory on. If a sample is too small, the results cannot reliably be generalized across a large population.
The method you choose before you commence testing might seem effective in theory, but too many stumbling blocks during the testing phase can influence the accuracy and reliability of the results.
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The methods you utilize to obtain your research—surveys, emails, in-person interviews, phone calls—will directly influence the type of results your study yields.
The nature of the information—and how far back it goes—affects the type of assumptions you can make. Extrapolating older data for a current hypothesis can significantly change the outcome of your testing.
Working within the deadline of when you need to submit your findings will determine the extent of your research and testing and, therefore, can heavily impact your results. Limited time frames for testing might mean not achieving the scope of results you were originally looking for.
Your study may require equipment and other resources that can become extremely costly. Budget constraints may mean you cannot acquire advanced software, programs, or travel to multiple destinations to interview participants. All of these factors can substantially influence your results.
So, now that you know how to determine your research limitations and the types you might experience, where should you document it? It’s commonly disclosed at the beginning of your discussion section , so the reader understands the shortcomings of your study before digging into the juicy bit—your findings. Alternatively, you can detail the constraints faced at the end of the discussion section to emphasize the requirements for the completion of further studies.
We hope this post will prepare you for some of the pitfalls you may encounter when conducting and documenting your research. Once you have a first draft ready, consider submitting a free sample to us for proofreading to ensure that your writing is concise and error-free and your results—despite their limitations— shine through.
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What they are and how they’re different (with examples)
By: Derek Jansen (MBA) | Expert Reviewed By: David Phair (PhD) | September 2022
If you’re new to the world of research, you’ve probably heard the terms “ research limitations ” and “ research delimitations ” being thrown around, often quite loosely. In this post, we’ll unpack what both of these mean, how they’re similar and how they’re different – so that you can write up these sections the right way.
Let’s start with the most important takeaway point of this post – research limitations and research delimitations are not the same – but they are related to each other (we’ll unpack that a little later). So, if you hear someone using these two words interchangeably, be sure to share this post with them!
Research limitations are, at the simplest level, the weaknesses of the study , based on factors that are often outside of your control as the researcher. These factors could include things like time , access to funding, equipment , data or participants . For example, if you weren’t able to access a random sample of participants for your study and had to adopt a convenience sampling strategy instead, that would impact the generalizability of your findings and therefore reflect a limitation of your study.
Research limitations can also emerge from the research design itself . For example, if you were undertaking a correlational study, you wouldn’t be able to infer causality (since correlation doesn’t mean certain causation). Similarly, if you utilised online surveys to collect data from your participants, you naturally wouldn’t be able to get the same degree of rich data that you would from in-person interviews .
Simply put, research limitations reflect the shortcomings of a study , based on practical (or theoretical) constraints that the researcher faced. These shortcomings limit what you can conclude from a study, but at the same time, present a foundation for future research . Importantly, all research has limitations , so there’s no need to hide anything here – as long as you discuss how the limitations might affect your findings, it’s all good.
Alright, now that we’ve unpacked the limitations, let’s move on to the delimitations .
Research delimitations are similar to limitations in that they also “ limit ” the study, but their focus is entirely different. Specifically, the delimitations of a study refer to the scope of the research aims and research questions . In other words, delimitations reflect the choices you, as the researcher, intentionally make in terms of what you will and won’t try to achieve with your study. In other words, what your research aims and research questions will and won’t include.
As we’ve spoken about many times before, it’s important to have a tight, narrow focus for your research, so that you can dive deeply into your topic, apply your energy to one specific area and develop meaningful insights. If you have an overly broad scope or unfocused topic, your research will often pull in multiple, even opposing directions, and you’ll just land up with a muddy mess of findings .
So, the delimitations section is where you’ll clearly state what your research aims and research questions will focus on – and just as importantly, what they will exclude . For example, you might investigate a widespread phenomenon, but choose to focus your study on a specific age group, ethnicity or gender. Similarly, your study may focus exclusively on one country, city or even organization. As long as the scope is well justified (in other words, it represents a novel, valuable research topic), this is perfectly acceptable – in fact, it’s essential. Remember, focus is your friend.
Ok, so let’s recap.
Research limitations and research delimitations are related in that they both refer to “limits” within a study. But, they are distinctly different. Limitations reflect the shortcomings of your study, based on practical or theoretical constraints that you faced.
