text:
Can a postdoc conduct collaborative research with people that are not their PI?
I am a junior faculty member at a research university. I am working with postdoc A  on a project. Postdoc A's PI, Dr. B, works in the same university, but is affiliated with another department. Prior to starting the project, postdoc A and I have agreed that we would work on the project during their spare time. We did not inform Dr. B about this arrangement. When the project is about half-way done, Dr.B found out that A is working with me on the project. Dr.B approached me and told me that all postdoc A's projects have to go through them, and claimed that postdoc A could get fired by the school if the school found out that postdoc A is publishing papers without their PI.
My questions:

Is it true that all projects of a postdoc have to go through their PI (even if the research is conducted during the postdoc's spare time)?
Do I have the obligation to let Dr.B know about my collaboration with postdoc A, given that we agree to work on the project during A's spare time? (I know as a courtesy to a colleague, I probably should have informed Dr.B about it before the project starts...)
What can I do at this point to make sure postdoc A's career is not affected by this incident?

"spare time" ... what's that?

"Can" is a very vague way to answer this, as it will depend on many factors - like the policies of Dr. B's lab, and if said postdoc wants their contract renewed. Or university policy (never underestimate the detail of university policy). But there's no universal, legally binding policy regarding this.
Do they? Yes, all the time. Probably a good 50% of the publications that came out of my postdoc were from outside my PI's lab.

It is likely not legally binding that postdoctoral activity must go through the supervisor, especially not if the additional work was beyond "normal working hours" and uncompensated. For example, if the postdoc had wished to donate time to a charity, no permission from the supervisor would be necessary. However,
Yes, you and Postdoc A had a professional obligation to inform Dr. B about your collaboration, especially if it was intended to result in publication and was not just for Postdoc A to learn something new. Dr. B has committed to training and advising Postdoc A, and Dr. B's financial and intellectual commitment to Postdoc A makes your unpaid collaboration possible. Also, you might be hard-pressed now (after the fact) to demonstrate convincingly that none of the work ever took time from the projects with Dr. B. Therefore,
You should take the fall for this one. Apologize to Dr. B, say that you should have known better and that you wrongly advised Postdoc A. Offer review and editing of the manuscript and possibly minor authorship to Dr. B. Or, collaboration on a follow-up project. Give a gift of coffee, tea, or a favorite food (I have found this to be a surprisingly effective apology strategy with academics -- citing my personal experience here).

Citation for all advice -- personal experience in the lab of another Dr. B, watching another Postdoc A.

Offers of (minor) authorship should come with assumptions of earning that authorship, one hopes.

Legally binding is a bit vague here - there can be grant restrictions on the tasks that a postdoc is paid to do.

Projects don't have to go through the PI, but it is considered professional courtesy that they do. The PI is paying for the postdoc's time and efforts.
You should have asked before starting the project.
In general, if a postdoc is hired to complete a certain task, then depending on the terms of the grant that they're under, it might be the case that the funding agency will get mad if it's discovered that the postdoc works on unrelated research activities.
Grant applications often have research effort descriptions, e.g. postdoc will devote 100% of their research efforts to task X, PI will devote 20% of their research efforts to task Y. So your claim that the postdoc worked on this in their spare time is irrelevant - they were doing research activities, and this took away from time they could have done other research activities related to the grant.
What can be done now? Apologize to Prof. B, and to the postdoc for putting them in an uncomfortable position. Be ready to write a very warm and glowing reference to the postdoc regardless of the outcome of your research efforts. I don't think this will have a lasting effect on their career assuming the work with their PI went well.

"The PI is paying for the postdoc's time and efforts." I don't know where such arrangement is common, but this is certainly not universally true. From what I have seen, postdocs are employed by universities and their salary comes from a government agency or an industrial partner. Some PIs behave as if they can do whatever they want with the grant money, propagate this misconception or even truly believe in it. How a postdoc can remind them this is not the case without risk to postdoc's career is a separate question.

The PI manages their funding. They are in charge of deciding who to hire, they are held accountable for their grant’s progress, they are the ones who decide whether the postdoc stays or goes, they’re the ones who need to answer why a goal wasn’t achieved. For most intents and purposes they’re in charge of the postdoc. That’s a simple fact. When a PI directs a person hired by a grant to do X and not Y, it’s precisely because they’re acutely aware that it’s not their money, so postdocs (or grad students and the PI too) can’t do whatever they want.

Is it true that all projects of a postdoc have to go through their PI (even if the research is conducted during the postdoc's spare time)?

No.  That would be blatantly unethical.  Authorship must be earned.  Being the PI is not sufficient to earn authorship in most disciplines.  You might also check your local labor law.

Do I have the obligation to let Dr.B know about my collaboration with postdoc A, given that we agree to work on the project during A's spare time?

No.  You have no obligation to report on any colleague's spare time.  It does not matter if they are golfing or doing research.


text:
Can I get a scholarship without a recommendation letter?
My country is currently on a rough armed conflict, a lot of the official organisations stopped working, I lost contact with my high school’s teachers and I am already abroad now . . . I can’t think of a way to get a recommendation letter, can I apply without one and how to explain my situation??

Unfortunately, we cannot answer that because it is very much dependent on individual factors. A motivation letter explaining your situation might be an idea that could help.

I advise many international students who, for many reasons, cannot get documents from their respective countries. There are several workarounds your issue:

Take a class at a local community college (in the US, in other countries, any open-admissions institution) and get a letter from that professor, then ask that professor to write your circumstances on that letter. This might be necessary, for example, if the place you are applying to requires a letter to be uploaded before they'll even look at your application.
Go directly to the admissions office at the institution you are applying to, and ask. Some institutions have specific guidelines for working with refugees.
Since you mention high school, I assume that you are applying to undergraduate institutions. Find out about high-school equivalency exams in your current country. In the US, this is the GED. You then take the GED classes, and get letters from those teachers.

source: Stack Exchange

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

sort: votes


text:
Assessing the Strength of my Graduate School Application
I will be putting together applications to apply to math PhD programs soon, and I've been struggling to select schools to apply to. I'm not sure how competitive of an applicant I actually am. I've talked to several of my professors and get very differing opinions every time. Some of them claim they haven't been on admissions committees in a very long time, and don't have any intuition as to what schools are looking for now days. One claimed my GPA (a 4.0) doesn't matter and schools won't care. A few others said it's a good asset. One claimed (the same one that said my 4.0 wouldn't matter) that without an REU, I need not apply to competitive schools. Several others claim that an REU would be an asset, but probably won't be the deciding factor whether I get into a school or not. Overall, I feel pretty lost and confused at what schools I should be looking at.
Some points to my application

4.0 GPA
Many upper-division courses in algebra, analysis, and topology
An "introduction to research" project, in which I completed a reading course with a professor (algebraic geometry) and presented my work at a symposium.
One letter writer is a distinguished professor who knows me decently well.
One letter writer is an associate professor who knows me extremely well. He both offered to do the research project with me and mentored me through it.
A post-doc who, again, is extremely familiar with me and I know will offer very high praise. I received my department's most prestigious scholarship with him as my letter writer.
Approx. 95 percentile for math subject test.
I taught mathematics at a juvenile detention center for 4 years.
I am currently founding the AMS chapter at my university.

Some points that I lack, which worry me:

No REU. I only decided that I wanted to do a PhD this last year, but I'm getting married this summer, which didn't really make doing an REU particularly conducive. There is a chance that I may be able to get involved in research this fall, but it's not guaranteed.
No graduate classes. I'll be taking a full graduate load this year, including real analysis, algebra, topology, and a semester of representation theory in the fall, and then complex analysis, number theory, and algebraic geometry in the spring. But none of these will appear on my transcript when I apply.

As I said before, my professors give me a very broad range of opinions on the strength of my application. One says that they never had the chance to do graduate classes as an undergrad and got into Michigan just fine. Another one says that without a strong list of graduate classes, I probably won't be given much of a second look. I could go on, but the point is that for any opinion one professor has given, I have the opinion of another professor that says something very, very different. And, of course, when I bring up that "Professor so-and-so said this," I get "Well, I haven't had to apply to grad school in a long time, so I don't really know," in response (or something like it). This trend is consistent even with the professors who know me best and will be writing my letters of recommendation.
Is there anyone out there that could vouch for the strengths/ weaknesses of my profile, who maybe knows a bit about PhD admissions? I'm wondering if part of the problem is that I simply have a weak profile, and my professors don't want to hurt my feelings? Please be honest--if I don't have a good application, I want to know it!

Unfortunately, this is really hard to tell externally (and even for admissions committees).  My department offers a graduate course entitled "Ring Theory" (which I have taught once).  It is actually completely a commutative algebra course.  We cover slightly more than an undergraduate course I took as an undergraduate, and about 3 weeks worth of material in the (supposedly introductory) graduate course I took when I was a grad student.  Standards and expectations vary, a lot.

