If you studied psychology in Georgia, or in most of the world, really, you learned statistics through SPSS. You opened the data, you clicked through menus, you got tidy output tables, and you wrote up your results. SPSS has been the default statistical software in psychology departments for decades, and that legacy runs deep.
But the world has moved on. R, an open-source statistical programming language, has become the standard in research-active psychology and in adjacent fields like data science. Most journal articles in serious psychology research now use R. Most cutting-edge methods, modern psychometrics, mixed-effects modeling, Bayesian analysis, machine learning are developed in R first, sometimes only in R.
If you’re still doing your analyses in SPSS, you’re working with a tool that’s increasingly out of step with how psychological research is actually conducted. In this post, I want to explain why the switch matters, what I personally gained by making it, and how to start the transition yourself.
My Own Switch
I first encountered R in 2016, when I started my PhD. That was my first practical exposure. I’d heard of R before, but I had assumed it was unnecessary. SPSS had a GUI, it produced clean output, and everything I needed to do seemed possible within it. Why complicate things by learning a programming language?
I was wrong. And once I started actually using R, the limitations of SPSS became impossible to ignore.
Today, R is my primary tool for everything: psychometric analysis, secondary data analysis of different datasets, building interactive applications, generating reports. I still use SPSS occasionally, mostly when teaching at universities in Georgia, because the curriculum is structured around SPSS and students need familiarity with the software they’ll be expected to use. But for my own research, SPSS is gone.
The transition wasn’t instant. It took time, frustration, and patience. But every researcher I know who has made the switch describes it the same way: a steep climb at first, followed by realizing they could now do things they hadn’t even imagined trying before.
The Replicability Problem
The first thing that pushed me toward R was replicability. SPSS does have a syntax option, you can save the commands underlying your point-and-click actions into a script and rerun them later. But almost nobody does. The vast majority of SPSS users work entirely through the GUI, clicking through menus, performing analyses, and never saving the code that produced their results.
This is a problem. If you don’t have the code, your analysis isn’t truly reproducible. If a reviewer asks how exactly you computed a result, you might not remember the precise sequence of clicks. If you need to rerun the analysis on a slightly different dataset, you have to redo all of those clicks manually. And if someone else wants to verify your work, they have to trust your written description of what you did.
R flips this entirely. There is no GUI for the analysis itself. Everything is code. Every analysis is, by definition, a script that you can save, share, version, modify, and rerun. You can hand someone your data and your script and they will reproduce your results exactly. This is the foundation of reproducible research, and it’s the standard the field is moving toward.
What R Lets You Do That SPSS Can’t
The replicability issue alone would be enough to justify the switch, but there are practical analyses where SPSS simply cannot keep up.
My own breaking point was multilevel weighted analysis using PISA, PIRLS, and TIMSS data. These large-scale assessment datasets have nested structures, students within schools, schools within countries, and they use complex sampling weights at multiple levels to produce nationally representative estimates. SPSS does support some two-level modeling, but when those two levels have different weights, SPSS loses its footing. The analyses don’t work properly, or they require workarounds that defeat the point.
In R, this is straightforward. Packages like survey, BIFIEsurvey, intsvy, and lme4 handle these complex sampling designs natively. If you want to do serious work with international large-scale assessment data, which I do for my PhD on PISA Georgia, you essentially have to use R.
Beyond complex sampling, R is also the natural home for modern psychometrics. Item Response Theory analysis with mirt, confirmatory factor analysis with lavaan, network analysis, Bayesian models, machine learning – all of this lives in R first. SPSS has equivalents for some of these, but they’re usually expensive add-ons, less flexible, and less well-maintained.
The Price Tag Nobody Talks About
Then there’s the cost. R is completely free. SPSS is not.
A student license for SPSS costs roughly 200 USD for one year. Let me put that number in context: at most universities in Georgia, a full semester of tuition is around 400 USD. We’re asking students to spend the equivalent of half a semester’s tuition just to access the software they need for their statistics coursework. For one year. Then they have to renew.
And it gets worse. Once a student graduates and leaves the academic context, they can no longer use student licenses. The full SPSS license costs roughly ten times the student price, sometimes more. So either you keep paying these enormous fees throughout your career, or you walk away from the only statistical tool you know.
