There are a range of statistical software packages available, some costly, other free, and some in between. Which to choose? Which to invest time to learn (Blood sweat, tears and frustration) and money to buy?

SPSS has it forte in that it has a pretty interface that is easy to use, with easily available results. There are even some useful macros available for it, like PROCESS, for Mediation and Moderation analysis, based on templates. (Also available for SAS.)

LISREL allows for SEM analysis, and has a free student version (with limits on number of variables), but is a little finicky. Mplus has been developed by the students of the guys behind LISREL, presumably to make it better. As of yet, I have no opinion on this. Fig_2a_ScholarlyImpact2015

However, the best, and possibly the one with the steepest learning curve, is also the most versatile and powerful. I am talking of R. There is a range of extensions that can be added; and a huge user base one can tap into for support… and best of all, it is free.

If starting out now, or preparing to move on from SPSS, one has to learn to write syntax, and each program has its own unique language. Rather then moving from program to program, one may want to go for a program one can grow and develop with. R seems to be that program, growing faster than any other, and now the second most used statistical program used in published research. (Have to wonder if it will do to statistical software what Wikipedia did to encyclopedias)



R Passes SAS in Scholarly Use (finally)

Way back in 2012 I published a forecast that showed that the use of R for scholarly publications would likely pass the use of SAS in 2015. But I didn’t believe the forecast since I expected the sharp decline in SAS and SPSS use to level off.


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