Your professors are right -- all of them. The problem seems to be that you are thinking of graduate school as an extension of college, as if it were just another level up the chain. The difference between graduate school and undergrad college is one of kind, not degree.
Admissions to graduate school can be managed by a committee with no input from professors, or solely by the individual professors who can be your advisors, and everything in between. At programs where there's an admissions committee, the process is more systematic with the committee preparing a spreadsheet with all candidates ranked by a combination of GPA, test scores, research experience, etc. Some even try a formulatic approach arriving at a single number that is then used to rank students. At places where the individual professors admit students directly to their own groups, to know what the professor wants is an exercise in mind reading.
You have to choose programs and potential advisors based on your own criteria and then apply. Some will reject you, and if the stars align, some will like you and reply with an offer.

Thank you for your response. I would upvote it, but I don't have the reputation. Does this mean that applying to graduate schools is a bit of roulette? Should I just apply to as many programs as possible and cross my fingers from there? Or is there a way to know what a program is looking for and select schools that I stand the best chance at acceptance?

Not so much a roulette, but finding a match, ie finding a program that is looking for a student like you. There are other answered questions on this site that address how to approach potential advisors, for example https://academia.stackexchange.com/questions/90725/how-should-i-phrase-an-important-question-that-i-need-to-ask-a-professor

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
Ask an expert in your field that you have never met to write a reference letter?
To be more specific, I work in pure mathematics and I am applying for a postdoc position in the US.
Prior to this, I have only had my recommendation letters written by my former advisors and PhD defense committee members.
However,  my recent research, as well as the scientific profile of the job I'm applying for, deviates from what I did as a PhD.  So I feel it more useful to have professor A (an expert in the field) reference me rather than those people.
But I merely have email correspondence with A and we have never met. Indeed, in the past year I released a paper which generalizes A's earlier work, and our emails are mainly technical discussions on his work and mine.
So my question is: in these circumstances, is it appropriate to ask A for a reference letter?

It sounds like an even better way to resolve this problem might be to try to arrange to go visit A, or vice versa. It sounds like it would be good for you two to know more about each other's work, and could lead to closer collaboration. And he'd likely be able to write a stronger letter after having an extended in-person discussion with you.

If you feel that A is sufficiently familiar with your work and your expertise and can comment upon those responsibly, then it is not inappropriate to ask him for a reference letter.
Your perception of your relationship maybe, however, different from his. And if that is the case he will probably politely turn you down (he is too busy, he does not feel comfortable doing it, etc.) in which case don't take it personally and don't hold it against him, and certainly don't force the matter. Having a weak letter at times is worse than not having one at all.
If the request was made to a complete stranger, it is possible that person may develop a bad impression due to this request out of the blue. But since you have been in correspondence with A, the worst that can happen really is that he would say 'no'.
To put in a personal perspective:

I have had an experience where an expert in the field, who has read some of my papers, and who has invited me for seminars (and hence whom I have personally met), turn down a request for a reference letter because he feel he is not in a position to write me an extremely strong letter.
I have had also an experience where an expert in the field agreed to write me a reference letter based on having read some of my papers and having conversed with some of my co-authors. We only had e-mail and Skype discussions after he agreed to write the letter and want to find out more about me and my interests.

Note that, if A agrees to write a letter, it will likely contain a disclaimer that he cannot speak about your teaching, collegiality, etc. So you will want to have other letters that do.

I switched the first word to Unless, so that it reads better, if this wrong feel free to reject my edit

@MarkRogers I think it's the double negative that makes it sound unclear.  Changing the first word to "unless" would give the wrong meaning, though.

@slicedtoad - ah your right, my bad.  My brain kept confusing the bolded point.

There is a subtle difference between nonnegative and positive, and the mathematician in me prefers the doubly-negated form. Of course, if you have a better word for inappropriate which is less confusing, I'm all ears.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/


text:
How should a social scientist deal with envy of disciplines that are more quantitatively rigorous?
I am a student in social science who got into quantitative research at the start of my graduate school. Since then, I have had the valuable opportunity to take many Statistics classes and become aware of this world I had not known before. However, with it comes the curse of envy, as I cannot help but feel that my discipline, albeit using quantitative methods, is not as sophisticated (i.e. using a method without understanding the assumption and the derivation, basically relying on canned statistical package that others recommend). Outside of academia, the industry job market has also spoken that these quant researchers are more valuable than I am.
With this attitude of mine, I've become increasingly cynical about my discipline. I don't feel that the work that we do is "scientific" and "accumulating knowledge." I don't think that my industry job market can be competitive. This obviously has harmful effect on both my mental health and my research. I just want to learn more stats, write more code, instead of doing the research of my field.
On rare moments of clarity, I suppose this envious feeling is turtle all the way down perhaps. I'm envious of the statisticians, but maybe the statisticians are envious of the mathematicians, etc. This is why I decide to ask Academia Stackexchange for perspective.
From my occasional conversations, I get a sense that some of my fellow students may have the same feelings. However, given the toxic nature of my thoughts, I can't really discuss them widely with friends, not to mention with my professors.
How to deal with these thoughts?
PS: The relevant xkcd that will inevitable show up in the comments :-)
PPS: Given the gravity and scope of this question, I don't feel entitled to picking a "correct" answer. Thus I will just let the community upvotes decide the visibility of the answers. I hope that the answerers don't mind -- thank you for your insights.

We are all envious of category theorists. (Disclaimer: No, we are not.) /sarcasm Good luck with the question. I truly wonder what interesting ideas can the wise people here give!

@gnometorule Don't you feel worried that they will one day decide to learn that 5% and take our jobs? Again, back to the "turtle all the way down" idea, several disciplines feel worried like that about economists taking their jobs :-)

@social_science_phd: Look at all the physicists and other quants on Wall Street. "They" have already decided to take the economists' jobs. At least where the pay is good. (I'm not discussing whether they are doing a good job there.)

Interestingly enough, 10 hours later we get this question on moving in the opposite direction.

Related: https://xkcd.com/435/

And while we're at xkcd, https://xkcd.com/1052/ applies also.

Well, sit down and learn what your statistical models imply. Problem solved.

Do your best to improve the quantitative rigor of the social sciences themselves, if you can, by producing your own quantitatively rigorous research. The lack of rigor in the social sciences is actually a pretty major problem.

I suggest you read The Golem by Harry Collins. It might not help with your immediate problem, but it is a convincing explanation of why the sciences need the social sciences just as much as they other way round. All academic disciplines have value.

@KyleStrand The slight problem with a sudden increase in rigor in the social sciences, I suspect, is that it would involve gutting them of pretty much every conclusion they have made in the last few centuries.  Secondly, it may leave them without any justification for making new ones. In other words, demand for rigor would be the kiss of death to the social sciences, sapping them of all their waffly vitality.

@Kaz I assume by "slight problem" you mean "benefit"? :D Seriously though, I'm not convinced that the social sciences are unsalvageable.

A quick aside: "basically relying on canned statistical package that others recommend" - unless you plan on switching fields and going into math, this is probably better (but depends on the particular package, and what exactly you do with it).  There's enough counter-intuitiveness in prob/stats where, doing it yourself without the right background, you could end up with bullshit numbers and not realize it.

@Izkata: The problem is that canned statistical packages don't actually help you avoid bullshit numbers; they're designed to implement a bewildering array of statistical concepts, but they're not designed to help you identify the right concepts for what you're doing. This is why it's such a problem that so many scientists (and not just social scientists) use them without understanding; their software tells them that p < 0.05, but it doesn't tell them that they applied the wrong test, failed to cross-validate, and have meaningless results.

Some call it soft science, I call it complex science. And complex systems are cool, your simplistic quantitative models are out of their league here. Let's just throw out the bullshitters that hide in all this complexity and we will do wonderfull things.

@ruakh Good point; it was the "that others recommend" that had me thinking advice on the right way to do something was included, but that's not necessarily what was meant

This question, though an interesting point of discussion, is actually the embodiment of the problem itself.  social_science_phd presumes that social science is somehow distinct from other science and by its nature is less quantitatively rigorous.  It is just the practitioners of social sciences who are less rigorous.  There's no reason why the standard for truth should change depending upon the topic, and this question presumes that to be the case.  So I think this question misses the point.

Speaking as one outside of the social sciences whose work has been strongly influenced by readings from social science, I think it may be clearer if you tease apart three concepts that are often conflated: rigor, funding, and importance:

The mathematical or analytical rigor of a subject makes it more difficult for outsiders to understand or hold an opinion on, and inaccessibility can make things seem more important, but all it really shows is that it is hard to understand.
The amount of money thrown at a subject is another easy proxy for importance, but all that really shows is either popularity or market structure.

