The Reformation: Can Social Scientists Save Themselves?(psmag.com)
psmag.com
The Reformation: Can Social Scientists Save Themselves?
http://www.psmag.com/navigation/health-and-behavior/can-social-scientists-save-themselves-human-behavior-78858
7 comments
> I agree with this article, but think that by singling out social sciences it fails to reveal the bigger picture: that all scientific fields are subject to these same mistakes.
I actually prefer them writing about a single (or a group of related) field(s) of study. There have been lots of articles asserting that all of science has these issues, but it's not clear to me that they all suffer to the same degree. For example, It's not clear to me that physics or astronomy suffer as from this type of behavior. I'm sure it does happen, but it doesn't seem to be as pervasive as in the social sciences and medicine. For social sciences it seems like it is happening often enough that it's affecting the trajectory of the field. To my knowlege, this isn't the case for most of physics and astronomy (for example).
Also, a lot (but not all) of the "mistakes" noted in the article were deliberate fabrications or unethical tampering of data. So I think labeling them as "mistakes" risks being too forgiving.
I actually prefer them writing about a single (or a group of related) field(s) of study. There have been lots of articles asserting that all of science has these issues, but it's not clear to me that they all suffer to the same degree. For example, It's not clear to me that physics or astronomy suffer as from this type of behavior. I'm sure it does happen, but it doesn't seem to be as pervasive as in the social sciences and medicine. For social sciences it seems like it is happening often enough that it's affecting the trajectory of the field. To my knowlege, this isn't the case for most of physics and astronomy (for example).
Also, a lot (but not all) of the "mistakes" noted in the article were deliberate fabrications or unethical tampering of data. So I think labeling them as "mistakes" risks being too forgiving.
Psychology tends to be picked on more because it's easier to understand and communicate. Researchers in psychology tend to be in areas that have names that are meaningful to humans (like "short term memory" or "interpersonal violence") rather than jargon ("systems" or "finite mass spectroscopy"). Really, psychology has the bad luck of having jargon that maps to lay knowledge -- imperfectly, but the reporters don't care.
Overall, most scientists are ethical. Very few people are deliberately falsifying their data.
But in every field, the incentives are not set up to do good science. That's a fact, and until it's understood there won't be any progress. Until then, you'll just see a slow grind of academia eating itself.
Overall, most scientists are ethical. Very few people are deliberately falsifying their data.
But in every field, the incentives are not set up to do good science. That's a fact, and until it's understood there won't be any progress. Until then, you'll just see a slow grind of academia eating itself.
> But in every field, the incentives are not set up to do good science. That's a fact, and until it's understood there won't be any progress. Until then, you'll just see a slow grind of academia eating itself.
I think this depends on what you mean by "doing good science". I agree that incentives in every field are not set up to do the _best_ science, but in many fields the incentives are not such that people are drawn to doing _bad_ science. So if academia is "eating itself", I don't think it can be universally attributed to incentives regarding good vs bad science.
I think this depends on what you mean by "doing good science". I agree that incentives in every field are not set up to do the _best_ science, but in many fields the incentives are not such that people are drawn to doing _bad_ science. So if academia is "eating itself", I don't think it can be universally attributed to incentives regarding good vs bad science.
No, it's the same in every field, because the process of getting tenure in about every field is the same: publish lots of times. "Lots" might be different in different fields, but it still comes down to "publish a lot." Publishing a lot is actually a lot closer to incentivizing bad science than not.
I'd encourage you to look past your pre-existing notions of what makes a "hard" science branch and actually talk to people who are the boots on the ground about what is really going on in academia.
I'd encourage you to look past your pre-existing notions of what makes a "hard" science branch and actually talk to people who are the boots on the ground about what is really going on in academia.
>> To actually run the experiment would have been to run the risk that a disordered environment has no effect on racial attitudes—a “null finding” that would mean he’d wasted his time, because journals aren’t usually interested in experiments that don’t prove something new.
