SAS, Stata, SPSS: A Comparison. Alan C. Acock

SAS, Stata, SPSS: A Comparison. Three software packages have emerged as the dominant tools for data management and standard statistical analysis. These are SAS, Stata, and SPSS. Each of these has advantages and disadvantages; none is sufficient for all the data analysis needs of the next generation of family scholars. Each package has a different vision, and it is important to understand these visions when making a choice. Before making a decision, a person should go to the University of California-Los Angeles' (UCLA) statistics portal that covers these and other packages (http://www.ats.ucla.edu/stat/). The portal has a page listing instructional material and even movies on many statistical packages, http://www.ats.ucla.edu/stat/seminars/. Reviewing the material will help you decide between packages. I will give you my impression of the vision each package has and what this means for the end user. The fiercest advocates of each package will surely disagree with some of my opinions, but I am making a concerted effort to be fair. Software packages are moving targets. Any attempt to say precisely what each of them can or cannot do will be wrong with the next release. Thus, saying a package cannot do something usually means that the development team is working on it. It is problematic to base your decision between packages on one or a couple capabilities. At the same time, the visions of these three packages suggest they will develop in different ways, and this direction may help you decide. We will consider SAS, SPSS, and Stata in that order. Some longtime users of SAS will tell you that it stands for "Statistical Analysis System," but SAS disavows this today as it defines itself as an enterprise computing system. SAS has moved beyond statistical analysis. Much of its development follows a shift from the early focus on academic statistical computation to servicing the more lucrative enterprise computing market. SAS is the first choice of many power users who spend several hours most days working on data management. SAS provides an extraordinary range of data analysis and data management tasks. Data management is the great strength of SAS. The most complex data sets we use hardly tap the capability of SAS for data management. Its ability to have many data files opened at the same time and its structured programming style in which data management and statistical analysis are separated are big attractions to those who make a living managing complex data sets. SAS is especially strong in analysis of variance (ANOVA), the general linear model, and their extensions. Indeed, there are few statistical or graphic tasks that you cannot do in SAS, but often you find that SAS is a long way down the list on ease of use. Because SAS was developed before the first PC or Mac was imagined, a researcher who only uses a PC or Mac will be frustrated. The fact that a PC program can be run on a 25-year-old mainframe time sharing system with minimum changes is simply irrelevant to most of us and certainly to the next generation of family scholars. The main objection family scholars voice about SAS is that it is difficult to learn. Graduate instruction on methods gets bogged down on how to use SAS rather than on methodological and statistical issues. SAS documentation is plentiful, but many users complain that it is written to show the power of SAS rather than to illustrate how to do something simple. Examples in the documentation show the most powerful task a procedure can do rather than how it will be used by 80% of the end users. This is good for power users who want to extend themselves to the most sophisticated applications, but it is frustrating for beginners. Although SAS has moved its vision beyond statistical analysis to enterprise applications, SAS is so big and its staff are so talented that it still is a leader in integrating new statistical developments-even if this is not its first priority. …