Social sciences actually deal with a lot of the really hard problems of society, the things that we all struggle with and don't know how to deal with well, like injustice and politics and social conflict.  We in the hard sciences and engineering like to pretend that we can solve these problems by the injection of new technologies, but all we can really do is create disruptions that destabilize the current order, following which society may become either more inclusive (e.g., the creation of the internet) or more exploitative (e.g., the creation of QoS protocols, leading to the current battle over net neutrality).
Social sciences are further challenged by problems of instrumentation (most of what they care about is really hard or inappropriate to measure), replicability (many phenomena are large enough or long enough duration that we've only got one or a few data points), and experimental controls (many interesting experiments cannot be performed because they would be horrifyingly unethical, e.g., isolating populations from the rest of society).
And yet... and yet I think the social sciences produce some of the most important work for us as humanity, because the work done therein is part of the reason that the arc of the moral universe bends toward justice.
So I think it's OK for a social scientist to envy the accessibility of data for people who only need to build a multi-billion dollar machine in order to do their research.  But don't envy them their field: your problems are just as important as theirs.

I wholeheartedly agree that social science problems are very important -- that's how I got interested in it in the first place. The problem is that I've become disheartened when I see (feel?) that social science isn't accumulating knowledge and moving towards solving those problems (given the difficulties you mentioned + trendy but sloppy quantitative work)

Trendy but sloppy quantitative work happens in every field: if you don't see it in another field, it's only because you don't know the field well enough to understand which bits are sloppy.

I can't +1 this AND the comment enough. Really, if you think your discipline isn't good enough, it is likely because you know what the average paper in your discipline looks like vs. the coolest, most influencial papers of other disciplines. You just never see the run-off-the-mills papers of other sciences, and/or you are not well-versed enough in their field to see their specific weaknesses.

A very much related question is here - the OP feels that all of Computer Science is just making shit up: http://academia.stackexchange.com/questions/26918/is-there-a-name-for-the-inverse-of-the-impostor-syndrome

As one of those people who gets data from multibillion dollar machines, I envy social scientists for the relevance (and one might also say importance) of their work.

+1 :) As a qualitative social scientists who dabbles in quant methods, I think this answer is excellent. We are also starting to see interdisciplinary work occurring, in which engineers and social scientists work together to solve issues in society. The engineers can make the technology, and the social scientists can pinpoint how it can be used to solve a complex social issue.

Background: I'm an academic in psychology that endeavours to apply innovative statistical techniques to answer psychological research questions. Over the years I have shared some of your concerns. While standard training in undergraduate and postgraduate psychology teaches many advanced statistical techniques, it is often taught at a high level that focuses on effectively using standardised software. In order to achieve my aims of applying innovative statistical methods, I spent years doing short courses and teaching myself a whole range of things: (a) a university mathematics curriculum in probability, mathematical statistics, calculus, linear algebra, etc; (b) proper statistical computing tools such as R, unix command line tools, and a wide range of other computing concepts; (c) more advanced statistics relevant to my area (e.g., Bayesian statistics, psychometrics, etc.).
I've also spent quite a bit of time interacting with people quantitatively trained in other areas (e.g., statisticians, biostatisticians, and mathematically focused practitioners). These interactions gave me a greater appreciation of what skills different fields do and don't possess. It also helped to clarify not only the quantitative shortcomings of my training, but also the strengths of my existing psychological training.
Some general principles

Think about what you are trying to achieve with your career. Plenty of social science academics and researchers function very effectively with only the standard understanding of quantitative methods. I've also seen many consulting areas related to psychology where it is the people that manage the client relationships that earn a lot more money, and the people doing the quantitative work get much less (to quote a phrase I've heard, the quants may be doing the "grunt work"). I just mention this to highlight the alternative perspective. Like me, you may find it fulfilling to be an academic who  applies cutting-edge quantitative techniques to their discipline.
If you have a passion for quantitative methods but your training is in the social sciences, you can always acquire more quantitatively rigorous skills. Do formal courses; watch videos; work through text books; work on projects that require a greater level of understanding.
Appreciate the skills that you have. If you judge your skills by the values of a different discipline, you are likely to be disappointed: knowledge of theories and empirical findings in your discipline; methodology, design measurement techniques relevant to your discipline; and so on. There are likely to be a whole range of skills that you take for granted that people in other more quantitatively rigorous disciplines would typically not possess.

Follow-up questions: balancing substantive and method work
1. How do you balance learning new techniques with achieving research outcomes?
I think this is an instance of the general trade-off between investing in self-improvement and producing valued output. To some extent this trade-off should be in the minds of all PhD students. And it does not stop when you move into academia. A few random thoughts:

A PhD is a period where you will have quite a bit of time to invest in learning new skills. It's important to make the most of it.
Pulling yourself up by your own bootstraps can teach you a lot. However, if you can find an advisor  that has treaded the path (e.g., someone who has the skills that you want to acquire) you can save a lot of time.
Engage in projects with tangible outcomes that stretch you but that you are capable of achieving. For example, you might do a study for your PhD that requires you to do some innovative statistical technique. In particular, in social science fields, there are plenty of opportunities to use your statistical skills to get co-authorship on papers with your less statistically literate colleagues.
Remember that quantitative skills are only part of being a well-rounded PhD student / academic. Its important to keep building up these other skills in parallel. Trying to produce a tangible output (like a publication or a thesis) will keep you grounded in terms of whether you are developing your skills in a balanced way.
Consider engaging in other career relevant activities that will further develop your quantitative skills. Typical examples include industry consulting, teaching, and statistical consulting.
The world of quantitative skills is very broad. It can be good to focus on particular areas. Academia values focus.
Balancing the trade-off between learning and producing output depends a lot of personal circumstances. How certain are you that the skills you are developing now are actually relevant to where you want to go? What short term pressures do you have to produce tangible outputs? How willing and able are you to delay immediate rewards for a longer term goal?

2. How do you deal with the issue that substantive research seems to be valued more than methodological work?
I think methodological work is valued, and you can do research that draws on both your substantive and methodological strengths.

Some of the most cited papers in the social sciences are methodological papers (e.g., Barron and Kenny's 1984 mediator-moderator paper has over 49,000 citations on Google Scholar).
Your quantitative skills can be highly valued in research collaborations. This can help you get co-authorship where perhaps other authors have more substantive expertise. Nonetheless, your background in the discipline will give you an advantage over a straight statistician because it will be easier for  you to communicate with the substantive collaborator.
You can make substantive contributions to your field using innovative quantitative methods. At the very least you should be able to interpret quantitative results better than others if you have deep understanding of quantitative methods. All these things can help with your research goals.

Your background is exactly like mine and ideally I would want to use cutting-edge quantitative tools in my research like you. How do you deal with the issue of balancing substantive and method work? 1) time -- I find that learning stats from the ground up takes a lot time, which is fine, except that it doesn't immediately benefit my research. 2) even if I invest in that time, substantive contribution is still valued more than method.

I've added a bit more info to my answer

I think one thing you should bear in mind is that the superiority and appropriateness of a research method is determined by the nature of the research question. While quantitative methods have the advantage of, for example, being precise, qualitative research methods still have its place. Qualitative research captures aspects of the physical/social world that does not lend themselves to quantification. One example of combining qualitative and quantitative research methods in social science is the book Soul Searching by Christian Smith, where you can see how face-to-face interviews provide unique insight into adolescent religiosity that is not revealed by quantitative survey questions. To overcome the feeling that social science is not "scientific", I would recommend the book How to Think Straight about Psychology by Stanovich, which gives a nice argument as to why psychology (and really social science in general) is scientific even though such disciplines do not use any fancy equipment or rigorous math.
If it would make you feel better, I simply point out even disciplines such as mathematics that is considered more "rigorous" and "hard" have their own issues that threaten the validity and interpretation of the discipline (e.g., Gödel's incompleteness theorems and the Löwenheim–Skolem theorem).

+1 for the first paragraph, but I don’t think your example at the end is accurate: I don’t know any mathematicians acquainted with the incompleteness or L-S theorems who would see them as threatening the “validity and interpretation” of maths.

@PLL Maybe I overstated my case, but if mathematicians cannot prove the consistency of a formal system, for example, then the danger is always there that maybe one day a new contradiction may be discovered. The L-S theorem shows that mathematical reality cannot be incorporated unambiguously into an axiomatic system. Such results complicate the issue of what a proper foundation of mathematics should be and make it hard to argue that math is a source of absolute truth.

"The L-S theorem shows that mathematical reality cannot be incorporated unambiguously into an axiomatic system." The Lowenheim-Skolem theorem concerns cardinalities of models of first order theories and elementary equivalence.  It certainly does not directly speak to mathematical reality.  (It is hard to argue that X is a source of absolute truth no matter what X is...)

