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R:
R is a free software package which is designed for use with command line only. While being a language is one of R's greatest strengths, it can make it harder to learn for those without programming experience. However, once learnt, you are no longer subject to price increases. The developer’s community ensures to constantly provide add-ons and also ensures that the software will continue to exist. R is extremely versatile in graphics, and generally good for people who really want to find out “what their data have to say”.
SAS:
SAS is the second most costly package. It can be used with, both, command line and graphical user interface (GUI). SAS is particularly strong on data management
(especially with large files), and good for cutting edge research. It covers many graphical and statistical tasks. The main focus is on business customers now.
SPSS:
SPSS is the first choice for the occasional user. However, it is the most expensive of the four. SPSS is clearly designed for point-and-click usage on the GUI. A command structure exists, but it is not well defined and sometimes inconsistent. SPSS is good for basic data management and basic statistical analysis, but rather weak in graphics. In the future, SPSS might be the weakest of the four packages with regard to the scope of statistical procedures it offers due to its main focus on business customers.
Stata:
Stata is designed for the usage by command line, but it also offers a GUI that allows for working with menus. The simple and consistent command structure makes it rather easy to learn. It is the cheapest of the packages that entail costs, and it offers additional reductions for the educational sector. Stata is relatively weak on ANOVA, but extraordinary on regression analysis and complex survey designs. Stata is completely focused on scholars. In the future, Stata may have the strongest collection of advanced statistical procedures.
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