IBM SPSS: A Senior Stats Swiss Army Knife That Scales With You
Benjamin Plotkin | TrustRadius Reviewer
June 28, 2019

IBM SPSS: A Senior Stats Swiss Army Knife That Scales With You

Score 8 out of 10
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  • IBM SPSS Statistics

Overall Satisfaction with IBM SPSS

As an IT professional in the Student Affairs division of the California State University system, I recently had the opportunity to undertake a data research project examining the impact of computer- vs. paper-based administration on writing proficiency assessment scores, across a large number of demographic dimensions. Being fortunate to have access to a very large number of cases (greater than 40,000), I needed to upgrade from spreadsheet statistical analysis packages to a professional statistical product -- both to expedite the overall number-crunching, but importantly to take advantage of automation opportunities such software packages can provide. I chose SPSS as it enjoys substantial support on my campus.
  • SPSS has been around for quite a while and has amassed a large suite of functionality. One of its longest-running features is the ability to automate SPSS via scripting, AKA "syntax." There is a very large community of practice on the internet who can help newbies to quickly scale up their automation abilities with SPSS. And SPSS allows users to save syntax scripting directly from GUI wizards and configuration windows, which can be a real life-saver if one is not an experienced coder.
  • Many statistics package users are doing scientific research with an eye to publish reproducible results. SPSS allows you to save datasets and syntax scripting in a common format, facilitating attempts by peer reviewers and other researchers to quickly and easily attempt to reproduce your results. It's very portable!
  • SPSS has both legacy and modern visualization suites baked into the base software, giving users an easily mountable learning curve when it comes to outputting charts and graphs. It's very easy to start with a canned look and feel of an exported chart, and then you can tweak a saved copy to change just about everything, from colors, legends, and axis scaling, to orientation, labels, and grid lines. And when you've got a chart or graph set up the way you like, you can export it as an image file, or create a template syntax to apply to new visualizations going forward.
  • SPSS makes it easy for even beginner-level users to create statistical coding fields to support multidimensional analysis, ensuring that you never need to destructively modify your dataset.
  • In closing, SPSS's long and successful tenure ensures that just about any question a new user may have about it can be answered with a modicum of Google-fu. There are even several fully-fledged tutorial websites out there for newbie perusal.
  • SPSS syntax is somewhat archaic, no doubt due to its long tenure and a desire to maintain backwards compatibility, so it may add to the learning curve for otherwise experienced script writers.
  • The syntax editing window is somewhat unwieldy when compared to modern IDEs, and can be laggy; sometimes mouse cursor accuracy is unpredictable when one attempts to select a particular word or line of syntax.
  • Counterintuitively, perhaps, SPSS has so many ways to achieve the same ends -- GUI, syntax, legacy functions vs. updated functions for visualizations, etc. -- that it can be hard for new users to find the "right" (quickest, simplest, most transparent) way to do things; it's an embarrassment of riches that may confuse and overwhelm novices.
Personally, SPSS allowed me to complete a complex and demanding statistical analysis project for my institution in a far shorter time than it would have taken with free statistical analysis packages; it gave me the ability to succeed without extensive training, and allowed me to dimensionalize and analyze my dataset with a very high level of confidence in the results.

Importantly, I was able to quickly complete my analysis project across many dozens of dimension thanks to SPSS syntax, which I was able to quickly duplicate and modify as needed, dimension by dimension.
SPSS facilitated my analysis project, which allowed me to deliver evidence of statistically-significant testing mode effects to my institution. My research is expected to influence testing administration policies and procedures going forward, ensuring that student test-takers are appropriately, efficiently, and effectively served in future.

Because I was able to deliver sophisticated and significant analysis quickly and easily, my department has received a great deal of positive attention from various university officials.
Two alternatives I considered before choosing SPSS are the programming languages R and Julia Statistics. Both are freely available, open-source packages for sophisticated statistical analysis and visualization. In my case, I preferred SPSS to these somewhat newer options due to the wide availability of support for SPSS, both on my campus and on the internet. While R and Julia Statistics are powerful programming languages, the fact that SPSS includes support to script in Python further secured SPSS as my number one choice.
SPSS is well-suited for academic environments, given the strong foothold it has in the educational and research institutions of the world; it helps if your institution has a licensing agreement as it is not free to use indefinitely (although a 14-day free trial is available). As it is a powerful and complex statistical analysis package, it is a good fit for large and otherwise unwieldy datasets and analytical projects.

SPSS is probably not a great fit for individual users doing simple statistical analysis, as much of this level of work can be accomplished at no cost using Google Sheets and an add-on such as the XLMiner package.