They show that a particular philosophical (temptingly idealistic) stance on mathematical truth doesn’t make sense, yes.  But mathematicians and philosophers of maths since then have accepted that, and come to terms with it.  Their philosophical implications are certainly interesting, but I think it’s fair to say: very few mathematicians, or philosophers of maths, would view them as any potential threat (philosophical or practical) to maths as a discipline.

If you were to look up from your examination of how green the grass is on the quantitative side of the fence, you might notice someone peering in the opposite direction (Surprise! That's me!).
Like you, I like learning theories on how the world works, but my early math background led me to a college major in Physics. Seeking more of a human connection in my work (through direct application), I transitioned to engineering in grad school, specifically simulation work. As I learned more about mathematical modelling, I was exposed to examples from other disciplines, eg. economics, and learned the hard truth about mathematical models: they can be quite subjective.
Mathematics is a normative discipline, so the results of modelling (statistical or otherwise) reflect only as much truth as your assumptions and simplifications leave behind. The 'softer' the discipline, the more assumptions you are forced to make in order to make the mathematics tractable. In many cases, the assumptions you make dictate the results of the model, in essence giving you an expensive, circular, and self-congratulatory pat on the back. So don't be dismayed by the lack of definitive answers delivered by social scientists; the quantitative folks aren't accomplishing as much as it might seem. It turns out the world is just hard to figure out.
The point: If you have an interest in the quantitative side of the social sciences, by all means try to transition your research in that direction; there are plenty of people modelling social behavior, for example (check out swarm intelligence). But do so without harboring any delusions about the existence of a mathematical panacea that will reveal The Capital-T Truth.  A wholesale degree change may be too expensive (or not, if you have the resources and patience), but moving within the department or to a suitable advisor at a different institution are options that might be of interest.
Full disclosure: I am closing in on my ME now and plan to do exactly that in the summer/fall. I will try to leverage fluid modelling experience to move to energy policy/decision/economic modelling.

This is the way to go. Learn the hard tools and then carve into the soft stuff. It's a lot harder to carve into the hard stuff with the soft tools.

I'm in a similar situation as you. I studied geography at Cambridge. I was interested in human geography -- I wanted to predict the growth of cities and calculate which side of the pavement people will walk on. Unfortunately, quantitative human geography has gone out of style at Cambridge and most other human geography institutions. Instead they argue about discourse, performativity and othering and read a lot of Foucalt. After a year or two I realized that most of what I was learning was insufficiently rigorous / quantitative to be of much use or interest. I experienced the same envy as you. I moved as far over to physical geography as I could but even that lacked rigor. By that point I had learned how to adeptly throw down nonsensical but convincing buzzwords, which enabled me to finish with a 1st class degree but a sense that the process was largely a charade. I vowed to do "real science" for grad school. I got an MS in Geochemistry and am now drifting towards geophysics as I complete my PhD. The whole time I've been getting more quantitative but wishing I'd started with the math and then gone soft instead of the opposite.
So, to answer your question: indulge your envy! Go quantitative ASAP. You'll find that you actually DID learn useful critical thinking and writing skills which the quant folks often lack. Combine that with theoretical rigor and you'll write killer papers.
Bear in mind that you cannot learn ALL the math. Sounds like you have a stats specialty; probably focus on that. Maybe get involved in an open source coding project which combines areas you already know with ones you want to learn.
Also, try and separate "good" from "bad" envy here. If you want to have more powerful analytical tools and answer harder questions, that makes sense. But if you are being seduced by pretty 3D figures and expensive research equipment, be careful! I've been led astray by cool gadgetry more than once...
One more thing... I learned to program from my friends during undergrad. I had friends in math and CS and their knowledge and enthusiasm was infectious. Knowing how to program in a couple of languages was the saving grace that really made the switch from soft to hard science possible for me.

Update: from hard science I switched to engineering

Background:  I have a Physics BS, a Masters in Cultural Anthropology (ethnography), and an Ph.D. cross disciplinary in mathematical methods in Psychology and Economics.   I am now an academic psychologist (professor) and love my job.  It consumes me in a good way.
Even though I love my job, I am deeply disenchanted with the field. It feels so wrong for the following two reasons:  I. The theory is culturally and linguistically circumscribed verbal dribble.  My training in ethnography has left me critical of the ahistorical, acontextual explanations we use in psychology. II. Most people are celebrating complexity rather than searching for parsimony, lawfulness, or regularity.
Being an intellectual outsider from the field gives me purpose.  I use my quantitative and statistical skills to search for structure in data that is theoretically meaningful.  I search for invariances rather than effects.  Not simple invariances, but deeper ones.  Is there only one shape of RT across all people and conditions?  Do errors reflect guessing in that on those trials no stimulus information was processed, or is there partial processing?  Can all ROC curves be described with a single factor model or do you need multiple factors (say from two processes)?  Sometimes, given my skill set, I can answer questions in new ways that others haven't seen or thought of or are unprepared to do.
My sense is that psychology desperately needs smart, talented young people who are going to challenge the status quo.  The need is recognized and people who do so are often rewarded.
Best,

I think your sentiment is fairly widespread in some quarters, and economics has been celebrated for its commitment to the quantitative method of generating new knowledge (but by the same token, after the Financial Crisis economics have been derided by its failure to venture outside its abstract models and understand actually existing economies, but that is an aside). However, as said by @Drecate, it really depends on your research question. There are many branches on the tree of knowledge, and many concepts used by, say, economists came from outside the discipline proper.
Quantitative work is fascinating and can yield lots of insights. But they may be more of the 'how'-type questions than the 'why'-type. If you are interested in, say, the Cuba Crisis, and how it came as close as it did, or why New York's and San Francisco's housing- and gentrification policies may differ, you will need a lot more than just quantitative work.
To me, a hallmark of the social sciences is that they meet the object of study where it is--some of it is uncovered through its many regularities with quantitative methods, but a vast number of socially interesting topics lie beyond such pursuits and call for decidedly qualitative work. For an interesting exposition on some of these issues, see Bent Flyvbjerg's article on case studies.

While I agree with you on the importance of qualitative method on certain question, the job market of my own discipline is very difficult for people doing qualitative work. And if a qual person fails his academic career, his industry potential is even worse. Given the contraction of the academic job market, these are not unwarranted stress I think.

I've had the same feeling in the past and I still have it to some extent. However, for me it is within areas of engineering. Personally, I switched fields within my major and started over again.
I was a Mechanical Engineering PhD student studying experimental fluid mechanics and at some point I realized I'm more interested in instrumentation, sensors, signal processing, etc. I also realized that I enjoy coding. I eventually got a master's degree and left the program. My plan was to work for a year or two and take my time to make up my mind about whether or not I want to go back to graduate school.
Getting a job was so difficult that it really did make me envy computer science graduates (since there were many more opportunities for them and as an undergrad I was very close to choosing CS). Once I got a job, I really didn't like the work that much anyway so I was really encouraged to go back to grad school. My biggest problem was that I was afraid of changing major since I felt like I didn't have the necessary background to study EE or CS. I also didn't think I would be admitted to any CS or EE program and I didn't have the extra cash to apply to too many grad programs. I ended up applying to ME programs again and once I joined a graduate program I tried to join research groups which were multidisciplinary. I eventually ended up in a group which does robotics work which combines all the areas I'm interested in. Personally, I'm extremely glad I made the change. I like the field much better and my research is much more interesting; however, I am also always aware that I'm a bit older than my peers and I'm also aware that I left a well paying job and a decent career for this and I'm silently afraid that I'll end up having trouble finding a job I like again (because I feel CS and EE majors get most the robotics jobs) and I'll end up settling for something similar to what I had in the first place....And that leads back to the envy thing you were talking about...I still envy CS and EE majors. Although not nearly as much as I used to.

