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Sad and very frustrating. Todd has put out so many excellent studies and for this to happen is a blow to a distinguished career. All it takes is one person to ruin multiple careers. To me, I feel like those who engage in these in activities are arrogant about ever getting caught, but also lazy in a way, as it is easier to exaggerate effects then run more subjects, reprocess, and rewrite the paper accurately. Sorry for the rant but tired of this bs that taints all of us.
Makes me more bitter about the peer review process because it isn't catching this stuff, and really isn't that what its biggest purpose is?
Makes me more bitter about the peer review process because it isn't catching this stuff, and really isn't that what its biggest purpose is?
Makes me more bitter about the peer review process because it isn't catching this stuff, and really isn't that what its biggest purpose is?
I firmly believe the peer review process is a very lazy, sloppy form of quality control for the scientific community and have felt that way for a long time. I don't think I'm alone in that some of the best-known folks (ridiculous numbers of grants, publish in the best places, etc.) I've worked with over the years have unquestionably done the worst work. Truthfully, peer review is a review of the writing moreso than the science. Sure, we can watch out for the big things (e.g. no or poor control group), point out some confounds that may have been missed or alternative interpretations, etc. That says little about the quality of the science though.
I'm convinced the things we ignore are the things that matter. I'm about to submit a manuscript that used an extremely well known measure (cited > 2000 times) in a standard way. The distribution of the data is such that even standard "Robust to departure" statistics are pretty tough to justify using. Yet I've never seen anyone do anything else. I doubt our sample is that unusual. So in those 2000 papers either no one ever looked, or they did and thought they wouldn't be able to publish so they swept it under the rug. Similarly - nearly every study has SOME issue with it. Equipment breaks, an RA screws up a couple sessions, physio data is noisy and has to get dropped etc.. Yet you'd never know that from most manuscripts (and in fact, if you are honest about such things your papers get rejected). Either I've happened to find the only labs in the country that aren't run perfectly, or everyone else just covers it up.
I genuinely believe we need a dramatic reform of the system and hope to contribute to that as I progress in my career. Its not an easy issue though because most of these things "can't" be caught unless you actually see the raw data (and oftentimes, not even then). With more advanced analyses (HLM/SEM/etc.) I always am left wondering how many papers are published where someone wrote their syntax wrong. Reviewers aren't going to say "send me your data files and syntax so I can check your numbers" though - in part due to time constraints, and in part due to social factors.
I actually DON'T worry about cases like this guy. These are extreme, likely quite rare, and not deemed acceptable to the field. I worry much more about the faculty member writing a paper based off the data collected by the RA who incorrectly explained a computer task to several participants and a project coordinator who took it upon themselves to "impute" a couple data points on an interview because they forgot to write it down, that was analyzed by the new grad student who forgot to include one of the lower-order terms in the interaction model, before being passed off to the post-doc who wrote the methods section not realizing that the computer task itself was programmed wrong by the investigator they borrowed it from and didn't work the way they thought it did. THAT is the stuff that scares me. Most PIs are not in a position to catch many of those errors (let alone the peer reviewers).
Replication. Over time the dust settles.
But no journal wants to publish replication studies. Or you attempt to replicate a study, find contrasting results, and the person whose idea you are arguing against pans your manuscript during the review process and you get a rejection. 🙄
Ollie, agreed with everything you said. Perhaps it's you and I that have been in labs that aren't perfect.
Bingo, Ollie hit the nail on the head. Truly, the best way to fight such corruption is to begin publishing articles that replicate findings and those that have no significant findings. However, that is a thought sell. Really, we are tip of the iceberg. How many replications of a study do you think a drug company does to have one significant study? I don't know...and neither does anyone not on their payroll. Think about that the next time you reach for a pill.
I agree that journals are unlikely to publish pure replication studies. An alternative way to go about it is to replicate a design or certain aspects of a study, and add a novel element to it. I've done this myself twice. It's not ideal - I agree that pure replication should be encouraged - but it can circumnavigate some of the obstacles while adding to the literature.
FWIW, I do know of one journal that requests your actual data set when you submit, although I imagine that this practice could also create IRB problems.
