Have you ever tried to calculate linear regression by hand? It can be a nightmare if you have a large dataset or covariate. Thankfully, the world of statistics has developed numerous statistical software programs to help you out. We’re going to talk about the top three that crop up – especially in job descriptions. Let’s talk **SPSS**, **Stata**, and **R**.

## SPSS

In the social sciences (like psychology and education), **SPSS** is considered the standard statistics program. In fact, it is usually the first piece of statistical software that people learn in undergraduate or graduate statistics programs.

The benefit of SPSS is that it is fairly easy to use for non-programmers. SPSS contains a large set of drop-down menus that make it fairly user-friendly. It covers most statistical methods pretty easily, including descriptive statistics, linear regressions, analysis of variance, and time-series methods. It also has the ability to add *modules*, which are like miniature programs to run more advanced techniques like survival calculations. Furthermore, viewing a dataset is pretty easy with the *variable view*

One of the drawbacks of SPSS is that it has difficulty handling higher order statistics like structural equation modeling. While it has a friendly point-and-click interface, it has limitations with programming additional techniques like forecasting. In addition, SPSS has a subscription license that has to be renewed on a 6-month, 1-year, or 2-year interval. This can be a bit rough on student-sized budgets (although there are student pricing options).

## Stata

**Stata** is a statistical program that is often used in the economic and financial sectors. Even though it is used in a smaller area, it has some distinct advantages.

Stata has a point-and-click interface, but it is friendly to programmers as well. You can easily combine statistical methods in the *command window* without having to navigate a lot of menus. It is also a little more intuitive than SPSS, with simple commands like typing *anova* and then typing some variables. In addition, Stata has a *perpetual license*, which mean that you only have to purchase it once, though updates to handle advanced techniques will cost you.

Some of the drawback to Stata is that it can be tricky to learn all the commands that you may want to use. For example, it has a nice way to do structural equation modeling, but coding can be lengthy, tricky, and confusing. Also, while it is fairly easy to manipulate variables within Stata, it doesn’t translate variable files to other programs, like SPSS, very well.

## R

**R** is a beast. What I mean is that it is quickly becoming the standard statistical software companies and schools are requesting potential employees or students to learn. But like SPSS and Stata, R has advantages and disadvantages.

R can handle almost any statistical techniques that you want to do with descriptive statistics, factor analysis, non-parametric test, and more. R can also be used to create almost any kind of graphic that you need or want. It is also easy to install specific *packages* (miniature programs for added functionality). Best of all (especially for students), it is completely free to download, modify, and use since it is considered open software. Programmers and statisticians are always adding new packages for methods like item response theory or propensity score analysis.

The major drawback to R is simultaneously its greatest advantage: coding. If you are a non-programmer, R can be daunting to learn because it is essentially programming the program to do what you want. You have to specify *exactly* what you want or need it do. This takes time and effort to really learn the method you want to use. Another drawback is that it can be cumbersome to use for advanced techniques like factor analysis or structural equation modeling.

## The Takeaway

SPSS, Stata, and R all have distinct advantages and disadvantages. Some are easy to learn while some are better for what you want to do but harder to learn. There are even some very specific ones like SAS and Mplus. Ultimately, to be the most versatile, you should pick the software that matches your needs and really dig into it. Happy statistics!

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