Item: IBM SPSS Statistics
Rating: 7
Author: Brandon Wreede

Overall Satisfaction with IBM SPSS Statistics

Use Cases and Deployment Scope

I like that you can simply click through the options in order to complete the analysis. The syntax feature is also great because you can save your work and re-use the same syntax again and again.It has also given us options with creative reporting and data displays allowing us to be really competitive in our market with offering high quality and visually appealing data analysis.IBM SPSS Statistics is one of the best tool I have used for statistical analysis and mathematical modeling. It has greater capability to handle large volumes of data.Fantastic experience while using IBM SPSS Statistics and would love to continue as no other better alternative is available.

Pros and Cons

Pros

The massiveness and extent of value it can unravel from data in processes that are "easy", intuitive, and "make sense".
LOVE using product for complexly designed survey analyses - they are lots of fun

Cons

That you have to repeat all analysis by hand, you cannot change what you did.
In terms of computer memory and processing power, IBM SPSS Statistics can be demanding, especially when working with huge datasets or carrying out complex analysis. Users with older or less powerful computers may face difficulties as a result, which could affect how well the software performs.

Return on Investment

Data quality and information is key to wellbeing of any organization
IBM SPSS Statistics gives you a platform to analyse the collected data, manage it, visualize the data

Operational Impact

For more than 6 years now, in our missions of baseline studies, market and impact assessments of projects; after some missions of large-scale information collection via paper and/or digital media, we have always opted for the use of IBM SPSS Statistics software as a tool for data entry and/or analysis. It is an excellent tool for data entry because it is easy to use when setting up a data entry mask and it is also easy to use by the data entry agents. We also use it regularly for statistical analysis (PCA, AFM, Student's t test, etc.) and sometimes econometric analysis (OLS, Oprobit, Probit, Logit, etc.). Therefore, it is a very useful software for statistical, graphical and sometimes econometric analyses of large databases.

Business Impact

PSS is more straight forward to use in comparison to its competitors, but it is more expensive. Open source alternatives such as R are great, but require knowledge in traditional code such as Python. IBM SPSS Statistics is more beginner friendly than R. Use IBM SPSS Statistics if you can afford it.

Alternatives Considered

Devart Python Connectors

I have on many occasions launched new versions of a big Python application in production, only to immediately drown in errors, caused by exceptions that were in turn caused by Python code where a single glance confirmed that it could never ever work and consequently had never been run: Not on a developer workstation, not in a unit test, and not in an integration test.

Key Insights

Do you think IBM SPSS Statistics delivers good value for the price?

Yes

Are you happy with IBM SPSS Statistics's feature set?

Yes

Did IBM SPSS Statistics live up to sales and marketing promises?

I wasn't involved with the selection/purchase process

Did implementation of IBM SPSS Statistics go as expected?

I wasn't involved with the implementation phase

Would you buy IBM SPSS Statistics again?

Yes

Other Software Used

Avionté Software, Azure Bot Service (Microsoft Bot Framework), Sage Intacct

Likelihood to Recommend

Plenty of output options are available with IBM SPSS Statistics, including tables, charts, and graphs. The application makes it simpler to evaluate and demonstrate outcomes of research.Allows users to use IBM SPSS Statistics with other statistical tools by being able to import and export data in various formats. It makes easier to integrate with multiple research workflows.Offers automation tools such as batch processing and syntax automation that can speed up repetitive tasks . It helps researches to focus more on data interpretation and analysisThere are numerous methods for dealing with missing data, such as mean substitution, multiple imputation, and maximum likelihood estimation. These tools enable researchers to successfully address the problem of missing data, resulting in reliable and accurate analysis.

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IBM SPSS Statistics Review