SPSS Statistics is a software package used for statistical analysis. It is now officially named "IBM SPSS Statistics". Companion products in the same family are used for survey authoring and deployment (IBM SPSS Data Collection), data mining (IBM SPSS Modeler), text analytics, and collaboration and deployment (batch and automated scoring services).
$99
per month
SAS Enterprise Miner
Score 8.2 out of 10
N/A
SAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.
I like the algorithms SAS uses better than SPSS. I have been writing SAS code since the mid 1980s and trust their development team. Also offer great refresher class to academics.
SAS EM has a very great set of machine learning and predictive analytics toolsets, which helped our organization achieve its goals. We used other tools, but for us, SAS EM was the most intuitive and easy to learn the tool and it provides greater data exploration and data …
SPSS was used for model development before SAS in my organization. SAS brought a bigger more complete integrated solution than SPSS had. It allowed users to easily prepare their data with SAS/Enterprise Guide and then use it with Enterprise Miner. The data preparation tools of …
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
SAS Enterprise Miner is world-class software for individuals interested in developing reproducible models in a reasonable amount of time. Perhaps the most useful part of SAS Enterprise Miner is the ability to compare models with other models without writing code. The ensemble modeling capabilities is the easiest way to do ensemble modeling I have come across. SAS Enterprise Miner is well-suited for beginning to advanced analysts who know something about advanced analytics. The software is not well-suited for analysts or companies that have little interest in advanced modeling.
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.
Enterprise Miner is really visual and lets you do a whole lot without actually going into the detailed options. For decent results, you should really explore the different advanced options though.
The recent versions of Miner allow users to use R code in Miner. You can then compare several models and approach to get the best performing model.
The resulting data is really well displayed and easy to understand (ex: the lift graph, score ranking, etc.)
Miner has the ability to integrate custom SAS code which allows the user to add functionalities that are specific to the project.
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.
With large data sets, SAS Enterprise Miner sometimes takes a long time to run. Sometimes you have to just leave your computer running while Enterprise Miner does its thing.
If you want complete control over the modeling framework, you have to take what Enterprise Miner does and customize it. SAS seems to be working hard on making things easier to customize, but it's not completely there yet.
The graphic capabilities of SAS Enterprise Miner leave a lot to be desired, especially in the era of self-service business intelligence software.
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
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.
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
I have contacted SAS twice in the past year and they have been super responsive both times. They solved my problem. I am also registered for an in-person class next month and they called today to tell me that it will be an online-only session. They apologized for the change and registered me for the online version. Super helpful!
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.
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.
SAS EM has a very great set of machine learning and predictive analytics toolsets, which helped our organization achieve its goals. We used other tools, but for us, SAS EM was the most intuitive and easy to learn the tool and it provides greater data exploration and data preparation capabilities compared to the other tools we used.
