sas ,spss.介绍

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spss】SPSS与SAS的客观评价

转自丁香园徐天和教授讲座

SAS 介绍

SAS的一般情况

Ú 制作单位：SAS公司

Ú 最新版本号：9.0(英文版)、8.2(中文版)

Ú 系统大小：>1G(完全安装)

Ú 英文网址

Ú http://www.sas.com

Ú 中文网址

Ú http://www.sas.com/offices/asiapacific/china

SAS的名称及含义

¨ SAS系统全称为Statistics Analysis System，最早由北卡罗来纳大学的两位生物统计学研究生编制，并于1976年成立了SAS软件研究所，正式推出了SAS软件。

¨ SAS是用于决策支持的大型集成信息系统，但该软件系统最早的功能限于统计分析，至今，统计分析功能也仍是它的重要组成部分和核心功能。

SAS概述一

¨ 经过多年的发展，SAS已被全世界120多个国家和地区的近三万家机构所采用，直接用户则超过三百万人，遍及金融、医药卫生、生产、运输、通讯、政府和教育科研等领域。

¨ 在英美等国，能熟练使用SAS进行统计分析是许多公司和科研机构选材的条件之一。

SAS概述二

¨ 在数据处理和统计分析领域，SAS系统被誉为国际上的标准软件系统，并在96～97年度被评选为建立数据库的首选产品。堪称统计软件界的巨无霸。

¨ 例如：在以苛刻严格著称于世的美国FDA新药审批程序中，新药试验结果的统计分析规定只能用SAS进行，其他软件的计算结果一律无效！哪怕只是简单的均数和标准差也不行！由此可见SAS的权威地位。

SAS的特点一

¨ SAS是由大型机系统发展而来，其核心操作方式就是程序驱动，经过多年的发展，现在已成为一套完整的计算机语言，其用户界面也充分体现了这一特点：它采用MDI（多文档界面），用户在PGM视窗中输入程序，分析结果以文本的形式在OUTPUT视窗中输出。

SAS的特点二

¨ 使用程序方式，用户可以完成所有需要做的工作，包括统计分析、预测、建模和模拟抽样等。

¨ 但是，这使得初学者在使用SAS时必须要学习SAS语言，入门比较困难。

SAS的特点三

¨ SAS的Windows版本根据不同的用户群开发了几种图形操作界面，这些图形操作界面各有特点，使用时非常方便。但是由于国内介绍他们的文献不多，并且也不是SAS推广的重点，因此还不为绝大多数人所了解。

SAS的缺点一

¨ 由于SAS系统是从大型机上的系统发展而来，在设计上也是完全针对专业用户进行设计，因此其操作至今仍以编程为主，人机对话界面不太友好，并且在编程操作时需要用户最好对所使用的统计方法有较清楚的了解，非统计专业人员掌握起来较为困难。

SAS的缺点二

¨ 此外，SAS极为高昂的价格和只租不卖的销售策略使得实力不足的个人和机构只能对他望而却步。

SPSS 介绍

SPSS的一般情况

¨ 制作单位：SPSS公司

¨ 最新版本号：11.5

¨ 系统大小：200兆（完全安装）

¨ 英文网址：http://www.spss.com

¨ 中文网址：http://www.spss.com.cn

SPSS的名称及含义

¨ SPSS是软件英文名称的首字母缩写，原意为Statistical Package for the Social Sciences，即“社会科学统计软件包”。

¨ 随着SPSS产品服务领域的扩大和服务深度的增加，SPSS公司已于2000年正式将英文全称更改为Statistical Product and Service Solutions，意为“统计产品与服务解决方案”，标志着SPSS的战略方向正在做出重大调整。

SPSS的特点一

¨ SPSS最突出的特点

– 以窗口方式管理数据；

– 以菜单方式展示各种分析方法；

– 以对话框展示出各种功能选择项。

SPSS的特点二

¨ 只要掌握一定的Windows操作技能，粗通统计分析原理，就可以使用该软件为特定的科研工作服务。

¨ 操作界面极为友好，输出结果美观漂亮（从国外的角度看），是非专业统计人员的首选统计软件。

SPSS的特点三

¨ SPSS采用类似EXCEL表格的方式输入与管理数据，数据接口较为通用，能方便的从其他数据库中读入数据。其统计过程包括了常用的、较为成熟的统计过程，完全可以满足非统计专业人士的工作需要。

SPSS的特点四

¨ 对于熟悉老版本编程运行方式的用户，SPSS还特别设计了语法生成窗口，用户只需在菜单中选好各个选项，然后按“粘贴”按钮就可以自动生成标准的SPSS程序。极大的方便了中、高级用户。

SPSS的缺点一

¨ 由于在SPSS公司的产品线中，SPSS软件属于中、低档（SPSS公司共有二十余个产品），因此从战略的观点来看，SPSS显然是把相当的精力放在了用户界面的开发上。

SPSS的缺点二

¨ 该软件只吸收较为成熟的统计方法，而对于最新的统计方法，SPSS公司的做法是为之发展一些专门软件，如针对树结构模型的Answer Tree，针对神经网络技术的Neural Connection、专门用于数据挖掘的Clementine等，而不是直接纳入SPSS，因此他们在SPSS中均难觅芳踪。

SPSS的缺点三

¨ 另外，其输出结果虽然漂亮，但不能为WORD等常用文字处理软件直接打开，只能采用拷贝、粘贴的方式加以交互。这些都可以说是SPSS软件的致命伤。

推荐参考资料

¨ 高惠璇

¨ SAS系统 SAS/Base软件使用手册

¨ SAS系统 SAS/STAT软件使用手册

¨ SAS系统 SAS/ETS 软件使用手册

¨ 中国统计出版社

推荐参考资料

¨ 胡良平

¨ 现代统计学与SAS应用

¨ Windows SAS 6.12 & 8.0 使用统计分析教程

¨ 军事医学科学出版社

推荐参考资料

¨ 张文彤

¨ SPSS 11.0统计分析教程（基础篇）

¨ SPSS 11.0统计分析教程（高级篇）

¨ 北京希望电子出版社

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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), 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 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 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. 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, 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 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 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

environ

SAS最新版本已经是9.2了，不知道什么时候能跟大家见面。