dissertation fake results

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One of my student researchers is likely falsifying some results/data. What are the consequences?

Relevant info and background:

I'm an engineering Post Doc at an American university. One of my roles is to basically function as a 'project manager' for a couple projects that have a number of Graduate-level RAs working on them. I have a good relationship with all the RAs and all are hard-working.

I'm convinced that one of the graduate RAs is falsifying computational results/data (also called "rigging data" by many) in some cases. Note that the individual appears to be doing this for only some cases, not all. I have many reasons to believe this, but here is a few: (1) inability to replicate various results, (2) finishing the work at a pace I think is not feasible, (3) finishing his work at home where he surely does not have the software environment to actually complete the work. There are also other reasons I believe this to be the case, but you get the point. I'm also convinced this has occurred for over 1 semester, so I probably need to report this since I am responsible for overseeing all the work. However, the student in general is a good person and hard worker. He has passed the preliminary exams and is finished with all classes - I'd hate to see him expelled from the university since he's this far into the program.

I have some questions:

What do you think could be the maximum punishment for this grad student/researcher? I'd feel terrible if it resulted in expulsion. I would think that you would have to receive at least one warning from the university before an expulsion, except in very extreme cases. I'd be fine if this resulted in suspension, and even losing funding, but for anything more I'd feel bad. What is the standard maximum punishment for these cases? Also, what is the most likely punishment?

What is the punishment for me if I don't report this problem? For instance, say I just pretended ignorance. It is extremely unlikely I would do this, but it's worth asking.

How common is this? I would think this happens once in a while - a grad student decides to be lazy and fabricate a small portion of the overall results to avoid working the weekend or something. An experienced professional would know this is seriously wrong, but not necessarily a mid-level PhD student.

Any advice from people with experience in this, professors, grad students, principle investigators, etc would be great

• supervision
• research-misconduct

• 84 This is a very serious issue. If you are confident the student is falsifying data, you have an ethical and professional obligation to report them, even if it ruins their career. Frankly, if they are falsifying data, the worst possible thing would be for them to continue into an academic career: at some point, their deception will be found out, and then all of their work since they received their degree will be discredited and they will probably be permanently ostracized from the academic community. The consequences they would receive if reported now, however severe, would be less damaging. –  Kevin Commented Jun 1, 2016 at 23:13
• 21 ...Thinking about your question some more, though, it looks to me like while you have good reason to be concerned about the student's behavior, you don't have strong proof that the student is in fact falsifying data. I would move forward cautiously. I'm not very experienced (I only have my terminal masters degree), so I'm not entirely sure what to recommend doing. Perhaps you could meet with a more experienced researcher in your lab to discuss your concerns and get advice on what steps forward you should take? –  Kevin Commented Jun 2, 2016 at 0:38
• 76 The "finishing his work at home" thing doesn't read as very strong evidence to me, unless there's more to the situation that's not apparent. Lots of work can be done by remote access, using tools like SSH, remote desktop, VNC, LogMeIn, etc. I even know physical laboratory experimentalists that have full remote access to their equipment and sensors. Unless there's some unique resource necessary for this work, that's strictly inaccessible over a network, you would need to rule out actual use of such mechanisms. –  Phil Miller Commented Jun 2, 2016 at 0:49
• 30 You should probably address the non-reproducible results before anything else. The most common reason people make non-reproducible results is honest mistakes, but even if the mistake is honest, once you're aware something is wrong it's not really honest to publish the data as if you think it's true. Even if he's not intentionally falsifying results this is a problem in itself. –  Owen Commented Jun 2, 2016 at 10:01
• 10 Even if (1), (2) and (3) are correct, I would first assume (without knowing more specifics) that the student is working honestly but incorrectly (in line with @Owen's comment). Students, and even faculty, make mistakes all the time. I would express my concerns to the student about the correctness of their work, and discuss in detail what they did to figure out what exactly went on. –  Kimball Commented Jun 2, 2016 at 12:14

12 Answers 12

You have suspicions, but the evidence, as you sketch it here, is circumstantial. You need hard proof. Then you can (and must) act.

Falsifying data is a capital crime in academia. It wastes time, possibly years of other people's work. Don't let it get through. This person, if they indeed falsified data and would come through with this, will taint anybody and anything they had to do with - you, your group, your department, your university. Their results will be worthless, and so will be the degree you bestow on them.

You would feel sorry for that person if expulsed; but how sorry would you feel for a person who for 2 years will try to reproduce this grad student's results and fail for no fault of their own? How about their life and career? An honest mistake is one thing, but faking data? You are feeling sorry for the wrong person here; you'll spare the guilty and will let the innocent being impaled? A grad student is sufficiently mature to know better than to produce "synthetic" data.

How about the person abetting such a fabrication? Frankly, if caught, depending on the power structure that person may get away with a milder penalty "for not knowing what was going on", but in principle they should get the same, if not a harsher penalty, because they certainly cannot claim they didn't know that this is wrong; and they know the repercussions.

How common is it? Hard to say, but there were a number of large scandals (Jan Hendrik Schoen comes to mind), there is probably a halo of minor such attempts. From my own anecdotal stock: I once heard the conspiracy theory that spectroscopists would intentionally introduce "innocent" wrong factors into published formulas that could be interpreted as honest mistakes to prevent competitors from progressing. I didn't believe it, however, once I had to use such a formula from a paper, and to be satisfied I rederived it and some of its "brothers" myself in a tortuous process taking several weeks; lo and behold: I found that one of them had an integer factor wrong. It goes without saying that I have no real reason to assume it was intentional, but the conspiracy theory still lodges in the back of the mind.

Bottom line: if he really fakes data, letting this happen is not an option ; but the evidence must be carefully and (important for fairness to the accused) confidentially vetted to establish whether this is indeed the case.

• 42 Great answer, but I disagree with "you must have incontrovertible proof". It is perfectly fine and indeed desirable to report strong suspicions based on less-than-solid proof to the PI, the dept. chair, or anyone else who has the ability to investigate the case and determine if misconduct occurred. Of course, in that case, when reporting suspicions the OP would make clear that they are suspicions and may turn out to be wrong. My point is that when suspicions are strong enough there is an ethical duty to report them, just like there is a duty to report a strongly suspected crime to the police. –  Dan Romik Commented Jun 2, 2016 at 3:42
• 2 @DanRomik In principle, I agree with you: "incontrovertible" may be too strong. Still, the evidence must be carefully and - initially - confidentially vetted and should be sufficiently close to certainty - more than anywhere, reputation is central in science. And even if one is wrongly accused and thus wrongly perceived as falsifying data, that person's career will take a dive, whether deservedly or not. I think this is the case one needs to worry about, not about harshly treating someone who has provably falsified. The knife cuts both ways. –  Captain Emacs Commented Jun 2, 2016 at 11:32
• 15 why not talk to the student and communicate your concerns with him? Based on the OP's question, it seems like he does have very legitimate cause for concern. However, going over his head and getting the administration involved would be a step I would take AFTER communicating with the student and letting him know that his methods "don't seem as rigorous as expected." If he ignores OP's admonishment/advice, then OP has to do what he has to do. But OP is, in fact, the superior directly in charge. –  sig_seg_v Commented Jun 2, 2016 at 11:53
• 4 @CaptainEmacs thanks for agreeing. I agree that "the evidence must be carefully and initially vetted". That is the point of having an investigation, which is what will happen before the student can be punished and certainly before any misconduct is made public (if indeed it ever is). US universities have well-oiled machinery for carrying out such processes, so I see no reason for the vetting of the evidence to be done by OP. OP will of course aid in the investigation by providing information and expertise, but acting as an investigator is way beyond the scope of a postdoc's job. –  Dan Romik Commented Jun 2, 2016 at 14:38
• To summarize, I suggest changing or removing the last sentence of your answer to make your otherwise great answer more precise. –  Dan Romik Commented Jun 2, 2016 at 14:39

This misconduct is considered the ultimate misconduct in the research community. The offender is often stripped of his credentials and because of the tight knit nature of the scientific community, even if the credentials are not stripped the researcher may never find work as a researcher again. It will impact the ability to secure funding in the future.

If you are aware of it, as you claim to be, you can also be affected, ESPECIALLY if your name is on or associated with the paper. Additionally, if you are the one who secured the grant, this could backfire for you trying to secure grants in the future.

This is not common or uncommon, some people purposefully falsify data to support their hypothesis, but it is not always inaccurate. Sometimes researchers choose to only highlight some information and not other so that their hypothesis is supported and this is a more grey area.

BOTTOM LINE: If you know your student is falsifying data, then don't allow them to do so, for their career and for yours.

Communicate with the student. Let the student know your concerns.

The question seems rigged to determine what penalty may be appropriate, and how to kindly dish out the pain.

However, if we show good faith, then maybe we don't need to be quite as secretive. Say, "This resembles trouble. Here are the concerns." Then, if the student is innocent, the student may be able to explain things, and learn importance of proactively make things more clear so that suspicions don't grow into bigger problems than warranted.

If the student did do something wrong, maybe the student can correct things before they get further out of hand. The situation may be more correctable before more resources (including time) get spent on a road that may be wrong.

In education, the goal is often to help people do better. A common assumption is that people are typically inexperienced, and mistakes may be made. The goal isn't to try to maximize penalty for people who may be struggling with new skills. The goal is to try to get people in a good situation, including experience doing things desirably (including doing things properly, and successfully).

So, to re-cap this quite simply:

• if you're absolutely convinced that something is completely wrong, then go through the formal steps of handling such problems (reporting the issue, and whatever consequences follow through).
• (If this communication results in more trouble being discovered, be ready to shift over to the first bullet point, as needed.)

• 3 My concern here is that if the student has falsified data, your suggestion is to give them a heads up. This will enable them to adjust their methods to avoid being found out in future. Falsifying data is not a teachable moment or a mistake. It is deliberate and deeply immoral, and should quite rightly permanently taint a researcher who does it. –  MJeffryes Commented Jun 2, 2016 at 16:07
• 15 "I cannot reproduce the results you obtained. Please write down your methods and provide to me all tools required to reproduce." is not giving him a heads up to adjust his methods to avoid being found out in the future. It is either bringing him back on the right track (not falsifying data anymore, for fear of being found out), or it won't change a thing, and then you can still decide to go to the chair/dean/whatever. –  Alexander Commented Jun 2, 2016 at 17:46
• 6 @MJeffryes : Giving them a heads up is completely intentional. Attempting to keep actions secretive would be a more adversarial move, and I don't recommend that until you start to determine that adversarial actions are required. At this point, I'm recommending to take the friendly approach. The intended goal is helping to correct an apparent problem. If you declare enemies too quickly, you can eliminate some potential opportunities to still resolve things while on more friendly terms. Don't try to begin the punishment process before non-speculatively knowing the penalty is warranted. –  TOOGAM Commented Jun 3, 2016 at 2:08
• 1 I wonder why none of the other more voted answers opt for this. Why isn't talking with the student the first option? Somehow it's implicitly assumed that whatever he's done he'll continue doing regardless... –  hjhjhj57 Commented Jun 3, 2016 at 21:29

What do you think could be the maximum punishment for this grad student/researcher?

Whatever the maximum punishment is, that punishment has been decided by the people running the university. If you consider your university to be a reasonably well-functioning institution (and I would hope you feel this way about the place where you have decided to spend several years of your career), you need to remember that the people making such decisions have much, much more experience than you in handling all different kinds of academic misconduct. Thus, the punishment is likely to have been well-calibrated over many years and based on a large amount of cumulative experience. What makes you think that your personal judgment on this question is more wise or likely to be correct than such a body of accumulated knowledge and experience?

By not reporting your suspicions, you would essentially be saying "I know better than everyone else what needs to happen to this student, so I will usurp the institution's right to properly bring the student to account for his actions and just act based on my own gut feeling to save myself from the feeling of guilt over the punishment that the student would receive (even though any such punishment would be 100% the student's fault)." This line of thinking is simply wrong. The punishment is not, and shouldn't be, your decision. You have a duty to report the misconduct, and by not doing so you would be making yourself complicit in all its many potentially harmful consequences, which were described quite well in the other answers.

• 6 +1 for "I usurp the institutions' rights to bring the student to account". –  Captain Emacs Commented Jun 1, 2016 at 23:33
• 19 Completely disagree: rules on the institution level are indeed built on accumulated experience, but not necessarily with the interest of either science or the PI. Rather based on maximizing the interest of the institution under their legal, financial and political constraints (which can be opposite to the interest and values of the OP). –  Dilworth Commented Jun 1, 2016 at 23:43
• 3 @Dilworth in principle you may be right, which is why I added the caveat "If you consider your university to be a reasonably well-functioning institution ...". If OP has serious cause for concern that the university is staffed with incompetent or corrupt people, that might call for extra caution. However, the default assumption should be that large US universities have well-tested and reasonable procedures for handling misconduct. Thinking that one knows better than everyone else is a common human cognitive bias; in this case it would almost certainly be an incorrect assumption to make. –  Dan Romik Commented Jun 2, 2016 at 14:29
• 2 Even large US universities have interests that may directly oppose the interests of the OP. It is not a case where the OP and the university have both the same goal, in which it is correct to assume that the university knows better than him how to act. It is about the possibility of completely contradicting goals. –  Dilworth Commented Jun 3, 2016 at 14:29

You don't need to kick up a big fuss about it.

While it is definitely the case that any case of data fabrication is worthy of the levels of punishment it incurs in academia, it is not very clear that this is actually happening here. And in any case, the repercussions of scientific falsification should be very clear at any level, even for undergraduate students.

Inability to replicate results is extremely common in all scientific fields, and the overarching likelihood is that the analysis or experiments were carried out incorrectly for some reason. In the vast majority of cases, that is all there is to the story.

Simply deal with this problem as you would with any other inexplicable scientific result. Walk through the entire protocol, troubleshooting all potential issue spots, and exclude variables as required. In the extremely unlikely case that you find that the student was actually falsifying data, you must report it, but it seems unlikely to me that it is going to be the case.

If this person is falsifying data now, this person will continue to do so later as a PI. While you're sure to feel bad about it, science as a whole requires you to address the situation. When the public loses faith in science, we all suffer.

There are many ways to address this in a discreet manner (to ensure your intuition is accurate). Why not have this person walk you through the data/analysis step by step from ground zero?

• 2 How do you know the person will continue to do so later? –  Jin Commented Jun 1, 2016 at 22:20
• 4 You don't know in any absolute sense, but it is the least assumption. –  dmckee --- ex-moderator kitten Commented Jun 1, 2016 at 22:26
• 14 @Jin It is expensive to verify results (people do not get funds to reproduce known results); therefore, trust is absolutely central in science. If someone falsifies data once, he cannot be trusted anymore. Everything that person claims to find out, especially an expensive to produce result, needs to be independently verified anyway; their testimony is unreliable; so why waste attention and grants on them ever again? A person once caught taking a bit of money out of the cash register showed a "fluid" morals once, they won't be let handling the cash again. –  Captain Emacs Commented Jun 1, 2016 at 22:44
• 3 @Jin The only question is whether the OP has hard proof that the person falsified in the past . It is irrelevant whether they will do later - for which I explained the reason above. Also, usually universities will have procedures in place to punish such transgressions, but what the precise consequences are, will depend on the uni and cannot be answered on SE. But you asked "How one knows that the person will do it later?" - and what I am trying to say is: it's not relevant whether they will really continue this or not - only that the costs for everybody in the future will be as if they do. –  Captain Emacs Commented Jun 1, 2016 at 23:14
• 2 @Jin I would agree if it were just about you and Jack; but it's not. I thus fine-tune my example: Jack has stolen from you. You give him a dire warning. People know he has handled your money in the past (you didn't mention anything to them) and thus think he is honest. They leave their wallets lying on the table. Jack is around. Do you warn them off? –  Captain Emacs Commented Jun 1, 2016 at 23:50

In terms of immediate authority, I assume you and the student in question both ultimately report to a professor. I expect that professor is one or more of the PI on the supporting grant, the student's thesis advisor, and your supervisor. I really hope you have a strong, trusting relationship with this professor, for a few reasons:

• they will be the first line of investigation and response in dealing with this situation, and likely carry more personal/reputational and institutional responsibility for it than you do
• the student has likely worked with them longer than you have (postdoc there maybe 1-2 years, vs ABD student)

Basically, you don't want to end up in a position where your actions lead the professor to hold this against you. That could lead to withdrawn/non-renewed funding for you, withheld or weakened recommendations for future positions, and so forth. You really need the professor on board with the suspicions before any wheels of process start moving.

If there's some administrator responsible for this sort of issue that you know and trust not to jump the gun, you could potentially speak to them first to get your concerns on record before bringing them to the professor, to avoid the risk of the professor trying to sweep them under the rug and/or throw you under the bus.

