IBM SPSS Statistics vs. IBM SPSS Modeler

I described earlier that the only scenarios where I use SPSS are those where we have legacy projects that were developed in the late 90s or early 2000s using SPSS, and for some reason, the project (data set, scope, etc.) hasn't changed in 24+ years. This counts for 1-2 out of around 80 projects that I run. Whenever possible, I actively have my team move away from SPSS, even when that process is painful.

Incentivized

Fast NLP analytics are very easy in SPSS Modeler because there is a built-in interface for classifying concepts and themes and several pre-built models to match the incoming text source. The visualizations all match and help present NLP information without substantial coding, typically required for word clouds and such. SPSS Modeler is good at attaining results faster in general, and the visual nature of the code makes a good tool to have in the data science team's repository. For younger data scientists, and those just interested, it is a good tool to allow for exploring data science techniques.

Incentivized

• 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.

Incentivized

• Combine text and data
• Provide facilities for all phases of the data mining process.
• Use a node and stream paradigm to easily and quickly create models.

Incentivized

• collaboration - SPSS lacks collaboration features which makes it near impossible to collaborate with my team on analysis. We have to send files back and forth, which is tedious.
• integration - I wish SPSS had integration capabilities with some of the other tools that I use (e.g., Airtable, Figma, etc.)
• user interface - this could definitely be modernized. In my experience, the UI is clunky and feels dated, which can negatively impact my experience using the tool.

Incentivized

• Has very old style graphs, with lots of limitations.
• Some advanced statistical functions cannot be done through the menu.
• The data connectivity is not that extensive.
• It's an expensive tool.

Incentivized

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

Incentivized

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.

Incentivized

The ability to do predictive modeling, text analytics for both structured & unstructured data, decision management, optimization, and support for various data sources

Incentivized

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

Incentivized

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.

Incentivized

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

The online support board is helpful and the free add ons are incredibly appreciated.

Incentivized

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.

Incentivized

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.

Incentivized

When it comes to investigation and descriptive we have found SPSS Statistics to be the tool of choice, but when it comes to projects with large and several datasets SPSS Modeler has been picked from our customers.

Incentivized

I am neutral because I have not had to look into scalability since I am using as a student.

Incentivized

• I found SPSS easier to use than SAS as it's more intuitive to me.
• The learning curve to use SPSS is less compared to SAS.
• I used SAS, to a much lesser extent than SPSS. However, it seems that SAS may be more suitable for users who understand programming. With SPSS, users can perform many statistical tests without the need to know programming.

Incentivized

• Positive - Ease of decision making and reduction in product life cycle time.
• Positive - Gives entirely new perspective with the help of right team. Helps expanding the portfolio.
• Negative - Needs to have good understanding about mathematical modelling, of which talent is rare and expensive. Hence, increase the costs for R&D and manpower.

Incentivized