This state of affairs has of course been remarked on before. Perhaps this could be rectified by creating an online journal with the title Null that only accepts papers recording experiments where the hypothesis was not proved. Of course it wouldn't make very interesting reading but after some time it might provide a useful reality check against novel claims, when a search in Null shows the x number of times a similar hypothesis was not proved. And the incentive to publish there would be to show that you are a credible scientist who sometimes, quite reasonably, comes up with hypotheses that when properly tested don't prove to be true.
This state of affairs has of course been remarked on before. Perhaps this could be rectified by creating an online journal with the title Null that only accepts papers recording experiments where the hypothesis was not proved. Of course it wouldn't make very interesting reading but after some time it might provide a useful reality check against novel claims, when a search in Null shows the x number of times a similar hypothesis was not proved. And the incentive to publish there would be to show that you are a credible scientist who sometimes, quite reasonably, comes up with hypotheses that when properly tested don't prove to be true.
Along similar lines, I've wondered whether an enterprising scientist could glamorize the search for null results by deliberately targeting previously published papers. So you don't just formulate random hypotheses, test them, and then report the null hypothesis. Instead, you pick out an influential paper and debunk it experimentally. I presume you'd consider suspiciousness when choosing your targets. E.g. a p-value just under 0.05 would be one signal you'd consider.
If journals like dramatic submissions, perhaps mythbuting previous papers would provide the requisite drama. Scientists out there, how do you think that would go over with journals?
If journals like dramatic submissions, perhaps mythbuting previous papers would provide the requisite drama. Scientists out there, how do you think that would go over with journals?
An online journal like in support of the null hypothesis like what you describe can be found here http://www.jasnh.com/ It's published online bi-yearly.
Thank you for that. And from scanning the titles of the papers listed there, it actually does look like interesting reading.
There are also Journals of Negative Results in a few fields, such as this one: http://www.jnrbm.com/
You'd have to get university tenure committees and national grant committees to consider publishing in Null to be prestigious, or else scientists will have no interest in it.
People publish in unprestigous
I'm a big fan of what the folks at the Center for Open Science[0] are trying to do. They are trying to build open tools [1] for the entire research process, and they are trying to encourage people to conduct more of their research in the open.
This would encourage open analysis on an ongoing basis, rather than waiting for publication before peer review begins in earnest. It would also allow people to establish a reputation for being strong in multiple aspects of conducting research, not just in getting research published. You can build a reputation by reviewing hypotheses, and helping researchers refine their hypotheses. You can build a reputation for helping ensure that people's statistical analyses are being done properly.
I understand that not all research should be done entirely in the open, but we can certainly move beyond the model of just opening up journal articles.
They are also developing the Reproducibility Project[2], which aims to encourage researchers to replicate existing studies rather than focus on doing new research. It aims to raise the prestige of doing this work as well.
[0] Center for Open Science: http://centerforopenscience.org
[1] Open Science Framework: https://osf.io/
[2] Reproducibility Project: https://osf.io/ezcuj/wiki/home/
This would encourage open analysis on an ongoing basis, rather than waiting for publication before peer review begins in earnest. It would also allow people to establish a reputation for being strong in multiple aspects of conducting research, not just in getting research published. You can build a reputation by reviewing hypotheses, and helping researchers refine their hypotheses. You can build a reputation for helping ensure that people's statistical analyses are being done properly.
I understand that not all research should be done entirely in the open, but we can certainly move beyond the model of just opening up journal articles.
They are also developing the Reproducibility Project[2], which aims to encourage researchers to replicate existing studies rather than focus on doing new research. It aims to raise the prestige of doing this work as well.
[0] Center for Open Science: http://centerforopenscience.org
[1] Open Science Framework: https://osf.io/
[2] Reproducibility Project: https://osf.io/ezcuj/wiki/home/
Oh bless us, it's the Hoax again. :( Set out to prove something reasonably obvious and straightforward (could you publish any old guff in Social Text at the time, even straightforward mathematical/scientific howlers? I'd bet my trousers on it), make a complete bags of doing so (if there's one time the editors of a social-science journal should feel reasonably happy accepting the physics content of an overview paper at face value it's when the author is a physics professor at a top-40 college publishing under his own name!), get praised and adulated forever after; for if Science teaches us anything, it is that it doesn't matter if your method is rubbish so long as you get the answer everyone wants and expects.