I agree that journals are unlikely to publish pure replication studies. An alternative way to go about it is to replicate a design or certain aspects of a study, and add a novel element to it. I've done this myself twice. It's not ideal - I agree that pure replication should be encouraged - but it can circumnavigate some of the obstacles while adding to the literature.
Ironically, I read that one of the retracted studies was at least partially replicated by another research team and the results were consistent with that of the retracted study. What's really odd to me is that it sounds like the grad student already had decent data and just committed fraud to make it better, which makes the whole thing all the more WTF? to me.
Again, this is why I have long stated that trying to publish empirical data/articles was probably one of the most unrewarding experiences I have ever had.
Once I got through the process (which by this time I already concluded had a poor ROI given the work/labor time), I was left with an article that would make little impact, get buried under the thousands of other articles published on the general topic that year, and that very few people will actually read (its true, folks). Ra-Ra!
Yep, the current publishing system as it is now does NOT encourage replication (this is true in hard science as well as psychology---hence the fear of getting "scooped")
I don't know how peer review would have caught this, though--there's no way for a peer reviewer to tell if the numbers in an article are "real." FWIW, I do know of one journal that requests your actual data set when you submit, although I imagine that this practice could also create IRB problems.
Again, this is why I have long stated that trying to publish empirical data/articles was probably one of the most unrewarding experiences I have ever had.
Once I got through the process (which by this time I already concluded had a poor ROI given the work/labor time), I was left with an article that would make little impact, get buried under the thousands of other articles published on the general topic that year, and that very few people will actually read (its true, folks). Ra-Ra!
FYI: Googled him and came up with his CV, pre-retraction. http://ccpweb.wustl.edu/pdfs/Savine_CV.pdf
You've got to give those details, when you catch them, when you publish a paper.
And finally, it is now much easier to include full data sets and analyses online with your publication. As an example, the new APA open access journal requires your data.
http://www.apa.org/science/about/psa...s-journal.aspx
The journal Decision Making requires your data set. My IRB said it was okay.
I *really* can't see most IRBs being okay with people turning over actual data sets to journals. Even if you remove explicitly identifying information, there's always the argument that certain combinations of variables could be identifying. In many cases, I think that's a stretch, but it is an argument IRBs make, and so I can't see them agreeing to having full data sets go to journals.
For every federal grant, you are required to note that you are willing to share your data with others. I really don't see why the IRB would care so long as the dataset is deidentified. If it is a small dataset, perhaps that could be an issue. But I have even heard of raw data sharing.
I think it is a good thing that there are journals with reviewers willing to take a look at the data (in case people did their analyses wrong). But it usually isn't going to solve the problem of falsified data.
Idk. I've seen IRBs have issues with people emailing deidentified data sets to collaborators, for example. I agree it's over the top in most cases, but it happens.
you have a particularly miserly IRB. But if there are issues, lets say with a small data set, if its is not important variables like age and gender can be left out and help mask identities.Idk. I've seen IRBs have issues with people emailing deidentified data sets to collaborators, for example. I agree it's over the top in most cases, but it happens.
It's a challenge. There are 18 hippa identifiers. The VA even counts the date of a visit as identifying in the context of no other data.
But no journal wants to publish replication studies. Or you attempt to replicate a study, find contrasting results, and the person whose idea you are arguing against pans your manuscript during the review process and you get a rejection. 🙄
Ollie, agreed with everything you said. Perhaps it's you and I that have been in labs that aren't perfect.
short cuts are tempting, especially with reliance on self-oversight.It also reminded me the most baffling thing about this situation to me--that his research was legitimately good (some of the retracted research has been replicated) and yet he doctored and faked data anyway. It's just bizarre.
I always wonder, with the "publish or perish" mindset, how often data is actually manipulated. Like you said, it's probably extremely tempting. It's something we might never know.short cuts are tempting, especially with reliance on self-oversight.
From my experience, data is often mistreated but not as blatantly as the cases above. Again my anecdotal experience, this is more likely in social psych rather than treatment outcome research (especially with the creation of clinicaltrials.gov).I always wonder, with the "publish or perish" mindset, how often data is actually manipulated. Like you said, it's probably extremely tempting. It's something we might never know.