Three statistical software: Comparison of SAS, Stata and SPSS

Strategically using general purpose statistics packages:
A look at Stata, SAS and SPSS

Chinese version (English version ):
Many people have asked about the difference between SAS, Stata and SPSS, which of them is the best. It can be imagined that each software has its own unique style and has its own advantages and disadvantages. This article provides an overview, but it is not a comprehensive comparison. People often have special preferences for the statistical software they use, hoping most people will agree that this is a true and fair comparative analysis of these software.

SAS
General usage. Thanks to its powerful functions and programming capabilities, SAS is very popular with advanced users. Based on this, it is one of the most difficult software to master. When using SAS, you need to write a SAS program to process and analyze the data. If an error occurs in a program, it is difficult to locate and correct the error.
Data management. In terms of data management, SAS is very powerful, allowing you to process your data in any way possible. It contains the SQL (Structured Query Language) process and can be used in the SAS data set for SQL query. However, it takes a long time to learn and master the data management of SAS software. In Stata or SPSS, the commands used to complete many complex data management tasks are much simpler. However, SAS can process multiple data files at the same time, making this work easier. It can process up to 32,768 variables and the maximum number of records allowed by your hard disk space.
Statistical analysis. SAS can perform most statistical analyses (regression analysis, logistic regression, survival analysis, variance analysis, factor analysis, and multi-variable analysis ). The best of SAS is its variance analysis, mixed model analysis, and multi-variable analysis. Its primary disadvantage is sequential and multivariate Logistic regression (because these commands are difficult ), and robust methods (it is difficult to complete robust regression and other robust methods ). Although the analysis of survey data is supported, the comparison with Stata is still quite limited.
Drawing function. Among all the statistical software, SAS has the most powerful plotting tool provided by the SAS/graph module. However, the learning of the SAS/graph module is also very professional and complex, and graph production mainly uses programming languages. Although SAS 8 allows interactive plotting by clicking the mouse, it is not as simple as SPSS.
Summary. SAS is suitable for advanced users. Its learning process is hard, and the initial stage will make people discouraged. However, it is favored by advanced users with powerful data management and the ability to simultaneously process a large number of data files.

Stata
General usage. Stata is widely welcomed by beginners and advanced users for its ease of understanding and powerful functions. You can enter only one command at a time (suitable for beginners) or multiple commands at a time (suitable for advanced users) through a Stata program ). In this way, even if an error occurs, it is easier to find and modify it.
Data management. Although Stata's data management capabilities are not as powerful as SAS, it still has many powerful functions and simple data management commands, making complex operations easy. Stata is mainly used to operate a data file at a time, and it is difficult to process multiple files at a time. With the introduction of STATA/SE, the number of variables in a Stata data file can reach 32,768, but you may not be able to analyze a data file when it exceeds the permitted range of computer memory.
Statistical analysis. Stata can also perform most statistical analysis (regression analysis, logistic regression, survival analysis, variance analysis, factor analysis, and some multivariate analysis ). Stata's biggest advantage may be regression analysis (which includes easy-to-use regression analysis feature tools), logistic regression (additional procedures that explain the results of Logistic regression, ease of use in ordered and multivariate Logistic regression ). Stata also has a series of good robust methods, including robust regression, robust standard erroneous regression, and other commands that contain robust standard false estimates. In addition, in the field of survey data analysis, Stata has obvious advantages and can provide investigation data analysis such as regression analysis, logistic regression, Poisson regression, and probability regression. Its shortcomings lie in variance analysis and traditional multi-variable methods (such as multi-variable variance analysis and discriminant analysis ).
Drawing function. As with SPSS, Stata provides interactive interfaces for drawing commands or mouse clicks. Unlike SPSS, it does not have a graphic editor. Among the three types of software, the syntax of its drawing command is the simplest, but the most powerful function. The image quality is also good and can meet the publishing requirements. In addition, these images provide the supplementary statistical analysis function. For example, many commands can simplify the creation of scatter graphs during regression discriminant.
Summary. Stata provides a better combination of ease of use and powerful functionality. Although it is easy to learn, it is very powerful in data management and many cutting-edge statistical methods. Users can easily download programs that are already in use by others, or write them by themselves, so that they can be closely integrated with Stata.