Edit to add 1:

Ultimately, though, resolving this situation now, while the student is still pre-PhD, is in their best interest. If they aren't doing anything wrong, then they'll learn how to conduct their work in a more traceable, transparent, supportable, and reproducible manner. If they are, there's at least a chance that they can get straight without a permanent black mark on their career. Once they've gotten that degree, any such allegation could lead to it being revoked, grants they've received being suspended or cancelled, etc. This is the last point in their career where they can learn appropriate boundaries and reasonably hope to rehabilitate themselves.

I think three statements that you make are just your impressions and as you know that these are your impressions, you are not completly sure that student in question falsifies the data. Otherwise, I think you would not have asked the question here.

The best thing to do in order to be sure 100 % is to replicate all results with this student in your office on your computer. Otherwise, I think your statements are just your own impressions, without any solid evidence.

If you see that data is falsified, then you should report it.

• 1 +1 This answer addresses an important aspect of the question. Whereas in the lab sciences the experiments are never 100% reproducible due to inevitable small environmental factors, in the computational realm everything, if properly documented, should be able to be verified and reproduced. Even random algorithms like Monte-Carlo can be exactly reproduced, especially in the testing stage, by seeding the pseudo-RNG with the same seed everytime. So if you want to make sure the student is doing his work: just get the code from him and run it on your own computer. –  Willie Wong Commented Jun 2, 2016 at 13:25
• 2 @Willie Wong, Fully agree. The code that uses student would be useful to understand if there is any falsifaction. As in programming stuff, it is more difficult to understand the code of others than writing its own, I think it is better to reproduce all results with the student. By doing this, OP can understand also the methodology used and can verify the data used. Unfortunately, in some fields like economics, most of papers are not reproducible ; timeshighereducation.com/news/… –  optimal control Commented Jun 2, 2016 at 13:46
• @WillieWong, I have the impression that computational experiments are also prone to variation due to hard-to-control environmental factors, including software versions and initial states of random number generators. Avoiding these pitfalls should be possible, as you say, but it doesn't seem straightforward to me ( journals.plos.org/ploscompbiol/article?id=10.1371/… ). –  Vectornaut Commented Jun 2, 2016 at 17:11
• 1 @Vectornaut: initial states of random number generators can be controlled by properly seeding it as I wrote. See, for example, the documentation for the Julia language . Software versions can be documented; and if open-source software is used, the older versions can usually be tracked down in the appropriate repositories. Avoiding these pitfalls is in fact quite straightforward (and in fact the rules in your linked article make it even more so). Compare to the laboratory sciences the amount of documentation... –  Willie Wong Commented Jun 2, 2016 at 17:27
• 2 ... is not more than what would be expected to go into a lab notebook keeping track of the conditions underwhich experiments are run etc. The fact that some individuals find it "hard" is more an indication that some computational scientists are not given the appropriate data management training that typical laboratory scientists would be given. In this day and age it should be the responsibility of the head of the lab (either the professor or the lab manager) to hold the students accountable for good data management practices. –  Willie Wong Commented Jun 2, 2016 at 17:30

I agree with Captain Emacs 's answer, but there is something missing that I feel is important, namely:

Ask the RA directly whether he is fabricating any data, and while asking tell him why it is wrong to do so, and also that if he really does it and anyone finds out he can be expelled. At the same time tell him that at this juncture the best thing to do now is to redo all tests properly, meaning that he records all the random seeds used so that his data is completely reproducible.

After that it is likely that the problem will be resolved more or less satisfactorily, because it is generally difficult to write a program that looks normal and yet find a special random seed that causes it to have special behaviour. (It is possible but increasingly improbable for larger-scale tests.)

Falsifying data is a big no-no. It's on par with (and possibly worse than) plagiarism. It can ruin careers, and can lead to a whole host of huge problems (we don't need any more Andrew Wakefields). So if the student is doing this, you absolutely must report it and cannot feel bad.

That said, from the information you provided, there really isn't strong evidence. If I were on that jury, I would acquit without a second thought.

1) Working from home: Can he connect to a network to access the needed software? Can he run the program in the lab, get the raw data (say in a text file, or spreadsheet) take it home and do post processing/analysis there?

2) Not replicating data: I've written programs and ran simulations that performed beautifully and satisfied all the tests. But when I get to the group meeting, it fails. Why? Because I changed something that "wouldn't affect the results or the existing tests" (Ha!) between the time I originally got it working and the meeting. Or maybe an initial guess was changed. It might only take a few minutes to fix on my own, but in a meeting/high pressure environment I can't fix it right there. To me, that seems like a plausible explanation. (And I'm assuming there's no randomization in the code, I've had Monte Carlo approaches give significantly different results depending on the seed used).

3) Working faster than you expect: I see two possible explanations for this: a) The student is better than you think. b) The student is worse than you think. For (a), perhaps the student is able to crank out code fast, when he hits his stride and has a good mental map of where to go and how things should fit together (this "gunslinger" approach can be effective, but also can let bugs show up that make data replication difficult). Or he has written scripts to run several computations simultaneously or overnight. For (b), perhaps the student "hacks" everything in the code. Hardcodes things that should not be hardcoded, for example. Messes with things that shouldn't be messed with. This can give the illusion of working fast, but results in unmaintainable or inconsistent code, essentially borrowing time from the future.

Obviously, you have access to more information than we do, so perhaps these explanations don't apply. I would suggest talking to the student about the results, though not in an accusing way. Ask him to explain the results, explain what he did, and how he did it. Look through the code that he uses with him, make sure you both understand it. Perhaps there's honest mistakes to be corrected. Maybe there isn't a problem. If he seems to have no idea what he did or can't explain the procedure, then you probably want to bring up your concerns with the PI. But, under no circumstances can you let data falsification continue.

Tell the student that this doubt exists but in a one-on-one situation

To clear this doubt, ask him to make his results fully reproducible . It is in his own interest to show that he did not falsify anything. Show him that there is "immediate danger" that this gets investigated.

If he did not falsify anything (and your doubts were wrong), then this is a viable route. It requires some effort, but of course it can be done (and should be done, anyway). Then no damage is done, you only force him to work more transparently.

Allow him to redact work, if no harm has been done yet

This is the only "easy way out" that is in my opinion acceptable. In particular if nothing has been published outside of the university, you can allow him to redact falsified material, in order to replace it with real work. This may be punishment enough at this stage: It may set him back half a year towards graduation! But it may also require additional measures, depending on the severity.

He may then learn a key lesson here: while you may get away in highschool and maybe even undergrad, once the work gets more closely reviewed, misbehavior, copypasting and data fabrication is likely to be discovered, and this is not a good way of working. A backlash could come any time, and may ruin his reputation.

In the case that he admits cheating on this project, I would consider also reviewing earlier work, too.

If harm has been done, you want to redact anyway

If anything of this has been published yet, your name or your professors name is likely to appear on it, or at least be associated with it. In this case, you really will want to have this resolved...

At this point, it may be necessary for your own reputation to trigger a formal investigation; partially to clear yourself from any responsibility.

In a nutshell,

1) you must get a decent proof of misconduct, and

2) if/when you get it, you should report it immediately, even if this means the definite end of his career

Falsifying data is a cardinal sin for a scientist, and as many others remarked, it may waste years of work for other scientists try to replicate (and possibly improve) the faulty results. I was once caught in a situation where my student was trying to replicate somebody else's results, and it seriously impacted her PhD work, since the original authors' selective reporting (they later found that their solution works only in very limited cases, but did not share that finding until much later).

Now, before you rush to your superiors , I would advise you to talk to student about his methodology. Explain him that as (de-facto) project leader you have responsibility to guarantee that all results conform to scientific standards and that after you went through his work, you suspect there might be a problem with his results, as a result of unintentional mistakes or inexperience on his part. Start with this - if he made unintentional mistake (or even multiple mistakes), he will probably more than glad to learn from them and work hard to correct them. For a PhD student, this is sufficiently vague and yet serious that, if he is honest, he will work really hard to correct the problems (and redo the experiments). In that case it is your decision whether you trust him enough to have him on the team, and if you don't want, you should simply explain to your superiors that he does not fulfill your criteria, since he makes too many mistakes, and you do not need such people on the project, period.

I understand that this is a very difficult task for you, since you will have to waste your own time to go through his work and make sense of it, and probably you have your hands full with other work.

On the other hand, it may be pretty easy to spot if he is really dishonest or trying to hide something, because if he took a shortcut the first time by falsifying the results, I very much doubt he will "waste" his time correcting them - more likely he will try to weasel out or start making excuses, which then really means a red flag, and gives you a really good grounds to either confront him directly (usually it won't be necessary, as he will probably start digging his hole deeper and deeper) or just go and report him to the superiors. Because, if his mistakes were unintentional or result of carelessness but he does not feel he needs to correct them, he still deserves to be reported and sanctioned - refusing to learn from mistakes that others point out and refusing to correct them is almost as bad as falsifying data.

I do advise against going to superiors based only on a hunch, because an accusation of falsifying data may ruin his career even if he is not guilty, and further graduate students may become reluctant to work with you, fearing the same treatment (and some may even interpret your actions in a way that you got rid of competition down the road, which is the last thing you need).

And, since you are his superior, you are guilty if you do nothing, and the problems get discovered in his further career. You must act.

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Research Fraud: Falsification and Fabrication in Research Data

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Although uncommon, it’s not unheard of that researchers are accused of falsifying and/or fabrication research. In addition to other malintent, like plagiarism, duplication in publishing or multiple submissions. The tricky part is finding and identifying true fraudulent activity, even through peer review processes. Even highly regarded journals have accepted articles that are suspected of research fraud. On the other hand, some researchers have had their careers and reputations compromised by false accusations. Even if the false accusation was cleared, intense media coverage on the accusation versus the acquittal can mean permanent damage to the researcher.

What is the difference between falsification and fabrication?

Any type of research fraud usually involves publishing conclusions, or even data, that were either made up or changed. There are two different types of research fraud; fabrication and falsification. Obviously, they are related. However, they are distinctly different.

Falsification essentially involves manipulating or changing data, research materials, processes, equipment and, of course, results. This can include altering data or results in a way where the research is not accurate. For example, a researcher might be looking for a particular outcome, and the actual research did not support their theory. They might manipulate the data or analysis to match the research to the desired results.

Fabrication, on the other hand, is more about making up research results and data, and reporting them as true. This can happen when a researcher, for example, states that a particular lab process was done when, in fact, it wasn’t. Or that the research didn’t take place at all, in the case of a study results from previous research were copied and published as original research.

Obviously, both falsification and fabrication of data and research are extremely serious forms of misconduct. Primarily because they can result in an inaccurate scientific record that does not reflect scientific truth. Additionally, research fraud deceives important stakeholders of the research, like sponsoring institutions, funders, employers, the readers of the research and the general public.

How to Detect Fabricated Data

Sometimes it’s easy to identify fraud and fabricated research. Maybe, for example, an evaluator of the research is aware that a particular lab doesn’t have the capability to conduct a particular form of research, contrary to the claims of the researcher. Or, data from the control experiment may be presented as too “perfect,” leading to suspicions of fabricated data.

Suspected data manipulation in research, fabrication or falsification is subject to reporting and investigation to determine if the intent was to commit fraud, or if it was a mistake or oversight. Most publishers have extremely strict policies about manipulation of images, as well as demanding access to the researcher’s data.

Image Manipulation in Research

Another fairly common fabrication relates to images that appear to have been manipulated. It should be noted that image enhancement is often acceptable, however any enhancement must relate to the actual data, and whatever image results from the enhancement must accurately represent the data. If an image is significantly manipulated, it must be disclosed in the figure caption and/or your “materials and methods” section of the manuscript.

So, the bottom line is that image manipulation in research is okay, as long as the manipulation is to improve clarity, no specific features are introduced, removed, moved, obscured or enhanced. Minor adjustments to brightness, color balance and contrast are acceptable if they don’t eliminate or obscure information that is present in the original image.

Protecting Your Reputation

A researcher’s worst nightmare might be the accusation of committing fraud. Of course, unintentional errors do happen, and unfortunately they can appear to be misconduct. However, it should be made clear that an honest error is not considered research misconduct.

To avoid any false accusations, make sure your research is 100% accurate and any methods and processes are expressed accurately. Ensure that any images that might be enhanced are noted as such, and include the original image with your submission. Keep records of all raw data; if falsification or fabrication are suspected, the journal or other investigative body will demand to review your information. Therefore, flawless records must be kept, analyzed and reported. Additionally, certain research topics, such as studies of human subjects, require a specific duration of data retention.

If you discover an accidental published error, follow the steps outlined in our article about retracting or withdrawing your research .

The Bottom Line

A researcher must take extra care to ensure that their data and research can not even be suspected of fraud via falsification and fabrication. You can do this by being transparent and honest about any and all research, data, analysis and conclusions. Remembering that the purpose of research is to further collective knowledge over supporting desired outcomes is key to ensuring integrity in research.

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Ethics of Science Writing

Data fabrication and falsification.

Group Members: Will Burnham, Kelly Heffernan, Alyssa Morgan, and Evan Russell 

The truth is always something that scientists and members of the scientific community should strive to achieve. Truth emerges at the intersection of transparency, trust, and honesty. These are the qualities that mark sound science which accurately disseminates findings and information. However, it is possible for scientists to fall short of this mark. Instead of scientists displaying truthful findings, we find that they choose the evidence they desire by manipulating it in their favor (Leng 2020, 159-172). This is known as data fabrication and falsification, and in this practice, scientists manipulate existing data or create new data with no basis in experimentation. Scientists are motivated to do this in order to form data that reflects their desired outcome or matches their hypothesis. This practice occurs more often in scientific writing than it would seem. As a result of fabricating and falsifying data, other scientists or researchers cite these papers that contain fabricated data and then the ongoing spiral of falsification commences. In the end, all the authors and or institutions that cited the original paper containing the falsified data are retracted once discovered. As data fabrication and falsification continues, the motivation for fabrication, the resulting implications and consequences, methods of detection, and preventative measures and ethical considerations will all be examined throughout this webpage in an effort to stop false data’s destructive path through science. 

Motivation for Fabrication & Falsification 

A frequent quote many people use today is “I believe in science”. There are t-shirts (see figure 1), book bags, and many Instagram posts espousing such a sentiment. This blind faith in science stems from the fact that society places scientists on a pedestal. They are expected to be trustworthy, unbiased, dedicated, and uncorrupted by ulterior motives. The reality, as shown in real life examples throughout this webpage, is much more nuanced. While many scientists do follow ethical practices, there are also many who fabricate and falsify the data they draw their conclusions from. This is a dangerous practice, as in fields like medicine where falsified results of drug trials can result in serious injury for future patients who are prescribed the drug. With all of these horrible consequences, why would scientists fabricate data?

Figure 1. This image shows a popular t-shirt design on the online retailer redbubble.com. The text on the t-shirt reads “Trust Science.” (Redbubble 2021). 

One reason for fabricating data could be that some scientists do not realize that they are using poor data practices. For instance, in one heavily cited study about data fabrication (Fanelli 2009, 1), only 33.7% of scientists admitted to questionable research practice that they themselves had conducted, while 72% reported that they had seen questionable research practices in their peers’ work. This shows that people are much less likely to admit faults in their own data than in others (see figure 2). For this reason, scientists could be subconsciously fabricating data and falsifying other items, but genuinely not realizing it due to their own inherent biases. 

Figure 2. This image is taken from (Fanelli 2009, 6). It shows the disparity between scientists admitting to their own mistakes, versus admitting to noticing peer’s mistakes in a survey. Note that QRP stands for questionable research practices. 

Another reason is that many scientists in modern times feel pressured to publish as many articles as they can, as quickly as they can. They believe this will build their reputations as scientists through sheer volume of increased citations. One researcher who was held in high esteem at Bentley University (Nurunnabi and Hossain 2019, 4) was found to have fabricated data in two of his most prominent studies. This kind of behavior can be explained by the need for many scientists to either publish work, or perish. It would be tempting for a researcher to fudge the numbers in order to reach a statistical significance threshold that is needed for publication. Without set ethical guidelines to follow, problems like this can quickly become prevalent. Another young researcher named Joachim Boldt (Mayor 2013, 1) fabricated data in research relating to safe blood plasma substitutes for diabetics. This resulted in people believing that certain plasma substitutes were safe for diabetics when they actually increased death and injury rates, as discovered by a meta-analysis (Mayor 2013, 1). Boldt’s desire for influence overcame his desire to genuinely help people, which may have resulted in patient deaths. 

