SAS vs. SPSS
From the UCLA, Statistics Department: Many people ask us about
the differences between SAS, Stata and SPSS or which package is the best
package. As you might imagine, each package has its own unique style and
its own strengths and weaknesses. This page gives a quick overview of the
style of each of the packages and the strengths and weaknesses of each, but
this is by no means a comprehensive comparison of the packages. Sometimes
people feel very passionately about the statistical packages they use; we hope
most will agree that this is a factual and dispassionate comparison of these
packages.
SAS
 General use. SAS is a
package that many "power users" like because of its power and programmability.
Because SAS is such a powerful package, it is also one of the most difficult
to learn. To use SAS, you write SAS programs that
manipulate your data and perform your data analyses. If you make a mistake
in a SAS program, it can be hard to see where the error occurred or how to
correct it.
 Data Management.
SAS is very powerful in the area of data management,
allowing you to manipulate your data in just about any way possible.
SAS includes proc sql that allows you to perform sql queries
on your SAS data files. However, it can take a long time to learn and
understand data management in SAS and many complex data management tasks can
be done using simpler commands in Stata or SPSS. However, SAS can work with
many data files at once easing tasks that involve working with multiple
files at once. SAS can handle enormous data files up to 32,768 variables and
the number of records is generally limited to the size of your hard disk.
 Statistical Analysis. SAS
performs most general statistical analyses (regression, logistic regression,
survival analysis, analysis of variance, factor analysis, multivariate
analysis). The greatest strengths of SAS are probably in its ANOVA,
mixed model analysis and
multivariate analysis, while it is probably weakest in ordinal and multinomial
logistic regression (because these commands are especially difficult), and robust
methods (it is difficult to perform robust regression, or other kinds of
robust methods). While there is some support for the analysis of survey
data, it is quite limited as compared to Stata.
 Graphics. SAS may have the
most powerful graphic tools among all of the packages via SAS/Graph.
However, SAS/Graph is also very technical and tricky to learn. The
graphs are created largely using syntax language; however, SAS 8 does have a
point and click interface for creating graphs but it is not as easy to use as
SPSS.
 Summary.
SAS is a package geared towards power users. It
has a steep learning curve and can be frustrating at first. However,
power users enjoy the its powerful data management and ability to work with
numerous data files at once.
Stata
 General Use. Stata is a
package that many beginners and power users like because it is both easy to
learn and yet very powerful. Stata uses one line commands which can be
entered one command at a time (a mode favored by beginners) or can be entered
many at a time in a Stata program (a mode favored by power users). Even if you
make a mistake in a Stata command, it is often easy to diagnose and correct
the error.
 Data Management.
While the data management capabilities of
Stata may not be quite as extensive as those of SAS, Stata has numerous
powerful yet very simple data management commands that allows you to perform
complex manipulations of your data with ease. However, Stata primarily works
with one data file at a time so tasks that involve working with multiple
files at once can be cumbersome. With the release of Stata/SE, you can
now have up to 32,768 variables in a Stata data file but probably would not
want to analyze a data file that exceeds the size of your computers memory.
 Statistical Analysis.
Stata performs most general statistical analyses (regression, logistic regression,
survival analysis, analysis of variance, factor analysis,
and some multivariate
analysis). The greatest strengths of Stata are probably in
regression (it has very easy to use regression diagnostic
tools), logistic regression, (add on programs are available that greatly
simplify the interpretation of logistic regression results, and ordinal
logistic and multinomial logistic regressions are very easy to perform).
Stata also has a very nice array of robust methods that are very easy to
use, including robust regression, regression with robust standard errors,
and many other estimation commands include robust standard errors as well.
Stata also excels in the area of survey data analysis offering the ability
to analyze survey data for regression, logistic regression, poisson
regression, probit regression, etc.). The greatest weaknesses in this
area would probably be in the area of analysis of variance and discriminant
function analysis.
 Graphics. Like SPSS, Stata
graphics can be created using Stata commands or using a point and click
interface. Unlike SPSS, the graphs cannot be edited using a graph editor.
The syntax of the graph commands is the easiest of the three packages and is
also the most powerful. Stata graphs are high quality, publication quality
graphs. In addition, Stata graphics are very functional for supplementing statistical
analysis, for example there are numerous commands that simplify the creation
of plots for regression diagnostics.

Summary. Stata
offers a good combination of ease of use and power. While Stata is
easy to learn, it also has very powerful tools for data management, many
cutting edge statistical procedures, the ability to easily download programs
developed by other users and the ability to create your own Stata programs
that seamlessly become part of Stata.
SPSS
 General use. SPSS is a
package that many beginners enjoy because it is very easy to use. SPSS
has a "point and click" interface that allows you to use pulldown menus to
select commands that you wish to perform. SPSS does have a "syntax" language
which you can learn by "pasting" the syntax from the point and click menus,
but the syntax that is pasted is generally overly complicated and often
unintuitive.
 Data Management.
SPSS has a friendly data editor that resembles Excel
that allows you to enter your data and attributes of your data (missing
values, value labels, etc.) However, SPSS does not have very strong
data management tools (although SPSS version 11 added commands for reshaping
data files from "wide" format to "long" format, and vice versa). SPSS
primarily edits one data file at a time and is not very strong for tasks
that involve working with multiple data files at once. There is no
limit to the number of variables or cases allowed in your SPSS data files
 you are only limited only by your
disk space.
 Statistical Analysis.
SPSS performs most general statistical analyses (regression, logistic regression,
survival analysis, analysis of variance, factor analysis,
and multivariate
analysis). The greatest strengths of SPSS are in the
area of analysis of variance (SPSS allows you to perform many kinds of tests
of specific effects) and multivariate analysis (e.g., manova, factor
analysis, discriminant analysis) and SPSS 11.5 has added some capabilities for
analyzing mixed models. The greatest weakness of SPSS are probably in
the absence of robust methods (we know of no abilities to
perform robust regression or to obtain robust standard errors), and the absence
of survey data analysis in the basic package (some procedures are available in an addon module
in SPSS version 12).
 Graphics. SPSS has a
very simple point and click interface for creating graphs and once you
create graphs they can be extensively customized via its point and click
interface. The graphs are very high quality and can be pasted into
other documents (e.g., Word documents or Powerpoint). SPSS does have a
syntax language for creating graphs but many of the features in the point
and click interface are not available via the syntax language. The
syntax language is more complicated than the language provided by Stata, but
probably simpler (but less powerful) than the SAS language.
 Summary. SPSS focuses on
ease of use (their motto is "real stats, real easy"), and it succeeds in this
area. But if you intend to use SPSS as a power user, you may outgrow
it over time. SPSS is strong in the area of graphics, but weak in more
cutting edge statistical procedures lacking in robust methods and survey
methods.
Overall Summary
Each package offers its own unique
strengths and weaknesses. As a whole, SAS, Stata and SPSS form a set of
tools that can be used for a wide variety of statistical analyses. With
Stat/Transfer it is very easy to convert data files from one package to
another in just a matter of seconds or minutes. Therefore, there can be
quite an advantage to switching from one analysis package to another depending
on the nature of your problem. For example, if you were performing
analyses using mixed models you might choose SAS, but if you were doing
logistic regression you might choose Stata, and if you were doing analysis of
variance you might choose SPSS. If you are frequently performing statistical
analyses, we would strongly urge you to consider making each one of these
packages part of your toolkit for data analysis.