SCS Short Courses, Winter 2002

SCS Courses Introduction to SPSS for Windows  Data Analysis Using SAS Assumption Violation in ANOVA Models: Problems and Solutions An Introduction to R

 Data Analysis Using 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.

The following zip file: spss.zip contains SPSS data sets and scripts used in the course. Copy this file (in binary mode) to an appropriate directory and use a zip extractor (such as pkunzip or WinZip) to expand the zip archive into the original files.


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Assumption Violation in ANOVA Models: Problems and Solutions

    The purpose of this course is to present an overview of the importance of examining the validity assumptions of the parametric F test in analysis of variance designs. Although researchers are often aware of the assumptions of the F test (e.g., normality, variance homogeneity), less often are they aware of the frequency with which social science data violate these assumptions, the problems that arise from using the F test when assumptions are violated, and the solutions that are available for dealing with assumption violation. The course will cover both one-way and factorial univariate designs.

An Introduction to R

    The statistical programming language and computing environment S has become the de-facto standard among statisticians. S has great built-in statistical functionality, including excellent facilities for drawing statistical graphs and for fitting linear, generalized linear, and nonlinear models. A wide range of "libraries" extends the capabilities of S to such areas as mixed models, survival analysis, and nonparametric regression. New statistical methods are often first made available in S.

     The S language has two major implementations: the commercial product S-PLUS, and the free, open-source R (also called "GNU S"). R, which is the focus of this short course, runs on Unix and Linux systems, Windows PCs, and Macintoshes. Although R is free, it is very high-quality software, to which many of the leading experts on statistical computing have contributed.

     A statistical package, such as SPSS, makes routine data analysis relatively easy, but it is relatively difficult to do things that are
innovative or nonstandard, or to add to the built-in capabilities of the package. In contrast, a good statistical computing environment also makes routine data analysis easy, but additionally supports convenient programming; this means that users can extend the already impressive facilities of R.

     The purpose of this short course is to show you how to do data analysis in R, including writing programs and constructing non-standard graphs. I assume that you are familiar with the statistical content of the course.

     The following topics will be covered:

1. Getting started with R; reading, manipulating, examining, and transforming data
2. Fitting linear and generalized linear models in R
3. Regression diagnostics with car (the Companion to Applied Regression library)
4. Writing programs and drawing graphs in R


     The text for the short course is J. Fox, An R and S-PLUS Companion to Applied Regression, Sage (in press).