Your instructor probably named a tool, and that settles it. If the choice is yours, here is the honest comparison for student coursework, based on what each one actually costs you in time.
The short answer
Use whatever your course teaches, because your marker is grading the output format they showed you. If the choice is genuinely open: SPSS for psychology, nursing, education and social science coursework; R for anything you will repeat, publish, or defend; Excel only for descriptives and simple tests where the dataset is small.
SPSS, fastest to a correct answer
Menu driven, so you can run an ANOVA in four clicks without knowing any syntax. Output tables are formatted close to APA already, which saves real time on write-up. The downsides are cost outside a campus licence, awkward reproducibility unless you save the syntax file, and limited flexibility for anything unusual. Always paste your steps into the syntax window and save the .sps file; it is the difference between reproducing your analysis in five minutes and redoing it from memory.
R, most capable and the steepest start
Free, handles anything from a t-test to a multilevel Bayesian model, and produces publication quality figures with ggplot2. The cost is the first two weeks. If your dissertation involves mixed models, structural equation modelling, or a dataset you will reanalyse repeatedly, the investment pays back. Use R Markdown so your analysis and write-up live in one document that regenerates when the data change.
Excel, fine within narrow limits
Perfectly adequate for descriptives, charts, correlation, a t-test or a simple regression through the Data Analysis ToolPak. It becomes a liability for anything with assumptions to check, repeated measures, or more than a few thousand rows, and it silently mangles data types, most notoriously turning gene names and ID codes into dates. Never use it as your only record of a dataset.
Stata and Python, the honourable mentions
Stata dominates economics and public health, has excellent panel and survival support, and its .do file culture makes reproducibility the default. Python with pandas and statsmodels is the right call when your analysis sits inside a larger data pipeline or when you need machine learning alongside inference. Both are better choices than Excel for any graded quantitative project.
What to do this week
Check your syllabus for the required tool. Install it before the first problem set rather than the night one is due. Then run the same simple analysis, a two group t-test on any dataset, in your chosen tool from start to finish, including exporting the table. Doing that once, without deadline pressure, removes most of the friction from every assignment afterwards.
Key takeaways
- → Use the tool your course teaches, the rubric follows its output
- → SPSS for speed and APA-shaped tables
- → R for anything repeated, published or defended
- → Excel only for descriptives and small clean datasets
- → Save syntax or scripts so your analysis is reproducible