How should a social scientist deal with envy of disciplines that are more quantitatively rigorous?

Question

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.

Answer 1

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.

Answer 2

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.

Answer 3

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).

Answer 4

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.

Answer 5

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.

Answer 6

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.

Answer 7

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,

Answer 8

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.

Answer 9

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

Answer 10

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.

Answer 11

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.

Answer 12

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 ..."

Answer 13

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.

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

Answer 14

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.