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While you can create publication-ready charts, the default outputs look like they are from 2005: gray backgrounds, basic colors, and non-intuitive editing. Compare this to the beautiful, interactive ggplot2 outputs from R or Python’s Seaborn. You will likely export SPSS data to another tool for final visualizations.

SPSS handles labeled survey data exceptionally well. You can define "1 = Male, 2 = Female," and all outputs will show the labels, not just numbers. It includes robust tools for recoding, computing new variables, and handling missing data (e.g., pairwise vs. listwise deletion). ibm spss

Verdict: 8.2/10 (Excellent for its target audience, but not for everyone) While you can create publication-ready charts, the default

SPSS’s syntax language is primitive. It lacks the vectorized operations, functional programming, or package ecosystem of R/Python. Loops and conditional logic are awkward. If your analysis requires a novel statistical method, you are stuck—SPSS cannot be extended in the way open-source platforms can. SPSS handles labeled survey data exceptionally well

However, the software industry has moved on. Modern, free, GUI-based alternatives (like JASP) offer the same ease with better graphics. And the programming world (R/Python) offers infinite flexibility at zero cost. IBM's slow innovation and high prices mean SPSS is no longer a wise personal investment.