Figure 3. This image shows the article authored prominently by Adeel Safdar. There is a retraction warning on it in order to show people that any conclusions or results drawn in it should not be relied upon for further research (Safdar et. al. 2015, 1). 

Another scientist who fabricated data is Adeel Safdar from McMaster University in Ontario (Safdar et al. 2015, 1). As shown in figure 3, His article was recently retracted from the Skeletal Muscle Journal, due to fabrication of two images that led to false conclusions. Recently, concerns were raised about the scientific ethics of Safdar after he was accused of torture and domestic abuse of his wife and family, which can be seen in figure 4. As Safdar’s character was taken into question, this led the scientific community to also reexamine his work, and discover instances of data fabrication. Once the scientist’s character is seen as flawed, their data collection and overall experimental designs will be checked thoroughly to make sure they were not also dishonest in their work. Many other papers of Safdar’s are being examined for data fabrication, and as of now three of his papers have been retracted. 

Figure 4. This image shows article headlines from The Hamilton Spectator, a Canadian newspaper. They outline the domestic violence charges Adeel Safdar has recently been accused of. (Editors of Hamilton Spectator 2021). 

Overall, there are many different reasons that motivate scientists either consciously or unconsciously to fabricate their data. No matter the reason they are engaging in this type of false science, the consequences continue to be severe and lasting on the entire scientific community. These consequences will be examined in the next section of this webpage. 

Implications & Consequences of Fabricating & Falsifying Data

Figure 5. This cartoon is from the Los Angeles Times created by Scott Adams. In this cartoon there are two researchers and/ or scientists who have realized that the results from their experiment were not what they had expected. Therefore, one of them brings up that they can just “adjust” (change) the data so that it “supports” what they had anticipated. This cartoon does a great job showing how fabricating and falsifying data takes place in the scientific community.  

Figure 6.  This is Table 1.: Potential Consequences* of Publication Fraud for Authors and Their Institutes from ‘Preventing Publication of Falsified and Fabricated Data: Roles of Scientists, Editors, Reviewers, and Readers’ in Journal of Cardiovascular Pharmacology. The table lists nine different punishments for fabricating and falsifying data in scientific writing.   

Figure 7. This newspaper is from Science Magazine and has the article of Luk van Parijs, a former MIT Researcher, and how his contract with MIT was terminated because he was found guilty of committing data fabrication in some of his published work on RNA interference.

Figure 8. The top two maps are from ‘Ireland after NAMA’ and show the median prices for properties in different counties of Ireland in 2010 and 2012. The bottom two maps are also from ‘Ireland after NAMA’ and show the change in median price for properties from 2010 to 2012, both in actual value and percent change.

Figure 9. These two graphs are from the article ‘Researcher at the center of an epic fraud remains an enigma to those who exposed him’ from Science Magazine, Tide of Lies. The graph titled, Total scientific output, shows the number of scientific papers that Yoshihiro Sato published throughout his career until he died in 2016. The graph titled, Clinical trials, shows the number of patients from 33 of his clinical trials.

Figure 10. This diagram is from the article ‘Researcher at the center of an epic fraud remains an enigma to those who exposed him’ from Science Magazine, Tide of Lies. It shows the top 12 trials performed by Yoshihiro Sato that had been widely cited by other researchers, scientists, institutions, etc. More specifically, this diagram shows when the trial took place, the number of patients involved in that trial, the number of references that were made to that trial by year and when all corresponding documents that had cited that trial were retracted.

It is evident from these examples alone that the falsification and fabrication of data does not come without consequences. Its effects can powerfully alter the course of individual lives and science as a whole, often for the worst.

Methods of Detecting Fabrication & Falsification 

Detecting flaws in data is the first step to recognizing and retracting falsified or fabricated data to prevent its far-reaching consequences. However, detecting data fabrication is not a simple process, requiring standards of measuring the accuracy of various types of data (Kitchin 2021, 41). As stated by data researcher, Rob Kitchin, “The fact that myself and my colleagues continually struggle to discover such information and have to extensively practice data wrangling has made it clear to us that there is still a long way to go to improve data quality and its reporting” (Kitchin 2021, 44). Kitchin highlights that even those who are well-versed in analyzing the quality of data struggle to determine just how reliable data truly is and if data should be retracted, or withdrawn from publishing. In short, there is no clear cut way to determine if data is true or false, or simply if it is influenced by bias. However, data falsification can be recognized and can result in the successful retraction of unreliable works, as shown in Figure 11 (Nurunnabi 2019, 116). Data falsification often has warning signs, which can be applied in a case-by-case manner to the various fields in which data can be relevant.

One major concern with data is that of representativeness: how much is the data actually revealing what it was attempting to measure in the first place (Kitchin 2021, 41)? Although misrepresentation is not always intentional in its resulting deceit, often one’s manner of interpreting and conveying information can blur the true meaning and intentions of data, which turns into data falsification when intentional. Between collecting data and spreading it, there is always a party in charge of its interpretation to bridge the gap between these two steps (Leng and Leng 2020, 159-172). Data cannot interpret itself, and this is often where the issue of representativeness becomes a problem (Leng and Leng 2020, 159-172). Processes, including extraction, abstraction, generalization, and sampling, involve instances where bias and lacking precision can step in (Kitchin 2021, 41). It is important to be aware that the method of data collection can affect how it is interpreted and conveyed, including factors such as sample size, demographics, and the population from which data is derived (Kitchin 2021, 41). For example, a study with a very small sample size will be much less reproducible and will have lower statistical power than if the same results were obtained from a larger sample size. In this case, any generalizations made from the data must be placed in the context of a small sample size to avoid drawing conclusions based on the data that would not be applicable to a larger sample size. This same thought process must be applied to all data. It is fair to conclude that certain deceiving aspects of data collection are not always easy to recognize, especially for the general public, but an awareness of where data can become flawed is crucial, as intentional misrepresentation is directly linked to data fabrication.

Despite the importance of having an individual awareness of where data may be falsified, whether intentionally or through unnoticed bias in action, there are international-level efforts to detect poor data (Kitchin 2021, 42). In fact, there have been formal measurement systems for classifying data as either proper or poor quality. These measurements, as shown in Figure 12, involve veracity, the completeness of the data, timeliness, coverage, accessibility, lineage, and provenance (Kitchin 2021, 42). However, not everybody follows these standards to ensure that the data is of proper high quality, and it can be challenging to apply such general standards to specific fields. Oftentimes, efforts are only made when flaws in the data would affect a larger crowd, meaning it would be more likely for people to notice any flaws (Kitchin 2021, 42). For these reasons, when analyzing data and research, it is crucial to think about who the researcher’s intended audience is and how this could affect the researcher’s motives and possible shortcomings.

One field where data fabrication is prevalent and especially dangerous is in clinical trials, which evaluate new treatments and medicines in the medical field. Accidents in data collection and analysis can happen. For this reason, it is vital that quality standards are established for researching, experimenting, and citing and that these standards are made aware to everyone involved with research. However, to help identify data falsification, prominent distinctions can be identified between accidents in data falsification and purposely falsified data (Marzouki, et al. 2005, 267). In one study, two clinical trials were examined and their data analyzed to determine if these data were reliable or flawed in some way (Marzouki, et al. 2005, 267). The first trial in question was the diet trial, surrounding a controlled trial of how fruits and vegetables in a diet affect patients with coronary heart disease, who were stated to be randomly assigned to the experimental and control groups (Marzouki, et al. 2005, 267). The other trial was that of a randomized drug trial, studying how drug treatment affected patients with mild hypertensions (high blood pressure) (Marzouki, et al. 2005, 267).  The data for both trials could be analyzed quantitatively, both separately and in comparison to one another, in order to come to recognize trends that could be possible indications of fabrication or falsification.

In an effort to recognize data falsification, the data was first checked for digit preference. Digit preference is defined as “the habit of reporting certain end digits more often than others” (Camarda, Eilers and Gampe 2015, 895). Human-recorded data often shows preference towards certain numbers, as opposed to machines, with humans often favoring either multiple of 5 or 10, or even numbers rather than odd, which can threaten the accuracy of data when nudging numerical data towards these favorable numbers (Camarda, Eilers and Gampe 2015, 895). For example, if a study’s data predominantly ends in even numbers, rather than odd, it can be assumed that possible rounding took place with the data to make the numbers seem more favorable. As a result, digit preference can be a strong indication of possibly falsified or unreliable data. Although chance can result in digit preference, the two clinical trials examined in the discussed study should have had a similar pattern of preference due to the randomization stated in creating the experimental and control groups (Marzouki, et al. 2005, 268). However, the results proved that the diet trial had notable differences in standard deviations for height and cholesterol measurements, while the digit presence for all variables was very strong (Marzouki, et al. 2005, 268). This led the researchers to believe that this data was not entirely raw and unaltered by its experimenters. 

Another major difference in these data included the magnitude of the P values in each data set (Marzouki, et al. 2005, 268). The P value is a statistical tool used in science that indicates the strength of evidence, with smaller P values referring to a greater significance of data, and analyzes the probability of data/results being reproduced by chance (Nuzzo 2014, 152). The P value is a strong motivator for scientists when experimenting, as a P value can determine how a researcher’s work is received, and, therefore, if it is cited or not. Therefore, analyzing P values is a necessary step to determine if data was falsified to make it appear as if data is more significant than it truly is. There were noticeable differences in mean and variance between baseline variables in the diet trial, as shown in Figure 12, showing that the groups were not randomly allocated as the author had claimed (Marzouki, et al. 2005, 268). Likewise, the P value, together with the significant difference in digit preference, served as evidence that further flaws in data did not occur by chance (Marzouki, et al. 2005, 268).Therefore, the lack of randomization in this process could be due to differences between the means of the baseline variables (Marzouki, et. al 2005, 268). Although one irregular pattern, such as digit preference, is not a definite indication of data fabrication, as it could simply be attributed to different people recording data for each group, the combination of digit preference, with differences in means and variances, points towards data fabrication. As exemplified by the analysis of this data, the tools used in evaluating data are specific to the field and presented data, as this clinical trial is mainly concerned with quantitative data. However, these specific statistical tools may not be useful in determining the validity of data from other fields that involve more qualitative analysis.

It is evident through this single example just how shaky the evaluation of data fabrication can be. How can we truly be sure that data is made up or true? Can one ever be 100% certain that chance played a part in certain results? This field of recognizing and abolishing data fabrication is constantly evolving as more cases are studied in various fields, but for now, we have to work with existing evidence and intuition. The difficulty in spotting data fabrication and falsification only supports the idea that this practice is dangerous to the integrity of science.

Preventative Measures & Ethical Practices 

Preventative measure and ethical practices are vital to creating an environment and culture in the scientific community that strives to prevent falsified and fabricated data. Preventative measures are found in two places in the research timeline. The research timeline is the sequential steps we take to conduct research. This begins with a question, followed by a hypothesis, testing, conclusion, and finally publishing your results.

Figure 14: Progression of the scientific process, and where to stop data fraud and falsification in the timeline.

The first section in the timeline is preventing falsified and fabricated data before it is created in the testing stage. Many examples of fraudulent work are caught after publication. For this reason, the second section focuses on snuffing out falsified and fabricated data after it has been created, but before it is published, so the incorrect information is not shared.

For a long time, science education has been about broadening the scope of our understanding. Now things may be shifting towards teaching ethics in science to students (Reiss, 1999). In a perfect world, science would be performed on a completely transparent and honest basis. While it is not possible for all scientists to be perfect, it is still important to create a culture of honest work. This can begin early in a student’s education. Educating students on data fraud will “… increase awareness, they will also encourage a mindset in which issues can be discussed earlier and easier” (Korte, 2017). By educating students on the dangers, they will be less likely to fabricate or falsify data. This increased awareness from education should also help break down the detrimental “publish or perish” culture found in labs around the world.

But learning shouldn’t stop with formal education up to, and through college. It is important that scientists continue to learn about ethics and honest work during their scientific careers. Many have explored the possibility of web based learning to continue our understanding of ethics in research. The ethicist Michael Pritchard found that post bachelors degree web based learning has the potential to create a collective responsibility for the research being done (Pritchard, 2005). This increased awareness for everyone involved in the research could help prevent the use of fraudulent and falsified data or have others notice before it is published. However, learning about ethical research conduct is often more subtle than a structured program, but it can be found in codes of ethics. A code of ethics is a guide of sorts that is adopted by a particular organization or scientific community that helps us distinguish right from wrong. Scientists are sometimes not even aware that they have created fraudulent data, but frequently referencing and being educated of the code of ethics, help us ensure we are operating within the guidelines that lead to “good science”. Many organizations draft or create their own code of ethics. For example, a group of students at Worcester Polytechnic Institute drafted a code of ethics for robotics, a relatively new field in the grand scheme of science. In this code they included a piece that stated “[To this end and to the best of my ability I will] not knowingly misinform, and if misinformation is spread do my best to correct it” (Ingram et al., 2010). Most codes of ethics or editorial policies will include a piece with this same idea, such as The Association of Clinical Research Professionals who set forth a code use in scholarly work. The code states researchers should “Report research findings accurately and avoid misrepresenting, fabricating or falsifying results” (ACRP, 2020). Enforcing that scientists adhere to their respective code will often mean that they are actively avoiding data fraud and fabrication by not spreading misinformation. Proactive solutions to catch and prevent problems before they

happen are better, but often are necessary to react when the latter methods fail.

While the scientific community generally prevents data fraud in the first place, it is something that happens, therefore it is important to have preventative measures for keeping fraudulent data from being published, this means performing checks like peer reviews and replica trials. Peer reviews are a great method for other members of the scientific community to review the publications to verify the work. Peer review can expose inconsistencies and false findings. This works by other members in your academic field reviewing your work by reading through it to identify pieces that don’t make sense or may be inconsistent with reality. There are many varieties and techniques for peer review but “Many believe [open review] is the best way to prevent malicious comments, stop plagiarism, prevent reviewers from following their own agenda, and encourage open, honest reviewing” (Elsevier, 2021). In the figure to the right, the steps of the peer review process are shown. It is very thorough and has multiple failsafe’s to catch fraudulent or falsified work. In addition to peer review some schools may have the resources for an in house reviewer. An in house review is someone with formal training in reviewing scholarly writing for things like reproducibility, accuracy, and the presence of falsified or fraudulent data. (give every paper a read for reproducibility).

Sometimes, it is found that peer review is not thorough enough to test the validity of a particular result from a study or research. This is the function of replica trials. If someone tells you it’s sunny outside, you will probably look out the window too before you head out, you never know, it could be raining. When researchers produce detailed methods, it allows other researchers to perform the exact same work and compare the results for validity. One challenge with replica trials is ensuring that a scientist’s work is in fact detailed enough to be reproducible. Detailed work means that another scientist may accurately reproduce the exact experiment and compare the resulting data to see that there is agreement between the independent trials. To ensure that scientists are producing replicable data there are several techniques that are seen as good solutions. A 2016 survey of scientists in a variety of fields found 90% of respondents deemed that experimental reproducibility could benefit from more robust experimental design, better statistics, and better mentorship (Baker, 2016). Another method of preventing data fraud through better reproducibility is simply not allowing people to fall into the temptation by keeping strict records of who is performing which experiments, and locking files after data collection to prevent manipulation. If researchers choose to keep data out, there must be a strong justification that the data should not be used. In the end, scrutiny and verification of others’ work by either method is an effective and respectful way to prevent the publication of fraudulent data. 

Preventing data fraud is an ongoing challenge for the scientific community, and methods of prevention are constantly improving as we educate future generations of scientists and review each other’s work with a more critical lens. Data falsification and fabrication is a widespread issue that has the power to put the integrity of science and the trustworthiness of researchers in danger. Although there is no definite solution to stopping data fraud, the problem must be addressed through spreading awareness and recognizing trends. By recognizing why scientists falsify and fabricate data and how it affects both individual scientists and the world of science as a whole, tools can be implemented to recognize and prevent data falsification. Science is the foundation for our understanding of how the world works, so it is our responsibility as scientists to continuously search for truth and uphold the prestige of science.

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Data Fabrication and Falsification and Empiricist Philosophy of Science

David b. resnik.

National Institute for Environmental Health Science, National Institutes of Health, 111 Alexander Drive, Box 12233, Mail Drop CU03, Research Triangle Park, NC, 27709, USA, vog.hin.shein@dkinser Phone: 919 541 5658 Fax: 919 541 9854

Scientists have rules pertaining to data fabrication and falsification that are enforced with significant punishments, such as loss of funding, termination of employment, or imprisonment. These rules pertain to data that describe observable and unobservable entities. In this commentary I argue that scientists would not adopt rules that impose harsh penalties on researchers for data fabrication or falsification unless they believed that an aim of scientific research is to develop true theories and hypotheses about entities that exist, including unobservable ones. This argument presents a challenge for constructive empiricists, such as van Fraassen. Constructive empiricists need to be able to explain why rules pertaining to data fabrication and falsification do not threaten their philosophy of science.

