I think I agree with Prof. Lumley: For me, R would likely be the platform of choice for most new statistical algorithm development. It has a steep learning curve, and I would therefore not recommend it for anyone who would not likely be involved in this kind of activity (unless my target audience could not afford simpler software that could handle the problems they will most likely encounter). Moreover, the learning curve could be even more difficult for many social scientists than for statisticians, mathematicians, engineers, physical scientists, who might more likely have more background in vector spaces and programming in other language(s).

I'm concerned that the time they spend learning R syntax might reduce substantially the time they have for learning statistical concepts that might be more important for them in the long term. I retract this reservations if, for example, the target audience already knows something about structural equation modeling and want to extend the R "sem" package for novel applications.

     hope this helps.  spencer graves

Thomas Lumley wrote:

On Thu, 12 Aug 2004, Rau, Roland wrote:


That is why would like to ask the experts on this list if anyone of you has
encountered a similar experience and what you could advise to persuade
people quickly that it is worth learning a new software?



One problem is that it may not be true. Unless these people are going to be doing their own statistics in the future (which is probably true only for a minority) they might actually be better off with a point and click interface. I'm (obviously) not arguing that SPSS is a better statistical environment than R, but it is easier to learn, and in 10 or 15 weeks they may not get to see the benefits of R.


-thomas

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