Stata vs. R vs. SPSS for Data Analysis

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.

Short video loop of a kid sitting at a computer and putting on sun glasses

Rock your research with the right tools!


STATA

Stata logo. Blue block lettering spelling out Stata.

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.

 

Pros

Cons

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

Additional resources:


R logo. Blue capital letter R wrapped with a gray oval.

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.

Pros

Cons

Free open-source software Steep learning curve
Strong online user community Can be slow
Programmable with more functions
for data analysis

Additional Resources:

  • 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

SPSS logo. Red background with white block lettering spelling SPSS.

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:

Gif of Kermit the frog dancing and flailing his arms with the words "Yay Statistics" in block letters above

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!

 

What’s In A Name?: From Lynda.com to LinkedIn Learning

LinkedIn Learning Logo

Lynda.com had a long history with libraries. The online learning platform offered video courses to help people “learn business, software, technology and creative skills to achieve personal and professional goals.Lynda.com paired well with other library services and collections, offering library users the chance to learn new skills at their own pace in an accessible and varied medium. 

However, in 2015—twenty years after its initial launch—Lynda.com⁠⁠ was purchased by LinkedIn. A year later, Microsoft purchased LinkedIn for $26.2 billion. And now, in 2019, Lynda.com content is available through the newly-formed LinkedIn Learning.

Charmander evolving into Charmeleon

Sometimes, evolution is simple (like when it gets you one step closer to an Elite-Four-wrecking Charizard). Sometimes, it’s a little more complicated (like when Microsoft buys LinkedIn which just bought Lynda.com).

The good news is that this change from Lynda.com to LinkedIn Learning includes access to all of the same content previously available. This means that, through the University Library’s subscription, you still have access to courses on software like R, SQL, Tableu, Python, InDesign, Photoshop, and more (many of which are available to use on campus at the Scholarly Commons). There are also courses on broader, related topics like data science, database management, and user experience

Setting up your own personal account to access LinkedIn Learning is where things get just a little trickier. As a result of the transition from Lynda.com to LinkedIn Learning, users are now strongly encouraged to link their personal LinkedIn accounts with their LinkedIn Learning accounts. Completing courses in LinkedIn Learning will earn you badges that are automatically carried over to your LinkedIn account. However, this additional step—using a personal LinkedIn account to access these course—also makes the information about your LinkedIn Learning as public as your LinkedIn profile. Because Lynda.com only required a library card and PIN, this change in privacy has received push-back from libraries and library organizations across the country.

Obi-Wan Kenobi looking confused with caption reading [visible confusion]

This new policy change doesn’t mean you should avoid LinkedIn Learning, it just means you should use it with care and make an informed decision about your privacy settings. Maybe you want potential employers to see what you’re proactively learning about on the platform, maybe you to keep that information private. Either way, you can get details on setting up accounts and your privacy settings by consulting this guide created by Technology Services.

LinkedIn Learning can be accessed through the University Library here.

Spotlight on DiRT Directory: Digital Research Tools

The DiRT logo.

As a researcher, it can sometimes be frustrating knowing that someone out there has created a useful tool that will help you with what you’re working on, but being unable to find it. Google searches prove fruitless, and your network of friends don’t necessarily know what you’re talking about. In that moment of panic and frustration, you may just need to get a little DiRT-y.

DiRT Directory: Digital Research Tools is a directory of research tools for scholarly use. Using TaDiRAH (the Taxonomy of Digital Research Activities in the Humanities), DiRT breaks down the stages of a research project, and groups tools that are relevant to each stage: Capture, Creation, Enrichment, Analysis, Interpretation, Storage, and Dissemination. Users can either search for tools using these categories — broken down into subcategories whose specificity helps to narrow down the many tools found in the DiRT Directory — through a search box or by tag. Personally, I feel that searching through the TaDiRAH categories allows you to find relevant tools, but also allows you to explore options that you may not have previously thought of as being available, making it the most fruitful way to browse tools.

One nice aspect of DiRT is its search platform. After you choose your category, you have the option to search within the category for these criteria: Platform, Cost, Exclude, License, and Research Objects, as well as sort order. For researchers concerned with cost, this tool is especially useful, as you can limit your search to what is in your budget.

After you complete your search, you are offered a list of different tools. Tools range from well-known sources, like Google Docs, to things you have probably never heard of before. Each source includes a description, outlining what kind of tool it is — online, software, etc. — what its capabilities are, and in many cases, a note on its past or future development. Each entry also includes a link to the tool’s website, their license, and the date of DiRT’s most recent update on the source information.

An example tool entry on DiRT for Scrivener writing software.

An example tool entry on DiRT for Scrivener writing software on the search page.

Finally, each tool has its own page that you can access from the search function. This page holds a wealth of information, including an expanded description that outlines the nitty gritty aspects of the tool — from platforms to cost bracket to tags. It also includes screenshots of the tool in action, a list of recent edits to the page, and a comments section. However, not all tools have the same level of detail in their pages.

capture2

Scrivener’s page, which includes a description, screenshots, a list of contributors, and a comments section.

While the selection presented on DiRT can be almost overwhelming, digging through DiRT can help you find the perfect tools for your project.

If you still can’t find what you want in DiRT Directory, or need some guidance in what to search for in the first place, stop by the Scholarly Commons, located in Main Library Room 306, open from 9am-6pm on weekdays. Or, email us! We are always happy to help you with your research needs.