SPSS vs. Excel vs. R vs. Jamovi for a thesis
Reviewed: August 19, 2026. Sources, review and corrections methodology
DIRECT ANSWER
Excel works for data entry, basic cleaning and summaries; SPSS offers a familiar interface and statistical procedures; Jamovi makes reproducible analysis approachable through an interface and R engine; R provides the most flexibility and automation. Choose a tool that correctly executes the method, preserves traceability and can be explained by the student.
Choosing software after defining the question and method avoids forcing the study into the available tool. No program decides which analysis is valid: it executes instructions and produces output that still needs verification and interpretation.
Quick comparison
| Criterion | Excel | SPSS | Jamovi | R |
|---|---|---|---|---|
| Entry and visual review | Strong | Adequate | Adequate | Requires a defined flow |
| Descriptive statistics | Yes | Yes | Yes | Yes |
| Advanced models | Limited | Broad, depending on license | Growing through modules | Very broad through packages |
| Reproducibility | Low with manual editing | Better with syntax | Saves analyses and options | High with scripts and projects |
| Cost | Depends on license | Commercial | Free | Free |
| Initial learning curve | Low | Low to medium | Low | Medium to high |
Excel
Excel is useful for inspecting tables, coding variables and simple summaries. Its main risk is manual cell editing without a record. Keep an immutable original dataset, document formulas and avoid mixing raw data and results in one sheet.
SPSS
Its interface lowers the barrier to common procedures. For traceability, paste or save syntax instead of relying only on clicks. Record version, institutional license, assumptions and exact procedure options.
Jamovi
Jamovi combines a spreadsheet interface with R-backed analyses. It is approachable for introductory work and stores settings in the project file. Modules expand its functions, but their versions should be documented and some specialized procedures may be unavailable.
R
R can automate cleaning, analysis, charts and reports. Its main advantage is that a script can rerun the workflow. Flexibility also requires control of packages, versions, objects and decisions. Do not copy code you cannot explain or validate.
How to choose
- Define the outcome, predictors, scales and objective.
- Specify the technique and its assumptions.
- Check what the program allows or teaches.
- Confirm that the tool exports usable tables and charts without losing precision.
- Preserve a reproducible record: syntax, script or option capture.
- Verify results with simple cases and investigate unexpected values.
A combination can be valid: capture in Excel, clean and analyze in R or Jamovi, and export results. The key is to avoid contradictory dataset versions and document each transformation.
For a practical introduction, read the data-analysis guide and analysis service. If you have not defined the method, begin with research guidance, not the software menu.
Primary and academic sources
These sources support the editorial review of this guide. Always verify the current rules of your own program.
- Introductory Statistics — OpenStax, Rice University
- Cochrane Handbook for Systematic Reviews — Cochrane
- R Manuals — The R Foundation
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