Great for novice users, but more experienced analysts might prefer R or Python
March 08, 2024
Great for novice users, but more experienced analysts might prefer R or Python
Score 6 out of 10
Vetted Review
Verified User
Modules Used
- IBM SPSS Statistics
Overall Satisfaction with IBM SPSS Statistics
IBM SPSS Statistics enables my organisation to address a range of data analytics challenges. We frequently use it for interrogating datasets, generating descriptive statistics, performing a range of hypothesis tests, and creating data visualisations. In my business area we tend to use it for regression modelling, particularly using the multiple linear regression and binary logistic regression approaches.
- Easily enables a range of statistical tests and procedures
- Easy-to-use interface
- More simple to learn for beginners
- Limited options for performing bespoke types of analysis
- Limited options for customising data visualisations
- Can be laggy with large datasets
- Enables data analysis to be performed sufficiently, with a lower risk of error (e.g., due to typos in coding)
- Easier learning curve for novice data analysts
- Not suitable for all data analysis work that the business does, meaning we have to use other software too
In general, IBM SPSS Statistics is useful in our organisation, because it easily enables a range of statistical procedures and tests to be performed. It is particularly suitable for our team members who only used SPSS at university (i.e., who have no coding experience), who are very familiar and comfortable with the software. Being able to use SPSS for data analysis is a great support for our analytical efficiency and quality.
However, SPSS isn't suitable for every type of data analysis we do. Sometimes we're working with incredibly large datasets that SPSS struggles with, and sometimes we need to perform bespoke statistical tests or procedures that aren't accommodated within SPSS. For this reason, we also use different software (R or Python) just as much as we use SPSS, if not more. It's a little bit inefficient to have different software for the same sets of tasks, so I anticipate we will move away from SPSS in the future.
However, SPSS isn't suitable for every type of data analysis we do. Sometimes we're working with incredibly large datasets that SPSS struggles with, and sometimes we need to perform bespoke statistical tests or procedures that aren't accommodated within SPSS. For this reason, we also use different software (R or Python) just as much as we use SPSS, if not more. It's a little bit inefficient to have different software for the same sets of tasks, so I anticipate we will move away from SPSS in the future.
I work for a company which is tasked with carrying out high-quality data analysis and reporting. IBM SPSS Statistics is used by many of our analysts to support these efforts, as it enables them to easily answer complex research questions that our stakeholders are interested in. Producing high-quality work helps to strengthen our reputation as a trustworthy and insightful provider of research and statistics.
In addition to IBM SPSS Statistics, we also use statistical programming languages such as R and Python to perform data manipulation and analysis.
In general, SPSS seems to be better for novice users, because it has a much more straightforward interface and an easier learning curve. It accommodates a broad array of statistical tests and procedures.
However, our more senior analysts tend to prefer R or Python. These are better at handling big data, and can perform various bespoke tasks that aren't available within SPSS. From an organisational perspective, it's also important to note that R and Python are free, whereas SPSS has quite a high licence cost.
In general, SPSS seems to be better for novice users, because it has a much more straightforward interface and an easier learning curve. It accommodates a broad array of statistical tests and procedures.
However, our more senior analysts tend to prefer R or Python. These are better at handling big data, and can perform various bespoke tasks that aren't available within SPSS. From an organisational perspective, it's also important to note that R and Python are free, whereas SPSS has quite a high licence cost.
Do you think IBM SPSS Statistics delivers good value for the price?
Not sure
Are you happy with IBM SPSS Statistics's feature set?
Yes
Did IBM SPSS Statistics live up to sales and marketing promises?
Yes
Did implementation of IBM SPSS Statistics go as expected?
Yes
Would you buy IBM SPSS Statistics again?
No