SAS vs. SPSS; Which One is Right for Your Career?
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Let me save you some potential reading time right from the start. If you are sure of your future career path, SAS vs SPSS is relatively easy. If the goal is to work for an enterprise/large corporation, SAS is the winner. However, if academia is your future, SPSS is likely all you need.
I have been teaching SAS programming for several years. You can learn more about SAS and understand more about it here (How to Learn SAS Programming Fast). I was introduced to SPSS first, but SAS came soon after. Since I have had a chance to experience both, it made sense to help others by comparing SAS and SPSS. I have to say, SAS won me over very quickly. SAS was more appealing early on for a straightforward reason. I just preferred writing code instead of the click-and-point interface of SPSS. You might choose NOT to code. I’m glad I made this decision as knowing how to program makes you much more valuable and harder to replace in the modern workplace.
In any case, let us compare SAS vs. SPSS in more detail.
Big-Picture Comparison Between SAS vs SPSS
The Statistical Package for the Social Sciences (SPSS) is an analytical tool widely used in social sciences, including educational psychology. However, it is also popular in other fields like the health sciences and marketing (Field, 2013). It is an excellent tool for non-statisticians due to its easy to navigate graphics user interface, with most basic data analysis capable of being accomplished through menus and dialog boxes without learning the SPSS language (Green & Salkind, 2010).
Statistical Analysis System (SAS) is a powerful suite of procedures, in addition to the well-known, well-documented, and flexible software language used to manipulate them (Dimaggio, 2013). Since its mass distribution in the 1970s, SAS has become the statistics industry standard and has accumulated large amounts of high-quality production code for multiple purposes. For example, many large companies and federal and local government public health agencies utilize the program (Dimaggio, 2013). It has robust data handling capabilities, automatically produces diagnostics, and quickly interprets plots and output.
SAS holds a 35.4% market share in advanced and predictive analytics with total revenue of $768.3 million. Although SPSS ranks second in the advanced analytics industry, its market share of 17.1% is only about half of SAS (IDC, 2015).
Data analytical tools include programming operations and windows operations (Minelli, Chambers, & Dhirai, 2012). SPSS executes user’s commands through interactive windows, with most features accessible via pull-down menus (Arbuckle, 2010). Its interactive nature is beneficial as the menus and dialog boxes offer visual reminders of the available options with each step in the analysis. It also provides a programming platform that uses a proprietary 4GL command syntax language. Some complex applications can only be programmed in syntax and are not accessible in menu structures (Levesque, 2005).
Although SAS provides a graphical point-and-click user interface for non-technical users and more advanced options through the SAS language, it is significantly more programming-oriented than SPSS (Salkind, 2010). SAS programs have a DATA step, which retrieves and manipulates data, usually creating a SAS data set, and a PROC step that analyzes the data. Each stage uses a series of statements to carry out its functions (Delwiche & Slaughter, 2012). Compared to other statistical packages, using SAS requires a relatively low coding knowledge, provided the only analyses already have PROCs. Outside of these, coding can be complicated, and any error will prevent the execution of the desired command (Delwiche & Slaughter, 2012).
Charts & Graphics
SPSS’s chart-builder enables you to produce various types of graphs. SPSS cannot automatically create a chart with the spreadsheet data; users designate their preferred chart-style in the graph builder (e.g., Horizontal Bar/Vertical Bar), variables, x-axis, and y-axis. SPSS provides standard statistical graphs but little else, and the chart produced is not usually visually appealing (Field, 2013).
In comparison, the graphic procedures in SAS are much more complex (Wicklin, 2013). To produce a graph requires coding using the SAS programming language. Users must write a Chart procedure followed by a series of statements regarding the group variables, sub-group variables, visual style, and all other elements shown in the chart (Wicklin, 2013). Although the SAS Graphics procedure is significantly more complicated than SPSS, it has its advantages: by weaving SAS macros into programming code, users can regenerate graphs on a routine basis, providing them with more flexibility and significantly reducing work volume (Zhu, Zeng, & Wang, 2010).
Flexibility in Model Building
The SPSS operation interface makes it highly convenient for entering data and manipulating rows and columns. Incorporating the 4GL command syntax language makes the software more flexible and allows users to customize their model based on the programming language. However, the syntax of SPSS is poor and has a complex command structure, and its menu-style interface is reported frequently as a source of frustration (Levesque, 2005). Although more challenging to use in advanced statistical analysis, the SAS system can provide more control than SPSS due to command-line interface/advanced editor coding (O’Rourke & Hatcher, 2013). Additionally, the output is clear.
Information Management Capacity
Information management has three levels: descriptive analytics (i.e., quantitatively describing the characteristics of a data sample), predictive analytics (i.e., using available information to predict future outcomes), and prescriptive analytics (i.e., identifying correct decisions based on these predictions and the effects of these decisions; De Bakker et al., 2005). SPSS can be used to build predictive models and conduct other analytics tasks. Its visual interface allows users to leverage statistical and data mining algorithms without programming. However, the menu-driven feature of SPSS is a significant obstacle in handling more complex predictive analysis and becomes significantly more cumbersome with prescriptive analysis (Van Barneveld, Arnold, & Campbell, 2012). On the other hand, SAS is considerably more robust in information management. For example, SAS has a specific predictive and prescriptive analytics module, which uses sophisticated and robust data analysis techniques to garner important information from large databases (Power, 2014).
To conclude, each statistical package has its strengths and weaknesses. SPSS is easy to learn and use. It includes a full range of data management systems and editing tools, provides in-depth statistical capabilities, and offers complete plotting, reporting, and presentation features. SAS can manage, alter, mine, and retrieve data from various sources and perform complex statistical analyses. It provides a graphical point-and-click user interface for non-technical users and more advanced options through the SAS language. Although not as easy to use and quick to learn as SPSS, SAS offers considerably greater possibilities concerning analysis. If used frequently, the commitment required to remember SAS becomes worthwhile.
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