SPSS vs. R vs. Excel: Which Should You Use for Your Research?

April 6, 2026 3 min read By Angel Reyes

You've been told to "run your stats" but nobody told you where to run them. SPSS, R, and Excel are the three tools graduate students encounter most often. Each has strengths and weaknesses, and the best choice depends on your skills, your committee, and your research design.

Excel: The Tool You Already Know

Best for: Basic descriptive statistics, data cleaning, simple charts

You already have Excel, and you already know how to use it. That's a real advantage. For calculating means, creating frequency tables, and building basic charts, it works fine.

Limitations: Excel has no built-in support for most inferential tests. It can do t-tests and basic ANOVA through the Data Analysis ToolPak, but it doesn't calculate effect sizes, it can't run non-parametric tests, and its statistical output doesn't meet APA formatting standards. For anything beyond descriptives, you'll outgrow it quickly.

Verdict: Use Excel for data entry, cleaning, and preliminary exploration. Don't rely on it for your main analyses.

SPSS: The Dissertation Standard

Best for: Most dissertation-level analyses, especially if you want a point-and-click interface

SPSS (Statistical Package for the Social Sciences) is the workhorse of social science research. It handles t-tests, ANOVA, regression, chi-square, reliability analysis, and more — all through dropdown menus. You don't have to write any code.

Strengths:

  • Point-and-click interface means no programming required
  • Widely taught in graduate programs
  • Committee members are likely familiar with it
  • Produces detailed output for most common tests
  • Can handle assumption checking and effect sizes with the right options

Limitations:

  • It's expensive ($99/month for the standard subscription)
  • The output is verbose and not APA-formatted by default
  • Advanced techniques (multilevel modeling, structural equation modeling) require add-on modules
  • Learning the menu system still takes time

Verdict: If your program provides a license and your committee uses SPSS, it's the path of least resistance. Most dissertation guides and tutorials assume SPSS.

R: The Free Powerhouse

Best for: Researchers who want maximum flexibility, reproducibility, or advanced analyses

R is free, open-source, and can do everything — from a simple t-test to complex structural equation models. The R ecosystem has packages for virtually any statistical method.

Strengths:

  • Completely free
  • Thousands of packages for specialized analyses
  • Reproducible scripts (you can share your exact analysis)
  • Beautiful publication-ready visualizations with ggplot2
  • Growing community of researchers who support each other

Limitations:

  • Steep learning curve if you've never coded before
  • Error messages can be cryptic and frustrating
  • Your committee may not know R, making it harder to get help
  • Setting up your environment takes time

Verdict: If you enjoy problem-solving and want a skill that will serve you beyond your dissertation, R is worth learning. But don't switch to R two months before your defense.

So Which One Should You Use?

Ask these three questions:

  1. What does your committee recommend? If your chair uses SPSS and can troubleshoot your output, use SPSS. Don't create unnecessary friction.
  2. What does your program provide? Many universities offer free SPSS licenses. Check your library or IT department.
  3. What will you need long-term? If you're planning an academic career, learning R will pay dividends. If you just need to finish your dissertation, use whatever gets you there.

The Hybrid Approach

Many successful students use a combination: Excel for data entry and cleaning, SPSS for main analyses, and free tools like G*Power for power analysis or online calculators for effect sizes. There's no rule saying you must use only one tool.

For more free options, check out our list of free statistics tools every graduate student should know.