The philosophical debate between realists, such as Boyd (1983) and Chakravartty (2007) , and constructive empiricists, such as van Fraassen (1980 , 1985 , 2001 ), focuses on the attitude one should take toward theories and theoretical entities. Realists hold that the aim of science is provide us with a true description of the world and that to accept a scientific theory (or hypothesis) is to believe that it is true and that the entities described by the theory, including entities we cannot observe, exist. Constructive empiricists hold that the aim of science is to provide us with an empirically adequate description of the world and that to accept a scientific theory (or hypothesis) one need only believe that it is empirically adequate: one need not believe that unobservable entities described by the theory exist. Both realists and constructive empiricists agree that there is a mind-independent external world and that physical objects exist; they are not idealists or relativists. They differ in how they conceive of the extent of our knowledge beyond the observable realm and our metaphysical commitments to unobservable entities ( Chakravartty, 2007 ).

Most scientists pay scant attention to metaphysical issues, such as the realism/empiricism debate ( Fine, 1996 ), but are significantly concerned about ethical ones, such as fraud, plagiarism, authorship attribution, conflict of interest, and protection of human and animal subjects in research (citation omitted for review). Although fraud is thought to be rare in science ( Fanelli, 2009 ), it draws considerable scrutiny from researchers, institutions, and sponsors because it can have far reaching negative consequences. Fraudulent research misleads scientists by sending them down blind alleys, destroys trust among researchers, sabotages scientific collaborations, weakens the public's support for research, and can cause considerable harm to society. For example, faked research data may lead to the approval of unsafe drugs or the construction of dangerous buildings.

Scientists who are caught conducting fraudulent research may face adverse social, financial, and legal consequences. The U.S. government prohibits misconduct in federally-funded research, which is defined as “fabrication, falsification, or plagiarism in proposing, performing, or reviewing research, or in reporting research results ( Office of Science and Technology Policy 2000 , p. 76262).” Scientists who are found to have committed any of these dishonest acts can face an array of penalties, including termination of employment by their institutions and a ban on receiving funds from government agencies. They may also face criminal penalties if they are convicted of defrauding the government (citation omitted for review). Most scientific journals have policies that prohibit fraud and require authors to retract or correct papers associated with an official finding of misconduct (citation omitted for review). Professional associations have also developed ethics codes that strongly endorse honesty in science. Is there any relationship between concerns about fraud in science and metaphysical issues, such as the realism/empiricism debate? In this commentary, I will explore the relationship between rules pertaining to fraud in research and debates about scientific realism. What are the philosophical commitments involved in taking an ethical or legal stance against fraud in science and what implications, if any, does that stance have for the dispute between realists and empiricists?

A distinction between observation and theory is a key pillar in constructive empiricism's philosophy of science, since constructive empiricists argue that we cannot know whether theoretical entities (i.e. things we cannot observe with the unaided senses) exist. Constructive empiricists do not claim that theoretical entities do not exist. Instead, they take an agnostic attitude toward the existence of atoms, electrons, deoxyribonucleic acid (DNA), cells, and so on ( van Fraassen 1980 , 1985 ).

Assuming empiricists can draw a coherent observation/theory distinction, how should they respond to rules concerning data fabrication and falsification? Does claiming that data have been fabricated or falsified commit one to believing in the existence of entities described by the data? Before we can address these questions, it is necessary to explore the concept of data in greater depth. While philosophers have paid considerable attention to the concept of observation, scientists rarely use the word ‘observation’ when reporting results but talk instead about data ( Bogen and Woodward, 1988 ). Data, the backbone of scientific inference, can be defined as recorded information used to support hypotheses, theories, or models in science. Data may be generated when human beings record their observations or when machines produce outputs. For example, if a zoologist observes a rare primate species in the wild, her recorded observations would be data. Data could also be produced by an automated deoxyribonucleic acid (DNA) sequencing machine that analyzes a biological sample.

Data often undergo several stages of processing before they are presented in scientific papers or reports. For example, consider functional magnetic resonance imaging (FMRI) data of brain activity used in neuroimaging research. The images reported in scientific papers provide an anatomical picture of the brain in black, gray, and white. Different colors, such as yellow, red, and blue, in the image indicate levels of metabolic activity in different areas of the brain. To produce these images, powerful magnetic fields are applied to an individual's brain. The magnetic fields cause protons in hemoglobin molecules in the brain to emit radio signals. When the magnetic field weakens, the radio signals from highly oxygenated areas of the brain (with more hemoglobin) deteriorate at a slower rate than radio signals in areas with less oxygen. Computer programs analyze the radio signal data to produce images with different colors corresponding to different levels of oxygenation or brain activity ( Bogen, 2009 ).

Data are often highly theory-dependent. First, scientists often use theories to generate data. For example, theories concerning magnetic fields, radio emissions, atomic and subatomic physics, and cellular metabolism are essential to producing FMRI images. Second, data often report information about things we cannot directly observe. In biomedicine, data may pertain to DNA, ribonucleic acid (RNA), proteins, and other macromolecules; cellular processes, such as cell signaling, cell death, and cell division; inflammatory responses; oxidative stress; and tissue damage.

Thus, the relationship between scientific data and observation is not straightforward ( Bogen and Woodward, 1988 ). A simplistic way of construing this relationship would be to say that data and observations are one and the same. Presenting data to support a theory or hypothesis is the same as reporting observations to support the theory or hypothesis. While realists may have no qualms about this way of viewing the relationship between data and observations, empiricists should take issue with it, because empiricists rely on a distinction between observation and theory and are skeptical about the existence of theoretical entities. Empiricists should not regard DNA sequence data, FMRI data, or other types of theory-dependent data as observations, unless they are willing to expand the scope of what can be observed. At the very least, empiricists must provide an account of the relationship between data and theory, and whether any types of scientific data should be treated as observations.

With this account of the relationship between observation and data in mind, we can now consider the philosophical import of rules pertaining to data fabrication and falsification. Data fabrication and falsifying both are forms of lying about the data reported in a scientific paper (citation omitted for review). Lying involves making a statement intended to mislead others, which could include making a statement that one knows or believes to be false or making a statement that omits some important information (i.e. not telling the whole truth) ( Bok 1979 ). The U.S. government defines data fabrication as “making up data or results and recording or reporting them” and data falsification as “manipulating research materials, equipment, or processes, or changing or omitting data or results such that the research is not accurately represented in the research record ( Office of Science and Technology Policy 2000 , p. 76262).” For example, a scientist who claims in a paper that he tested a chemical on 100 rodents but only used 50 and made up the data for the other 50 would be fabricating data. A scientist who tests a chemical on 100 rodents but omits or changes results from 50 rodents, to provide better support for his hypothesis, would be falsifying data.

Most scientific papers include a materials and methods section that explains how data were produced/acquired and analyzed. It is important to carefully describe how data were generated/acquired and analyzed in a paper or report so that other scientists can validate the work and replicate experiments (citation omitted for review). Data fabrication and falsification involve not only lying about the data, but also lying about how the data were generated/acquired or analyzed.

One of the most infamous cases of data fabrication in recent memory occurred when Seoul National University scientist Woo Suk Hwang and his research team published two papers in Science ( Hwang et al, 2004 , 2005 ), claiming to have produced patient-specific embryonic stem cells through somatic-cell nuclear transfer (SCNT). In SCNT, the nucleus is removed from an unfertilized egg and a nucleus from a somatic cell is inserted into the egg. The donor nucleus reprograms the egg, which can begin cell division. Stem cells can be harvested from the developing embryo to create cell lines for transplantation. If the embryo is implanted in a womb, the resulting offspring would be a clone of the individual that donated the nucleus. SCNT has been performed in animals successfully to produce cloned sheep, dogs, and mice, but it has not been performed in humans. Hwang's research, if substantiated, would have represented an important advance in the field of regenerative medicine, since cell lines produced by this process would be immunologically compatible with the patient's body, which would reduce the risk of tissue rejection (citation omitted for review).

Though Hwang was hailed as a national hero in South Korea following the publication of the two papers, suspicions concerning the legitimacy of his work emerged in the fall of 2005, when an anonymous informant told the South Korean investigative news program PD Notebook that his research was fraudulent. PD Notebook began investigating the case, and Seoul National University launched its own investigation. A university committee concluded in December 2005 that Hwang's data were faked. Hwang's papers included microscopic images of human cell lines that he said were produced by SCNT. Hwang also provided data concerning other characteristics of the cell lines, including genetic and immunological analyses. The committee asked three laboratories to perform tests to determine whether the cells reported in the paper matched the cells from the donors. The laboratories compared DNA from cell lines reported in the papers to DNA from the donors and found that they did not match. Hwang was dismissed from Seoul University, barred from receiving research funding from South Korea, and convicted of embezzlement and bioethics violations. He was sentenced to two years in prison, though his sentence was suspended. Science retracted both papers. The incident caused considerable embarrassment for South Korean researchers and had a negative impact on the public's perception of stem cell research ( Cyranoski, 2006 , citation omitted for review).

Hwang's papers reported fabricated data pertaining to things that are not observable with the unaided senses, including cells lines, blastocysts, embryos, DNA, and histocompatibility complexes (proteins on the surface of cells) ( Hwang, 2004 , 2005 ). He received a harsh punishment for his fabrications. Hwang is not the only person to have received a prison sentence for scientific fraud, however. In 2006, University of Vermont researcher Eric Poehlman was sentenced to serve a year and a day in federal prison for defrauding the federal government as a result of fabricating or falsifying data on fifteen federal grant applications worth $2.9 million and seventeen publications over a ten-year period (citation omitted for review). Although most researchers do not go to jail for fabricating or falsifying data, they may face other career-threatening consequences, such as loss of employment or a ban on receipt of federal funding.

The harsh sanctions imposed on researchers who are found to have fabricated or falsified data pertaining to unobservable things present a potential problem for empiricism. Hwang lost his job and was sentenced to prison for faking data pertaining to stem cells. This seems like an onerous penalty to impose on someone if one does not believe that one of the goals of stem cell research is to determine the truth about stem cells. The fact that scientist impose tough sanctions on colleagues who lie about data that describe unobservable things supports the view that scientists have realist aims. They are interested not only in obtaining truth about the observable realm (i.e. empirical adequacy) but also in obtaining truth about things we cannot directly observe, such as stem cells.

Suppose that Hwang were not a scientist working for the government but were instead a science fiction writer who had published a novel on stem cell research. If this were the situation, he would not be charged with data fabrication, even if his novel included pages of fictional data. People who write works of fiction do not face charges of data fabrication or falsification because the audience does not expect a work of fiction to report the truth. When people read a work of fiction, they understand that statements contained in the work are not intended to be true and that the people, places, and things described in the work may not exist. This is not the case when scientists, funders, and others read scientific papers and reports. Fiction does not deal with reality, but science does.

This problem for empiricism can be stated more formally as an argument:

• Scientists have rules pertaining to data fabrication and falsification and these rules are enforced with significant punishments, such as loss of funding, termination of employment, etc.
• Scientific data frequently describe entities that are not observable with the unaided senses.
• If scientists have rules pertaining to data fabrication and falsification that are enforced with significant punishments and the data frequently describe unobservable entities, then they believe that an aim of scientific research is to develop true theories and hypotheses about entities that exist, including unobservable ones.
• Therefore, scientists believe that an aim of scientific research is to develop true theories and hypotheses about entities that exist, including unobservable ones (from 1, 2 and 3).
• We should not adopt a philosophy of science that is contrary to practice of science (i.e. what scientists believe and do) without good reason.
• Constructive empiricism holds that the aim of science is not to develop true theories and hypotheses concerning unobservable entities but to develop theories and hypotheses that are empirically adequate (van Fraassen's definition of empiricism).
• Constructive empiricism is contrary to the practice of science (from 4 and 6).
• Therefore, we should not accept constructive empiricism (from 5 and 7).

Constructive empiricists have several ways of responding to this argument. First, they can deny the third premise by claiming that the rules concerning data fabrication and falsification pertain to what is observable; penalties are imposed on scientists for making claims about things that can be observed. There is no need to assume that an aim of science is to develop true theories or hypotheses concerning unobservable thing since data fabrication and falsification have an adverse impact on science's claims about what we can observe. Thus, scientists can condemn data fabrication and falsification without assuming that science has realist aims.

This is a perfectly reasonable response to science's rules pertaining to data fabrication and falsification, if one assumes that data only describe observable entities. An empiricist who is willing to expand the range of what counts as observable can legitimately claim that penalizing scientists for data fabrication or falsification involves no commitment to realist aims. However, this does not seem to be the position that van Fraassen and other empiricists. Van Fraassen's view implies that stem cells are not observable. If one adopts this view, it is difficult to justify the harsh penalties imposed on Hwang for fabricating stem cell data.

Second, constructive empiricists could deny the third premise by arguing that data fabrication and falsification is prohibited in science because it is deceptive, and deception is unethical. A scientist who fabricates or falsifies data is not being honest about how the data were generated or analyzed, because the scientist has not followed the methods and procedures described in the paper or report. One does not need to assume that science has realist aims to regard this type of deception as unethical. A scientist who fabricates or falsifies data is not playing by the rules of science.

While it is correct that data fabrication and falsification involve a deviation from the methods and procedures described in a paper or report, there is more to fabrication and falsification than this. The reason why deviation from methods and procedures described in a paper or report is harshly condemned is that it involves lying about the data, which leads to unreliable or unrepeatable results. Hwang's fabricated data could lead scientists to adopt false hypotheses, theories, and beliefs about stem cells. If a scientist made a deviation from the methods and procedures described in the paper that had no impact on the data or results, then this deviation would not be treated as fabrication or falsification. For example, suppose a sociologist said that he used a particular statistical program, such as Statistical Analysis System (SAS), to analyze the data reported in a paper, when he really used a different, but similar program, such as Statistical Packages for Social Scientists (SPSS), but that this had no impact on the data analysis. If this problem were discovered, he might be asked to submit a correction to the journal, but he would not be charged with fraud.

Third, constructive empiricists can deny the fifth premise by claiming that it is acceptable to adopt a philosophy of science that is contrary to the practice of science ( Wylie, 1986 ). Philosophy of science is a normative discipline that seeks to evaluate and criticize science. By reflecting on the nature of scientific reasoning, language, and knowledge, philosophers can make suggestions for reforming scientific practice. If the rules adopted by scientists penalize researchers for making false claims about things we cannot observe, then perhaps the rules need to be changed, so that they conform to empiricist principles. For example, rules pertaining to data fabrication and falsification would apply only to data that describes things that can be observed. Misreporting or manipulating data pertaining to unobservable things would be contrary to good scientific practice, but it would not be regarded as data fabrication or falsification. Empiricists have long acknowledged that many researchers subscribe to scientific realism, but they seldom take this fact to constitute a sound argument for realism, since philosophical arguments should appeal to considerations that are independent of scientific practice ( Fine, 1996 ).

This response raises a larger issue concerning the philosophy of science: should philosophy of science be normative or descriptive? Prior to Thomas Kuhn's (1962) landmark book The Structure of Scientific Revolutions , philosophy of science was regarded as a normative discipline that focused on the logic of science and the justification of scientific hypotheses, theories, and methods ( Kitcher 1995 ). Kuhn and his followers argued that the history, sociology, psychology, and economics of science have an important bearing on the philosophy of science, and that philosophers can ill afford to ignore scientific practice ( Lakatos, 1980 , Hull, 1990 , Kitcher, 1995 ). Scientific practice teaches us important lessons about the norms, goals, and traditions of research, and how scientists accept, reject, or revise theories and hypotheses ( Kuhn 1962 ).

Kuhn's ideas have transformed the philosophy of science, and most philosophers today recognize the relevance of the practice of science to the philosophy of science (Giere, 1985, Laudan, 1984 ). Acknowledging that the philosophy of science should be accountable to the practice of science does not imply that philosophy of science must faithfully follow every aspect of scientific practice, as this would undermine critical reflection on the practice of science. Philosophy of science still has an important role to play in criticizing or evaluating scientific practice even if it should be attentive to the history, sociology, psychology, and economics of science ( Kitcher, 1995 ).

