Comparison of SAS, Stata and SPSS

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 is generally used. 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. SAS
8. You can click the mouse to perform interactive drawing, but 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 is generally used. 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 is generally used. 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 image is of excellent quality and can be pasted into other files (Word
Documentation 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. The overall evaluation of each software has its own uniqueness, and it is inevitable that it has 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.