The question of which statistical software to use inevitably arises at some point in the training of every psychology researcher. Conversations in faculty hallways, doctoral student WhatsApp groups, and academic forums revolve recurrently around the same names: SPSS, R, and JASP. Each has its staunch advocates and habitual critics, but the reality is more nuanced than any passionate debate might suggest. The best choice depends on specific factors such as the type of analysis needed, the institutional context, the willingness to invest time in learning, and, yes, the budget as well.
This article does not aim to declare a winner but rather to offer an honest comparison that helps each researcher make the most appropriate decision for their particular situation. To do so, we will examine the three tools across several practical dimensions that affect the day-to-day work with data: ease of use, analytical coverage, cost, reproducibility, and community support.
Ease of use and learning curve
SPSS remains the most accessible option for someone who has never worked with statistical software. Its graphical interface with drop-down menus replicates the logic of any office software program: select variables, choose an analysis, configure options, and obtain results. An undergraduate student can perform a t-test or an ANOVA in their first practical session without needing to write a single line of code. This immediacy has undeniable pedagogical value and explains why many universities continue to use it as their reference tool in statistics courses.
JASP offers a similar experience in terms of ease of use, with the additional advantage of a more modern interface and results that dynamically update as analysis parameters are modified. JASP's design philosophy prioritizes clarity: results are displayed directly in APA format, tables are clean, and options are logically organized. For a user coming from SPSS, the transition to JASP is practically immediate.
R, on the other hand, requires a significant time investment before even the most basic analyses can be performed. It is necessary to become familiar with the language syntax, understand concepts such as objects, functions, and packages, and get used to debugging code errors. However, this initial difficulty should not be dismissed as an insurmountable obstacle. With resources such as the online R tutorials for psychology and user-friendly environments like RStudio, most researchers can reach a functional level within a few weeks of regular practice. And once the initial curve is overcome, productivity with R tends to surpass that of graphical interfaces, especially when working with repetitive analyses or complex datasets.
Analytical coverage and flexibility
In terms of available analyses, R has no real competition. Its ecosystem of more than 20,000 packages covers everything from the most basic analyses to cutting-edge techniques that have just been published in methodological journals. If a new statistical method is published today, it will probably be implemented in an R package within a few months. This speed of adoption is possible because any researcher can create and share a package, which makes R a living platform that evolves at the pace of science.
SPSS adequately covers the classical analyses of psychological research. T-tests, ANOVAs (including repeated measures and mixed designs), multiple regression, exploratory factor analysis, reliability, nonparametric tests, and frequency analyses work correctly and produce complete results. However, when needs go beyond the conventional, SPSS shows its limits. Structural equation models require AMOS (a separate program with its own license), multilevel models are possible but with limited configuration options, and more recent techniques such as network analysis or latent class models are not natively available.
JASP occupies an interesting middle ground. It offers classical frequentist analyses alongside their Bayesian equivalents, which is a considerable advantage. Its SEM module allows confirmatory factor analyses and basic mediation models, and additional modules gradually expand its repertoire. However, for highly specialized or customized analyses, JASP may fall short. Its strength lies not in covering every possible analysis but in making the most common analyses accessible, correct, and well presented.
Cost and reproducibility
In terms of cost, the difference is clear. R and JASP are completely free and open source. SPSS requires a commercial license that, depending on the version and subscription type, can cost between 1,200 and 5,000 euros annually for professional licenses, with reduced prices for universities. Many researchers access SPSS through their university's institutional license, but this advantage disappears when they finish their contract or change institutions. It is not uncommon for a researcher who has trained exclusively in SPSS to find themselves without access to the software when starting a postdoctoral stay in another country or when working independently.
Reproducibility is another area where differences are substantial. R is inherently reproducible because every analysis is recorded in a script that can be re-executed at any time, shared with collaborators, or attached as supplementary material to an article. JASP has incorporated the ability to save analyses in files that another user can open and review, which represents a significant advance over the typical SPSS workflow. In SPSS, although it is possible to work with syntax (and it is advisable to do so), most users work with menus and do not keep a systematic record of their analytical steps. This hinders peer review and replication.
At a time when psychology is experiencing a replication crisis and there is growing emphasis on open science practices, the ability to document and share the analyses performed is not a minor detail. It is a matter of scientific responsibility that an increasing number of journals and funding agencies explicitly require.
Practical recommendations
If you are starting an undergraduate degree in psychology and your university uses SPSS, learn SPSS. There is no point in swimming against the institutional current when you are just starting out, and what matters at that stage is understanding statistical concepts, not the software. But if you are beginning a master's or doctoral program, seriously consider learning R. The time investment is more than recovered during the thesis years, and the ability to program analyses is an added value increasingly appreciated in both the academic and non-academic job market.
If your priority is to perform Bayesian analyses in an accessible way, JASP is probably your best option. If you need maximum flexibility and plan to conduct methodologically advanced research, R is irreplaceable. And if you work in an environment where SPSS is the lingua franca and you need to collaborate with colleagues who only use that program, maintaining competence in SPSS still makes practical sense. The ideal, in any case, is not to limit yourself to a single tool. Knowing the strengths of each one allows you to choose the most appropriate for each project, and that versatility is what distinguishes a researcher with methodological maturity.