Choosing the right software for data analysis is one of those decisions that can greatly facilitate a researcher's work or turn it into a constant source of frustration. The current landscape offers options for all profiles, from programs with graphical interfaces that allow analyses to be performed without writing a single line of code to programming languages that offer virtually unlimited flexibility. There is no universally superior tool, because the optimal choice depends on the type of analysis needed, the user's level of experience, the available budget, and the institutional context in which one works.
What has changed in recent years is the general trend of the field. Psychology, as a discipline, is progressively moving toward open-source tools and greater analytical transparency. This does not mean that commercial software has lost its relevance, but rather that free alternatives have reached a level of maturity that makes them completely viable for most research needs. Understanding the strengths and limitations of each option is the first step toward making an informed decision.
SPSS: the historical standard
SPSS (Statistical Package for the Social Sciences) has been the tool of choice in psychological research for decades. Its graphical interface based on drop-down menus makes it an accessible option for researchers without programming training, and its presence in most universities has created an enormous user base that makes it easy to find tutorials, manuals, and help in specialized forums. For classical analyses such as t-tests, ANOVA, linear regression, correlations, or chi-square tests, SPSS works efficiently and produces output that, while not the most visually elegant, contains all the necessary information.
However, SPSS has limitations that become evident when analytical needs become more sophisticated. Structural equation models require the additional AMOS module (with its own license cost), Bayesian analyses are limited, and data visualization options fall far short of what other tools offer. Moreover, its commercial licensing model represents a significant cost, especially for independent researchers or institutions with tight budgets. The current version of IBM SPSS Statistics costs approximately 100 euros per month on its subscription plan, which can be prohibitive outside the university context.
Another aspect worth considering is reproducibility. Although SPSS allows saving the syntax of the analyses performed, its typical mouse-click-based workflow makes it difficult to document exactly what was done and in what order. This can be problematic when a reviewer asks to repeat an analysis with some modification or when, months later, we need to remember how we obtained a specific result.
R: power and flexibility
R is a free, open-source programming language and statistical environment that has become the de facto standard in many areas of quantitative research. Its main advantage is extensibility: any conceivable statistical technique is probably implemented in one of the more than 20,000 packages available on CRAN, the official R repository. From confirmatory factor analyses with lavaan to multilevel models with lme4, through meta-analyses with metafor or network analysis with qgraph, R covers virtually any analytical need that may arise in psychological research.
The learning curve is, without a doubt, the main barrier to entry. R requires writing code, which is intimidating for many researchers accustomed to graphical interfaces. However, this initial investment pays off quickly. Code written in R is completely reproducible, can be shared with collaborators or reviewers, and allows automating repetitive tasks that in SPSS would require dozens of manual clicks. Furthermore, the integration of R with writing tools such as R Markdown or Quarto allows generating complete reports where text, analyses, and figures coexist in a single document, eliminating the possibility of errors from incorrectly copying a number from a statistical output.
For those starting with R, RStudio (now called Posit) offers an integrated development environment that greatly facilitates the experience. The R user community in psychology is also very active, with blogs, YouTube tutorials, and textbooks that explain the most common analyses step by step using real psychological data. Resources such as William Revelle's psych package or the PsyTeachR tutorials from the University of Glasgow are specifically designed for behavioral science researchers.
JASP: the best of both worlds
JASP was created as a project by the University of Amsterdam with a clear objective: to offer the analytical power of R with the ease of use of SPSS, and to do so completely free of charge. The interface is clean and intuitive, results are presented directly in APA format, and analyses update in real time when parameters are modified. For many researchers in psychology, JASP represents the perfect balance between accessibility and rigor.
One of JASP's most distinctive features is its native support for Bayesian statistics. While Bayesian analyses in SPSS are rudimentary and in R they require programming knowledge, JASP allows calculating Bayes factors with the same ease as a p-value: by selecting variables and clicking. This has contributed significantly to democratizing the Bayesian approach in psychology, enabling researchers without advanced programming training to explore this perspective complementary to traditional frequentist statistics.
JASP also has its limitations. Being a relatively young application, its catalog of available analyses is smaller than that of R or SPSS. The most specialized analyses (complex mixed models, survival analysis, certain types of multilevel analysis) may not be available or may have limited options. Additionally, its module system, although constantly expanding, does not offer the same granularity in analysis configuration as R code. For research projects with standard analytical needs, JASP is an excellent option. For highly specialized analyses, it will likely need to be supplemented with R.
Choosing according to context
The decision about which software to use should not be dogmatic. In a master's thesis where time is limited and analyses are relatively standard, JASP or SPSS may be the most pragmatic option. In a doctoral thesis project that will span several years and will likely require sophisticated analyses, investing in learning R is a strategic decision that will yield long-term benefits. And in many cases, the best approach is to combine tools: use JASP for quick initial explorations, R for definitive analyses and the generation of publication-ready figures, and perhaps Python if the project involves text processing, machine learning, or handling very large datasets.
Before you submit: the program you pick matters far less than whether the analysis survives peer review. If your manuscript is already written, run it through the AI Paper Reviewer: a free Reviewer 2 style pre-review that tells you, in under a minute, which single problem is most likely to sink the submission. It reads the same whether you ran your analyses in SPSS, R or JASP, because what reviewers question is the decision, not the software that produced it.
What should not happen, and unfortunately happens frequently, is that the choice of software determines the analyses performed rather than the other way around. First, the research questions and the analyses needed to answer them are defined, and then the tool that best fits those needs is chosen. A researcher who only knows SPSS may be tempted to simplify their analyses to fit within the program's capabilities, and that is a methodological error worth avoiding. Continuous training in analytical tools is an essential part of any researcher's professional development, as important as staying current with the theoretical literature of their field.