All three produce correct results for standard analyses. The differences are in cost, reproducibility and how much of your time the software consumes.
SPSS
Menu-driven, so productive within days. Widely taught, which means supervisors and co-authors can usually follow what you did.
- Strengths: shallow learning curve, familiar to reviewers, good for standard tests, survey analysis and descriptive work
- Weaknesses: expensive licensing, point-and-click work is hard to reproduce unless you save syntax, limited for newer methods
- Best for: researchers who need standard analyses done reliably without becoming programmers
R
Free, open source, and effectively unlimited in what it can do. Every method appears in R first, usually years before commercial packages.
- Strengths: free forever, fully reproducible by design, unmatched graphics through ggplot2, packages for meta-analysis, mixed models and Bayesian analysis
- Weaknesses: steep initial learning curve, package quality varies, error messages are unhelpful to beginners
- Best for: anyone doing meta-analysis, complex modelling, or planning a research career
Stata
Command-driven with a menu fallback. Strong in economics, epidemiology and the social sciences, and the log file makes reproducibility close to automatic.
- Strengths: excellent documentation, superb for panel data, survey weighting and survival analysis, commands are readable months later
- Weaknesses: licence cost, smaller community than R, graphics are functional rather than beautiful
- Best for: epidemiology, health economics and any work with complex survey designs
Choosing
- What does your department license? Access after graduation matters.
- What can your supervisor or co-authors check?
- What does your method require? Some analyses effectively only exist in R.
- How long will you be doing research? R repays its learning curve over a career, not over one project.
A defensible answer is to learn R and keep SPSS for the analyses you need done today.
What reviewers actually require
Not a particular package — only that you name it with its version and any packages used, because results can differ between implementations. Whichever you choose, keep the script or syntax.
We work in all three, plus Python, SAS and NVivo, and supply the scripts so your analysis stays reproducible.