Since philosophy of science can and should maintain a critical distance from scientific practice, constructive empiricist challenges to the fifth premise in the argument have considerable merit. However, constructive empiricists still must provide a good reason why we should accept a philosophy of science that seems to be inconsistent with the practice of charging researchers with fabricating or falsifying data concerning unobservable things. EConstructive empiricists might argue that their view can be justified on independent grounds. For example, according to the pessimistic induction argument advanced by Laudan (1977 , 1981) , we should be skeptical about the existence of unobservable entities, since the history of science contains many examples of entities postulated by theorists, such as phlogiston, the ether, a vital force, and epicycles, which scientists no longer believe exist. According to the underdetermination argument advanced by van Fraassen (1980) , the proof for any scientific theory is always tentative, because indefinitely many theories may fit the data (i.e. they are empirically equivalent). Since different empirically equivalent theories may postulate the existence of different theoretical entities, we should be skeptical about the ontological claims made any theory.

It may indeed be the case that constructive empiricism can be established on independent grounds that have nothing to do with scientists' attitudes toward data fabrication and falsification. Assessing empiricism as a philosophy of science is an issue beyond the scope this paper (see Chakravartty, 2007 ). All I have attempted to show in this commentary is that the rules pertaining to data fabrication and falsification in science present a problem for constructive empiricism because they seem to commit scientists to a realist understanding of their work. Constructive empiricists need show why this aspect of the scientific practice does not constitute a credible objection to their view.

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Peer-reviewed

Research Article

How Many Scientists Fabricate and Falsify Research? A Systematic Review and Meta-Analysis of Survey Data

* E-mail: [email protected]

Affiliation INNOGEN and ISSTI-Institute for the Study of Science, Technology & Innovation, The University of Edinburgh, Edinburgh, United Kingdom

• Daniele Fanelli

• Published: May 29, 2009
• https://doi.org/10.1371/journal.pone.0005738
• Reader Comments

The frequency with which scientists fabricate and falsify data, or commit other forms of scientific misconduct is a matter of controversy. Many surveys have asked scientists directly whether they have committed or know of a colleague who committed research misconduct, but their results appeared difficult to compare and synthesize. This is the first meta-analysis of these surveys.

To standardize outcomes, the number of respondents who recalled at least one incident of misconduct was calculated for each question, and the analysis was limited to behaviours that distort scientific knowledge: fabrication, falsification, “cooking” of data, etc… Survey questions on plagiarism and other forms of professional misconduct were excluded. The final sample consisted of 21 surveys that were included in the systematic review, and 18 in the meta-analysis.

A pooled weighted average of 1.97% (N = 7, 95%CI: 0.86–4.45) of scientists admitted to have fabricated, falsified or modified data or results at least once –a serious form of misconduct by any standard– and up to 33.7% admitted other questionable research practices. In surveys asking about the behaviour of colleagues, admission rates were 14.12% (N = 12, 95% CI: 9.91–19.72) for falsification, and up to 72% for other questionable research practices. Meta-regression showed that self reports surveys, surveys using the words “falsification” or “fabrication”, and mailed surveys yielded lower percentages of misconduct. When these factors were controlled for, misconduct was reported more frequently by medical/pharmacological researchers than others.

Considering that these surveys ask sensitive questions and have other limitations, it appears likely that this is a conservative estimate of the true prevalence of scientific misconduct.

Citation: Fanelli D (2009) How Many Scientists Fabricate and Falsify Research? A Systematic Review and Meta-Analysis of Survey Data. PLoS ONE 4(5): e5738. https://doi.org/10.1371/journal.pone.0005738

Editor: Tom Tregenza, University of Exeter, United Kingdom

Received: January 6, 2009; Accepted: April 19, 2009; Published: May 29, 2009

Copyright: © 2009 Fanelli. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The author is supported by a Marie Curie Intra European Fellowship (Grant Agreement Number PIEF-GA-2008-221441). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The author has declared that no competing interests exist.

Introduction

The image of scientists as objective seekers of truth is periodically jeopardized by the discovery of a major scientific fraud. Recent scandals like Hwang Woo-Suk's fake stem-cell lines [1] or Jan Hendrik Schön's duplicated graphs [2] showed how easy it can be for a scientist to publish fabricated data in the most prestigious journals, and how this can cause a waste of financial and human resources and might pose a risk to human health. How frequent are scientific frauds? The question is obviously crucial, yet the answer is a matter of great debate [3] , [4] .

A popular view propagated by the media [5] and by many scientists (e.g. [6] ) sees fraudsters as just a “few bad apples” [7] . This pristine image of science is based on the theory that the scientific community is guided by norms including disinterestedness and organized scepticism, which are incompatible with misconduct [8] , [9] . Increasing evidence, however, suggests that known frauds are just the “tip of the iceberg”, and that many cases are never discovered. The debate, therefore, has moved on to defining the forms, causes and frequency of scientific misconduct [4] .

What constitutes scientific misconduct? Different definitions are adopted by different institutions, but they all agree that fabrication (invention of data or cases), falsification (wilful distortion of data or results) and plagiarism (copying of ideas, data, or words without attribution) are serious forms of scientific misconduct [7] , [10] . Plagiarism is qualitatively different from the other two because it does not distort scientific knowledge, although it has important consequences for the careers of the people involved, and thus for the whole scientific enterprise [11] .

There can be little doubt about the fraudulent nature of fabrication, but falsification is a more problematic category. Scientific results can be distorted in several ways, which can often be very subtle and/or elude researchers' conscious control. Data, for example, can be “cooked” (a process which mathematician Charles Babbage in 1830 defined as “an art of various forms, the object of which is to give to ordinary observations the appearance and character of those of the highest degree of accuracy” [12] ); it can be “mined” to find a statistically significant relationship that is then presented as the original target of the study; it can be selectively published only when it supports one's expectations; it can conceal conflicts of interest, etc… [10] , [11] , [13] , [14] , [15] . Depending on factors specific to each case, these misbehaviours lie somewhere on a continuum between scientific fraud, bias, and simple carelessness, so their direct inclusion in the “falsification” category is debatable, although their negative impact on research can be dramatic [11] , [14] , [16] . Henceforth, these misbehaviours will be indicated as “questionable research practices” (QRP, but for a technical definition of the term see [11] ).

Ultimately, it is impossible to draw clear boundaries for scientific misconduct, just as it is impossible to give a universal definition of professional malpractice [10] . However, the intention to deceive is a key element. Unwilling errors or honest differences in designing or interpreting a research are currently not considered scientific misconduct [10] .

To measure the frequency of misconduct, different approaches have been employed, and they have produced a corresponding variety of estimates. Based on the number of government confirmed cases in the US, fraud is documented in about 1 every 100.000 scientists [11] , or 1 every 10.000 according to a different counting [3] . Paper retractions from the PubMed library due to misconduct, on the other hand, have a frequency of 0.02%, which led to speculation that between 0.02 and 0.2% of papers in the literature are fraudulent [17] . Eight out of 800 papers submitted to The Journal of Cell Biology had digital images that had been improperly manipulated, suggesting a 1% frequency [11] . Finally, routine data audits conducted by the US Food and Drug Administration between 1977 and 1990 found deficiencies and flaws in 10–20% of studies, and led to 2% of clinical investigators being judged guilty of serious scientific misconduct [18] .

All the above estimates are calculated on the number of frauds that have been discovered and have reached the public domain. This significantly underestimates the real frequency of misconduct, because data fabrication and falsification are rarely reported by whistleblowers (see Results), and are very hard to detect in the data [10] . Even when detected, misconduct is hard to prove, because the accused scientists could claim to have committed an innocent mistake. Distinguishing intentional bias from error is obviously difficult, particularly when the falsification has been subtle, or the original data destroyed. In many cases, therefore, only researchers know if they or their colleagues have wilfully distorted their data.

Over the years, a number of surveys have asked scientists directly about their behaviour. However, these studies have used different methods and asked different questions, so their results have been deemed inconclusive and/or difficult to compare (e.g. [19] , [20] ). A non-systematic review based on survey and non-survey data led to estimate that the frequency of “serious misconduct”, including plagiarism, is near 1% [11] .

This study provides the first systematic review and meta-analysis of survey data on scientific misconduct. Direct comparison between studies was made possible by calculating, for each survey question, the percentage of respondents that admitted or observed misconduct at least once, and by limiting the analysis to qualitatively similar forms of misconduct -specifically on fabrication, falsification and any behaviour that can distort scientific data. Meta-analysis yielded mean pooled estimates that are higher than most previous estimates. Meta-regression analysis identified key methodological variables that might affect the accuracy of results, and suggests that misconduct is reported more frequently in medical research.

Electronic resources were searched during the first two weeks of August 2008. Publication and journal databases were searched in English, while the Internet and resources for unpublished and “grey” literature were searched using English, Italian, French and Spanish words.

Citation databases.

The Boolean string “research misconduct” OR “research integrity” OR “research malpractice” OR “scientific fraud” OR “fabrication, falsification” OR “falsification, fabrication” was used to search: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (A&HCI), Conference Proceedings Citation Index- Science (CPCI-S), BIOSIS Previews, MEDLINE, Business Source Premier, CINAHL Plus, SPORTDiscus, Library, Information Science & Technology Abstracts, International Bibliography of the Social Sciences, America: History & Life, Teacher Reference Center, Applied Social Sciences Index And Abstracts (ASSIA), ERIC, Index Islamicus, CSA linguistics and language behaviour, Physical Education Index, PILOTS, Social Services Abstracts, Sociological Abstracts, Proquest Dissertation & Theses, ECONLIT, Educational Research Abstracts (ERA) Online, Article First, Economic and Social Data Service, Francis, Geobase, Georefs, Global Health (CABI), Index to Theses, International Bibliography of the Social Sciences (IBSS), IEEE Xplore, INSPEC, JSTOR, Mathematical Sciences Net (MathSciNet), PubMEd, Russian Academy of Sciences bibliographies, Sciencedirect, Teacher Reference Center, EMBASE, EMBASE Classics, PSYCHINFO.

Scientific journals.

The Boolean string “research misconduct” OR “research integrity” OR “research malpractice” OR “scientific fraud” OR “fabrication, falsification” OR “falsification, fabrication” was used to search: Interdisciplinary Science Reviews, American Journal of Sociology, Annual Review of Sociology, PNAS, Issues in Science & Technology, Journal of Medical Ethics, PLoSONE, Science and Engineering Ethics, Sociology of Health & Illness, Minerva, The Scientific World Journal, Social Science Research, Social Studies of Science, Science in Context.

Grey literature databases.

The Boolean string “research misconduct” OR “research integrity” OR “research malpractice” OR “scientific fraud” OR “fabrication, falsification” OR “falsification, fabrication” was used to search: SIGLE, National Technical Information Service, British Library Collections, British Library Direct, Canadian Evaluation Society, Bioethics Literature Database.

The Italian string “etica AND ricerca” was used in: CNR database.

The French string “scientifique AND “ethique” OR “fraude” OR “faute” OR “enquete” OR “sondage” was used in: LARA -Libre acces aux rapports scientifiques et techiques

Internet search engines.

The Boolean string “research misconduct” OR “research integrity” OR “research malpractice” OR “scientific fraud” OR “fabrication, falsification” OR “falsification, fabrication”, the Spanish Boolean string “ética cientifica” OR “faltas éticas” the French Boolean string “faute scientifique” OR “éthique scientifique” were used to search: ScienceResearch.com, Scirus.

Titles and available abstracts of all records were examined, and the full text of all potentially relevant studies was retrieved. The references list of the retrieved studies and of other documents was also examined in search of potentially relevant papers.

Only quantitative survey data assessing how many researchers have committed or observed colleagues committing scientific misconduct in the past were included in this review. Surveys asking only opinions or perceptions about the frequency of misconduct were not included.

To allow direct quantitative comparison across data sets, studies were included only if they presented data in frequency or percentage categories, one of which was a “never” or “none” or “nobody” category - indicating that the respondent had never committed or observed the behaviour in question. Studies lacking such a category, or presenting results in statistical formats that prevented the retrieval of this information (e.g. mean and standard deviation) were excluded. Respondents of any professional position and scientific discipline were included, as long as they were actively conducting publishable research, or directly involved in it (e.g. research administrators). Surveys addressing misconduct in undergraduate students were excluded, because it was unclear if the misconduct affected publishable scientific data or only scholastic results.

This review focused on all and only behaviours that can falsify or bias scientific knowledge through the unjustified alteration of data, results or their interpretation (e.g. any form of fabrication and falsification, intentional non-publication of results, biased methodology, misleading reporting, etc…). Plagiarism and professional misconduct (e.g. withholding information from colleagues, guest authorship, exploitation of subordinates etc…) were excluded from this review. Surveys that made no clear distinction between the former and latter types of misconduct (e.g. that asked about fabrication, falsification and plagiarism in the same question) were excluded.

Any available data on scientists' reaction to alleged cases of misconduct was extracted from included studies. Since these data provided only additional information that was not the focus of the review, survey questions that did not distinguish between data manipulation and plagiarism were included in this section of the results, but clearly identified.

Validity assessment

Surveys that did not sample respondents at random, or that did not provide sufficient information on the sampling methods employed where given a quality score of zero and excluded from the meta-analysis. All remaining papers were included, and were not graded on a quality scale, because the validity and use of quality measures in meta-analysis is controversial [21] , [22] . Instead of using an arbitrary measure of quality, the actual effect of methodological characteristics on results was tested and then controlled for with regression analysis. In the tables listing study characteristics, the actual words reported in the paper by the authors are quoted directly whenever possible. The few cases where a direct quotation could not be retrieved are clearly indicated.

Data abstraction

For each question, the percentage of respondents who recalled committing or who observed (i.e. had direct knowledge of) a colleague who committed one or more times the specified behaviour was calculated. In the majority of cases, this required summing up the responses in all categories except the “none” or “never” category, and the “don't know” category.

Some studies subdivided the sample of respondents according to a variety of demographic characteristics (e.g. gender, career level, professional position, academic discipline, etc…) and disaggregated the response data accordingly. In all these cases, the data was re-aggregated.

Given the objectivity of the information collected and the fact that all details affecting the quality of studies are reported in this paper, it was not necessary to have the data extracted/verified by more than one person.

Quantitative data synthesis

Mean pooled effect size was calculated assuming a random effects model, and homogeneity was tested with Chochran's Q test. Differences between groups of studies were tested using inverse variance weighted one-way ANOVA. The combined effect of independent variables on effect sizes was tested with inverse variance weighted regression assuming a random effects model and estimated via iterative maximum likelihood.

To avoid the biasing effect of multiple outcomes within the same study, all meta-analyses on the main outcome of interest (i.e. the prevalence of data fabrication, falsification and alteration) were conducted using only one outcome per study. For the same reason, in the regression analysis, which combined all available effect sizes on data fabrication, falsification and alteration, studies that had data both on self- and on non self- where used only for the former.

The regression model first tested the combined effect of three methodological factors measured by binary variables (self- vs non-self- reports, handed vs mailed questionnaire, questions using the word “falsification” or “fabrication” vs questions using “alteration”, “modification” etc…). Then, the effect of several study characteristics was tested (year when the survey was conducted, surveys conducted in the USA vs anywhere else, surveys conducted exclusively on researchers vs any other, biomedical vs other types of research, social sciences vs natural sciences, medical consultants and practitioners vs other). To avoid over-fitting, each study characteristic was tested independently of the others.

Questions on behaviours of secondary interest (questionable research practices) where too diverse to allow meaningful meta-analysis, so they were combined in broad categories for which only crude unweighted parameters were calculated. All statistical analyses were run on SPSS software package. Meta-analyses were conducted using the “MeanES”, “MetaF” and “MetaReg” macros by David B. Wilson [24] .

Publication bias-Sensitivity analysis

The popular funnel-plot-based methods to test for publication bias in meta-analysis are inappropriate and potentially misleading when the number of included studies is small and heterogeneity is large [25] , [26] . However, the robustness of results was assessed with a sensitivity analysis. Pooled weighted estimates for effect size and regression parameters were calculated leaving out one study at a time, and then compared to identify influential studies. In addition, to further assess the robustness of conclusions, meta-analyses and meta-regression were run without logit transformation.

Flow of included studies

Electronic search produced an initial list of 3276 references. Examination of titles and abstracts, and further examination of the references lists in the retrieved papers and in other sources led to a preliminary list of 69 potentially relevant studies. Of these, 61 were published in peer-reviewed journals, three were dissertations theses, three were published in non-peer reviewed popular science magazines, one was published in a book chapter, and one was published in a report. All studies were published in English except for one in Spanish.

After examination of full text, 33 studies were excluded because they did not have any relevant or original data, two because they presented data exclusively in a format that could not be used in this review (e.g. means and standard deviations), eight because their sample included non-researchers (e.g. students) and/or because they addressed forms of academic misconduct not directly related to research (e.g. cheating on school projects), five because they do not distinguish fabrication and falsification from types of misconduct not relevant to the scopes of this review ( Table S1 ). Therefore, 21 studies were included in the review. Three of these did not match the quality requirements to be included in the meta-analysis. Data from these three studies was only used to estimate crude unweighted means for QRP and more generic questions, and not for analyzing the main outcome of interest (data fabrication, falsification, modification). Therefore, the meta-analysis was conducted on 18 studies ( Figure 1 ).