Yup. Not to mention that Social Text isn't peer reviewed, and fake papers have made their way into _many_ different journals, including physics ones.
Here's what makes me sad about this. There are probably useful bits of information collected by social scientists all the time that will never see the light of day because there is no requirement to publish the data. But more than that, it simply doesn't matter what format the data is in, or if its well presented. It just has to be accessible. As the ability and scope of data mining methods grows, every piece of scientific data will be fodder, not for humans, but for machines looking back over the entirety of scientific output to draw new conclusions from information we simply ignore today. I would go as far as to say that if you feel your work has any value at all, you have a duty to make your data available, so that it can be mined at a later date.
My understanding of why this isn't often the case in the Social Sciences is that data is usually extremely expensive to collect. The researchers usually spend many, many hours writing proposals, revising it, resubmitting it, until they finally have the funding to collect the data their interested in. Then they actually have to get people into a laboratory, or send someone out to do field work (sometimes halfway across the world), or any number of expensive procedures.
And then, after data is collected, they begin analyzing it and seeing what are the interesting patterns in the data. And this takes a lot of time, and often you just want to show one thing at a time. In the current environment of publish-or-perish, and grant providers asking what you've done with their money, you might want to take that data you worked so hard to obtain, and milk quite a few publications out of it. If you openly release the data, then others can easily find interesting patterns in your data before you can, beating you to publication, and diminishing your track record for future grants, tenure, etc.
So your point is extremely valid. But the counter point of why open data isn't feasible is, if a researcher does all the hard legwork necessary to get to the interesting analysis stage, why shouldn't they reap the rewards of their hard work (namely, publications and recognition).
A few things have been proposed: verification projects (one particular other research group gains access to your data and verifies your results, but is not allowed to use the data otherwise), and grace periods (you have to release your data 3 years after a study or something, giving you time to milk out publications, but still allowing for general verification later).
Generally speaking, true scientific verification should also involve the complete recollection of the data. But when data collection is extremely expensive, this is typically infeasible.
[I work in a field that's almost all open access, and research data is almost always available upon request. So I agree with you. But data is typically incredibly cheap in my field, so the problem above doesn't really apply.]
And then, after data is collected, they begin analyzing it and seeing what are the interesting patterns in the data. And this takes a lot of time, and often you just want to show one thing at a time. In the current environment of publish-or-perish, and grant providers asking what you've done with their money, you might want to take that data you worked so hard to obtain, and milk quite a few publications out of it. If you openly release the data, then others can easily find interesting patterns in your data before you can, beating you to publication, and diminishing your track record for future grants, tenure, etc.
So your point is extremely valid. But the counter point of why open data isn't feasible is, if a researcher does all the hard legwork necessary to get to the interesting analysis stage, why shouldn't they reap the rewards of their hard work (namely, publications and recognition).
A few things have been proposed: verification projects (one particular other research group gains access to your data and verifies your results, but is not allowed to use the data otherwise), and grace periods (you have to release your data 3 years after a study or something, giving you time to milk out publications, but still allowing for general verification later).
Generally speaking, true scientific verification should also involve the complete recollection of the data. But when data collection is extremely expensive, this is typically infeasible.
[I work in a field that's almost all open access, and research data is almost always available upon request. So I agree with you. But data is typically incredibly cheap in my field, so the problem above doesn't really apply.]
nods This is the exact same situation as entrepreneurs and their fear of others "stealing their ideas." For years and years people would hoard their ideas, ask people to sign NDAs and generally be paranoid dicks about their work. But in reality ideas almost never get stolen! They arise independently, sure, and on a long enough time line good ideas will be acted on, but in the near-term this is almost never the case.
The situation for scientists is exactly the same. On a timeline of writing papers (3 years from original research, say 5 papers). No one is going to steal your data and get it published first! It could happen, and so people are afraid. But it won't because your data is hard to interpret, and people are lazy and they people have their own data anyway.
