SCS Short Courses, Fall 2003

SCS Courses Data Analysis Using SAS Introduction to SPSS for Windows Data Screening

Data Analysis Using SAS for Windows

This short course provides a basic introduction to the Statistical Analysis System (SAS). Sessions One and Two provide an overview of SAS and its underlying logic; an explanation of the use of the Display Manager System to run a SAS job; an introduction to the SAS Data step for reading, transforming, and storing data; and a demonstration of how statistical analyses may be performed in SAS Insight.

Sessions Three and Four will concentrate on SAS programming techniques to modify data and enhance SAS output. More statistical procedures will be introduced for general linear models.

Introduction to SPSS for Windows

This course presents the basics of the Statistical Package for the Social Sciences (SPSS). Session One will introduce the computing concepts of SPSS, the different facilities for reading data into an SPSS spreadsheet, and saving SPSS data files for future use. At the end of the first session, participants should be able to run simple programs, including some statistical procedures.

Sessions Two and Three will cover basic data modifications, transformations and other functions including the uses of SPSS system files. More statistical procedures will also be introduced, with an emphasis on the use of graphical methods for examining univariate and bivariate relationships. Session Four will cover Analysis of Variance and Least Squares Regression. As with previous sessions, graphical techniques will be demonstrated.

Data Screening

Data screening is the condom of data analysis: an important, but frequently overlooked step. While data analysis often focuses on summarization, model fitting, and numbers, data screening emphasizes exposure, preparation for modeling, checking the adequacy of assumptions, and graphical display. Come learn about Safe Stats!

This workshop covers a variety of practical aspects of data screening, including:

• Entering and checking raw data
• Assessing univariate problems (distribution shape, outliers)
• Assessing bivariate problems (linearity, regression diagnostics)
• Assessing multivariate problems (multivariate normality, detecting multivariate outliers)
• Dealing with missing data

Examples are presented using SAS software, along with a collection of general-purpose SAS macros for applying some of these techniques to any data set.

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