Likelihood to Recommend SPSS is well-suited for the following: 1) User Behavior Analysis: SPSS handles large datasets to analyze user behavior data. 2) Customer Satisfaction / Foundational Surveys: SPSS facilitates analysis of quant data from satisfaction surveys, keeping us informed about customer needs and preferences. 3) A/B test analysis: SPSS statistical tools for A/B test analysis, which helps optimize user experience of our products. Scenarios where SPSS are less appropriate: 1) Qualitative Data Analysis: I do not use SPSS for open-ended survey responses/qual data. 2) Live/in-vivo data analysis: SPSS is not ideal for real-time data processing. 3) Complex Data Integration: SPSS isn’t the best fit for complex data integration tasks
Read full review SAS Enterprise Guide is good at taking various datasets and giving analyst/user ability to do some transformations without substantial amounts of code. Once the data is inside SAS, the memory of it is very efficient. Using SAS for data analysis can be helpful. It will give good statistics for you, and it has a robust set of functions that aid analysis.
Read full review Pros 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. Read full review I think the most useful aspect of SAS Enterprise Guide is the ability to use a point-and-click interface to create graphics, transform data, and perform statistics. The best part is that SAS Enterprise Guide creates base SAS code from the process, making it easy to reproduce analyses. SAS Enterprise Guide makes creating summary statistics about as easy as it gets. If one doesn't know proc means or proc tabulate, one can use SAS Enterprise Guide instead. The time-series forecasting procedures within SAS Enterprise Guide produce fairly good results. SAS Enterprise Guide makes time-series model comparisons relatively straight-forward. Read full review Cons It would be beneficial to have AMOS as part of the SPSS package instead of purchasing it separately. It would be beneficial to have other statistical tests, such as PROCESS, be part of the standard SPSS tests instead of having the need to run a syntax to have it installed. My dataset tends to be smaller, and I have never had any issues with using SPSS. I heard that SPSS may not be optimal when handling large datasets. Read full review SAS Studio has some great examples that can be implemented. Adding a filter to the output datasets for one. Some issues around having to enter my password every time I open it up. Some people are having this issue and others aren't. SAS admin is at a loss to work out why it's occurring. Read full review Likelihood to Renew Both money and time are essential for success in terms of return on investment for any kind of research based project work. Using a Likert-scale questionnaire is very easy for data entry and analysis using IBM SPSS. With the help of IBM SPSS, I found very fast and reliable data entry and data analysis for my research. Output from SPSS is very easy to interpret for data analysis and findings
Read full review On account of current user experience and the organization-wide acceptance.
Read full review Usability Probably because I have been using it for so long that I have used all of the modules, or at least almost all of the modules, and the way SPSS works is second nature to me, like fish to swimming.
Read full review It's not all bad, but I don't believe that an enterprise purchase of SAS is worth the expense considering the widely available set of tools in the data analytics space at the moment. In my company, it's a good tool because others use it. Otherwise, I wouldn't purchase a new set of it because it doesn't have some of the better analytical functions in it.
Read full review Reliability and Availability SPSS can tend to crash when I am trying to do a lot of data. This can slow me down when I need to do a lot of data
Read full review Performance SPSS does the job, but it can be slow. I do have to plan a lot of time to get through a huge amount of data.
Read full review Support Rating I have not contacted IBM SPSS for support myself. However, our IT staff has for trying to get SPSS Text Analytics Module to work. The issue was never resolved, but I'm not sure if it was on the IT's end or on SPSS's end
Read full review I did not use the technical support of SAS EG. I can say that I have had hard time to find online tutorials or projects for SAS EG. For instance, it is hard to find completed researches or designed algorithms used with SAS EG. Sometimes it just depends on user's skill set and experience with databases and programming.
Read full review Implementation Rating Have a plan for managing the yearly upgrade cycle. Most users work in the desktop version, so there needs to be a mechanism for either pushing out new versions of the software or a key manager to deal with updated licensing keys. If you have a lot of users this needs to be planned for in advance.
Read full review I've not worked hands-on with the implementation team, but there were no escalations barring a few hiccups in the deployment due to change in requirement & adoption to our company's remote servers.
Read full review Alternatives Considered I have used R when I didn't have access to SPSS. It takes me longer because I'm terrible at syntax but it is powerful and it can be enjoyable to only have to wrestle with syntax and not a difficult UI.
Read full review Why I prefer SAS EG: Data processing speed is much faster than that R Studio. It can load any amount of data and any type of data like structured or unstructured or semi-structured. Its output delivery system by which we have the output in PDF file makes it very comfortable to use and share that file to clients very easily. Inbuilt functions are very powerful and plentiful. Facility of writing macros makes it far away from its competitors.
Read full review Scalability I am neutral because I have not had to look into scalability since I am using as a student.
Read full review Return on Investment IBM SPSS has allowed me to quickly analyze data for research. IBM SPSS has allowed me to complete analyses in order to submit research findings to conferences and complete manuscripts. IBM SPSS has enabled me to meet research objectives set out in grant proposals. Read full review Positive (cost): SAS made a bundle that include unlimited usage of SAS/Enterprise Guide with a server solution. That by itself made the company save a lot of money by not having to pay individual licences anymore. Positive (insight): Data analysts in business units often need to crunch data and they don't have access to ETL tools to do it. Having access to SAS/EG gives them that power. Positive (time to market): Having the users develop components with SAS/EG allows for easier integration in a production environment (SAS batch job) as no code rework is required. Read full review ScreenShots