The Scholarly Commons is a wonderful resource if you are working on a project that involves statistical analysis. In this post, I will highlight some of the great resources the Scholarly Commons has for our researchers. No matter what point you are at in your project, whether you need to find and analyze data or just need to figure out which software to use, the Scholarly Commons has what you need!
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 Stata, R, and SPSS with regards to quantitative data analysis and provide links to additional resources. Every data analysis software I talk about in this post is 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.
Among researchers, Stata is often credited as the most user-friendly data analysis software. Stata is popular in the social sciences, particularly economics and political science. It is a complete, integrated statistical software package, meaning it can accomplish pretty much any statistical task you need it to, including visualizations. It has both a point-and-click user interface and a command line function with easy-to-learn command syntax. Furthermore, it has a system for version-control in place, so you can save syntax from certain jobs into a “do-file” to refer to later. Stata is not free to have on your personal computer. Unlike an open-source program, you cannot program your own functions into Stata, so you are limited to the functions it already supports. Finally, its functions are limited to numeric or categorical data, it cannot analyze spatial data and certain other types.
|User friendly and easy to learn||An individual license can cost
between $125 and $425 annually
|Version control||Limited to certain types of data|
|Many free online resources for learning||You cannot program new
functions into Stata
- STATA YouTube Channel: A great resource for troubleshooting problems in Stata.
- A Gentle Introduction to STATA by Alan C. Acock: A great reference for getting started with Stata available through the Scholarly Commons collection.
- Stata.com Resources for learning STATA: Lot of information on how to execute specific functions in Stata.
- The University Library’s Guide on STATA: A great place to find links to additional resources on Stata.
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 the same features as Stata such as a point-and-click user interface, a command line, savable files, and strong data analysis and visualization capabilities. It also has some capabilities Stata does not because 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.
|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.
|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|
- 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!
Exciting news for anyone interested in learning the basics of statistical and qualitative analysis software! Registration is open for workshops to be held throughout spring semester at the Center for Innovation in Teaching and Learning! There will be workshops on ATLAS.ti, R, SAS, Stata, SPSS, and Questionnaire Design on Tuesdays and Wednesdays in February and March from 5:30-7:30 pm. To learn more details and to register click here to go to the workshops offered by CITL page. And if you need a place to use these statistical and qualitative software packages, such as to practice the skills you gained at the workshops stop by Scholarly Commons, Monday-Friday 9 am- 6 pm! And don’t forget, you can also schedule a consultation with our experts here for specific questions about using statistical and qualitative analysis software for your research!
The University of Illinois Center for Innovation in Teaching & Learning (CITL) has registration open for their fall line-up of workshops. These are the same workshops that have been offered by ATLAS in the past. The workshops show participants how to use statistical and qualitative analysis software, as well as social science data. Registration is free of charge to UIUC faculty, instructors, staff, and students. All workshops run from 5:30 to 7:30 PM, and all but the ATLAS.ti workshops will take place in room G8a in the Foreign Languages Building (the ATLAS.ti workshop’s location will be announced soon). This semester’s schedule is as follows:
- 9/20: R I: Getting Started with R
- 9/21: SAS I: Getting Started with SAS
- 9/27: R II: Inferential Statistics
- 9/28: SAS II: Inferential Statistics with SAS
- 10/4: Stata I: Getting Started with Stata
- 10/5: SPSS I: Getting Started with SPSS
- 10/6: ATLAS.ti I: Introduction – Qualitative Coding
- 10/11: Stata II: Inferential Statistics with Stata
- 10/12: SPSS II: Inferential Statistics with SPSS
- 10/13: ATLAS.ti II: Data Exploration and Analysis
- 10/18: Questionnaire Design
For more information about the individual workshops, or to get a look at the workshop tutorials, head to the Statistics, Data and Survey Wiki. To register for these workshops, head to the CITL Workshop Registration Form. If you have any questions or concerns, please e-mail firstname.lastname@example.org.