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https://doi.org/10.1371/journal.pone.0005738.g001

Study characteristics

Table 1 lists the characteristics of included studies and their quality score for inclusion in meta-analysis. Included surveys were published between 1987 and 2008, but had been conducted between 1986 ca and 2005. Respondents were based in the United States in 15 studies (71% ca of total), in the United Kingdom in 3 studies (14% ca), two studies had a multi-national sample (10% ca) and one study was based in Australia. Six studies had been conducted among biomedical researchers, eight were more specifically targeted at researchers holding various positions in the medical/clinical sciences (including pharmacology, nursing, health education, clinical biostatistics, and addiction-studies), six surveys had multi-disciplinary samples, one surveyed economists.

https://doi.org/10.1371/journal.pone.0005738.t001

Quantitative data analysis

Scientists admitting misconduct..

When explicitly asked if they ever fabricated or falsified research data, or if they altered or modified results to improve the outcome (see Table S2 , questions 1, 4, 6, 8, 10, 17, 26), between 0.3% and 4.9% of scientists replied affirmatively (N = 7, crude unweighted mean: 2.59%, 95%CI = 1.06–4.13). Meta-analysis yielded a pooled weighted estimate of 1.97% (95%CI: 0.86–4.45), with significant heterogeneity (Cochran's Q = 61.7777, df = 6, P<0.0001) ( Figure 2 ). If only questions explicitly using the words “fabrication” or “falsification” were included ( Table S2 , questions 3, 6, 10, 26), the pooled weighted estimate was 1.06% (N = 4, 95%CI: 0.31–3.51)

Area of squares represents sample size, horizontal lines are 95% confidence interval, diamond and vertical dotted line show the pooled weighted estimate.

https://doi.org/10.1371/journal.pone.0005738.g002

Other questionable practices were admitted by up to 33.7% of respondents ( Table S2 ) ( Figure 3 , N = 20 (six studies), crude unweighted mean: 9.54%, 95%CI = 5.15–13.94).

N indicates the number of survey questions. Boxplots show median and interquartiles.

https://doi.org/10.1371/journal.pone.0005738.g003

Consistently across studies, scientists admitted more frequently to have “modified research results” to improve the outcome than to have reported results they “knew to be untrue” (Inverse Variance Weighted Oneway ANOVA Q(1,4) = 14.8627, P = 0.011)

In discussing limitations of results, two studies [19] , [27] suggested that their results were very conservative with respect to the actual occurrence of misconduct, while the other studies made no clear statement. Non-response bias was recognized as a limitation by most surveys. One study employed a Random-Response technique on part of its sample to control for non-response bias, and found no evidence for it [28] (see Discussion for further details).

Scientists observing misconduct.

When asked if they had personal knowledge of a colleague who fabricated or falsified research data, or who altered or modified research data ( Table S3 , questions, 1, 6, 7, 10, 20, 21, 29, 32, 34, 37, 45, 54) between 5.2% and 33.3% of respondents replied affirmatively (N = 12, crude unweighted mean: 16.66%, 95%CI = 9.91–23.40). Meta-analysis yielded a pooled weighted estimate of 14.12% (95% CI: 9.91–19.72) ( Figure 4 ). If only questions explicitly using the words “fabrication” or “falsification” were included ( Table S3 , questions 1, 6, 7, 10, 17, 21, 29, 32, 37, 45, 54), the pooled weighted estimate was 12.34% (N = 11, 95%CI: 8.43–17.71)

https://doi.org/10.1371/journal.pone.0005738.g004

Between 6.2% and 72% of respondents had knowledge of various questionable research practices ( Table S3 ) ( Figure 3 , N = 23 (6 studies), crude unweighted mean: 28.53%, 95%CI = 18.85–38.2). When surveys asked about more generic questions (e.g. “do you have knowledge of any cases of fraud?” [29] , [30] ) or defined misconduct in more comprehensive ways (e.g. “experimental deficiencies, reporting deficiencies, misrepresentation of data, falsification of data” [30] ) between 12% and 92% replied affirmatively ( Table S3 ) (N = 10 (seven studies), crude unweighted mean: 46.24, 95%CI = 16.53–75.95).

In discussing their results, three studies [27] , [29] , [31] considered them to be conservative, four [30] , [32] , [33] , [34] suggested that they overestimated the actual occurrence of misconduct, and the remaining 13 made no clear statement.

Scientists reporting misconduct.

Five of the included studies asked respondents what they had done to correct or prevent the act of misconduct they had witnessed. Around half of the alleged cases of misconduct had any action taken against them ( Table 2 ). No study asked if these actions had the expected outcome. One survey [27] found that 29% of the cases of misconduct known by respondents were never discovered.

https://doi.org/10.1371/journal.pone.0005738.t002

Factors influencing responses.

Methodological differences between studies explained a large portion of the variance among effect sizes (N = 15, one outcome per study, Table 3 ). Lower percentages of misconduct were reported in self reports, in surveys using the words “falsification” or “fabrication”, and in mailed surveys. Mailed surveys had also higher response rates than handed-out surveys (Mean: 26.63%±2.67SE and 48.53%±4.02SE respectively, t-test: t = −2.812, df = 16, P = 0.013), while no difference in response rates was observed between self- and non-self-reports (Mean: 42.44±6.24SE and 44.44±5.1SE respectively, t = −0.246, P = 0.809) and between surveys using or not “fabrication or falsification” (Mean: 42.98%±6.0SE and 44.51±4.76SE respectively, t = −0.19, P = 0.85). Excluding all surveys that were not mailed, were not self-reports and that did not use the words “falsification” or “fabrication” yielded a maximally conservative pooled weighted estimate of 0.64% (N = 3, 95%CI: 0.25–1.63).

https://doi.org/10.1371/journal.pone.0005738.t003

When the three methodological factors above where controlled for, a significant effect was found for surveys targeted at medical and clinical researchers, who reported higher percentages of misconduct than respondents in biomedical research and other fields ( Table 3 ). The effect of this parameter would remain significant if Bonferroni-corrected for multiple comparisons. If self- and non-self- reports were tested separately for the effect of study characteristics (one characteristic at a time), a significant effect was found only in self-reports for year when survey was conducted (k = 7, b = −0.1425±0.0519, P = 0.006) and a nearly significant effect was found again in self-reports for survey delivery method (k = 7, b = −1.2496±0.6382, P = 0.0502)

Sensitivity analysis

Self-report admission rates varied between 1.65% -following the removal of Kalichman and Friedman (1992) [35] - and 2.93% -following the removal of Martinson et al. (2005) [19] ( Figure 5 ). Reports on colleagues' misconduct varied between 12.85% (when Tangney (1987) [32] was removed) and 15.41% (when Titus et al. (2008) [31] was removed ( Figure 6 ). Weighted pooled estimates on non-logit-trasformed data yielded self- and non-self- admission rates of 2.33% (95%CI 0.94–3.73%) and 14.48% (95%CI: 11.14–17.81%) respectively, showing that the results are robust and conservative.

Plots show the weighted pooled estimate and 95% confidence interval obtained when the corresponding study was left out of the analysis.

https://doi.org/10.1371/journal.pone.0005738.g005

Plots show the weighted pooled estimate obtained when the corresponding study was left out of the analysis.

https://doi.org/10.1371/journal.pone.0005738.g006

Results of the regression analysis were robust to the leave-one-study-out test: the four significant variables remained statistically significant when anyone of the studies was excluded ( Table S4 ). The largest portion of variance was explained when Titus et al. (2008) [31] was removed (R 2  = 0.9202). Meta-regression on non-transformed data showed similar trends to that on transformed data for all four parameters, but only two parameters remained statistically significant (self-/non-self- and delivery method, P<0.0001 and p = 0.0083 respectively), and the overall portion of variance explained by the model was lower (R 2  = 0.6904).

This is the first meta-analysis of surveys asking scientists about their experiences of misconduct. It found that, on average, about 2% of scientists admitted to have fabricated, falsified or modified data or results at least once –a serious form of misconduct my any standard [10] , [36] , [37] – and up to one third admitted a variety of other questionable research practices including “dropping data points based on a gut feeling”, and “changing the design, methodology or results of a study in response to pressures from a funding source”. In surveys asking about the behaviour of colleagues, fabrication, falsification and modification had been observed, on average, by over 14% of respondents, and other questionable practices by up to 72%. Over the years, the rate of admissions declined significantly in self-reports, but not in non-self-reports.

A large portion of the between-studies variance in effect size was explained by three basic methodological factors: whether the survey asked about self or not, whether it was mailed or handed out to respondents, and whether it explicitly used the words “fabrication” and “falsification”. Once these factors were controlled for, surveys conducted among clinical, medical and pharmacological researchers appeared to yield higher rates of misconduct than surveys in other fields or in mixed samples.

All the above results were robust with respect to inclusion or exclusion of any particular study, with perhaps one exception: Martinson et al. (2005) [19] , which is one of the largest and most frequently cited surveys on misconduct published to date. This study appears to be rather conservative, because without it the pooled average frequency with which scientists admit they have committed misconduct would jump to nearly 3%.

How reliable are these numbers? And what can they tell us on the actual frequency of research misconduct? Below it will be argued that, while surveys asking about colleagues are hard to interpret conclusively, self-reports systematically underestimate the real frequency of scientific misconduct. Therefore, it can be safely concluded that data fabrication and falsification –let alone other questionable research practices- are more prevalent than most previous estimates have suggested.

The procedure adopted to standardize data in the review clearly has limitations that affect the interpretation of results. In particular, the percentage of respondents that recall at least one incident of misconduct is a very rough measure of the frequency of misconduct, because some of the respondents might have committed several frauds, but others might have “sinned” only once. In this latter case, the frequencies reported in surveys would tend to overestimate the prevalence of biased or falsified data in the literature. The history of science, however, shows that those responsible of misconduct have usually committed it more than once [38] , [39] , so the latter case might not be as likely as the former. In any case, many of the included studies asked to recall at least one incident, so this limitation is intrinsic to large part of the raw data.

The distinction made in this review between “fabrication, falsification and alteration” of results and QRP is somewhat arbitrary. Not all alterations of data are acts of falsification, while “dropping data points based on a gut feeling” or “failing to publish data that contradicts one's previous research” (e.g. [19] ) might often be. As explained in the introduction, any boundary defining misconduct will be arbitrary, but intention to deceive is the key aspect. Scientists who answered “yes” to questions asking if they ever fabricated or falsified data are clearly admitting their intention to misrepresent results. Questions about altering and modifying data “to improve the outcome” might be more ambiguously interpreted, which might explain why these questions yield higher admission rates. However, even if we limited the meta-analysis to the most restrictive types of questions in self-reports, we would still have an average admission rate above 1%, which is higher than previous estimates (e.g. [11] ).

The accuracy of self-reports on scientific misconduct might be biased by the effect of social expectations. In self-reports on criminal behaviour, social expectations make many respondents less likely to admit a crime they committed (typically, females and older people) and make others likely to report a crime they have not really committed (typically, young males) [40] . In the case of scientists, however, social expectations should always lead to underreporting, because a reputation of honesty and objectivity is fundamental in any stage of a scientific career. Anyone who has ever falsified research is probably unwilling to reveal it and/or to respond to the survey despite all guarantees of anonymity [41] . The opposite (scientists admitting misconduct they didn't do) appears very unlikely. Indeed, there seems to be a large discrepancy between what researchers are willing to do and what they admit in a survey. In a sample of postdoctoral fellows at the University of California San Francisco, USA, only 3.4% said they had modified data in the past, but 17% said they were “willing to select or omit data to improve their results” [42] . Among research trainees in biomedical sciences at the University of California San Diego, 4.9% said they had modified research results in the past, but 81% were “willing to select, omit or fabricate data to win a grant or publish a paper” [35] .

Mailed surveys yielded lower frequencies of misconduct than handed out surveys. Which of the two is more accurate? Mailed surveys were often combined with follow-up letters and other means of encouraging responses, which ensured higher response rates. However, the accuracy of responses to sensitive questions is often independent of response rates, and depends strongly on respondents' perception of anonymity and confidentiality [43] , [44] . Questionnaires that are handed to, and returned directly by respondents might better entrust anonymity than surveys that need to be mailed or emailed. Therefore, we cannot rule out the possibility that handed out surveys are more accurate despite the lower response rates. This latter interpretation would be supported by one of the included studies: a handed out survey that attempted to measure non-response bias using a Random-Response (RR) technique on part of its sample [28] . Differently from the usual Direct Response technique, in RR, respondents toss coins to determine whether they will respond to the question or just mark “yes”. This still allows admission rates to be calculated, yet it guarantees full anonymity to respondents because no one can tell whether an individual respondent answered “yes” to the question or because of chance. Contrary to author's expectations, response and admission rates were not higher with RR compared to DR, suggesting that in this handed out survey non-response bias was absent.

The effect of social expectations in surveys asking about colleagues is less clear, and could depend on the particular interests of respondents. In general, scientists might tend to protect the reputation of their field, by minimizing their knowledge of misconduct [27] . On the other hand, certain categories of respondents (e.g. participants at a Conference on Research Policies and Quality Assurance [30] ) might have particular experience with misconduct and might be very motivated to report it.

Surveys on colleagues' behaviour might tend to inflate estimates of misconduct also because the same incident might be reported by many respondents. One study controlled for this factor by asking only one researcher per department to recall cases that he had observed in that department in the past three years [31] . It found that falsification and fabrication had been observed by 5.2% of respondents, which is lower than all previous non-self reports. However, since one individual will not be aware of all cases occurring around him/her, this is a conservative estimate [31] . In the sensitivity analysis run on the regression model, exclusion of this study caused the single largest increase in explained variance, which further suggests that findings of this study are unusual.

Another critical factor in interpreting survey results is the respondents' perception of what does and does not constitute research misconduct. As mentioned before, scientists were less likely to reply affirmatively to questions using the words “fabrication” and “falsification” rather than “alteration” or “modification”. Moreover, three surveys found that scientists admitted more frequently to have “modified” or “altered” research to “improve the outcome” than to have reported results they “knew to be untrue”. In other words, many did not think that the data they “improved” were falsified. To some extent, they were arguably right. But the fuzzy boundary between removing noise from results and biasing them towards a desired outcome might be unknowingly crossed by many researchers [10] , [14] , [45] . In a sample of biostatisticians, who are particularly well trained to see this boundary, more than half said they had personally witnessed false or deceptive research in the preceding 10 years [46] .

The grey area between licit, questionable, and fraudulent practices is fertile ground for the “Mohammed Ali effect”, in which people perceive themselves as more honest than their peers. This effect was empirically proven in academic economists [28] and in a large sample of biomedical researchers (in a survey assessing their adherence to Mertonian norms [47] ), and may help to explain the lower frequency with which misconduct is admitted in self-reports: researchers might be overindulgent with their behaviour and overzealous in judging their colleagues. In support of this, one study found that 24% of cases observed by respondents did not meet the US federal definition of research misconduct [31] .

The decrease in admission rates observed over the years in self-reports but not in non-self-reports could be explained by a combination of the Mohammed Ali effect and social expectations. The level and quality of research and training in scientific integrity has expanded in the last decades, raising awareness among scientists and the public [11] . However, there is little evidence that researchers trained in recognizing and dealing with scientific misconduct have a lower propensity to commit it [47] , [48] , [49] . Therefore, these trends might suggest that scientists are no less likely to commit misconduct or to report what they see their colleagues doing, but have become less likely to admit it for themselves.

Once methodological differences were controlled for, cross-study comparisons indicated that samples drawn exclusively from medical (including clinical and pharmacological) research reported misconduct more frequently than respondents in other fields or in mixed samples. To the author's knowledge, this is the first cross-disciplinary evidence of this kind, and it suggests that misconduct in clinical, pharmacological and medical research is more widespread than in other fields. This would support growing fears that the large financial interests that often drive medical research are severely biasing it [50] , [51] , [52] . However, as all survey-based data, this finding is open to the alternative interpretation that respondents in the medical profession are simply more aware of the problem and more willing to report it. This could indeed be the case, because medical research is a preferred target of research and training programs in scientific integrity, and because the severe social and legal consequences of misconduct in medical research might motivate respondents to report it. However, the effect of this parameter was not robust to one of the sensitivity analyses, so it would need to be confirmed by independent studies before being conclusively accepted.

