Decomposition: The Statistics Software Signal

January 8, 2013
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(This article was first published on Econometric Sense, and kindly contributed to R-bloggers)

From:

Decomposition: The Statistics Software Signal

http://seanjtaylor.com/post/39573264781/the-statistics-software-signal

"When you don't have to code your own estimators, you probably won't understand what you're doing. I'm not saying that you definitely won't, but push-button analyses make it easy to compute numbers that you are not equipped to interpret."

I agree that statistics is a language best communicated and understood via code vs. a point and click GUI.

However, particularly interesting is his view of how the use of a given software package may relate to the quality of research:

"SPSS: You love using your mouse and discovering options using menus. You are nervous about writing code and probably manage your data in Microsoft Excel." (see the linked article for similar remarks)


 To be fair, STATA, SPSS, SAS and R have coding environments, and as a user of both SAS and R products I don't see why using PROC REG in SAS is any less sophisticated than the 'lm' function in R. Nor do I see any difference in coding an estimator or algorithm in R vs. SAS IML.

In fact, there has been a long running discussion for over a year now on SAS vs. R on LinkedIn and in my opinion it all it has established is that R certainly provides a powerful software solution for many researchers and businesses. 

It would be interesting to quantify and test Taylor's theory.

UPDATE: see You say Stata I Say SAS: software signaling and social identity theory.

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