One thing to bear in mind is that our ability to evaluate the solution to a problem is directly proportional to certainty with which we can answer questions about it. Very well defined problems can have very well defined answers. Less well defined questions necessarily have less well defined answers. The most well defined questions allow you to use numbers to solve them, but not all important questions fall into that category.
Social scientists aren't lazy, and there are many fields of social inquiry where statistical analysis doesn't really add to the conversation. To be clear, there are lazy scientists who could be doing things better, but you have to ask yourself what a detailed statistical analysis will contribute. We live in a culture that glorifies the scientific and assumes that if you can put a number to something then you know more than someone who can't, but that's not always the best way to approach things.
Consider an engineer who wants to determine the ultimate strength of a new type of metal alloy. They build a bunch of metal rods and stick it in a machine that pulls the rod apart until it breaks. They do this 30 times, recording the force required to break the rod each time, and then do some statistics to show that 90% of their rods broke within +/- 5% of 30,000 PSI. This is a very well defined problem that admits a very well defined quantitative solution.
Consider now an engineer who wants to build the strongest bridge possible subject to a $10 million dollar cost limit. This is a much more complicated problem, and given the huge number of design decisions it's not possible to actually find the single most optimal solution, but they can try many different possible designs and settle on the one that gives the highest strength while meeting all specified constraints. In this case the question is complex but admits a simple evaluation criteria (total strength), so it's easy to tell which of a hundred proposed designs is "correct"- the design with the highest strength.
Now consider an engineer tasked to build the most environmentally friendly bridge. Suddenly you don't even necessarily know how to formulate reasonable questions, much less find the "correct" solution. There is no well defined measure of "environmentally friendly". One bridge might generate a million tons of CO2 in the atmosphere to build, while another bridge might generate ten million tons of CO2. The second bridge runs through sensitive wetlands and will probably kill a species of endangered turtle. Is an extra 9 million tons of CO2 in the atmosphere worth killing and endangered turtle? We can quantify the possible alternatives precisely, but the question does not admit an optimal well-defined solution.
Likewise, psychology has some very well defined questions with very well defined answers. The military has done a lot of research into what it takes to train someone to shoot and kill another person on command. In WW2 a man named SLA Marshall did simple observational studies that showed that around 75% of troops would not shoot another human and did not fire their weapons in combat even if their own life was in danger. Thus, the psychological question "How many people are willing to kill another to save their own life?" is quantitatively and unambiguously answerable: about 25%.
There are more complex questions that have less well defined answers. The military's next question was how they could get more people to fire their weapon in combat. They experimented with a number of techniques, but in Dave Grossman's book On Killing he reports that by the time Vietnam rolled around the military was able to increase the firing rate from 25% to 90%. We can pose and answer a second question analogous to the engineer who wanted to build the strongest bridge: What training technique should the military use to maximize the firing rate of their soldiers?
However, Grossman and others have proposed that the increase in firing rate has increased the incidence of PTSD and related psychological disability by causing people to perform actions (with horrific consequences) that they are not naturally prepared to do. This raises a third question that is less well defined in the sense that the engineer's third question is not well defined. "How should we train our soldiers?" If you train them to be too aggressive then rates of PTSD and psychological trauma go up. If they're not aggressive enough then your soldiers die on the battlefield. Balancing those two concerns does not admit a simple measure that we can maximize or measure.
Difficult questions arise in every discipline- it just so happens that the natural sciences tend to ask questions that are clearer and unambiguous than the social sciences. That doesn't mean that the social sciences are less rigorous or less important, but it does mean that non-qualitative approaches are more prevalent in the social sciences.
http://www.gocomics.com/calvinandhobbes/1986/11/26

I am an engineer with a minor in sociology and was a winner of one my University's most prestigious sociology awards as an undergrad in engineering. I am deeply inspired by a professor with three degrees, civil, mechanical and sociology who studied extensively on the effect of industrialization.
I am in the reverse situation. I know that what I am learning is more accessible to a career, more rigorous as you say, but at the same time I am keenly aware that it is not fundamentally transforming my society and the human experience.
How can I continue my work in a bubble when there are so much inequality in the world, so much negligence in the world, how is it that my little equations will change the fundamentally racist social hierarchy in America, lift people out of poverty, repair the hurt felt by Native Americans that drives them to abuse and suicide or stop exploitation of third world workers in this new age slavery?
How is flappy bird going to do all this, what is the merit of creating the new facebook, what is the point for accelerating the processor by one more microsecond or create a voice activated keyboard?
The question ultimately lies your realistic outlook on life. Many people lose sight to what they truly wish to do when money suddenly the biggest factor in their life. I know countless engineers now studying computer science because the field has more job opening, this is not what they want to do. People in software development often works without very deep understanding of the background theory, and many people find it difficult to swallow. Many go back to school, but most stay in industry. Engineers envies physics and math majors, applied math envies pure math majors, biologist envies biochemists, software people envies computer science people, computer science people envies pure math people and you know the pure math majors are going to envy software people when it comes time to graduate...and the cycle continues.
So my two cents is to see the connection between one's work and what one wishes to do, there has to be a connection some how and work along that connection. Einstein found the truth about our universe while working as a clerk, I wonder what was going through his mind as a clerk, I wonder if he ever realized that he was going to be remembered forever by the entire human race. In my engineering work I find opportunity to work with local disadvantaged communities and native communities and I do my part in establishing a bridge. No matter what career you wind up going to, you will be able to see a connection and that connection has a fundamental value that is going to go beyond any monetary or social gain.

I agree with you about the importance of social science problems -- that is the reason why I'm in it in the first place. However, the further I am in my studies, the more I feel that due to the lack of rigor + the inherent complexity of social problems, social scientists aren't solving anything either. That's how I lose my initial passion.

I think that's what makes social problems so much more interesting to tackle. Even with rigor, we can see a painful mismatch. For example, for decades the field of game theory has been attempted to use in social situations but failed because of limitations of this theory. But then again there are so many different areas that worthwhile to explore, such as machine learning algorithms that predicts social behavior. Predicting whether a person is going to sit in an empty seat on bus maybe an interesting social problem that uses CS. Social science is begging for new theories and approaches

And that is a problem to be solve with someone who is interested in both sociology (or psychology) and computer science, not someone who is purely interested in either. So most of the interesting things are on the boundary not within separate disciplines

I think that the relationship between statistical math and real science has to lie at a deeper level.  It's the level of the modeling of reality.  Let's take Johannes Kepler for example.  He came along after Tycho Brahe and before Isaac Newton.  He spent about 20 years doing a detailed analysis of Brahe's observations, and managed to determine that the planets followed an elliptical path around the sun.  But why ellipses? Why not some other shape?  Kepler didn't have an answer.
It wasn't until Newton came along, and came up with the mechanical model of gravitation and inertia, and added some new, high powered math to the mix, that ellipses suddenly "made sense".  If you apply Newton's laws to the problem you find out that the paths have to be ellipses, to a first approximation.  The reason I say approximation is that there are second order effects due to the attraction between the planets, and there are third order effects due to the fact that the planets follow Einstein's laws of motion, which are ever so slightly different than Newton's, in this situation.
So, did Kepler waste 20 years of his life?  I think not.  But I wouldn't blame a contemporary of his who wondered what all this mathematical analysis was really all about.
So, with regard to sociology, I'd say that they've been applying increasingly sophisticated mathematical methods and very, very primitive models to the field of study for the last 50 years.  That's more or less the computer era, if you start with transistorized circuits.  Transistors made computers much faster and much cheaper than they were before.  And it made number crunching on a massive scale a feasible approach.
Why are the models of sociology so primitive, when compared to physics, chemistry, or biology?  I'm not sure, but I think it's because the system under study is fundamentally more complex.  Physics, Chemistry, and Biology make advances in their models by abstracting out the aspects that "don't matter".  Separating out what matters and what doesn't matter in sociology strikes me as ultimately difficult.  There's more, but this is a far as I get, with limited brainpower.

In your question you focus a lot on quantitative issues. But "quantitative" doesn't mean "accurate" or "better". As they say, "lies, damn lies, and statistics". You can use the most rigorous analytical techniques and get the narrowest confidence intervals and lowest p-values ever seen in your discipline, but if you're using convenience sampling and getting a less than 10% response rate your results are meaningless, regardless of how numerically sound they are. Likewise, if you ignore pertinent confounding variables, you might get answers which are technically correct, but informatively useless. (See Simpson's Paradox.)
If you haven't already, please read Feynman's "Cargo Cult Science" essay, particularly the part about the rats. (Yes, Feynman was sexist - just please read through the sexism and focus on what he's saying about Science.) Good science is trying to properly understand the world, and that takes more than just running a large amount of numbers though a sophisticated algorithm - even one where you know all the assumptions and derivations.
In some respects, physics has it easy. The common joke with physicists' approach is "imagine a frictionless spherical cow in a vacuum". Even physicists will laugh a bit at that, because they know a lot of their success is due to oversimplifying problems. But they don't feel too bad about laughing as it's still success. They're able to simplify and isolate their study systems in ways that are impossible or immoral for a social scientist. What's more, they know where their methods start to fail and just have to throw up their hands and quit.
Social scientists don't really get that luxury. They have to deal with humans as they are - messy, complex, interconnected and chaotic. You can't deal with the equivalent of a frictionless, spherical human in a vacuum. One, they wouldn't display the behavior you're interested in, and two, you'll never get IRB approval to actually do the test. That's part of where social scientist hate comes from - they're forced to deal with messy systems with very limited tools, trying their best and often, understandably, making a botch of things.
That's not to say that there aren't a bunch of cruddy social scientists out there, or that a more rigorous approach wouldn't benefit them. It's just that throwing "quantitative" at the problem is cargo cult science. Better statistical techniques are worthless if your study system has ill-defined confounding variables or is intrinsically under-sampled.
Regarding jobs, yes, employers are looking for quantitative skills, but mainly because that's what's easy to filter CVs for. What they're really after is analytical skills. Can you take this complex problem, with these difficult restrictions, put bounds on the system of study, and come up with a good analysis of the key question of interest despite all the mess?
Keep in mind that the goal is not to be "quantitatively rigorous" but to be scientifically rigorous. Quantitative techniques are just a tool to do that, and might not be the main or even best tool for it. Also, even though employers say they want quantitative skills, what they really want is analytical skills, and a good social scientist should have those. In fact, in some areas they may even have an advantage, as they're used to dealing with the complexity of the human condition. You can't just say "imagine a spherical homeowner ..."

