I agree with Jake that C and even Fortran will be with us much longer
than Stata, R or the next new programming fad. And C's syntax is very
similar to a large number of current and almost certainly future
languages. I would also bet that the S language will outlast R (much
like how it has outlasted S itself) and Stata. There is an elegance
to it.
Learning C or Fortran teaches people programming and algorithms at a
more fundamental level than interpreted languages. Even if we use
interpreted languages for day-to-day computing, it's important that we
teach the fundamentals at least to methodologists. This is a general
issue not limited to just the social sciences. A hint of what we
(even in technical departments such as statistics and engineering)
have lost in teaching computing can be gleaned by remembering that
Fortran and C are already high level programming languages---i.e.,
they abstract from the hardware. But, alas, we now days often don't
think of them as such.
It would be great if I could get more students to learn C by reading
Kernighan & Ritchie and programming by reading "Structure and
Interpretation of Computer Programs"---full text is on line at
http://mitpress.mit.edu/sicp/full-text/book/book.html.
Salon recently had an interesting article entitled "Why Johnny can't
code" which argues that the absence of experience with BASIC (you
remember, "goto 20" etc) has resulted in this generation of students
not being able to code even if they can whip up menus in Visual
Basic---students now jump past the simple line-programming
language. (the article is in error on one point: one actually *can*
easily download and run BASIC on a modern OS if one wants to, but
almost no one does). My generation used to rant "X is too old to
code". In some coding circles a new slogan has developed: "too young
to code" (this slogan is not yet as accurate as the previous one).
Fortunately, there is China and India.
Cheers, Jas.
=======================================
Jasjeet S. Sekhon
Associate Professor
Travers Department of Political Science
Survey Research Center
UC Berkeley
http://sekhon.berkeley.edu/
V: 510-642-9974 F: 617-507-5524
=======================================
Jake Bowers writes:
> Hi All,
>
> Somewhat off topic, but something I've been wondering about.
>
> What do you think of this:
>
> "Stats packages may come and go but you can always come home to
> Fortran(C)."
>
> It seems to me that entire computing environments change radically
> every decade or so, and particular optimized ways of interacting with
> data and instantiating current statistical practice also change
> around that fast. Since most of us hope to have multi-decade careers,
> I often wonder if it would be better for graduate students to learn
> two programming languages upon arriving at graduate school (or
> hopefully earlier): (1) one that can do most or all of what we
> currently think are best practices in data analysis (say, R or Stata
> now, or in earlier years, SPSS or SAS or CSA or OSIRIS (unfortunately
> I have never used OSIRIS although I hear it was great)) AND (2) a
> lower level programming language like fortran or C that will probably
> exist in some way or another for the entire data-analytic life of a
> given scholar.
>
> This way, when, say, us old guys don't want to install neural
> interfaces in order to analyze our data (say, our brains are not
> plastic enough for them to be effective or we don't want holes in our
> heads), and R is no longer supported and doesn't do what we need (not
> because of lack of flexibility but because in this future we only do
> enormous genetic and MCMC and permutation style estimation with
> billions of operations and R is at its core a slow memory hog) ----
> then we can roll our own using good old Fortran or C --- since the
> neural operating systems will still be written in versions of those
> languages.
>
> Anyway, this post does not answer the question of exactly which
> specialized package to learn. Jim Battista put my point of view more
> elegantly that I could have done: "You win the jackpot -- you get to
> learn them all." I do wonder, however, whether ensuring that folks
> know something more enduring and lower level would allow for greater
> adaptability and flexibility as we try to get work done while we
> watch the fads flow by.
>
> Best,
>
> Jake
>
>
> Jake Bowers
> currently:
> Robert Wood Johnson Health Policy Scholar, '05-'07
> Institute for Quantitative Social Science
> Harvard University
>
> on leave:
> Assistant Professor of Political Science
> Faculty Associate in the Center for Political Studies, ISR
> University of Michigan
>
> http://www.umich.edu/~jwbowers
>
>
>
>
> On Oct 5, 2006, at 10:04 AM, Walt Borges wrote:
>
> > Gosh, everyone's going to have a different opinion on this, especially
> > on the relative value of preparing tables and graphs for publication
> > submissions.
> >
> >> From an analysis standpoint, and as someone who learned all these
> > programs over the last two years, I would list the pros and cons thus:
> >
> > SPSS -- pro: Good point and click features
> > Relatively easy to work with data
> > con: Expensive on a students' budget
> > Program coding is not intuitively based
> >
> > STATA -- pro: Intuitive and simple program coding
> > Decent point and click
> > Simple, logical command structure
> > You can create "do-file" programs that process
> > data and run analytical programs; these
> > can be assembled into master do-files that
> > allow you to work off the
> > original
> > data set each session, rather than saving
> > modified data sets (which can quickly become
> > confusing, especially when several team members are
> > working with the data)
> > Decent graphics.
> > Relatively inexpensive for students
> >
> > con: Inevitably you will need to deal with a data or
> > graphics issue that will send you to
> > the
> > seven-volume manual. Someone in the
> > department better have one you can use,
> > because it's real expensive. The good news is
> > that you will probably find the instructions there
> > and understand them.
> > In-program help files are incomplete and often
> > not helpful. Example: The STATA
> > typeface makes the percent sign,
> > which is used in graphics commands,
> > look like a capital N on screen. I
> > spent
> > four hours using the on-screen help in
> > trying unsuccessfully to format a graph
> > before going to the manual and discovering
> > this
> > annoying little problem.
> > The program for time-series analysis is not
> > fully developed, but the alternative
> > software is far more expensive.
> >
> >
> > R -- pro: Price: freeware.
> > Exceptionally versatile: you do the
> > programming,
> > so your abilities are the limits of
> > its
> > capabilities. Library of add-on
> > components for
> > specialized analysis.
> > Easy access to outside modules (such as
> > WinBugs
> > for Bayesian analysis)
> > Excellent graphic capabilities.
> >
> > con: Non-intuitive, object-based programming
> > structure (however, if you are
> > familiar with computer programming
> > structures, this is not too much of a
> > problem).
> > Importing data sets (major pain in the butt).
> >
> > Personally, although I learned SPSS first, I prefer Stata for most
> > projects, although time series analysis is easier in E-Views and RATS
> > (both very expensive). The trick is learning how to format data in
> > sets
> > in Stata using do-files. Once you learn that, Stata is a cheap, quick
> > and easy way to conduct most basic analysis. For advanced analysis and
> > Baysian, R is really the only choice.
> >
> > Walt Borges
> > Doctoral candidate
> > University of Texas-Dallas
> >
> >
> > -----Original Message-----
> > From: Political Methodology Society
> > [mailto:[log in to unmask]] On
> > Behalf Of Michael Plenty
> > Sent: Wednesday, October 04, 2006 4:42 PM
> > To: [log in to unmask]
> > Subject: [POLMETH] R vs. Stata vs. SPSS
> >
> > My name is Mike and I work as an intern for a major consulting firm in
> > Washington,DC.
> >
> > My company uses SPSS, as most undergraduate programs and companies,
> > however,
> > now that I'm beginning the thinking process for graduate school, most
> > schools, in particular Northwestern, UChicago, and Yale, all
> > recommend I
> > become familiar with Stata and R.
> >
> > Are there any major differences between the three programs?
> >
> > If so, what? and what resources are out there to help me adjust?
> >
> > Michael Plenty
> >
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