Contrasted to that, delimitations reflect the choices that you made in terms of the focus and scope of your research aims and research questions. If you want to learn more about research aims and questions, you can check out this video post , where we unpack those concepts in detail.
This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...
Good clarification of ideas on how a researcher ought to do during Process of choice
Thank you so much for this very simple but explicit explanation on limitation and delimitation. It has so helped me to develop my masters proposal. hope to recieve more from your site as time progresses
Thank you for this explanation – very clear.
Thanks for the explanation, really got it well.
This website is really helpful for my masters proposal
Thank you very much for helping to explain these two terms
I spent almost the whole day trying to figure out the differences
when I came across your notes everything became very clear
thanks for the clearly outlined explanation on the two terms, limitation and delimitation.
Very helpful Many thanks 🙏
Excellent it resolved my conflict .
I would like you to assist me please. If in my Research, I interviewed some participants and I submitted Questionnaires to other participants to answered to the questions, in the same organization, Is this a Qualitative methodology , a Quantitative Methodology or is it a Mixture Methodology I have used in my research? Please help me
How do I cite this article in APA format
Really so great ,finally have understood it’s difference now
Getting more clear regarding Limitations and Delimitation and concepts
I really appreciate your apt and precise explanation of the two concepts namely ; Limitations and Delimitations.
This is a good sources of research information for learners.
thank you for this, very helpful to researchers
Very good explained
Great and clear explanation, after a long confusion period on the two words, i can now explain to someone with ease.
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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
Being open about what you could not do in your research is actually extremely positive, and it’s viewed favorably by editors and peer reviewers. Writing about your limitations without reducing your impact is a valuable skills that will help your reputation as a researcher.
Areas you might have “failed,” in other words, your limitations, include:
Your limitations don’t harm your work and reputation. Quite the opposite, they validate your work and increase your contribution to your field.
Limitations are quite easy to write about in a useful way that won’t reducing your impact. In fact, it’ll increase it.
Study design limitations, impact limitations, statistical or data limitations, other limitations, how to describe your limitations, where to write your limitations, structure for writing about a limitation, writing up a broader limitation, dealing with breakthroughs and niche-type limitations, dealing with critical flaws, curb your enthusiasm: manage expectations.
Regrettably, the publish-or-perish mentality has created pressure to only come up with successful results. It’s also not too much to say that journals prefer positive studies – where the findings support the hypothesis.
But success alone is not science. Science is trial and error.
So it’s important to present a well-balanced, comprehensive description of your research. That includes your limitations. Accurately reporting your limitations will:
Adding clear discussion of any negative results and/or outcomes as well as your study limitations makes you much better able to provide your readers (including peer reviewers) with:
These are good things. There’s even a journal for failure ! That’s how important it is in science.
Some authors find it hard to write about their study limitations, seeing it as an admission of failure. You can do it, and you don’t have to overdo it, either.
These might include the procedures, experiments, or reagents (or funding) you have available. As well as specific constraints on the study population. There may be ethical guidelines, and institutional or national policies, that limit what you can do.
These are very common limitations to medical research, for example. We refer to these kinds as study design limitations. Clinical trials, for instance, may have a restriction on interventions expected to have a positive effect. Or there may be restrictions on data collection based on the study population.
Even if your study has a strong design and statistical foundation there might be a strong regional, national, or species-based focus. Or your work could be very population- or experimental-specific.
Your entire field of study, in fact, may only be conducive to incremental findings (e.g., particle physics or molecular biology).
These are inherent limits on impact in that they’re so specific. This limits the extendibility of the findings. It doesn’t however, limit the impact on a specific area or your field. Note the impact and push forward!
Perhaps the most common kind of limitation is statistical or data-based. This category is extremely common in experimental (e.g., chemistry) or field-based (e.g., ecology, population biology, qualitative clinical research) studies.
In many situations, testing hypotheses, you simply may not be able to collect as much data or as good quality data as you want to. Perhaps enrollment was more difficult than expected, under-powering your results.
Statistical limitations can also stem from study design, producing more serious issues in terms of interpreting findings. Seeking expert review from a statistician, such as by using Edanz scientific solutions , may be a good idea before starting your study design.
The above three are often interconnected. And they’re certainly not comprehensive.
As mentioned up top, you may also be limited by the literature. By external confounders. By things you didn’t even see coming (like how long it took you to find 10 qualified respondents for a qualitative study).
