SPSS – Statistical Package for Social Sciences
SPSS is a popular statistical analysis software package, which stands for Statistical Package for Social Sciences. SPSS is popular in statistical analysis for its user-friendly interface and extensive capabilities, including add-ons like Amos and Clementine. Researchers first developed SPSS in 1968. Since its development, researchers have widely used SPSS in both industry and university research applications.
SPSS, like E-views, Stata, MStat, and SAS, offers basic capabilities like descriptive statistics, regression, and other tools in its base package for common applications. In addition, through various add on modules, it can cater to the more advanced needs of high end multivariate analysis, neural networks, conjoint analysis, etc. it offers the convenience of automating several tasks such as data cleansing and organizing, along with creating charts and other types of output. It automates tasks like data coding, missing values analysis, and import/export from tools like Excel.
The SPSS screen offers two viewing modes: one for data entry, where entered or imported data appear. The labels row appears separately as a grayed-out area, unlike in Excel. The ‘Variable View’ allows variable editing, while the ‘Data View’ is where data entry occurs. The second view, ‘Variable View,’ displays variable properties like name, type, length, label, and alignment. It allows easy editing of Excel files without complex import/export processes.
It also has the inherent windows properties such as cut, copy, paste, find, replace, etc., which makes it easy for a non-SPSS user to gain familiarity with the system, particularly if one has experience using MS Office tools.
The key selling point of SPSS is its expansive data analysis options. It performs various analyses, including hypothesis testing, T-tests, ANOVA, correlation, cluster analysis, and more. It automates tasks, requiring only variable selection and output, and handles the rest. In the context of SPSS, it’s important to mention that using AMOS and Clementine, two of its’ most popular add-on packages (not modules), one can access the high end functionality within it.
Researchers use Amos for Structural Equation Modeling and Path analysis, while they use Clementine for high-end data mining. Amos is probably one of the simplest and easiest to use Path analysis software available. They can dynamically graph each chain of variables without programming, with results available almost immediately. Clementine offers
There are several other packages in the market which are strong competition to SPSS in terms of functionality. Despite being a powerful software, SPSS is not without its shortcomings. For instance, when it comes to time series analysis, it offers limited capabilities. Similarly, MATLAB is a powerful mathematical package used where programming needs are extensive.
SPSS vs. SAS
SAS is another leading statistical package with extensive programming capabilities. Unlike SPSS, SAS does not offer the easy to use point-and-click interface as extensively, although for programming needs, SAS is considered a more powerful tool. It was preferred over SPSS historically due to the ease of programming, which in SPSS was considered far more complex and difficult. However, the modern versions of SPSS command a lot more respect in terms of programming capability.
Time series analysis is another function which is much more extensive in SAS. However, SAS offers the flexibility to perform a variety of functions which may or may not be possible through SPSS.