R doesn’t have this problem. You download it. You install it. You use it forever. There’s no license to renew, no institutional gatekeeping, no career-stage pricing trap. Whether you’re a student, a researcher at a wealthy institution, a researcher in Georgia, or an independent consultant, you have access to exactly the same tool.
This is not a minor consideration. It’s a major one, especially for researchers and students in countries where institutional budgets don’t stretch to expensive software licenses.
The Learning Curve Is Real
I won’t pretend R is easy. It’s harder to learn than SPSS, and it takes longer. With SPSS you point, you click, you get results, and those results come with familiar labels and clean tables. With R you have to type commands. You have to remember function names. You have to debug your own errors. The early stages can be genuinely frustrating, especially for students who are simultaneously trying to learn statistical concepts and a programming language at the same time.
That dual learning curve is the hardest part. If you’re learning what a t-test is for the first time, and at the same time trying to figure out how to write the R code for it, you can end up confused about both. This is why SPSS persists in introductory statistics teaching, it lets students focus on the concepts without the additional barrier of programming.
But once you understand the basics, R becomes easy to use. The investment pays off rapidly. And the modern R ecosystem has made the learning curve much gentler than it used to be.
RStudio and the Tidyverse Help
If you’re going to learn R, do it through RStudio. It’s a development environment that wraps around R and makes everything more approachable: a clean interface with panels for your script, your console, your data, and your plots, all visible at once. It’s free, well-maintained, and now the standard way that most people interact with R.
You should also lean heavily on the tidyverse, a collection of packages (dplyr, ggplot2, tidyr, and others) designed to make data manipulation and visualization more intuitive and consistent. The tidyverse uses a coherent set of design principles that make code easier to read and easier to write. When I teach R, I teach it through RStudio and tidyverse, not through base R. The base language can come later.
These two together have transformed what learning R feels like. It’s still harder than learning SPSS at the very beginning, but the gap is much smaller than it used to be.
Where to Start
If you’re considering making the switch, my advice is simple: start as soon as possible. The longer you delay, the more analyses you’ll have done in SPSS that you’ll later need to redo or migrate. The earlier in your career you make the switch, the more your future work will benefit from it.
The specific resource I recommend is R for Data Science by Hadley Wickham and Garrett Grolemund. It’s freely available online, it’s exceptionally well-written, and the exercises actually require you to think, they don’t just walk you through commands, they push you to solve real problems. That book taught me R, and I still recommend it to everyone who asks.
Install RStudio. Pick a small dataset, your own thesis data, or any open dataset you find interesting. Work through the early chapters of R for Data Science. Replicate one or two analyses you previously did in SPSS, just to see the differences side by side. The first few weeks will be slow. After that, things accelerate quickly.
What About GUIs?
If you really can’t give up the GUI experience, there are options. Jamovi is a free, open-source statistical platform that runs on top of R and provides a point-and-click interface. It’s an excellent middle ground for people coming from SPSS who want to ease into the R ecosystem without immediately writing code. I occasionally use it myself when I want something fast and visual.
JASP is another similar option, particularly strong for Bayesian analyses. Both are free, both are well-maintained, and both can serve as bridges from SPSS to full R.
But these are bridges, not destinations. Eventually, if you want full flexibility and the ability to do modern psychological research, you’ll need to learn R properly.
Looking Back
Do I regret not making the switch earlier? A little earlier transition would have been nice, would have saved me time later. But it is what it is. The important thing is that I made the switch, and now I can’t imagine working any other way.
Do I miss anything about SPSS? Honestly, no. The familiar output tables, the clean GUI, the predictable workflow, none of it outweighs what R offers. SPSS feels limiting once you’ve experienced what’s possible elsewhere.
The future of psychological research is being written in R. The methodological innovations, the open-science tools, the reproducible workflows, the integration with modern data science – all of it is happening in R. If you want to be part of that future, you need to be working in the same tools.
The transition is hard. The benefits are enormous. And the longer you wait, the further behind you’ll be.
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Giorgi Tchumburidze
May, 2026