Once you’ve identified possible limitations in your work, you need to get to the real point of this post – describing them in your manuscript.
Use the perspective of limitations = contribution and impact to maximize your chances of acceptance.
Reviewers, editors, and readers expect you to present your work authoritatively. You’re the expert in the field, after all. This may make them critical. Embrace that. Counter their possibly negative interpretation by explaining each limitation, showing why the results are still important and useful.
Limitations are usually listed at the end of your Discussion section, though they can also be added throughout. Especially for a long manuscript or for an essay or dissertation, the latter may be useful for the reader.
Giving a specific number is useful for the reader and can guide your writing. But if it’s a longer list, no need to number them. For a short list, you can write them as:
But this gets tiring for more than three limitations (bad RX: reader experience).
So, for longer lists, add a bit of variety in the language to engage the reader. Like this:
An expert editor will be happy to help you make the English more natural and readable.
After your lead-in sentence, follow a pattern of writing on your findings and related limitation(s), giving a quick interpretation, back it with support (if needed), and offer the next steps.
This provides a complete package for the reader: what happened, what it means, why this is the case, and what is now needed.
In that way, you’ve admitted what may be lacking, but you’ve further established your authority. You’ve also provided a quick roadmap for your reader. That’s an impactful contribution!
It might not always be logical or readable to give that much detail. As long as you fully describe and justify the limitation, you’ve done your job well.
Your study looked at a weight intervention over 6 months at primary healthcare clinics in Japan. The results were generally. But because you only looked at Japanese patients, these findings may not be extendible to patients of other cultures/nationalities, etc.
That’s not a failure at all. It’s a success. But it is a limitation. And other researchers can learn from it and build on it. Write it up in the limitations.
Finding: We found that, in the intervention group, BMI was reduced over 6 months.
Interpretation (and support): This suggests a regimen of routine testing and measurement followed by personalized health guidance from primary physicians had a positive effect on patients’ conditions.
Support: Yamazaki (2019) and Endo et al. (2020) found similar results in urban Japanese clinics and hospitals, respectively.
Limitation and how to use it: While these are useful findings, they are limited by only including Japanese populations. This does not ensure these interventions would be as effective in other nations or cultures. Similar interventions, adapted to the local healthcare and cultural conditions, would help to further clarify the methods.
Now you’ve stated the value of your finding, the limitation, and what to do with it. Nice impact!
Another hurdle you may hit is when your results are particularly novel or you’re publishing in a little-researched field. Those are limitations that need to be stated. In this case, you can support your findings by reinforcing the novelty of your results.
When breaking new ground, there are probably still many gaps in the knowledge base that need to be filled. A good follow-up statement for this type of limitation is to describe what, based on these results, the next steps would be to build a stronger overall evidence base.
It’s possible that your study will have a fairly “critical” flaw (usually in the study design) that decreases confidence in your findings.
Other experts will likely notice this (in peer review or perhaps on a preprint server, they should notice it), so it’s best to explain why this error or flaw occurred.
You can still explain why the study is worth repeating or how you plan to retest the phenomenon. But you may need to temper your publication goals if you still plan to publish your work.
No one expects science to be perfect the first time and while your peers can be highly critical, no one’s work is beyond limitations. This is important to keep in mind.
Edanz experts can help by giving you an Expert Scientific Review and seeking out your limitations.
Our knowledge base is built on uncovering each piece of the puzzle, one at a time, and limitations show us where new efforts need to be made. Much like peer review, don’t think of limitations as being inherently bad, but more as an opportunity for a new challenge.
Ultimately, your limitations may be someone else’s inspirations. Include them in your submission when you get published in the journal of your choice.
All research faces problems: Being honest impresses people much more than ignoring your limitations.
There is no "one best way" to structure the Research Limitations section of your dissertation. However, we recommend a structure based on three moves : the announcing , reflecting and forward looking move. The announcing move immediately allows you to identify the limitations of your dissertation and explain how important each of these limitations is. The reflecting move provides greater depth, helping to explain the nature of the limitations and justify the choices that you made during the research process. Finally, the forward looking move enables you to suggest how such limitations could be overcome in future. The collective aim of these three moves is to help you walk the reader through your Research Limitations section in a succinct and structured way. This will make it clear to the reader that you recognise the limitations of your own research, that you understand why such factors are limitations, and can point to ways of combating these limitations if future research was carried out. This article explains what should be included in each of these three moves :
There are many possible limitations that your research may have faced. However, is not necessary for you to discuss all of these limitations in your Research Limitations section. After all, you are not writing a 2000 word critical review of the limitations of your dissertation, just a 200-500 word critique that is only one section long (i.e., the Research Limitations section within your Conclusions chapter). Therefore, in this first announcing move , we would recommend that you identify only those limitations that had the greatest potential impact on: (a) the quality of your findings; and (b) your ability to effectively answer your research questions and/or hypotheses.
