![]() ![]() ![]() ![]() Even so, the gaps are in the most complicated analyses–some mixed models, gee, complex sampling, etc. And when you’re trying to learn a new, highly complicated statistical method is not the time to learn a new, highly complicated stats package.įor these reasons, I recommend that everyone who plans to do research for the foreseeable future learn two packages. I know, it’s hard enough to find the time to start over and learn one. But if you can, it will save you grief later on. There are many great books, online tutorials, and workshops for learning all the major stats packages.īut I also recommend you choose one as your primary package and learn it really, really well. The defaults and assumptions and wording are not the same across packages. Knowing how yours handles dummy coding or missing data is imperative to doing correct statistics. Which one? Mainly it depends on the field you’re in. Social scientists should generally learn SPSS as their main package, mainly because that is what their colleagues are using. You can then choose something else as a backup–either SAS, R, or Stata, based on availability and which makes most sense to you logically. ![]()
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