Fortran still exists? I thought it remained only in the scripting language
of SAS (and, I suppose, SPSS). Next you will tell me that COBAL is still
used for development. With small to medium size datasets you can do
ANYTHING in a spreadsheet (I am talking thousands or even tens of thousands
of observations these days). But the advantage of commercial or widely
examined open source software is that you don't have to conduct a tedious
audit of your math to be sure that your results are correct.
-----Original Message-----
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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