We use the word potential impact because we often do not know the degree to which different factors limited our findings or our ability to effectively answer our research questions and/or hypotheses. For example, we know that when adopting a quantitative research design, a failure to use a probability sampling technique significantly limits our ability to make broader generalisations from our results (i.e., our ability to make statistical inferences from our sample to the population being studied). However, the degree to which this reduces the quality of our findings is a matter of debate. Also, whilst the lack of a probability sampling technique when using a quantitative research design is a very obvious example of a research limitation, other limitations are far less clear. Therefore, the key point is to focus on those limitations that you feel had the greatest impact on your findings, as well as your ability to effectively answer your research questions and/or hypotheses.
Overall, the announcing move should be around 10-20% of the total word count of the Research Limitations section.
Having identified the most important limitations to your dissertation in the announcing move , the reflecting move focuses on explaining the nature of these limitations and justifying the choices that you made during the research process. This part should be around 60-70% of the total word count of the Research Limitations section.
It is important to remember at this stage that all research suffers from limitations, whether it is performed by undergraduate and master's level dissertation students, or seasoned academics. Acknowledging such limitations should not be viewed as a weakness, highlighting to the person marking your work the reasons why you should receive a lower grade. Instead, the reader is more likely to accept that you recognise the limitations of your own research if you write a high quality reflecting move . This is because explaining the limitations of your research and justifying the choices you made during the dissertation process demonstrates the command that you had over your research.
We talk about explaining the nature of the limitations in your dissertation because such limitations are highly research specific. Let's take the example of potential limitations to your sampling strategy. Whilst you may have a number of potential limitations in sampling strategy, let's focus on the lack of probability sampling ; that is, of all the different types of sampling technique that you could have used [see Types of probability sampling and Types of non-probability sampling ], you choose not to use a probability sampling technique (e.g., simple random sampling , systematic random sampling , stratified random sampling ). As mentioned, if you used a quantitative research design in your dissertation, the lack of probability sampling is an important, obvious limitation to your research. This is because it prevents you from making generalisations about the population you are studying (e.g. Facebook usage at a single university of 20,000 students) from the data you have collected (e.g., a survey of 400 students at the same university). Since an important component of quantitative research is such generalisation, this is a clear limitation. However, the lack of a probability sampling technique is not viewed as a limitation if you used a qualitative research design. In qualitative research designs, a non-probability sampling technique is typically selected over a probability sampling technique.
And this is just part of the puzzle?
Even if you used a quantitative research design, but failed to employ a probability sampling technique, there are still many perfectly justifiable reasons why you could have made such a choice. For example, it may have been impossible (or near on impossible) to get a list of the population you were studying (e.g., a list of all the 20,000 students at the single university you were interested in). Since probability sampling is only possible when we have such a list, the lack of such a list or inability to attain such a list is a perfectly justifiable reason for not using a probability sampling technique; even if such a technique is the ideal.
As such, the purpose of all the guides we have written on research limitations is to help you: (a) explain the nature of the limitations in your dissertation; and (b) justify the choices you made.
In helping you to justifying the choices that you made, these articles explain not only when something is, in theory , an obvious limitation, but how, in practice , such a limitation was not necessarily so damaging to the quality of your dissertation. This should significantly strengthen the quality of your Research Limitations section.
Finally, the forward looking move builds on the reflecting move by suggesting how the limitations you have discuss could be overcome through future research. Whilst a lot could be written in this part of the Research Limitations section, we would recommend that it is only around 10-20% of the total word count for this section.
A title page is required for all APA Style papers. There are both student and professional versions of the title page. Students should use the student version of the title page unless their instructor or institution has requested they use the professional version. APA provides a student title page guide (PDF, 199KB) to assist students in creating their title pages.
The student title page includes the paper title, author names (the byline), author affiliation, course number and name for which the paper is being submitted, instructor name, assignment due date, and page number, as shown in this example.
