(This article was first published on

From: **Econometric Sense**, and kindly contributed to R-bloggers)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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