Several answers here and yours suggest that quantitative tools are not always suitable to the task, which I agree 100%. The only problem is that many social science disciplines are self-imposing a quant monopoly. Economics is a prime example, political science and sociology are following suit. It puts tremendous pressure on graduate student to follow that trend, cargo cult or not.

Social disciplines are already as rigorous as their "rivals"
While being counterintuitive at it seems, let us return back to the basic question: where does the rigor of the natural disciplines come from? Being able to collect data and reduce them to formulas? But aren't they exactly what social scientists do everyday: observing and finding patterns? It isn't because social disciplines are so primitive that they aren't as quantitative as their rivals, it's because quantitative tools are so primitive that they can't be used effectively in them. There is no distinction between the two, albeit the surface it seems.
While you envy them, they envy themselves: How should I deal with discouragement as a graduate student? What do mathematics researchers do if they aren't good? There is nothing wrong to say Einstein is the genius, but it doesn't mean anybody else is less important. That's what we call "teamwork". You have accepted a fact that at this very moment, your thoughts are toxic. This is deadly important, because only when you accept it, you will magically feel better immediately*. Then, with the relief in your hand, use it to find out how your work is important to the techie guys. It's already there, you just hasn't seen it yet. When and only when you find your own value, then your envy can stop.

*Psychology 101. How could social sciences not be wonderful?

Two suggestions, from someone who is in a quantitative science that is "one adjacent" to social science, and who often scowls at my colleagues for ignoring social science and re-inventing the wheel from time to time:
Add More Rigor: Take more statistics classes, and in your own work, ground your analysis in statistically defensible methods. Economics, for example, has started running more randomized controlled trials, and social science has been responsible for a lot of work in social network analysis, agent based modeling, and other quantitative methods.
Don't Worship Math: Math =/= Rigor, as much as people like to conflate the two. This often occurs in my field - there will be some very elegant math, with a great many accompanying proofs, that doesn't actually map to anything in the real world.
This has also been a criticism leveled against economics more recently - that sophisticated mathematical models have been used to cloak an element of magical thinking and give things a veneer of rigor without it actually being there.

authors: عبدالله محسن

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/


text:
Would a professor write a bad letter of recommendation?
I asked a professor from my online graduate degree to write a letter of recommendation. It was when I first started looking at graduate school, so was not prepared with what you would usually want to give a professor, especially one you had never met in person. The exchange went like this
Me: "Dr. XXXXX,
I was a student of yours at XXXX from 2011-2014. I will be separating from the military in the fall and am looking to pursue another Master's degree in Food Science. I hope to fuse this with my work at XXXX and my previous biochemical engineering degree in order to work as an R&D chef focused on Nutrition. I was wondering if you wouldn't mind endorsing a letter of recommendation for me. It would be a great help in my application process. Thanks so much for your time."
Professor:
"Yes, I will be happy to provide a letter of recommendation to you. Please, let me know specific details and whom it should be sent to."
Me:
"Thanks so much for the quick response. I'm actually looking at colleges right now, but as soon as I get them narrowed down I will send you the contact information. As far as specific details, is there anything you are particularly looking for? Or should I just include the classes I attended that you taught? GPA? The focus of my studies? Once again, thanks so much for your help. I really appreciate it."
Professor:
"I do not need any additional information. Let me know when you need letter to be sent."
Is this basically, you didn't come to me prepared. Now you're getting a bad letter? Or maybe my record was looked up and showed something bad? I got all A's in the classes and was the supervisor of my capstone project which was also an A. Since then I've sent  my resume and some facts to work off of as well. Communication had been normal since then, usually a quick response with short replies. I feel like I may just be being paranoid, but I figured I'd get some unsolicited opinions on whether it's worth the risk (if there is a risk) to use his letters.
The professor is a Dean at the college and has been exposed to my work the most. I've contemplated possibly asking politely what happened during that exchange, but I can't for the life of me figure out how I would even approach that. One letter has already been written and submitted to a school.

Related (really!): Why do academics frequently write very short email replies?

Unrelated quibble: you "figure [you'd] get some unsolicited opinions"…but the purpose of your question is precisely to solicit opinions, so they're not unsolicited >_>

As someone who did an undergrad at a university where EVERYONE was running around asking for way too many recommendation letters, I suspect, given that it is from an online degree program, that the professor has several pre-written letters and he will just replace your name into the letter and send it to all the institutions. I doubt it will be a bad letter, though it may not be a particularly helpful letter either (as it will probably be pretty general).

Relevant comic.

If you got A's, you should be one of his best students. I didn't teach for long enough to get students asking me for letters, but I wouldn't need any reminders of my best students. - I would probably have a form letter that my best students matched with sentences I could delete if they weren't true: On time. Asked intelligent questions. Did good work. Top of class. etc... Stop being paranoid, and good luck with your future endeavors.

Related: http://imgur.com/gallery/h0ynXoA (photo of document, SFW)

I would read these responses as: "I am happy to write you a letter of recommendation, I am busy, and just want know where to send my letter".
As a professor, I get literally hundreds of emails everyday, many of which, like yours, I want to reply to, but I don't have time to write elaborate or detailed responses. I read the responses you got as terse but professional.
If I am approached by a student I don't feel I can write a good recommendation letter for, I usually tell them this, and suggest they ask someone else. I think this is common practice.

This is the correct reply and just the way the exchange would have gone for  one of my students. You should be pleased.
The professor will not need any information because the academic records and personal recollection provide everything that is necessary to put in the reference letter. If you had to provide information then the reference provided would be less worthy. Anyone can write from information provided, but to do it from personal experience will be so much more valuable.
The fact that reference will be sent direct to the colleges will give it so much more weight. A confidential reference from a professor that remembers teaching you is the best you could ever get.
Be more grateful and less paranoid.

Just to reiterate a point that is often misunderstood: a letter of recommendation that mostly refers to second-hand information is not positive. The best LORs refer to first-hand experience of the letter-writer with the student. What's the point of having the letter refer to things that are documented in transcripts and such? It adds nothing. Nothing. (Unless there's either positive or negative trouble/action that can be explained by adding information...)
So, when I write letters, the main point is to speak of my first-hand knowledge of the student's past accomplishments, my perception of their interest, motivation, and potential, based on my direct contact with them... and "looking them in the eye".
Email contact is more tenuous, sure, but, still, it has been known to be an adequate mode of communication "first-hand", not just a notifier of events elsewhere.
So, if your former teacher has a similar attitude as mine, then (s)he'd have no need at all for any further information... because his/her letter would, and should, be based on first-hand knowledge of you.

I think you nailed it. Just a guess on my part, but I wouldn't be surprised if the O.P.'s revelation ("I was the supervisor of my capstone project which was also an A") is the first-hand knowledge that is coming into play here.

A professor will not likely tell you that he would be "happy to provide a letter of recommendation" and then proceed to write you a terrible letter of recommendation. Much likelier, if the professor felt they could not in full honesty write you a compelling letter of recommendation, they would let you know that you would probably be better off finding someone who could write you a stronger letter of recommendation (i.e. someone who recognizes some other qualities in you that would let them write a stronger letter of recommendation). The fact that the professor has indicated he is happy to write you a letter of recommendation means that he is happy to write you a letter of recommendation. To say anything to that effect and then proceed to write a poor letter of recommendation would just be mean-spirited and dishonest.

It does not sound like he is going to intentionally write you a bad letter of recommendation. Presumably, he has access to the classes you have taken and the grades you have gotten (potentially you entire transcript). That said, not wanting to know more about what you have been up to is not a great sign. It is much easier to write a good letter of reference that is tailored to a application, if you have seen the application.

The answer to the question title, as opposed to the specifics of your case, is "Yes, it can happen".
A bad letter of recommendation may be unintentional - either because the recommender does not actually know you, and this shows by him not discussing first-hand experience, or because the letter may be short and generic etc. But if you're not sure someone has a high opinion of you, it is not impossible he recalls something negative and will let that mix in with what he might write. Finally, in some cases, recommenders get contacted by future employers/academic institutions, and if they don't follow up with some positive banter, that might be work in your disfavor.
So, bottom line: If at all possible, get a letter of recommendation by meeting with the recommender in person, telling him what you plan on doing, etc. Otherwise it might be a bit of a risk.

