SPSS vs R for Humanitarian Sector Impact Evaluation
Both tools can produce rigorous results. The right choice depends on your team's skills, your donor's expectations, and the complexity of your evaluation design.
Quick verdict
- Your team has no programming background
- Your donor or institution requires a point-and-click audit trail
- You are running standard surveys with Likert scales and descriptive statistics
- You need AMOS for Structural Equation Modelling
- Speed of delivery matters more than reproducibility
- Your evaluation involves complex multilevel or mixed models
- You need full reproducibility and open-source transparency
- Visualisation quality is a deliverable, not an afterthought
- Your team includes at least one analyst comfortable with code
- The project budget does not include a software licence
Why this comparison matters in the humanitarian context
Impact evaluation in the humanitarian sector operates under constraints that do not exist in commercial research. Timelines are compressed by funding cycles. Teams are often distributed across low-bandwidth environments. Donors — whether USAID, ECHO, UN agencies, or bilateral funders — increasingly require not just results, but documented, reproducible methodology.
Against this backdrop, the choice between SPSS and R is rarely a pure technical question. It is a question of institutional capacity, donor expectation, and the trade-off between accessibility and analytical power.
This guide does not declare a winner. It maps the decision honestly so that evaluation leads, M&E officers, and research coordinators can make the choice that fits their specific context.
Head-to-head comparison
| Criterion | SPSS | R |
|---|---|---|
| Learning curve | Low — GUI-driven, familiar to researchers trained in social sciences | Steep — requires comfort with scripting, though RStudio lowers the barrier significantly |
| Cost | Licence required (~$1,200–3,000/year). Discounts available for NGOs and academic institutions | Completely free and open-source |
| Reproducibility | Possible via syntax files, but often ignored in practice | Excellent — R Markdown and Quarto enable fully reproducible reports |
| Statistical depth | Strong for standard tests (t-test, ANOVA, regression, factor analysis) | Virtually unlimited — thousands of packages covering any method |
| Visualisation | Basic charts; limited customisation | ggplot2 produces publication-quality graphics with full control |
| Data handling | Comfortable up to ~1M rows; variable and value labels are a strength | Handles large datasets efficiently; tidyverse makes wrangling intuitive |
| Mixed methods integration | Limited — primarily quantitative | Packages exist for text analysis, qualitative coding integration, and GIS |
| Donor acceptance | Widely accepted; name recognition helps in institutional settings | Accepted by most major donors when methodology is documented |
| Team portability | Analysis tied to the person who built the file | Scripts are portable, versionable, and shareable via Git |
| Offline use | Fully offline once installed | Fully offline; packages can be installed from local source |
Four real-world scenarios
Where should each tool sit in your evaluation workflow?
Post-distribution monitoring survey
A rapid PDM with 400 households, Likert-scale food security indicators, and a two-week deadline before donor reporting. The M&E officer has SPSS training from university.
Recommendation: SPSS
Speed and familiarity outweigh reproducibility here. Standard descriptives, cross-tabs, and a Mann-Whitney test will cover the analysis. SPSS delivers this in hours.
Multi-country programme evaluation
A five-country evaluation with panel data, control and treatment groups, and a requirement for difference-in-differences estimation. The final report will be published openly.
Recommendation: R
DiD models, heteroscedasticity-robust standard errors, and ggplot2 maps are all handled cleanly. The R Markdown report becomes the audit trail.
Beneficiary satisfaction index
Developing a composite index from 18 indicators across three sectors. The research team wants to validate the index structure and present results to a non-technical steering committee.
Recommendation: SPSS + R
Run the factor analysis and reliability testing in SPSS for speed. Produce the final visualisations and index documentation in R for quality and reproducibility.
Conflict-affected context — limited connectivity
Field teams in a fragile context with intermittent power and no internet. Data collected on tablets, analysis done on a single laptop by a national staff analyst.
Recommendation: R (offline)
R is free, runs fully offline, and a pre-configured RStudio environment with all packages installed can be distributed on a USB drive — zero licence cost.
The hybrid approach — why you do not have to choose
Many high-performing evaluation units use both tools at different stages. SPSS handles the early data cleaning and exploratory work where speed and label management matter. R takes over for the modelling, visualisation, and final report generation.
This division is not a compromise — it is a rational allocation of each tool's strengths. The key is documenting the handoff point clearly so that any team member or external reviewer can follow the analytical chain.
If your organisation is moving toward a single-tool approach, R is the stronger long-term investment. The skills are transferable across sectors, the community is larger, and the cost of zero is hard to beat when operating under constrained budgets.
Five questions to make your decision
- Does your team write code? If no one on the team is comfortable with scripting, SPSS will deliver faster results. If even one analyst is code-literate, R becomes viable.
- What does your donor require? Some frameworks (IFRC, Sphere) reference specific tools. Check the Terms of Reference before choosing.
- Will the analysis be published? Open publication demands open, reproducible methods. R + GitHub is the standard for credible public evaluation.
- How large and complex is the dataset? Standard cross-sectional survey? SPSS is sufficient. Longitudinal panel, geospatial layer, or text data? R.
- What is the organisational trajectory? If your organisation is building internal data capacity over five years, investing in R now saves licence costs and builds transferable skills.
Need help designing your evaluation methodology?
Claryon works with humanitarian organisations, UN agencies, and development institutions to design rigorous, context-appropriate evaluation frameworks — and to implement them using the right tools for your team and your funders.