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.

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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:

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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!

 

Lightning Review: How to Use SPSS

“A nice step-by-step explanation!”

“Easy, not too advanced!”

“A great start!”

           Real, live reviews of Brian C. Cronk’s How to Use SPSS: A Step-By-Step Guide to Analysis and Interpretation by some of our patrons! This book, the Tenth Edition of this nine-chapter text published by Taylor and Francis, is ripe with walkthroughs, images, and simple explanations that demystifies the process of learning this statistical software. Also containing six appendixes, our patrons sang its praises after a two-hour research session here in the Scholarly Commons!

           SPSS, described on IBM’s webpage as “the world’s leading statistical software used to solve business and research problems by means of ad-hoc analysis, hypothesis testing, geospatial analysis and predictive analytics. Organizations use IBM SPSS Statistics to understand data, analyze trends, forecast and plan to validate assumptions and drive accurate conclusions’ is one of many tools CITL Statistical Consulting uses on a day-to-day basis in assisting Scholarly Commons patrons. Schedule a consultation with them from 10 am to 2 pm, Monday through Thursday, for the rest of the summer!

           We’re thrilled to hear this 2018 title is a hit with the researcher’s we serve! Cronk’s book, and so many more works on software, digital publishing, data analysis, and so much more make up our reference collection – free to use by anyone and everyone in the Scholarly Commons!

Register for Spring 2017 Workshops at CITL!

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!

Getting Over Data Anxiety

For some people, looking at a spreadsheet brings back bad memories of sixth grade Pre-Algebra classes and mediocre grades. Unlearning your fear of numerical data may take time and patience, but will ultimately leave you a better, well-rounded researcher. Here are a few tips on how to start the process of embracing numbers in your research and life.

  1. Find data that interests you. If you weren’t someone with a heavy interest in math, the mere sight of an equation may make you shiver. That’s why it’s important to begin your journey with data by practicing with data that actually keeps you absorbed in your work. If you’re invested in the outcome, you’re more likely to put in the time and effort to learn skills and best practices that will ultimately make using data easier for you in the long run.
  2. Find some guidance. Staring at a sheet of numbers while scratching your head isn’t always a great plan. Find resources that will help you. Here at the University of Illinois, the Scholarly Commons, Research Data Service, and the Center for Innovation in Teaching & Learning offer frequent Savvy Researcher workshops to help people learn how to use data and corresponding software. You can also schedule a consultation request with a Scholarly Commons expert online, or e-mail the Scholarly Commons for simple questions. If you want to keep to yourself, there are also a number of data analysis LibGuides, which you can peruse.
  3. Start simple.  Don’t try to learn R in a day. You’ll end up frustrated and discouraged. Take some time to survey your options, and start simple, with Excel and SPSS, for example. Each software has unique things that it can do, and figure out a system that works for you.
  4. Understand why you’re doing this. Everyone has had the moment where they look at their research and think to themselves, “Why am I doing this? Why didn’t I go into the private sector?” It’s understandable. Looking at some of the incredible things people are doing with numerical data can help you remember why it is that you’re taking the time to learn these skills — which will actually be very marketable if you do decide to go into the private sector, just saying — and what you can do with them. A few favorite projects of mine are Institute for Health Metrics and Evaluation’s GBD Compare visualization, The Daily Routines of Famous Creative People by Mason Currey, and Two Centuries of US Immigration.

Just like any skill, learning how to handle and understand numerical data takes time and effort. But mastering data will add depth to your research, and allow you to present your findings in new, interactive ways.

Register for Fall 2016 CITL Workshops

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 atlas-training@illinois.edu.

ATLAS Workshops

This semester, ATLAS is conducting several short evening courses to show participants how to use statistical, GIS (geographic information systems), and qualitative software, as well as social science data. Statistical & GIS workshops are available to all University of Illinois faculty, instructors, staff, and students.

The Spring 2015 Workshop Schedule
02/24/2015 – ATLAS.ti 1: Introduction – Qualitative Coding
03/03/2015 – ATLAS.ti 2: Data Exploration and Analysis

02/11/2015 – ArcGIS 1: Introduction to ArcCatalog and ArcMap
02/18/2015 – ArcGIS 2: Introduction to ArcToolbox

02/25/2015 – SPSS 1: Getting Started with SPSS
03/04/2015 – SPSS 2: Inferential Statistics with SPSS

03/11/2015 – Stata 1: Getting Started with Stata
03/18/2015 – Stata 2: Inferential Statistics with Stata

03/10/2015 – SAS 1: Getting Started with SAS
03/17/2015 – SAS 2: Inferential Statistics with SAS

02/10/2015 – R 1: Getting Started with R
02/17/2015 – R 2: Inferential Statistics
04/01/2015 – R 3: R Studio

03/31/2015 – Questionnaire Design

Registration Details:
http://www.surveygizmo.com/s3/1957979/workshop-registration

ATLAS Offers Data Consulting, Free Online Tools, Workshops, and More

The college of LAS has recently purchased college-wide licenses for the online survey research tools: Surveygizmo and Qualtrics.  These accounts are free to faculty and students.  To request an account please use the following link: http://www.surveygizmo.com/s3/1781704/surveytool

ATLAS also offers:

— A free questionnaire design workshop  and assistance with programing online surveys:http://www.atlas.illinois.edu/services/stats/consulting/

–An open computer lab with knowledgeable staff ready to answer your questions about quantitative and qualitative research and programs:  2043 Lincoln Hall,(9-5 M-Th, 9-3 F)

–Free workshops: http://www.surveygizmo.com/s3/1708941/workshop-registration

–Classroom demonstrations  for our supported programs: http://www.atlas.illinois.edu/services/stats/tutorials/


Here is the ATLAS Fall Workshop Schedule
http://www.surveygizmo.com/s3/1708941/workshop-registration

10/08/2014  – ATLAS.ti Introduction – Qualitative Coding

9/24/2014 – ArcGIS 1: Introduction to ArcCatalog and ArcMap
10/01/2014 – ArcGIS 2: Introduction to ArcToolbox

10/07/2014 – SPSS 1: Getting Started with SPSS
10/142014 – SPSS 2: Inferential Statistics with SPSS

10/22/2014 – Stata 1: Getting Started with Stata
10/29/2014 – Stata 2: Inferential Statistics with Stata

10/21/2014 – SAS 1: Getting Started with SAS 
10/28/2014 – SAS 2: Inferential Statistics with SAS

09/23/2014 – R: Getting Started with R
09/30/2014 – R 2: Inferential Statistics

11/04/2014 – Survey Research


Do you need help locating data for a project or thesis?  Do you need assistance preparing your data for analysis?  ATLAS holds Data Service hours in the Library’s Scholarly Commons (306 Main Library).  For more information please visit: http://www.library.illinois.edu/datagis/


For more information about any of these services, please visit:
http://www.atlas.illinois.edu/services/stats/consulting/