Title page setup is covered in the seventh edition APA Style manuals in the Publication Manual Section 2.3 and the Concise Guide Section 1.6
Student papers do not include a running head unless requested by the instructor or institution.
Follow the guidelines described next to format each element of the student title page.
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Paper title | Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms. |
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Author names | Place one double-spaced blank line between the paper title and the author names. Center author names on their own line. If there are two authors, use the word “and” between authors; if there are three or more authors, place a comma between author names and use the word “and” before the final author name. | Cecily J. Sinclair and Adam Gonzaga |
Author affiliation | For a student paper, the affiliation is the institution where the student attends school. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author name(s). | Department of Psychology, University of Georgia |
Course number and name | Provide the course number as shown on instructional materials, followed by a colon and the course name. Center the course number and name on the next double-spaced line after the author affiliation. | PSY 201: Introduction to Psychology |
Instructor name | Provide the name of the instructor for the course using the format shown on instructional materials. Center the instructor name on the next double-spaced line after the course number and name. | Dr. Rowan J. Estes |
Assignment due date | Provide the due date for the assignment. Center the due date on the next double-spaced line after the instructor name. Use the date format commonly used in your country. | October 18, 2020 |
| Use the page number 1 on the title page. Use the automatic page-numbering function of your word processing program to insert page numbers in the top right corner of the page header. | 1 |
The professional title page includes the paper title, author names (the byline), author affiliation(s), author note, running head, and page number, as shown in the following example.
Follow the guidelines described next to format each element of the professional title page.
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Paper title | Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms. |
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Author names
| Place one double-spaced blank line between the paper title and the author names. Center author names on their own line. If there are two authors, use the word “and” between authors; if there are three or more authors, place a comma between author names and use the word “and” before the final author name. | Francesca Humboldt |
When different authors have different affiliations, use superscript numerals after author names to connect the names to the appropriate affiliation(s). If all authors have the same affiliation, superscript numerals are not used (see Section 2.3 of the for more on how to set up bylines and affiliations). | Tracy Reuter , Arielle Borovsky , and Casey Lew-Williams | |
Author affiliation
| For a professional paper, the affiliation is the institution at which the research was conducted. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author names; when there are multiple affiliations, center each affiliation on its own line.
| Department of Nursing, Morrigan University |
When different authors have different affiliations, use superscript numerals before affiliations to connect the affiliations to the appropriate author(s). Do not use superscript numerals if all authors share the same affiliations (see Section 2.3 of the for more). | Department of Psychology, Princeton University | |
Author note | Place the author note in the bottom half of the title page. Center and bold the label “Author Note.” Align the paragraphs of the author note to the left. For further information on the contents of the author note, see Section 2.7 of the . | n/a |
| The running head appears in all-capital letters in the page header of all pages, including the title page. Align the running head to the left margin. Do not use the label “Running head:” before the running head. | Prediction errors support children’s word learning |
| Use the page number 1 on the title page. Use the automatic page-numbering function of your word processing program to insert page numbers in the top right corner of the page header. | 1 |
The library building will be open from 9:00am-3:00pm on Friday, March 29th. Our services will be available online 7:45am-4:30pm for your convenience.
The limitations of the study are those characteristics of design or methodology that impacted or influenced the application or interpretation of the results of your study. They are the constraints on generalizability and utility of findings that are the result of the ways in which you chose to design the study and/or the method used to establish internal and external validity.
Always acknowledge a study's limitations. It is far better for you to identify and acknowledge your study’s limitations than to have them pointed out by your professor and be graded down because you appear to have ignored them.
Keep in mind that acknowledgement 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.
Acknowledgement of a study's limitations also provides you with an opportunity to demonstrate to your professor 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 findings 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?
Structure: How to Structure the Research Limitations Section of Your Dissertation . Dissertations and Theses: An Online Textbook. Laerd.com.
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 your paper.
Here are examples of limitations you may need to describe and to discuss how they possibly impacted your findings. Descriptions of limitations should be stated in the past tense.
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.
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 to offer 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 the need for future research.
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, consult with a librarian! In cases when a librarian has confirmed that there is a lack of 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 that this limitation can serve as an important opportunity 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 in future research to revise the specific method for gathering data.