There needs to be pressure from a lot of different places to get the point across, but the reality is that sharing is ultimately a great resource for everyone but in the near term not a threat to your ability to publish!
The situation for scientists is exactly the same. On a timeline of writing papers (3 years from original research, say 5 papers). No one is going to steal your data and get it published first! It could happen, and so people are afraid. But it won't because your data is hard to interpret, and people are lazy and they people have their own data anyway.
There needs to be pressure from a lot of different places to get the point across, but the reality is that sharing is ultimately a great resource for everyone but in the near term not a threat to your ability to publish!
This state of affairs is completely arse backwards. In hard sciences, where direct quantitative measurement is made (e.g. the sulfur isotopic composition of a mineral in a rock), those measurements, and details of how they were made are published in the paper. If you have the paper you have the data.
How is it that 'sciences' that do not make direct measurements, and instead rely on statistical analysis to separate weak signals from noise in very very indirect measurements can get away _without_ publishing raw data? The burden of proof must be higher for such weak signals, and that must include the raw data (e.g. all the survey results). I am unsurprised people get away with making shit up in these disciplines if we esentially are taking their word for it.
How is it that 'sciences' that do not make direct measurements, and instead rely on statistical analysis to separate weak signals from noise in very very indirect measurements can get away _without_ publishing raw data? The burden of proof must be higher for such weak signals, and that must include the raw data (e.g. all the survey results). I am unsurprised people get away with making shit up in these disciplines if we esentially are taking their word for it.
> A few things have been proposed: verification projects, and grace periods.
Is glorifying (and sending grants to) data collectors on the list?
Is glorifying (and sending grants to) data collectors on the list?
You mean to say, granting more wide recognition for the collection itself, rather than the analysis stemming from collection?
Possibly, I don't know. Mostly this is just my understanding from talking to peers in the social sciences and asking why they don't have open access like my field.
I think the problem there though, is knowing what questions to ask (what data to collect) is the hardest part, but fits much more in line with the analysis aspect (ie forming a hypothesis) than collection (conducting a designed experiment). So an ideal researcher would show good work in both areas, not just one.
Possibly, I don't know. Mostly this is just my understanding from talking to peers in the social sciences and asking why they don't have open access like my field.
I think the problem there though, is knowing what questions to ask (what data to collect) is the hardest part, but fits much more in line with the analysis aspect (ie forming a hypothesis) than collection (conducting a designed experiment). So an ideal researcher would show good work in both areas, not just one.
Exactly that. We can quite well be at awe to people that managed to collect difficult sets of data, instead of the people that made a superficial analysis on it and discovered something. While still reserving some respect for the people that do difficult analysis, of course.
That problem is not specific to the social sciences. As far as I know, only physics are making some inroads on that change of attitude, but even there it's restricted to a few fields where data collection is extremely expensive (much more than at social sciences).
I think that's one much needed change to adapt science to our current world.
That problem is not specific to the social sciences. As far as I know, only physics are making some inroads on that change of attitude, but even there it's restricted to a few fields where data collection is extremely expensive (much more than at social sciences).
I think that's one much needed change to adapt science to our current world.
This (edit: I mean this comment of mine) is off-topic, so I'll demote it to the bottom, but since I've spent the last month telling people what isn't appropriate on HN, I want to point out something that is.
This thread contains excellent comments, especially wmacmil's: https://news.ycombinator.com/item?id=7676945. It's great when someone with directly relevant experience adds so substantive a comment on an interesting question, and gets properly upvoted. Nearly all the other comments right now are substantive and insightful too.
This thread contains excellent comments, especially wmacmil's: https://news.ycombinator.com/item?id=7676945. It's great when someone with directly relevant experience adds so substantive a comment on an interesting question, and gets properly upvoted. Nearly all the other comments right now are substantive and insightful too.
This is off-topic, so I'll demote it to the bottom in a little while, but since I've spent the last month telling people what isn't appropriate on HN, I want to point out something that is.