SPSS
General usage. SPSS is very easy to use, so it is most accepted by beginners. It has an interactive interface that can be clicked. You can use the drop-down menu to select the command to be executed. It also learns its "Syntax" language through copying and pasting, but these syntaxes are usually very complex and not intuitive.
Data management. SPSS has a user-friendly data Editor similar to excel, which can be used to input and define data (missing values, value tags, and so on ). It is not a powerful data management tool (although some commands for increasing data files are added in version 11 of SPS, the effect is limited ). SPSS is also mainly used to operate a single file. It is difficult to process multiple files at the same time. Its data files have 4096 variables, and the number of records is limited by your disk space.
Statistical analysis. SPSS can also perform most statistical analyses (regression analysis, logistic regression, survival analysis, variance analysis, factor analysis, and multi-variable analysis ). It has the advantages of variance analysis (SPSS can test a variety of special effects) and multi-variable analysis (Multivariate variance analysis, factor analysis, discriminant analysis, etc ), version 5 also provides the hybrid model analysis function. The disadvantage is that there is no robust method (robust regression cannot be completed or a standard error is obtained), and there is no investigation data analysis (the module that completes some of the processes is added in version 12 of China ).
Drawing function. The interaction interface of SPSS graph is very simple. Once you draw a graph, you can click it to modify it as needed. This graphic is of excellent quality and can be pasted into other files (such as Word files or PowerPoint ). SPSS also has programming statements for plotting, but it cannot produce some interactive interface plotting results. This statement is more difficult than the STATA statement, but easier (less functional) than the SAS statement ).
Summary. SPSS is committed to simplicity (its slogan is "real statistics, indeed simple") and success. However, if you are an advanced user, you will lose interest in it over time. SPSS is a strong drawing skill. Due to the lack of robust and investigation methods, it is weak to handle cutting-edge statistical processes.

Overall rating
Each software has its own uniqueness, and it will inevitably have its own weakness. In general, SAS, Stata, and SPSS are a set of tools that can be used for multiple statistical analyses. You can use STAT/transfer to convert different data files in seconds or minutes. Therefore, you can select different software based on the nature of the problem you are dealing. For example, if you want to use a hybrid model for analysis, you can select SAS; for logistic regression, select Stata; for variance analysis, the best choice is, of course, SPSS. If you are often engaged in statistical analysis, we strongly recommend that you collect the above software into your toolkit for data processing.

English version: SAS

General use. SAS is a package that uses "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 shortdes 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 compute complex data management tasks can be done using simpler commands in Stata or SPSS. however, SAS can work with your 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, regression Val analysis, analysis of variance, factor analysis, multivariate analysis ). the greatest strengths of SAS are probably in its ANOVA, Mixed Model Analysis
And multivariant analysis, while it is probably weakest in ordinal and multinomial logistic regression (because these commands are especially difficult), 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 combines 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 done
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 privileges. 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 wocould 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, regression Val 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, role robust regression, regression with robust standard errors, and role 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 wowould probably be in the area of analysis of variance and traditional mutivariate
Methods (e.g. manova, discriminant analysis, etc .).
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
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 usage and power. while Stata is easy to learn, it also has very powerful tools for data management, knife 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 requires 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. SPSS data files can have 4096
Variables and the number of records is limited only by your disk space.
Statistical analysis. SPSS performs most general statistical analyses (regression, logistic regression, regression Val 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 your kinds of tests of specific effects) and multivariate analysis (e.g. manova, factor analysis, discriminant analysis) and SPSS 11 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 ), the absence of survey data analysis (we know of no tools in this area ).
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 does 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 usage 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 using Ming 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 executing Ming statistical analyses, we wocould stronugly urge you to consider making each one of these packages
Part of your toolkit for data analysis.