Self-reported data -- whether you are relying on pre-existing self-reported 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 must take what people say, whether in interviews, focus groups, or on questionnaires, at face value. However, self-reported data contain several potential sources of bias that should be noted as limitations: (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, or documents and, for whatever reason, access is denied or otherwise limited, the reasons for this need to be described.
Longitudinal effects -- unlike your professor, who can devote years [even a lifetime] to studying a single research problem, the time available to investigate a research problem and to measure change or stability within a sample is constrained by the due date of your assignment. Be sure to choose a topic 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, talk to your professor.
Cultural and other types of bias -- we all have biases, whether we are conscience of them or not. Bias is when a person, place, or thing is viewed or shown in a consistently inaccurate way. It is usually negative, though one can have a positive bias as well. 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 way you have ordered events, people, or places and 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 that if you detect bias in prior research, it must be acknowledged, and you should explain what measures were taken to avoid perpetuating bias.
Fluency in a language -- if your research focuses on measuring the perceived value of after-school tutoring among Mexican American ESL [English as a Second Language] students, for example, and you are not fluent in Spanish, you are limited in being able to read and interpret Spanish language research studies on the topic. This deficiency should be acknowledged.
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.
Information about the limitations of your study is 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 a pilot 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 later studies.
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 reframe 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 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 ask a particular question in a survey 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 any future study. An underlying goal of scholarly research is not only to prove what works, but to demonstrate what doesn't work or what needs further clarification.
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.
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 in an attempt to hide its flaws is a big turn off to your readers. A measure of humility goes a long way!
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 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.
A Note about 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 judgement about the methodological technique chosen [e.g., single life history study versus focus group interviews] rather than the number of respondents used.
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.
At Sakana AI, we have pioneered the use of nature-inspired methods to advance cutting-edge foundation models. Earlier this year, we developed methods to automatically merge the knowledge of multiple LLMs . In more recent work, we harnessed LLMs to discover new objective functions for tuning other LLMs. Throughout these projects, we have been continuously surprised by the creative capabilities of current frontier models. This led us to dream even bigger: Can we use foundation models to automate the entire process of research itself?
One of the grand challenges of artificial intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used to aid human scientists, e.g. for brainstorming ideas or writing code, they still require extensive manual supervision or are heavily constrained to a specific task.
Today, we’re excited to introduce The AI Scientist , the first comprehensive system for fully automatic scientific discovery, enabling Foundation Models such as Large Language Models (LLMs) to perform research independently. In collaboration with the Foerster Lab for AI Research at the University of Oxford and Jeff Clune and Cong Lu at the University of British Columbia, we’re excited to release our new paper, The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery .
In our report:
The AI Scientist is designed to be compute efficient. Each idea is implemented and developed into a full paper at a cost of approximately $15 per paper. While there are still occasional flaws in the papers produced by this first version (discussed below and in the report), this cost and the promise the system shows so far illustrate the potential of The AI Scientist to democratize research and significantly accelerate scientific progress.
We believe this work signifies the beginning of a new era in scientific discovery: bringing the transformative benefits of AI agents to the entire research process, including that of AI itself. The AI Scientist takes us closer to a world where endless affordable creativity and innovation can be unleashed on the world’s most challenging problems.
For decades following each major AI advance, it has been common for AI researchers to joke amongst themselves that “now all we need to do is figure out how to make the AI write the papers for us!” Our work demonstrates this idea has gone from a fantastical joke so unrealistic everyone thought it was funny to something that is currently possible.
The remainder of this post provides a more detailed summary of The AI Scientist. Read on for:
For more details and many more example papers, please see our full scientific report . We are also releasing open source code and full experimental results on our GitHub repository.
The AI Scientist is a fully automated pipeline for end-to-end paper generation, enabled by recent advances in foundation models. Given a broad research direction starting from a simple initial codebase, such as an available open-source code base of prior research on GitHub, The AI Scientist can perform idea generation, literature search, experiment planning, experiment iterations, figure generation, manuscript writing, and reviewing to produce insightful papers. Furthermore, The AI Scientist can run in an open-ended loop, using its previous ideas and feedback to improve the next generation of ideas, thus emulating the human scientific community.
Conceptual illustration of The AI Scientist . The AI Scientist first brainstorms a set of ideas and then evaluates their novelty. Next, it edits a codebase powered by recent advances in automated code generation to implement the novel algorithms. The Scientist then runs experiments to gather results consisting of both numerical data and visual summaries. It crafts a scientific report, explaining and contextualizing the results. Finally, the AI Scientist generates an automated peer review based on top-tier machine learning conference standards. This review helps refine the current project and informs future generations of open-ended ideation.