The lack of statistical significance for the effect of country, professional position and other sample characteristics is not strong evidence against their relevance, because the high between-study variance caused by methodological factors limited the power of the analysis (the regression had to control for three methodological factors before testing any other effect). However, it suggests that such differences need to be explored at the study level, with large surveys designed specifically to compare groups. A few of the included studies had done so and found, for example, that admission rates tend to be higher in males compared to females [42] and in mid-career compared to early career scientists [19] , and that they tend to differ between disciplines [41] , [53] . If more studies attempted to replicate these results, possibly using standardized methodologies, then a meta-analysis could reveal important correlates of scientific misconduct.

In conclusion, several surveys asking scientists about misconduct have been conducted to date, and the differences in their results are largely due to differences in methods. Only by controlling for these latter can the effects of country, discipline, and other demographic characteristics be studied in detail. Therefore, there appears to be little scope for conducting more small descriptive surveys, unless they adopted standard methodologies. On the other hand, there is ample scope for surveys aimed at identifying sociological factors associated with scientific misconduct. Overall, admission rates are consistent with the highest estimates of misconduct obtained using other sources of data, in particular FDA data audits [11] , [18] . However, it is likely that, if on average 2% of scientists admit to have falsified research at least once and up to 34% admit other questionable research practices, the actual frequencies of misconduct could be higher than this.

Supporting Information

Studies excluded from the review.

https://doi.org/10.1371/journal.pone.0005738.s001

(0.14 MB DOC)

Self-report questions included in review, and responses.

https://doi.org/10.1371/journal.pone.0005738.s002

(0.07 MB DOC)

Non-self report questions included in the review, and responses.

https://doi.org/10.1371/journal.pone.0005738.s003

(0.11 MB DOC)

Sensitivity analysis for meta-regression model.

https://doi.org/10.1371/journal.pone.0005738.s004

Acknowledgments

I wish to thank Nicholas Steneck, Tom Tregenza, Gavin Stewart, Robin Williams and two anonymous referees for comments that helped to improve the manuscript, and Moyra Forrest for helping to search the literature.

Author Contributions

Conceived and designed the experiments: DF. Performed the experiments: DF. Analyzed the data: DF. Wrote the paper: DF.

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Research fraud: the temptation to lie – and the challenges of regulation

Professorial Fellow in Law and Psychiatry, The University of Melbourne

Disclosure statement

Ian Freckelton has received funding from the Australian Research Council.

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Most scientists and medical researchers behave ethically. However, in recent years, the number of high-profile scandals in which researchers have been exposed as having falsified their data raises the issue of how we should deal with research fraud.

There is little scholarship on this subject that crosses disciplines and engages with the broader phenomenon of unethical behaviour within the domain of research.

This is partly because disciplines tend to operate in their silos and because universities, in which researchers are often employed, tend to minimise adverse publicity.

When scandals erupt, embarrassment in a particular field is experienced for a short while – and researchers may leave their university. But few articles are published in scholarly journals about how the research fraud was perpetrated; how it went unnoticed for a significant period of time; and how prevalent the issue is.

We need to start to look more deeply into who engages in research misconduct; why it happens; what the patterns of such behaviour are; how we can identify it; and how we can deter it.

Recent exposures have brought home the point in a confronting way. Two are of particular relevance to Australia.

Recent cases of research misconduct

In April 2016, a former University of Queensland professor, Bruce Murdoch, received a two-year suspended sentence after pleading guilty to 17 fraud-related charges. A number of these arose from an article he published in the European Journal of Neurology, which asserted a breakthrough in the treatment of Parkinson’s disease.

The sentencing magistrate found that Murdoch forged consent forms for study participants and that his research was “such as to give false hope to Parkinson’s researchers and Parkinson’s sufferers”.

She found there was no evidence at all that Murdoch had conducted the clinical trial on which his purported findings were based.

Murdoch’s plea of guilty and evidence that he was suffering from severe depression and dealing with a cancer diagnosis were factors that resulted in his jail sentence being suspended.

In 2015, Anna Ahimastos, who was employed at the Baker IDI Heart and Diabetes Institute in Melbourne, admitted to fabricating research on blood-pressure medications published in two international journals.

The research purported to establish that for patients with peripheral artery disease (PAD), intermittent claudication (a condition in which exercise induces cramping pain in the leg) treatment with a particular drug resulted in significant improvements.

It had significant ramifications for treatment of PAD and, presumably not coincidentally, also for uptake of the drug. Ahimastos’ research was later retracted from the Journal of the American Medical Association following an internal investigation by the Baker Institute. However, while she lost her employment, she was not criminally charged.

In recent years, other research fraud cases have been reported around the world, such as that involving anesthesiologist Scott Reuben , who faked at least 21 papers on his research on analgesia therapy.

His work sought to encourage surgeons to move away from the first generation of non-steroidal anti-inflammatories (NSAIDs) to multi-modal therapy utilising the newer COX-2 inhibitors.

Reuben was a prominent speaker on behalf of large pharmaceutical companies that produced the COX-2 drugs. After it emerged that he had forged the name of an alleged co-author and that in a study that purported to have data in relation to 200 patients, no data existed at all, he was charged with criminal fraud in relation to spurious research between 2000 and 2008.

He was sentenced to six months’ imprisonment after the plea on his behalf emphasised the toll that the revelations had taken upon his mental health.

In 2015, in the most highly publicised criminal case in the area so far, biomedical scientist Dong Pyou-Han , at Iowa State University, was sentenced to 57 months’ imprisonment for fabricating and falsifying data in HIV vaccine trials. He was also ordered to pay back US$7.2 million to the government agency that funded his research.

Harm caused by fake research

More commonly, though, instances of comparable fraud have not resulted in criminal charges – in spite of the harm caused.

In the Netherlands, for instance, over 70 articles by social psychology celebrity psychologist Diederik Stapel were retracted. His response was to publish a book, entitled in English Derailment , “telling all” about how easy it was to engage in scholarly fraud and what it was that led him to succumb to the temptation to do so.

The book gives memorable insights into the mind of an academic fraudster, including his grandiose aspirations to be the acknowledged leader in his field:

“My desire for clear simple solutions became stronger than the deep emotions I felt when I was confronted with the ragged edges of reality. It had to be simple, clear, beautiful and elegant. It had to be too good to be true.”

And then there’s the notorious case of the Japanese scientist Haruko Obokata , who claimed to have triggered stem-cell abilities in regular body cells.

An inability to replicate her findings resulted in an investigation. The inquiry revealed not just fraud in her postdoctoral stem cell research, but major irregularities in her doctorate. This resulted in the removal of her doctoral qualification, retraction of the papers, professional disgrace and resignation from her employment.

But the ripple effect was much wider. Obokata’s co-author/supervisor committed suicide. There was a large reduction in government funding of the research establishment that employed her. Her line of research into cells that have the potential to heal damaged organs, repair spinal cords and treat diseases such as Alzheimer’s and diabetes was discredited, and grave questions were asked about the academic glitches that allowed her to obtain her PhD.

Despite this, Obokata too published a book denying impropriety and displacing responsibility for her conduct onto others.

Accountability issue

It is easy to dismiss such examples of intellectual dishonesty as aberrations – rotten apples in an otherwise healthy scholarly barrel – or to speak of excessive pressures on researchers to publish.

But there is a wider accountability issue and a cultural problem within the conduct and supervision of research, as well as with how it is published.

A review of the 2,047 retractions listed in PubMed as of May 2012 found that 67.4% were attributable to misconduct. This included fraud or suspected fraud (43.4%), duplicate publication (14.2%) and plagiarism (9.8%).

This does not prove that the incidence of retractions is rising, and it may be that researchers and journal editors are getting better at identifying and removing papers that are either fraudulent or plainly wrong, but it strongly suggests that the checks and balances are too often inadequate until problems are belatedly exposed.

As for cultural issues, a 2012 survey by the British Medical Journal of more than 2,700 researchers found that 13% admitted knowledge of colleagues “inappropriately adjusting, excluding, altering or fabricating data” for the purpose of publication.

Why are researchers tempted to fake results?

The temptation for researchers to fake their results can take many forms .

It can be financial – to acquire money, to save money and to avoid losing money. It can be to advance one’s career. It can also be a desire to attract or maintain kudos or esteem, a product of narcissism, and the expression of an excessive commitment to ambition or productivity.

It can be to achieve ascendancy or retribution over a rival. Or it can be the product of anxiety about under-performance or associated with psychiatric conditions such as bipolar disorder.

What all of these motives have in common is that their outcome is intellectual dishonesty that can have extremely serious repercussions.

A difficulty is that research fraud is not difficult to perpetrate if premeditated.

• Peer review

The check and balance of peer review in publication has, at best, a modest prospect of identifying such conduct.

In peer review, the primary data are not made available to the reviewer. All that the reviewer can do is scrutinise the statistics, the research methodology and the plausibility of the interpretation of the data.

If the fraud is undertaken “professionally”, and a study’s results are modestly and sensibly expressed, the reviewer is highly unlikely to identify the problem.

In 1830, the mathematician Charles Babbage classified scientific misconduct into “hoaxing” (making up results, but wanting the hoax at some stage to be discovered), “forging” (fabricating research outcomes), “trimming” (manipulating data) and “cooking” (unjustifiable selection of data).

Experience over the past 20 years suggests that outright forging of results is the most successful mechanism employed by the academically unscrupulous, although those who engage in forging often also tend to engage in trimming, cooking and plagiarism – their intellectual dishonesty tends to be expressed in more than one way.

Removing temptations

The challenges include how we can remove the temptations of such conduct.

Part of the answer lies with clear articulation of proprieties within codes of conduct. But much more is required.

A culture of openness in respect of data needs to be fostered. Supervision and collaboration need to be meaningful, rather than tokenistic. And there needs to be an environment that enables challenge to researchers’ methodologies and proprieties, whether by whistleblowers or others.

Publishers, journal editors and the funders of scholarly research need to refashion the culture of scholarly publication to reduce the practice of gift authorship, whereby persons who have not really contributed to publications are named as authors. The issue here is that multiple authorship can cloud responsibility for scholarly contribution and blur responsibilities for oversight across institutions by ethics committees.

Journals need to be encouraged to be prepared to publish negative results and critiques and analyses of the limitations of orthodoxies.

When allegations are made, they must be investigated in a way that is going to command respect and confidence from all stakeholders. There is much to be said for the establishment of an external, government-funded Office of Scholarly Integrity. This could be based on the model of the US Office of Research Integrity , which is resourced and empowered to investigate allegations of scholarly misconduct objectively and thoroughly.

Finally, there is a role for the criminal law to discourage grossly unethical conduct in research.

Where funders are swindled of their grants, institutions are damaged by fraud and research conduct is brazenly faked, such conduct is so serious as to justify the intrusion of the criminal law to punish, deter and protect the good name of scholarly research.

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Harvard professor who studies dishonesty is accused of falsifying data

Juliana Kim

Francesca Gino has been teaching at Harvard Business School for 13 years. Maddie Meyer/Getty Images hide caption

Francesca Gino has been teaching at Harvard Business School for 13 years.

Francesca Gino, a prominent professor at Harvard Business School known for researching dishonesty and unethical behavior, has been accused of submitting work that contained falsified results.

Gino has authored dozens of captivating studies in the field of behavioral science — consulting for some of the world's biggest companies like Goldman Sachs and Google, as well as dispensing advice on news outlets, like The New York Times, The Wall Street Journal and even NPR .

But over the past two weeks, several people, including a colleague, came forward with claims that Gino tampered with data in at least four papers.

Harvard releases report detailing its ties to slavery, plans to issue reparations

Gino is currently on administrative leave. Harvard Business School declined to comment on when that decision was made as well as the allegations in general.

In a statement shared on LinkedIn , the professor said she was aware of the claims but did not deny or admit to any wrongdoing.

"As I continue to evaluate these allegations and assess my options, I am limited into what I can say publicly," Gino wrote on Saturday. "I want to assure you that I take them seriously and they will be addressed."

The scandal was first reported by The Chronicle of Higher Education earlier this month. According to the news outlet, over the past year, Harvard had been investigating a series of papers involving Gino.

University Of South Carolina President Resigns After Plagiarizing Part Of Speech

The university found that in a 2012 paper, it appeared someone had added and altered figures in its database, Max H. Bazerman, a Harvard Business School professor who collaborated with Gino in the past, told The Chronicle.

The study itself looked at whether honesty in tax and insurance paperwork differed between participants who were asked to sign truthfulness declarations at the top of the page versus at the bottom. The Proceedings of the National Academy of Sciences , which had published the research , has retracted it.

Shortly after the story, DataColada , a group of three investigators, came forward with similar accusations. After examining a number of Gino's works, the team said they found evidence of fraud spanning over a decade, most recently in 2020.

Acclaimed Harvard Scientist Is Arrested, Accused Of Lying About Ties To China

"Specifically, we wrote a report about four studies for which we had accumulated the strongest evidence of fraud. We believe that many more Gino-authored papers contain fake data. Perhaps dozens," DataColada wrote.

The group said they shared their concerns with Harvard Business School in 2021.

Gino has contributed to over a hundred academic articles around entrepreneurial success, promoting trust in the workforce and just last year published a study titled, "Case Study: What's the Right Career Move After a Public Failure?"

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Academics underestimate willingness of PhDs to use fake data

Around one in 12 postgraduate researchers would publish fraudulent results if it helped them get ahead, says study.

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About one in 12 PhD students would publish fraudulent results if it helped them to get ahead in academia, a study suggests.

In an international study that surveyed almost 800 doctoral candidates, researchers presented PhD researchers with a scenario in which data had been fabricated and asked whether they would be happy to proceed to publication.

In the first part of the study, involving 440 PhD candidates recruited from social science or psychology departments in Dutch universities, almost all spotted the use of fraudulent data but 8 per cent said they would publish if they felt under pressure to do so, explains the study, published in  Frontiers in Psychology .

A replication study involving 198 PhD candidates from the medical and psychology faculties at a Dutch university found similar results, while a third study that polled 127 social science PhD students in Belgium found that 13.4 per cent would publish the dodgy data.

“Many of those we interviewed came up with good arguments for publishing what they knew was fabricated data, such as ‘if this is what it takes to finish my PhD’,” said the study’s lead author, Rens van de Schoot, professor of statistics at Utrecht University .

While the proportion of those willing to use fake data “was not high, it is also not zero”, he added, stating that most of the 36 Dutch academic leaders they also interviewed predicted that cheating would be unthinkable for PhD students.

While the study was confined to the Netherlands and Belgium, Professor van de Schoot said he believed the trend “could be even worse in other parts of the world as PhDs in the Netherlands are university employees and protected by certain rights under Dutch law”. “They don’t have that same status in many countries,” he said.

The study sought to explore whether ethical leadership made a difference to dishonesty levels, describing various checks on research transparency and ethics to respondents. “You might have expected training or education in ethics to make a difference, but it didn’t,” Professor van de Schoot said.

Instead, he said, universities should seek to create a “safe space for PhD researchers to share their uncertainties” where “someone with power can be made available to ask if what we’re doing here is right”.

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PhD thesis and fake results on papers

I am nearly to the end of my PhD and I am probability writing this post when it is too late. The studies I did during these years were faked by my supervisor: he wrote two papers and he added a fake graph on each on of them. Now that I am at the end of this PhD, he said to me that I can write my thesis not necessarily based on the paper results, but I could just write my personal results as they are. I am doing a job like this, however there is my name (as first outhor!) on that two papers. The question is: even if I do not fake anything in my thesis, is there any possibility that the commission would ask and blame me some "strange" results resulting in that papers?

Hmm, this doesn't sound good. Any reason you decided to publish papers with fake results? This is an impossible situation really. 1. If you come clean and retract your papers, you probably won't get a PhD and will never work in Science again. 2. If you add these results to your thesis, it's likely no one will ever know that these results were fake and you can on as normal. 3. If you don't add the results to your thesis, people will definitely ask why eg in your viva, you could then say it was work that your supervisor did. It's your decision to make. It's too easy to say do the right thing (option 1) in this case, because there's a lot at stake for you personally, and you were probably manipulated in one way or another by your supervisor, so it's not entirely your fault. On the other hand, the integrity of Science depends on the integrity of its researchers. Whatever you choose to do, you need to distance yourself from this work and your supervisor as soon as you can.

I think I agree with ToL. Get away from that person and do not continue that terrible practice that he has taught you to do that utterly discredits science. If you're not coming clean then I think ToL's 3rd suggestion might be best. You'd have to prepare the answer about it being your supervisor's work. They will ask why you are first author so you need to have some response (I guess you could say you did most of the writing - if you did?). But can I ask why your supervisor has made this suggestion about not including those papers? I mean - why should he care if those papers are included in your thesis or not?

