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).
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Tableau Desktop
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Tableau Desktop is a data visualization product from Tableau. It connects to a variety of data sources for combining disparate data sources without coding. It provides tools for discovering patterns and insights, data calculations, forecasts, and statistical summaries and visual storytelling.
If I didn't want to code, IBM SPSS would be after JMP and Tableau, and before SAS and R. The user interface is very clunky compared to the analytics software I stated. You could definitely learn to do basic analysis faster in SAS than SPSS. I selected SPSS to test the …
I also use or have used Tableau, Excel, and R (wasn’t able to list R above). Tableau is better for visualizations, Excel works for generalized/more basic statistical analysis but lacks more complex features, and R has been difficult for me to master and lacks the UI and ease of …
Compared to Stata, python and MPlus, SPSS is more user friendly especially for beginners. It displays data and output in easily readable formats and makes statistics fun and easy. However, Stata, python and MPlus are more ideal for complex statistical methods like structural …
Two alternatives I considered before choosing SPSS are the programming languages R and Julia Statistics. Both are freely available, open-source packages for sophisticated statistical analysis and visualization. In my case, I preferred SPSS to these somewhat newer options due …
Select IBM SPSS because it is a program that is easier to use than RStudio [which] requires the application of much more work hours to master and make the most of each of the tools it offers.
I use Stata for tasks that SPSS cannot support, but ultimately SPSS has a short learning curve, strong statistical processing, and a mature tool set. SAS is also mature, but more programming based. JMP tries to 2nd guess what I need. NOTE: R (open source) is a great option …
I have used SAS, R, Systat, Stata, Excel, Access and I continually find myself using SPSS to do the same set of tasks I would be using the previously named software to do. It's an all encompassing software that is very easy to use but can become as powerful an analytical tool …
Graphics within SPSS provide you with a general framework for understanding your data, so that you will be better able to interpret the complex inferential procedures that follow.