The AI Scientist has 4 main processes, described next.
Idea Generation . Given a starting template, The AI Scientist first “brainstorms” a diverse set of novel research directions. We provide The AI Scientist with a starting code “template” of an existing topic we wish to have The AI Scientist further explore. The AI Scientist is then free to explore any possible research direction. The template also includes a LaTeX folder that contains style files and section headers, for paper writing. We allow it to search Semantic Scholar to make sure its idea is novel.
Experimental Iteration . Given an idea and a template, the second phase of The AI Scientist first executes the proposed experiments and then obtains and produces plots to visualize its results. It makes a note describing what each plot contains, enabling the saved figures and experimental notes to provide all the information required to write up the paper.
Paper Write-up . Finally, The AI Scientist produces a concise and informative write-up of its progress in the style of a standard machine learning conference proceeding in LaTeX. It uses Semantic Scholar to autonomously find relevant papers to cite.
Automated Paper Reviewing . A key aspect of this work is the development of an automated LLM-powered reviewer, capable of evaluating generated papers with near-human accuracy. The generated reviews can be used to either improve the project or as feedback to future generations for open-ended ideation. This enables a continuous feedback loop, allowing The AI Scientist to iteratively improve its research output.
When combined with the most capable LLMs, The AI Scientist is capable of producing papers judged by our automated reviewer as “Weak Accept” at a top machine learning conference.
Here, we highlight some of the machine learning papers The AI Scientist has generated, demonstrating its capacity to discover novel contributions in areas like diffusion modeling, language modeling, and grokking. In our full report, we do a deeper dive into the generated papers and provide more analysis on their strengths and weaknesses.
Language modeling, limitations and challenges.
In its current form, The AI Scientist has several shortcomings. We expect all of these will improve, likely dramatically, in future versions with the inclusion of multi-modal models and as the underlying foundation models The AI Scientist uses continue to radically improve in capability and affordability.
In our report, we dive deeper into The AI Scientists’s current limitations and challenges ahead.
We have noticed that The AI Scientist occasionally tries to increase its chance of success, such as modifying and launching its own execution script! We discuss the AI safety implications in our paper.
For example, in one run, it edited the code to perform a system call to run itself. This led to the script endlessly calling itself. In another case, its experiments took too long to complete, hitting our timeout limit. Instead of making its code run faster, it simply tried to modify its own code to extend the timeout period. Here are some examples of such code modifications it made:
These issues can be mitigated by sandboxing the operating environment of The AI Scientist. In our full report, we discuss the issue of safe code execution and sandboxing in depth.
As with many new technologies, The AI Scientist opens up a Pandora’s box of new issues. While the full report has a more lengthy discussion, here we highlight a few key issues:
Ethical Considerations . While The AI Scientist may be a useful tool for researchers, there is significant potential for misuse. The ability to automatically create and submit papers to venues may significantly increase reviewer workload and strain the academic process, obstructing scientific quality control. Similar concerns around generative AI appear in other applications, such as the impact of image generation.
Furthermore, the Automated Reviewer, if deployed online by reviewers, may significantly lower review quality and impose undesirable biases on papers. Because of this, we believe that papers and reviews that are substantially AI-generated must be marked as such for full transparency.
As with most previous technological advances, The AI Scientist has the potential to be used in unethical ways. For instance, it has the potential to be deployed to conduct unethical research. It could also lead to unintended harm if The AI Scientist conducts unsafe research. For example, if it were encouraged to find novel, interesting biological materials and given access to “cloud labs” where robots perform wet lab biology experiments, it could (without its overseer’s intent) create new, dangerous viruses or poisons that harm people before we realize what has happened. Even in computers, if tasked to create new, interesting, functional software, it could create dangerous computer viruses. The AI Scientist current capabilities, which will only improve, reinforces that the machine learning community needs to immediately prioritize learning how to align such systems to explore in a manner that is safe and consistent with our values.
Open Models . In this project, we used various proprietary frontier LLMs, such as GPT-4o and Sonnet, but we also explored using open models like DeepSeek and Llama-3. Currently, proprietary models such as Sonnet produce the highest quality papers. However, there is no fundamental reason to expect a single model like Sonnet to maintain its lead.