Be very pleased this instructor is willing to give you a recommendation letter. If you are super curious what he will say, ask him for a CC sent to you, to put in your resume file. Just in case you change your mind and chose another school. You won't have to bother him again since you have a copy of the letter and can just forward it. It may work different for school ref. Letters but every Supervisor etc. I have every asked for a reference sent me a copy anyway.

Depending on cultures and context, some recommendation letters are NOT copied to the applicants. This is, for instance, the usual procedure when applying for lectureships in the United Kingdom

In the US, this would be completely inappropriate. See the discussions at http://academia.stackexchange.com/questions/1152/ and http://academia.stackexchange.com/questions/61576/.


text:
Math teaching job after PhD in the United States for a foreigner
I am currently at the beginning of my math PhD career at a university in the U.S. after getting higher and college education in Europe (where I am from). I have always liked teaching and math, but I'm realizing that even though I am doing quite well in pure math research, I find it really stressful and I don't think I would like to that for the rest of my life. I enjoy much more teaching, and I would like to that in the United states. Now my question is in regards of the following 3 different opportunities. From what I understood, I could search for a job as a professor at:

High school
Community College
Liberal arts school

Is the salary increasing from 1 to 3? This is what I would expect, but I heard that, for example, the salary of a high school teacher is higher than the salary of a no tenure track position/ lecturer even at a good university. Is this true?
Would the PhD be enough to teach in a high-school? And also, is it going to be much harder for a non American to get hired at a high school rather than a college?
Would a position in either of the 3 types of institution give me vacations for the summer? I assume high schools are closed in the summer, but maybe colleges will require to teach during summer.
Any other suggestions or insights on the matter would be greatly appreciated, and I apologize if the question is a little vague, but I guess others might be in my same situation.

Hi, and welcome to Academia.SE! You are likely to get more focused and helpful answers if you focus on one key question; right now, you've got a lot of different questions and thoughts all together here.  Can I please suggest that you edit this down to your most important question?  You can ask the others as additional, separate questions if you like.

Is the salary increasing from 1 to 3?

Generally, yes.

This is what I would expect, but I heard that, for example, the salary of a high school teacher is higher than the salary of a no tenure track position/ lecturer even at a good university. Is this true?

The median 2012-2013 starting salary for US high school teachers was (ugh) $36,141.  This varies considerably based on the state: see here.  There is so much variation in the nature of a non-tenure track university position as to dissuade me from trying to look up figures, but in the cases I know of non-tenure track teaching positions at universities which are (i) full-time and (ii) with a PhD, their starting salaries are at least $45K.
In 2013 the median annual salary for US high school teachers was $55,360.  Teachers generally have strong unions and get steady annual raises, which is something that university professors have had a lot of trouble with in recent years, and non-tenure track faculty even more so.  Most people I know who are PhDs and have been in non-tenure track teaching positions for at least ten years are making $50K to $60K.  I would not be surprised if the median salary for these positions was less than $55K.
Note also that the average US high school teacher certainly does not have a PhD.  (In fact, fewer than one percent of such teachers have a PhD.  This is such a small percentage that the "converse percentage" of PhDs who go on to teach high school must be rather small as well.  The last link is to an article which explores this a bit.)  In the realm of high school teaching, having a PhD puts you into a very elite group which is competitive for the best teaching assignments, the best students, and so forth.  If you have a PhD, I think you would have an easier time bringing home a salary upwards of $75K as a high school teacher than as a non-tenure track college faculty member.

Would the PhD be enough to teach in a high school?

In many states, public high school teachers need to be certified.  My understanding is that certification is not require for private high schools (though this too probably varies by state) and I know of at least some private high schools which do not require it.  Anyway, though I have not done it, my understanding is that certification is a fairly easy process compared to getting a PhD.  In fact many students with teaching interests acquire this certification before they graduate, so it can't be that bad.

And also, is it gonna be much harder for a non american to get hired at a high school rather than a college?

Sorry, I don't really know.  My guess is that if you do not already have a green card and are looking for immigration status coming from your teaching job, that will be harder.  If you are a permanent resident, speak fluent and intelligible English, and have a PhD (again, this confers highly elite status among high school teachers) from an American university, you're going to look like a catch.  Do recall that America is a nation of immigrants.  If you're teaching in an urban environment, there will be many other foreigners and first generation Americans milling about.  If you go to a small town, maybe they'll chase you out with pitchforks...but probably only if you have bolts sticking out of your neck.

Would a position in either of the 3 types of institution give me holidays for the summer? I assume high schools are closed in the summer, but maybe colleges will require to teach during summer.

Yes, you should get summers off in all three positions.  For high school this is of course one of the major perks of the job.  In a college or university they will probably offer you summer teaching, and they may pay you little enough to make that attractive, but you certainly don't have to.
Let me put in a vote for high school teaching.  America needs competent, intelligent, serious high school teachers more than anything else I can think of.  Like many jobs, it has some negative aspects, but it's truly important, good work.

My local grocery store poached teachers off the school system by offering better wages, benefits, and more job security.  You can continue to bag groceries no matter what tantrums the politicians throw.  you can put your phd to better use bagging my groceries.

I would also note the stress that comes with teaching in public schools is far greater than teaching in colleges. Most high school teachers are unhappy unless they're  private high schools.

Some additional information about the possibility of teaching high school are given in the answers and comments to the Mathematics Educators Stack Exchange question Devising backup plan to become HS teacher as a late undergrad.


text:
Working on a Homework Question I Encountered Before Taking the Course
This year, I have been studying ahead for some graduate math courses I will be taking in the fall. I have been working on some exercises from a classic textbook and discussing a few of them with a peer. Furthermore, occasionally, if I am very stuck I may read about the question or similar ones on StackExchange for some hints or to have a brief peek at an answer to give me some ideas (I may carefully read the first few lines and see some terms that stick out or some notation).
My question is: what should I do if I happen to encounter a question I have worked on when I self-studied the material in a homework assignment, especially for a question where I got some form of help either from a peer or StackExchange?
Would it be acceptable to answer the question, without looking at the answer I wrote before but also without attributing my (hypothetical) latest solution to help I received when I first did the question? Let's say at this point I only vaguely remember how I answered that question before and I reasoned through the steps of the problem without active assistance from those sources. I am wondering if I have any responsibility to make attributions at that point. I would definitely at least cite any sources that I actively used in my latest attempt at the problem.
I feel that the answer is complicated because it is possible to learn a common idea that was not one's own and then assimilate it into one's way of thinking. Even if one may not have originated the idea of using a technique for a certain problem, now they are aware of it and they use it according to their own understanding of what it does.
Now, if I am conscious that my answer to the homework question is heavily influenced by something I saw from, say, StackExchange in the past, to the point where it is not very much the product of my own thinking and is just reciting what I saw before, I would cite that.
As far as I can tell, this sort of situation likely occurs often with students and passes without acknowledgment because it almost never results in an answer that is too similar to a peer's or an answer from the internet. But I still wonder if there are pitfalls I should be aware of or if I am overthinking this.

Just how you learn to answer a question is not relevant. The test is to determine if you have learned to figure it out. That learning how to do it relied on a variety of sources doesn’t matter. Textbooks, lectures, office hours, tutors, friends - learning is learning.

I don't see any violation or any plagiarism (per se) unless you explicitly copy from another source without attribution.
Learning isn't about doing everything yourself without help. If it were, academics would be unemployable as irrelevant. The fact that you studied a topic before you took a course is to be admired. Instructors normally answer questions of students. It is part of the process. I favor minimal hints for such questions, but students often need help to work around a block. It is a natural part of learning.
And, I don't think you need to avoid what you wrote down before if it helps. The review will be helpful, as would attempting the solution again without review. But using them for "hints" only as necessary might be the best path.
If you want, though I wouldn't consider it necessary, you could add a note to any such homework answer that you have seen and solved this problem in the past with help.
Normally homework is to help you learn something and imprint ideas in your brain. It is usually graded as the carrot/stick to make you do it rather then just ignoring the potential benefits in the hope that you will "remember stuff". You have done that. Not a problem.
There was a time when students at a very prestigious university studying math wouldn't think about "taking" a course unless they had audited it first. Even if the questions asked in the second viewing were different, they had some idea about how to attack them.
Yes, you are overthinking it.