He knows that I can hardly discuss and defend a fake result like that ones he added on the two papers. For this reason he says to me that I do not necessary have to write down everything is reported on the papers. He could add the first fake result on the first of the two papers as at that time I just trusted him but, after a discussion, I realized how he figure out that result... Later on, he published a further paper, without asking me any authorization, I just realized I was author of a paper after it was already pubblished! I will not mension the second paper at all on my thesis. There is even more! He just published a third paper on my data, without let me know anything. And the name of the first outhor (which should be me!) is wrong! If someone ask me anything about that paper I will answer: "I don't know anything about that and I am not the author of that paper as that is not my name!" You can imagine the troubles he is giving me as I can hardly figure out a logically coherent test of about 100 pages...

I would seriously feel like exposing him and making him lose his career. But it isn't fair that you should suffer yourself (and it is likely that you would - eg by not getting your PhD and losing your own reputation). I think he knows this - which is how he has trapped you. Sort of like an abuse case - the abused person doesn't tell because they think they will be implicated somehow (and in this case - I think you would - because you made some mistakes eg by letting him publish in your name). How about this... - Can you exclude all the "fake" findings from your thesis - Successfully defend your thesis and be clear to the examiners that the papers not included (if they ask about them) aren't actually your work - even though they are in your name - Then once you have received your PhD on the basis of your work (none of which is made up) - just move away and start anew somewhere - never listing those papers as yours (and if anyone asks, say they were published in your name without your permission) This may be what ToL has already suggested - just written in a different format.

Quote From guaio1: He knows that I can hardly discuss and defend a fake result like that ones he added on the two papers. For this reason he says to me that I do not necessary have to write down everything is reported on the papers. I still don't understand this... I mean... how did you (or he) write a decent Discussion section if the "fake" results don't even make sense/can't be defended logically? I am not suggesting that you do include them in your thesis. I am just trying to make sense of the situation. Probably my ignorance comes from not knowing your field (and it is probably wise not to disclose it here!).

I just know that he provided as final proof of the phenomena a graph that is totally invented! Behind that graph there should be some further measurements and calculations, none ever did anything like that. And you can write down a small paper without engaging in details, but you can't write down a thesis on it! Long story short, I will write my thesis based on my personal considerations, that comprend a quite large introduction, a review of similar papers, and dissertations about my measurements. I can add only one of the three papers already published (the first one!), except the last graph, that I would substitude with some qualitative considerations. Other way I could include that graph, if someone ask me details about it, I can say that I wasn't the author of it (as he suggested me once long time ago) I still don't know whether this is possible or not... However, I am not that kind of person who fakes results and I alternate a kind of depression with intense writing and studies... That's what happen on a PhD like this

I would ensure that your thesis does not include anything faked. If there is any subsequent investigation into these papers then it's hard to make the case to take away your PhD if it's based on only the legitimate data. On the papers, frankly, if what you report is 100% accurate then there's a good chance, it's not the first time your supervisor has engaged in research misconduct. It seems pretty blatant for a first offence. Does your discipline make use of PubPeer? https://pubpeer.com/ It might be worth checking to see whether queries have been raised about any of these three papers or any other articles by him. Serial cheats tend to get found out, and then you risk being dragged in. So I'd consider once you have your PhD being that whistle-blower yourself. With that possibility in mind, you might want to consider whether there are ways to correct the scientific record without full retraction e.g. through a correction. It depends how egregious the fraud is. Even if you decide not to, make sure now that any email evidence / early versions of the papers, anything you have that points to your innocence is stored somewhere off the university network, so you can always access it. Final thought, if these papers have any potential to cause harm by not being retracted e.g. this is a Macchiarini type of issue http://www.bbc.co.uk/news/magazine-37311038 then I do think you have a moral duty to set the record straight.

Guaio1, how long have you known that these data are fake? Is it only recently or has it been for a while? If this matter came to light and it was being investigated or there was some form of review, you might be asked these questions. It is a serious ethical and practical dilemma you are in, and I agree with the others in that you do need to distance yourself from your supervisor and any false data and claims made. (Including ensuring your thesis does not rely on any of this data). Is there any way you might be able to get some legal advice on this issue?

I really feel for you Gauio. You are in a very difficult position. you have a few options at this stage and all involve a bit of risk. 1) include forged data in thesis and hope know one ever notices 2) write your thesis without fake data, and be prepared to explain why you don't have the additional graphs and analysis that were in papers in your thesis 3) just walk away now and start a new Phd somewhere else. so option 1; if you or your supervisor ever gets found out or investigated you will lose your Phd and your academic reputation. This could happen next year, 2 years time or 10 years time . you will never know and it is largely out of your control because it depends on careless and risky your supervisor decides to be in the future. You can move on and distance yourself from him, but if he proceeds with this line of conduct there is a chance he will get found out down the line. google Deidrick Staple_ he is a social psychologist who forged most of his data, was eventually found out and 10 Phd students who had over the years completed Phd with him got their Phd award taken off them. maybe he never gets found out; but there are a lot of ways he could. If he is too careless people will doubt his findings, if not now, maybe in the future (I think this is what happened to Staple) if he puts other students in your position, they may be forced to 'out' him. Option 2: this to me is a better option, less likely you will get your phd award taken away from you (if it is awarded). but here's the rub__ how do explain the difference between the papers and the thesis without arousing suspicion?? i don't know your field and maybe if your examiners haven't read your papers you could get away with it... but they could easily google you and find your papers when they are reading the thesis. so how do you explain it?? and if your arouse their suspicion, you will then be accused of including forged data in a published paper... I'm not even fully sure of the consequences of this but I would imagine its the end to academic career and you may not get your phd. Option 3: very drastic, but you start again and this time 4 years you have phd and nothing to worry about. response continued in second message

continued from abve How many years have you spent at this phd,now? If it's two or less, personally I would walk away for sure. I know even two years, they have been blood, sweat and tears years, but in grand scheme of things I would chalk it down to bad luck and walk away. If its more than two years the choice becomes difficult and you have a lot of soul searching to do. everyone is different and everyone has different motivations and reasons for wanting an academic career. personally I don't think any career is worth the kind of drama and turmoil this may bring. If it were me and I'm in year 5, I would still walk and just go do a practical masters and move on with my life . but factored into my decision is the fact that I don't love academia anymore, and wouldn't like all that stress of waiting to be found out, It would suck, but hopefully i could get a job to fund masters and maybe even talk to the university about allowing fee waiver for practical masters, given the circumstances.

The time spent on this PhD is already too much. I just kept going trying to figure out how to manage this issue and only now I've got the idea of writing this here.

Were those papers written right at the start of your PhD career/based on data from earlier on, eg from a masters? It is just that that could be a reason for not including them in your thesis. I know that there is a rule that says something like only work done for the PhD directly can be included in the thesis - eg not borrowing from your masters (or you would be getting double credit for it - which isn't allowed). Is this a loophole you could use? Or - were other people substantively involved - eg. other authors? I think if they had substantial involvement - eg. analysed the data (which it seems like your supervisor did)... then you wouldn't include it in your thesis as it wouldn't be entirely your work. Could this be the reason you give if asked about why they are not in your thesis? You could say it was entirely joint work - ie. 50% each if there are only you two as authors - so you couldn't claim full credit for it by including it in your thesis? The good thing is - if you don't include them in your thesis then if your supervisor's terrible behaviour ever comes to light and there is an investigation, then you would know your thesis is sound and you shouldn't lose your PhD. I still think that if you can get away with not including them in the thesis and have a strong and solid reason WHY, then you'll be OK. Because your thesis will be "clean". As someone else has said, you could be the whistleblower yourself. I think that is something to decide once you have your PhD and are confident that the content of your thesis is completely sound. Personally I would just make a new start elsewhere but make sure I kept evidence of everything to prove my innocence the best I could later if needed. The most important thing is to decide now is surely about your thesis. All the best

I second newlease36's advise. If possible, write your thesis excluding fake data. If asked, say that you only put in data if you contributed significant effort in generating it.

More consequences of fake data, read http://www.abc.net.au/news/2014-12-12/university-of-queensland-professor-on-fraud-charges/5964476

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• Knowledge Base
• Dissertation
• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on August 30, 2022 by Tegan George . Revised on July 18, 2023.

A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean—any evaluation should be saved for the discussion section .

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Table of contents

How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs. discussion vs. conclusion, checklist: research results, other interesting articles, frequently asked questions about results sections.

When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.

Here are a few best practices:

• Your results should always be written in the past tense.
• While the length of this section depends on how much data you collected and analyzed, it should be written as concisely as possible.
• Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like “appears” or “implies.”
• If you have other results you’d like to include, consider adding them to an appendix or footnotes.
• Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe store: first discuss the shoes as a whole, then the sneakers, boots, sandals, etc.

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If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .

Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.

The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:

• A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
• A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
• A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion.

A note on tables and figures

In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .

As a rule of thumb:

• Tables are used to communicate exact values, giving a concise overview of various results
• Graphs and charts are used to visualize trends and relationships, giving an at-a-glance illustration of key findings

Don’t forget to also mention any tables and figures you used within the text of your results section. Summarize or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

A two-sample t test was used to test the hypothesis that higher social distance from environmental problems would reduce the intent to donate to environmental organizations, with donation intention (recorded as a score from 1 to 10) as the outcome variable and social distance (categorized as either a low or high level of social distance) as the predictor variable.Social distance was found to be positively correlated with donation intention, t (98) = 12.19, p < .001, with the donation intention of the high social distance group 0.28 points higher, on average, than the low social distance group (see figure 1). This contradicts the initial hypothesis that social distance would decrease donation intention, and in fact suggests a small effect in the opposite direction.

Figure 1: Intention to donate to environmental organizations based on social distance from impact of environmental damage.

In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.

For each theme, start with general observations about what the data showed. You can mention:

• Recurring points of agreement or disagreement
• Patterns and trends
• Particularly significant snippets from individual responses

Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .

When asked about video games as a form of art, the respondents tended to believe that video games themselves are not an art form, but agreed that creativity is involved in their production. The criteria used to identify artistic video games included design, story, music, and creative teams.One respondent (male, 24) noted a difference in creativity between popular video game genres:

“I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.”

Responses suggest that video game consumers consider some types of games to have more artistic potential than others.

Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.

It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .

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I have completed my data collection and analyzed the results.

I have included all results that are relevant to my research questions.

I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .

I have stated whether each hypothesis was supported or refuted.

I have used tables and figures to illustrate my results where appropriate.

All tables and figures are correctly labelled and referred to in the text.

There is no subjective interpretation or speculation on the meaning of the results.

You've finished writing up your results! Use the other checklists to further improve your thesis.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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The results chapter of a thesis or dissertation presents your research results concisely and objectively.

In quantitative research , for each question or hypothesis , state:

• The type of analysis used
• Relevant results in the form of descriptive and inferential statistics
• Whether or not the alternative hypothesis was supported

In qualitative research , for each question or theme, describe:

• Recurring patterns
• Significant or representative individual responses
• Relevant quotations from the data

Don’t interpret or speculate in the results chapter.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

George, T. (2023, July 18). How to Write a Results Section | Tips & Examples. Scribbr. Retrieved September 9, 2024, from https://www.scribbr.com/dissertation/results/

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Understanding Misinformation: The Tale of Fake News and Fake Reviews

Type of degree.

Computer Science and Software Engineering

Misinformation has been long issues in the global communities because of the booming usage of social networks, online retail platforms and so on. The wide spreading of the massive amount of misinformation has recently become a global risk. Therefore, effective detection methods on misinformation is required to combat bad influence. In this dissertation study, we make the following three contributions by focusing on two types of misinformation detection, namely, fake news detection and fake review detection. The first contribution of this study is the fake news engagement and propagation path framework or FNEPP, in which we devise a novel fake news detection technique from a social-context perspective. The widespread fake news on social media has boosted the demand for reliable fake news detection techniques. Such dissemination of fake news can influence public opinions, allowing unscrupulous parties to control the outcomes of public events such as elections. More recently, a growing number of methods for detecting fake news have been proposed. Most of these approaches, however, have significant limitations in terms of timely detection of fake news. To facilitate early detection of fake news, we propose FNEPP - a unique framework that explicitly combines multiple social context perspectives like news contents, user engagements, user characteristics, and the news propagation path. The FNEPP framework orchestrates two collaborative modules - the engagement module and the propagation path module - as composite features. The engagement module captures news contents and user engagements, whereas the propagation path module learns global and local patterns of user characteristics and news dissemination patterns. The experimental results driven by the two real-world datasets demonstrate the effectiveness and efficiency of the proposed FNEPP framework. The second contribution of the dissertation lies in an emotion-aware fake review detection framework. Customers are increasingly relying on product reviews when making purchasing decisions. Fake reviews, on the other hand, obstruct the value of online reviews. Thus, automatic fake review detection is required. Previous research devoted most efforts on examining linguistic features, user behavior features, and other auxiliary features in fake review detection. Unfortunately, emotion aspects conveying in the reviews haven’t yet been well explored. After delving in the effective emotion representations mined from review text, we design and implement the emotion-aware fake review detection framework anchored on ensemble learning. The empirical study on the two real-world datasets confirms our model's performance on fake review detection. To investigate how people perceive fake and real reviews differently in terms of emotion aspects, we prepare 200 real product reviews and 200 fake reviews, and random assign 20 reviews to each participant to determine the level of authenticity, credibility, and believability based on 1 - 100 scale. The results from an LIWC-22 emotion analysis intuitively demonstrate people's perception on fake reviews from the aspect of emotions. The last contribution of the dissertation study is a two-tier text network analysis framework. As the global COVID-19 pandemic boosted the demand of online shopping, the number of online reviews increased dramatically on online shopping platforms. More often than not, customers have the tendency of referring to the product reviews before making buying decisions when products are not physically presented. Fake reviews are designed to influence buyers' purchasing decisions. Existing research devoted their efforts on designing automatic fake review detection systems; however, a text network analysis on fake reviews is missing. To close this technological gap, we construct a two-tier text network analysis framework guiding the investigation of the network-level characteristics and text characteristics of fake reviews. We conduct the extensive experiments driven by the Amazon product review dataset using Gephi. We unfold key findings on guiding the design of next-generation fake-review detection systems.

https://etd.auburn.edu//handle/10415/8328

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• Knowledge Base
• Dissertation
• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on 27 October 2016 by Bas Swaen . Revised on 25 October 2022 by Tegan George.

A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean – any evaluation should be saved for the discussion section .

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Table of contents

How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs discussion vs conclusion, checklist: research results, frequently asked questions about results sections.

When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.

Here are a few best practices:

• Your results should always be written in the past tense.
• While the length of this section depends on how much data you collected and analysed, it should be written as concisely as possible.
• Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like ‘appears’ or ‘implies’.
• If you have other results you’d like to include, consider adding them to an appendix or footnotes.
• Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe shop: first discuss the shoes as a whole, then the trainers, boots, sandals, etc.

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If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .

Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.

The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:

• A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
• A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
• A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion.

A note on tables and figures

In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .

As a rule of thumb:

• Tables are used to communicate exact values, giving a concise overview of various results
• Graphs and charts are used to visualise trends and relationships, giving an at-a-glance illustration of key findings

Don’t forget to also mention any tables and figures you used within the text of your results section. Summarise or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

Figure 1: Intention to donate to environmental organisations based on social distance from impact of environmental damage.

In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.

For each theme, start with general observations about what the data showed. You can mention:

• Recurring points of agreement or disagreement
• Patterns and trends
• Particularly significant snippets from individual responses

Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .

‘I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.’

Responses suggest that video game consumers consider some types of games to have more artistic potential than others.

Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.

It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .

Prevent plagiarism, run a free check.

I have completed my data collection and analyzed the results.

I have included all results that are relevant to my research questions.

I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .

I have stated whether each hypothesis was supported or refuted.

I have used tables and figures to illustrate my results where appropriate.

All tables and figures are correctly labelled and referred to in the text.

There is no subjective interpretation or speculation on the meaning of the results.

You've finished writing up your results! Use the other checklists to further improve your thesis.

The results chapter of a thesis or dissertation presents your research results concisely and objectively.

In quantitative research , for each question or hypothesis , state:

• The type of analysis used
• Relevant results in the form of descriptive and inferential statistics
• Whether or not the alternative hypothesis was supported

In qualitative research , for each question or theme, describe:

• Recurring patterns
• Significant or representative individual responses
• Relevant quotations from the data

Don’t interpret or speculate in the results chapter.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Swaen, B. (2022, October 25). How to Write a Results Section | Tips & Examples. Scribbr. Retrieved 9 September 2024, from https://www.scribbr.co.uk/thesis-dissertation/results-section/

Is this article helpful?

Other students also liked

What is a research methodology | steps & tips, how to write a discussion section | tips & examples, how to write a thesis or dissertation conclusion.