Differences Between SPSS vs SAS

With the increasing use of technology, data is generated for every little thing. To analyze this data and we need statistics to further give us value and help us predict and provide data trends. SPSS and SAS are tools that help statistical analysis easier. SPSS is comprehensive and flexible. SAS is a programming language that comes with its own suite.

Let us understand the differences that are there between SPSS and SAS. Although the main motto of both SPSS and SAS tools is statistical analysis, business growth, the variance in the actual work that they do will be seen further.

Head to Head Comparison Between to SPSS and SAS (Infographics)

Below are the top 6 differences between SPSS and SAS:

Key Differences Between SPSS and SAS

Though both SPSS vs SAS is used for statistical data analysis, they have some significant differences, which are as follows:

SPSS stands for “Statistical Package for the Social Sciences” and was launched in 1968. It is mainly used in scientific research for analyzing all sorts of data. It is also used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations and data miners. SPSS was developed for the social sciences and was the first statistical programming language for the PC. SPSS is also useful in reporting, which provides tables and charts which can be easily copy-pasted. SAS, on the other hand, does not have a user interface. It is used for advanced analytics, business intelligence, data management and predictive analysis.
SAS is tougher to learn than the point and click interface of SPSS. SPSS is easier to learn as it provides paste functionality. All tables, charts can be easily passed by the click of a button. SAS, on the other hand, does not have any such functionality. It is difficult to customize things in SAS as one has to have coding knowledge to build things as per the customized requirements. SPSS also provides an interface that makes it easier to learn. SPSS Documentation is much better and gives better clarity on algorithms used for statistical procedures. Modeling is easier done in SPSS, but SAS can provide more control thanks to command-line interface/advanced editor coding. The SAS Enterprise is not as good a visual interface as the SPSS.
Data processing is faster in SAS as compared to SPSS. SPSS does process data quickly but only when it is small in amount. When data gets larger, is it difficult to handle it through SPSS. SAS can easily handle large amounts of data. It provides different features like sorting and splitting the data, which makes it easier for SAS to handle big chunks. SPSS, on the other hand, provides a perpetual license while SAS provides a yearly license which makes it costlier than SPSS. SAS is almost 1.75 times as expensive in upfront cost for a single installation as SPSS. The graphical capabilities of SAS and SPSS are purely functional; However, making minor changes to graphs is possible to customize your plots and visualizations in SAS, and SPSS fully can be very cumbersome or even impossible.

SPSS and SAS Comparison Table

Following are the lists of points, describes the comparisons between SPSS and SAS.

Basis for Comparison	SPSS	SAS
Basic Difference and History	SPSS is basically software that is used in statistical analysis. It is an abbreviation for Statistical Package for the Social Sciences. It is also used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations and data miners. SPSS was developed for the social sciences and was the first statistical programming language for the PC. It was developed in 1968 at the University of Stanford, and eight years later, the company SPSS Inc. was founded, which was bought by IBM in 2009.	SAS is a programming language and has a suite developed for advanced analytics, business intelligence, data management and predictive analysis. It stands for Statistical Analysis System. SAS was developed at North Carolina State University and was primarily developed to be able to analyze large quantities of agriculture data.
Purpose and Usability	SPSS is an amazing tool for people who are not statisticians. It has a user-friendly interface and has easy to use drop-down menus. It can be used in many fields, but it mainly plays an important role in social sciences.	SAS is supposed to have a large amount of high-quality production code for various purposes. It is considered to be leading in the commercial analytical space. SAS has strong handling capabilities, and its software updates are in a controlled environment, which makes it well tested.
Data Processing	SPSS can be used when data is smaller than, say, 100 MB. It will provide data appropriately.	When data is present in a huge crunch then SAS is more powerful and provides various facilities like sorting or splicing the data.
Ease of Learning	SPSS has an easy interface where a user does not need to learn to code. It has a paste function that creates syntax for steps executed in a user interface.	SAS uses Proc SQL, which makes it’s coding easy to learn for those who know SQL. You can easily learn SAS from scratch.
Advantages	SPSS is used in many universities. It has a very good interface with complete documentation. It provides click and play functionality which makes writing code easier using the ‘Paste’ button. Also, it has very good official support for any issues that the user would face.	SAS, on the other hand, is majorly used in the industry. It has a flow-based interface that provides a drag and drop facility. It is capable of handling large datasets.
Disadvantages	SPSS is costly and has different licenses for different functionalities. Its syntax is limited, and it is comparatively slow towards adopting new technologies. Also, it cannot handle large datasets.	SAS is also costly. For options that are not involved in the user interface, you will need to write code and customize accordingly. There are different programs for different visualizations or data mining.

Conclusion

As a result, though both SPSS and SAS are very helpful in the analysis of data, they are different in their own ways. The different functionalities that they perform help an organization knowing its value, and they provide a way of improving and increasing its market value. Hence you should ideally have a mix of both SPSS and SAS to optimize both costs and analytical flexibility.

Popular Course in this category