A slightly different approach: If you actually run into this situation, just reach out to the instructor and ask about their expectations.
There is a gray area and corresponding cultural expectations about whether some particular idea is "common knowledge" which vary by level in an academic environment.
When I teach students an introductory programming class, I instruct them to cite any StackOverflow questions and similar sources whose ideas they used to complete the assignment. But I don't expect most researchers to do that with boilerplate code in research software. Why? Adapting boilerplate is effectively common knowledge for experienced programmers, but is a substantial skill that I'm trying to teach at the introductory level.
I can't imagine caring about this for, e.g., a math class however, especially at the graduate level. If you've already seen an argument I thought it was important for you to think about, or already worked through the question before, or what have you...Great, I expect you to find something else productive to do with your time that furthers your career goals. One usually conveys and expects earlier graduate students to write more detailed proofs for introductory classes to exhibit that they understand what's going on, but that's at best tangential to worrying about plagiarism and citations. The grades already largely don't matter anyway.


text:
Manuscript exceeding page limit
I have submitted a manuscript to a conference. They accepted double-blind maniscripts. The page limit was 8-10 pages. My manuscript exceeded the page limit by a bit. I had to make a last-minute change and then the manuscript extended a bit into 11th page. I didn't check the page length during the final submission, which was a big mistake. Will the conference consider my manuscript?

You could email the conference organizers. I have no involvement in this conference so I'm not sure how I'm supposed to know.

depends upon how strict they are. we cannot answer the question.

How much is 'a bit'? I would strongly suggest figuring out how to get it down to 10 pages and letting the conference organizers know. If only a few lines over, go and look at paragraphs that have only a few words on the last line and edit to shorten by that one line. Usually fairly easy to do...

It depends. If you have a very high impact conference that will end up rejecting lots of good submissions, then yes, they might not want to look at papers by careless authors who did not check their final manuscript before submitting. The conference organization software is probably not set up to generate desk rejects, as this option will cause even more complaints.
If you have a conference with a more reasonable acceptance rate in the range of about 20% to 50%, they might not use a silly rule for cutoffs and recognize that last minute changes can throw of the page limit. Reviewers might comment on exceeding the length, but if it is only a couple of lines, they will tend to be reasonable and not apply stricter standards. I for example would be worried about a paper that is longer by a page or more because it feels that the authors want me to tell them what to cut, which is their job and not mine. It also raises an issue of fairness.
If you cannot wait for a decision because there is a deadline for an alternative conference looming, you might write to the PC chair. If this is a top conference, the PC chair might still get annoyed by you as they will have a lot of questions like this. In a conference with only a few hundred submissions, the PC chair will have a bit more bandwidth to deal with nervous authors.
In your interactions with the conference, please be mindful that most of the work is done by volunteers (or graduate students who have been drafted) who do not see any of the conference fees but will (in my experience) be hounded by the organizing institution to fill out forms, give accounts, and sign statements.


text:
Mendeley citation standardization
I've started using Mendeley and noticed that the automatically generated references don't always standardize - for example sometimes a doi would show up, but sometimes not. I try checking the info to make sure that all that's needed is in there when I import documents.
Due to this I've started typing in citations by hand. But I would still like to use the handy citation so it doesn't interrupt my flow. Has anyone had this problem? Is there something else I should do?

Mendeley attempts to extract metadata (title, authors etc) from article PDFs by searching through the PDF content. Since different publishers format their PDFs in different ways, sometimes Mendeley gets more information (e.g. a DOI) and sometimes it gets less. This is not a problem unique to Mendeley – Zotero has struggled with it, too.
There is an article on the Mendeley Support website called 'How does the automatic document details extraction work?' that explains the process in slightly more detail.
Automated metadata extraction is likely to remain imperfect until publishers consistently produce PDFs with standardised data. Several organisations have made progress on this, but it is not yet a solved problem. Until then, if you want good-quality references then it may be best either to generate them manually or to edit Mendeley's automated references by adding missing information.


text:
Is copying lab results a form of plagiarism?
A quick question, which I'm asking out of curiosity rather than an actual problem.
Plagarism and how to detect/combat/punish it seems to be a common topic on this SE. When I was an undergraduate, 20 years ago, I don't remember ever plagerising a book or other student, or any of my classmates doing the same (at least not that I knew of).
However, I did a practical science in which we had 1 or 2 full-day lab workshops every week. It was very common for most of the students in these labs not to get the "expected" result, due largely to inexperience and poor technique. It was equally common for all the students to then copy, and subtly modifiy, the results of the few students who did get it to work. They would then independently use this in writing up the assignments by themselves.
Would this be considered a form of plagerism in a modern academic environment? If so, what should science students with poor practical results do to write up their assignments?
EDIT: after comments, I feel compelled to make clear these were undergraduate labs with previously verified results. We were not undermiming scientific process here.

This is collusion in the assessment regime. However, scientifically, this is even worse, because it creates hidden dependencies between allegedly independent measurements. For instance, Millikan measured the electrical elementary charge, and subsequent studies seemed to have been so strongly influenced by it that they only slowly drifted away from Millikan's measurement, although it was biased by a wrong model assumption.

Some universities seem to have started using the term plagiarism more broadly, to refer to almost any kind of misconduct that has something to do with sourcing.  (See some of the references cited in this question for examples.)  Especially in that context, copying someone else's lab results certainly might get treated as plagiarism by some schools.
But that doesn't really get to the heart of the matter; for instance, I don't see a difference between copying someone else's results and just making up numbers (say, based on calculations of what should have happened).  The actual misconduct here is the fraudulent reporting.
But perhaps this is splitting hairs: this is unambiguously misconduct, it's just not clear that it's specifically plagiarism.
As for what students who get poor results should do, I think this is case where it's pretty clear that one should just ask the professor or TA.  I expect the answer would usually be to write up the results as they are, with the inevitable conclusion that the data didn't match the theory.  In some cases the course might have some "typical" results on file, and might ask students to write it up based on those rather than their results.  (Which is almost the same as what students were doing anyway, but without the dishonesty.)

Plagiarism is typically defined in terms of taking the work of another person and passing it off as your own (see, e.g., the Wikipedia page).  It does not matter whether the material that you steal is words, data, pictures, etc.
Thus, I would classify the practice of copying lab results that you describe as plagiarism.  It could also be categorized as other types of academic dishonesty as well, such as falsification of experimental results.

The Wikipedia link limits plagiarism to "language, thoughts, ideas, or expressions". I am not sure data/results qualify. Just because it is not plagiarism does not mean it is ethical.

@StrongBad If you look down below, you'll see broader definitions, including "words, ideas, or work products," which is the approximate sentiment that I hold.

The definition of plagiarism and academic misconduct has not changed substantially over the years. In some cases there is more awareness and edge cases like self plagiarism are more well defined. Copying results has always been academic misconduct, although I am not sure that it would ever be labeled plagiarism. Modifying the results is not plagiarism, but is academic misconduct.
As for how to write up the results, it really depends on what the instructor wants. It could be the instructor wants the students to explain what went wrong, or maybe interpret the result in light of what is known. They might also want you to collect the data again, or be fine with sharing of data, or even have an example dataset that they will share.


text:
Bad recommendation letter from previous postdoc adviser
I am completing my first postdoc and I had a pretty bad experience with my current adviser. I finally got to publish something, contribute in a grant writing etc., but overall it hasn't been great...
Now, I am going to start a new postdoctoral appointment under the supervision of a faculty I have been working with for several years and I will apply for tenure track positions once this last appointment has ended. I am pretty sure that my new adviser will be able to provide very good recommendation letters for me in the future. So the question is, will I need to provide recommendation letters from my current adviser? What if those are not good?

When applying for your first tenure track job most search committees will expect that your PhD supervisor and current supervisor will be amongst the N letters of recommendation that you provide. While having your PhD supervisor and current supervisor write letters is not a strict requirement their absence would be noticed. The other letters can come from anyone that knows you well and is experienced enough to write letters for a TT position. There is no expectation that you have letters from all supervisors that you worked with.
That said, you should talk with your current supervisor about the possibility of getting a letter. There maybe some positions they are willing to recommend you for and there may be cases where you need N+1 letters and you only have N.

In my field in the USA, you normally only need to send 3 letters of recommendation:

One of them should be your dissertation supervisor (if diss < 5 years old)
One should be a current supervisor (such as your new postdoc advisor)
One should be from a committee member or someone else who knows your work well

As long as you have three, I think you're fine in not including a letter from the  postdoc advisor that you're having problems with.
Note: Some programs require more. Even if n number of letters are required, there is no requirement that the n be comprehensive.
tl;dr: Letters of recommendation are used to indicate that some (at least n) faculty think highly of you, not necessarily that all do.

In my field, for tenure-track research positions, I think it's expected that you get 4-6 letters, even if the application only requires 3.  (I think many explicitly state 4, but I can't remember.)  In our case, 1 should be a teaching letter.

Different departments ask for different numbers of letters, even in the same field, even within the same country.