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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    IBM SPSS Statistics

    Score8 out of 10
    N/ASPSS 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).

    $105

    per month per user

    pandas

    Score10 out of 10
    N/Apandas is an open source, BSD-licensed library providing high-performance data structures and data analysis tools for the Python programming language. pandas is a Python package providing expressive data structures designed to make working with “relational” or “labeled” data both easier. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.N/A
    Pricing
    IBM SPSS Statisticspandas
    Editions & Modules
    Base
    USD 3,830
    one-time fee per user
    Standard
    USD 8,440
    one-time fee per user
    Professional
    USD 16,900
    one-time fee per user
    Premium
    USD 25,200
    one-time fee per user
    Monthly subscription
    USD 105
    per month per user
    Annual subscription
    USD 1,188.00
    per year per user
    No answers on this topic
    Offerings
    Pricing Offerings
    IBM SPSS Statisticspandas
    Free Trial
    YesNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Community Pulse
    IBM SPSS Statisticspandas
    Considered Both Products
    IBM
    No answer on this topic
    Open Source
    No answer on this topic
    Key User Insights
    Would buy again
    94%
    Would buy again
    49 Answers
    No answers on this topic
    Delivers good value for the price
    91%
    Delivers good value for the price
    42 Answers
    No answers on this topic
    Happy with the feature set
    98%
    Happy with the feature set
    51 Answers
    No answers on this topic
    Lived up to sales and marketing promises
    97%
    Lived up to sales and marketing promises
    34 Answers
    No answers on this topic
    Implementation went as expected
    97%
    Implementation went as expected
    35 Answers
    No answers on this topic
    Features
    IBM SPSS Statisticspandas
    Platform Connectivity
    Comparison of Platform Connectivity features of IBM SPSS Statistics and pandas
    Feature
    IBM SPSS Statistics
    -
    Ratings
    pandas
    8.5
    1 Ratings
    2% above category average
    Connect to Multiple Data Sources00 Ratings8.01 Ratings
    Extend Existing Data Sources00 Ratings8.01 Ratings
    Automatic Data Format Detection00 Ratings10.01 Ratings
    MDM Integration00 Ratings8.01 Ratings
    Best Alternatives
    IBM SPSS Statisticspandas
    Small Businesses
    No answers on this topic
    RapidMiner
    Score8.9 out of 10
    Medium-sized Companies
    Alteryx Platform
    Score9 out of 10
    Anaconda
    Score8.8 out of 10
    Enterprises
    Alteryx Platform
    Score9 out of 10
    IBM Watson Studio
    Score10 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    IBM SPSS Statisticspandas
    Likelihood to Recommend
    8.5
    (116 ratings)
    10.0
    (1 ratings)
    Likelihood to Renew
    8.5
    (23 ratings)
    -
    (0 ratings)
    Usability
    8.0
    (15 ratings)
    10.0
    (1 ratings)
    Availability
    6.0
    (1 ratings)
    -
    (0 ratings)
    Performance
    6.0
    (1 ratings)
    -
    (0 ratings)
    Support Rating
    6.4
    (12 ratings)
    -
    (0 ratings)
    Implementation Rating
    8.7
    (7 ratings)
    -
    (0 ratings)
    Configurability
    5.0
    (1 ratings)
    -
    (0 ratings)
    Ease of integration
    5.0
    (1 ratings)
    -
    (0 ratings)
    Product Scalability
    5.0
    (1 ratings)
    -
    (0 ratings)
    Vendor post-sale
    5.0
    (1 ratings)
    -
    (0 ratings)
    Vendor pre-sale
    5.0
    (1 ratings)
    -
    (0 ratings)
    User Testimonials
    IBM SPSS Statisticspandas
    Likelihood to Recommend
    IBM
    IBM SPSS Statistics is well suited for pretty much any data analytic scenario. It can handle extremely complex and large-scale datasets with ease. It especially shines if you have to do any kind of analyses that involve significance testing. Being able to do any number of significance tests (i.e., t-tests, chi-square, ANOVAs, etc.) right inside the tool is very valuable. The only scenario I would say it is less appropriate is if you need to work on a very small dataset and answer very simple questions, like frequencies or averages. In those cases, something like Excel could probably do the job just as easily.
    Incentivized
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    Open Source
    Pandas are great for quick and relatively simple analytics and visualizations
    Pandas work well for exploratory ad-hoc analytic work
    But , We had little success in implementing complicated predictive analytics. And large data sizes can be a problem.
    Incentivized
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    Pros
    IBM
    • 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.
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    Open Source
    • It is easy to do statistical analysis
    • It is easy to clean the data
    • It is easy to produce graphs and charts to visualize
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    Cons
    IBM
    • Cost is becoming prohibitive.
    • Availability of procedures in the base package seems to be dwindling.
    • The copy-and-paste function from output to Excel is not as easy as it once was (now I have to do a "paste special").
    • Text and date handling are terrible.
    • Need to include AI-based NLP for survey verbatims and other text-based fields. This is becoming increasingly important in the CX world, yet SPSS seems to be ignoring it.
    Incentivized
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    Open Source
    • There are a lot of libraries and ways to do visualization. Sometimes it is very confusing.
    • Error handling can be a challenge. Sometimes the error messages do not provide valuable clues for the debugging.
    • In our case, there are a bunch of different frameworks and libraries working together. I would rather work with one framework, well tuned for my use case
    Incentivized
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    Likelihood to Renew
    IBM
    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
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    Open Source
    No answers on this topic
    Usability
    IBM
    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
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    Open Source
    Over the years, we tried a lot of different frameworks and tools, homegrown and commercial. Pandas provide the best results.
    It is lightweight, flexible and easy to implement.
    Incentivized
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    Reliability and Availability
    IBM
    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
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    Open Source
    No answers on this topic
    Performance
    IBM
    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.
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    Open Source
    No answers on this topic
    Support Rating
    IBM
    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
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    Open Source
    No answers on this topic
    Implementation Rating
    IBM
    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.
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    Open Source
    No answers on this topic
    Alternatives Considered
    IBM
    If you have made it this far, you should have a very good idea of how SPSS stacks up the competition (data processing and analytics tools). Even the free ones, such as r Studio or Stata, are leaps and bounds ahead of SPSS. IBM is resting on a reputation developed nearly 30 years ago and has shown no desire to improve.
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    Open Source
    All these frameworks are great for gathering data and providing some initial analysis. But for real performance debugging work one needs more than tools provided by this tools. That's where the pandas excel.
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    Scalability
    IBM
    I am neutral because I have not had to look into scalability since I am using as a student.
    Incentivized
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    Open Source
    No answers on this topic
    Return on Investment
    IBM
    • 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.
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    Open Source
    • Performance debugging was time consuming and mostly poorly automated exploratory process. Once we started use pandas for these tasks, it really moved the needle. Pandas are instrumental to provide actionable insights. As a result we were able to improve notably cloud software resource utilization and performance
    • Analytics implemented with pandas allow us to detect and. address problems in our APIs before they are notable to our customers
    Incentivized
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    ScreenShots

    IBM SPSS Statistics Screenshots

    Screenshot of SPSS Statistics Forecasting. This enables users to build time-series forecasts regardless of their skill level.Screenshot of SPSS Statistics Regression. These predict categorical outcomes and apply nonlinear regression procedures.Screenshot of IBM SPSS Statistics Neural Networks. These can discover complex relationships and improve predictive models.Screenshot of IBM SPSS Statistics Curated Help. These can interpret correlation output.Screenshot of IBM SPSS Statistics AI Output Assistant interprets statistical output in easy to consume language