Posted on by Abigail Sewall
As you do research with larger amounts of data, it becomes necessary to graduate from doing your data analysis in Excel and find a more powerful software. It can seem like a really daunting task, especially if you have never attempted to analyze big data before. There are a number of data analysis software systems out there, but it is not always clear which one will work best for your research. The nature of your research data, your technological expertise, and your own personal preferences are all going to play a role in which software will work best for you. In this post I will explain the pros and cons of R and SPSS with regards to quantitative data analysis and provide links to additional resources. Both data analysis software mentioned in this post are available for University of Illinois students, faculty, and staff through the Scholarly Commons computers and you can schedule a consultation with CITL if you have specific questions.
R and its graphical user interface companion R Studio are incredibly popular software for a number of reasons. The first and probably most important is that it is a free open-source software that is compatible with any operating system. As such, there is a strong and loyal community of users who share their work and advice online. It has a point-and-click user interface, a command line, savable files, and strong data analysis and visualization capabilities. Users with more technical expertise can program new functions with R to use it for different types of data and projects. The problem a lot of people run into with R is that it is not easy to learn. The programming language it operates on is not intuitive and it is prone to errors. Despite this steep learning curve, there is an abundance of free online resources for learning R.
Pros | Cons |
|---|---|
| Free open-source software | Steep learning curve |
| Strong online user community | Can be slow |
| Programmable with more functions for data analysis |
- Introduction to R Library Guide: Find valuable overviews and tutorials on this guide published by the University of Illinois Library.
- Quick-R by DataCamp: This website offers tutorials and examples of syntax for a whole host of data analysis functions in R. Everything from installing the package to advanced data visualizations.
- Learn R on Code Academy: A free self-paced online class for learning to use R for data science and beyond.
- Nabble forum: A forum where individuals can ask specific questions about using R and get answers from the user community.
SPSS is an IBM product that is used for quantitative data analysis. It does not have a command line feature but rather has a user interface that is entirely point-and-click and somewhat resembles Microsoft Excel. Although it looks a lot like Excel, it can handle larger data sets faster and with more ease. One of the main complaints about SPSS is that it is prohibitively expensive to use, with individual packages ranging from $1,290 to $8,540 a year. To make up for how expensive it is, it is incredibly easy to learn. As a non-technical person I learned how to use it in under an hour by following an online tutorial from the University of Illinois Library. However, my take on this software is that unless you really need a more powerful tool just stick to Excel. They are too similar to justify seeking out this specialized software.
Pros | Cons |
|---|---|
| Quick and easy to learn | By far the most expensive |
| Can handle large amounts of data | Limited functionality |
| Great user interface | Very similar to Excel |
Additional Resources:
- OpenLearn- Getting Started with SPSS: A free and open online class for learning to use SPSS for data analysis.
- LinkedIn Learning: SPSS Statistics Essentials Training: Free online class for learning the basics of SPSS.
- How to use SPSS: A step-by-step guide to analysis and interpretation by Brian Cronk: This book is a beginner’s guide to using SPSS for data analysis available through the Scholarly Commons collection.
Thanks for reading! Let us know in the comments if you have any thoughts or questions about any of these data analysis software programs. We love hearing from our readers!