Did you drop a negative somewhere, or add an extra? I have trouble following your sentence. My best guess at an interpretation is that you've spent a lot of time telling people "no, this is actually on-topic" and now you want to point out something that truly is off-topic?
If so, why do you feel this is off-topic? Like yesterday's article on parapsychology (https://news.ycombinator.com/item?id=7666575), this article offers useful insight into the ways in which the ideals of the "scientific method" differ from the realities of published science. I think a lot of people here find this of interest, and I don't think this particular slope is that slippery.
Did you drop a negative somewhere, or add an extra? I have trouble following your sentence. My best guess at an interpretation is that you've spent a lot of time telling people "no, this is actually on-topic" and now you want to point out something that truly is off-topic?
If so, why do you feel this is off-topic? Like yesterday's article on parapsychology (https://news.ycombinator.com/item?id=7666575), this article offers useful insight into the ways in which the ideals of the "scientific method" differ from the realities of published science. I think a lot of people here find this of interest, and I don't think this particular slope is that slippery.
I don't think that sentence is worded wrong, but it's late and I may be missing it. In any case: no! The article was wonderfully on-topic and the thread was excellent.
Edit: Oh, I get it now. "This" in "This is off-topic" meant "this meta comment I am currently posting". The article was superb. Sorry for the confusion! I will edit the GP.
Edit: Oh, I get it now. "This" in "This is off-topic" meant "this meta comment I am currently posting". The article was superb. Sorry for the confusion! I will edit the GP.
That makes much more sense, and wasn't an interpretation I'd considered. Thanks!
Should we expect more from social science?
Has psychology ever been able to yield the same practical findings as fields such as Electromagnetics or has it merely reflected fluid social trends.
The real story here is to avoid taking the social sciences seriously.
Has psychology ever been able to yield the same practical findings as fields such as Electromagnetics or has it merely reflected fluid social trends.
The real story here is to avoid taking the social sciences seriously.
Well, social science has told us a lot more about the structure of society, power plays, etc, than all "practical findings in fields such as Electromagnetics".
If you want "the same practical findings" from it, it's ovbvious that you ain't gonna get them. Social sciences do not produce light bulbs or combustion engines, not they are meant to.
If you want "the same practical findings" from it, it's ovbvious that you ain't gonna get them. Social sciences do not produce light bulbs or combustion engines, not they are meant to.
>>not they are meant to.
Yes that my point
The real problem is that people confuse psychology with science.
Well, I see where you're getting at, but I can't really blame them.
Science just means "an area of systematic knowledge", since time immemorial. It's not just about a certain methodology to go about.
So, they shouldn't confuse it with a "hard science", but it's a science in the common sense nonetheless.
Science just means "an area of systematic knowledge", since time immemorial. It's not just about a certain methodology to go about.
So, they shouldn't confuse it with a "hard science", but it's a science in the common sense nonetheless.
I just started working in a research lab at a hospital that creates finite element cardiac models based on data taken during heart surgeries on sheep along with MRI images taken at various intervals pre and post surgery. Although I'm still new to this position, it seems that our own methods are subject to just as much deception. We basically want our models to coordinate with the actual heartbeat at only two exact moments, during the beginning of contraction and relaxation. If I've understood what's been done before, modeling these two brief periods during a single heart beat are all that are needed for publication.
I bring this up not to criticize my lab, obviously our work is meant to be a progression towards getting more and more accurate models. I just think that it shows that even something that is considered hard science is subject to many of the same faults as anything else. There are so many parameters and considerations to take into account that I don't think the end goal is to build a comprehensive theory that explains computational modeling of physiological function in a similar way to how Newton's laws predict the motions of the planets. The goal is simply to create a model that works for the purposes of helping diagnose and treat people more accurately.
It seems that a crisis is imminent in the coming age of computational, statistical, and mathematical applications to all fields where researchers are not properly taught to distinguish between data science and building actual theories. Just as there is a humungous gap between using a computer and actually coding, there is an equivalent difference between being able to collect & analyse data with a computer, and able to actually build a substantial theory that can describe a vast number of phenomena and result.