We anticipate that all frontier LLMs, including open models, will continue to improve. The competition among LLMs has led to their commoditization and increased capabilities. Therefore, our work aims to be model-agnostic regarding the foundation model provider. We found that open models offer significant benefits, such as lower costs, guaranteed availability, greater transparency, and flexibility. In the future, we aim to use our proposed discovery process to produce self-improving AI research in a closed-loop system using open models.
The Role of a Scientist. . Ultimately, we envision a fully AI-driven scientific ecosystem including not only LLM-driven researchers but also reviewers, area chairs and entire conferences. However, we do not believe that the role of a human scientist will be diminished. If anything, the role of a scientist will change and adapt to new technology, and move up the food chain.
The introduction of The AI Scientist marks a significant step towards realizing the full potential of AI in scientific research. By automating the discovery process and incorporating an AI-driven review system, we open the door to endless possibilities for innovation and problem-solving in the most challenging areas of science and technology.
But while the current iteration of The AI Scientist demonstrates a strong ability to innovate on top of well-established ideas, such as Diffusion Modeling or Transformers, it is still an open question whether such systems can ultimately propose genuinely paradigm-shifting ideas. Will future versions of The AI Scientist be capable of proposing ideas as impactful as Diffusion Modeling, or come up with the next Transformer architecture? Will machines ultimately be able to invent concepts as fundamental as the artificial neural network, or information theory?
We believe The AI Scientist will make a great companion to human scientists, but only time will tell to the extent to which the nature of our human creativity and our moments of serendipitous innovation can be replicated by an open-ended discovery process conducted by artificial agents.
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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 ...
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.
Some Examples of Limitations in Research are as follows: Example 1: Research Title: ... 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 ...
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 ...
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.
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 ...
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.
s are not the last thing reviewers read in the paper.Start this "limitations" paragraph with a simple topic. sentence tha. signals what you're about to discu. s. 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 ...
Limitations of a dissertation are potential weaknesses in your study that are mostly out of your control, given limited funding, choice of research design, statistical model constraints, or other factors. In addition, a limitation is a restriction on your study that cannot be reasonably dismissed and can affect your design and results.
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.
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 ...
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 ...
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.
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 ...
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.
Writing the limitations of the research papers is often assumed to require lots of effort. However, identifying the limitations of the study can help structure the research better. Therefore, do not underestimate the importance of research study limitations. 3. Opportunity to make suggestions for further research.
Any limitation influences a research paper. It is unknown how much and to what extent any limitation affects other limitations, but it does create a cascading domino effect of ever-increasing interactions that compromise findings and conclusions. ... This is a sample of limitations and a few of their component variables under the rubric of a ...
Once you have a first draft ready, consider submitting a free sample to us for proofreading to ensure that your writing is concise and error-free and your results—despite their limitations— shine through. Whether you're a veteran researcher or a novice to the field, every study has its limitations. Here's how to identify them.
Research Limitations. Research limitations are, at the simplest level, the weaknesses of the study, based on factors that are often outside of your control as the researcher. These factors could include things like time, access to funding, equipment, data or participants.For example, if you weren't able to access a random sample of participants for your study and had to adopt a convenience ...
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 ...
Limitations are usually listed at the end of your Discussion section, though they can also be added throughout. Especially for a long manuscript or for an essay or dissertation, the latter may be useful for the reader. Writing on your limitations: Words and structure. This study did have some limitations. Three notable limitations affected this ...
Also, whilst the lack of a probability sampling technique when using a quantitative research design is a very obvious example of a research limitation, other limitations are far less clear. Therefore, the key point is to focus on those limitations that you feel had the greatest impact on your findings, as well as your ability to effectively ...
Example. Paper title. Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize major words of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms.
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. ... A Note about Sample Size Limitations in Qualitative Research . Sample sizes are typically smaller in qualitative research because, as the study goes on, acquiring more ...
More examples of generated papers are below. The remainder of this post provides a more detailed summary of The AI Scientist. Read on for: An Overview of how The AI Scientist works. More Examples of generated papers and innovations discovered by The AI Scientist. Known Limitations and Challenges faced by the current version of The AI Scientist.
Motivated by the great success of classical generative models in machine learning, enthusiastic exploration of their quantum version has recently started. To depart on this journey, it is important to develop a relevant metric to evaluate the quality of quantum generative models; in the classical case, one such example is the (classical) inception score (cIS). In this paper, as a natural ...