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• Author: Xitao Fan, Akos Felsovalyi, Stephen A. Sivo, Sean C. Keenan
• Publisher: SAS Publishing
• Publish Date: 01 January, 2003
• ISBN: 1590471415

Acknowledgments

Putting all the pieces together for this project has taken more than what we originally expected.

During the process, it has been our pleasure to work with the patient and supportive members of the

Books by Users program. We are especially grateful for two members of BBU who have made our

project possible. From the very beginning of the project, Julie Platt has given us great encouragement

and support, as well as her understanding and patience, even at a time when our project appeared to

be faltering. Efficient, helpful, and pleasant, John West has kept us on the left path in the later stage

of the project, and finally guided us to bring the project to fruition.

We are very thankful for the technical reviewers who have provided us with constructive comments

and have pointed out our errors. Our gratitude goes to Jim Ashton, Brent Cohen, Michael Forno, Phil

Gibbs, Sunil Panikkath, Mike Patetta, Jim Seabolt, Paul Terrill, and Victor Willson, for their time and

effort in scrutinizing our draft chapters. We, of course, take full responsibility for any errors that

remain.



 

As the title of this book clearly indicates, the purpose of this book is to provide a practical guide for

using the SAS System to conduct Monte Carlo simulation studies to solve many practical problems

encountered in different disciplines. The book is intended for quantitative researchers from a variety

of disciplines (e.g., education, psychology, sociology, political science, business and finance,

marketing research) who use the SAS System as their major tool for data analysis and quantitative

research. With this audience in mind, we assume that the reader is familiar with SAS and can read and

understand SAS code.

Although a variety of quantitative techniques will be used and discussed as examples of conducting

Monte Carlo simulation through the use of the SAS System, quantitative techniques per se are not

intended to be the focus of this book. It is assumed that readers have a good grasp of the relevant

quantitative techniques discussed in an example such that their focus will not be on the quantitative

techniques, but on how the quantitative techniques can be implemented in a simulation situation.

Many of the quantitative techniques used as examples in this book are those that investigate linear

relationships among variables. Linear relationships are the focus of many widely used quantitative

techniques in a variety of disciplines, such as education, psychology, sociology, business and finance,

agriculture, etc. One important characteristic of these techniques is that they are all fundamentally

based on the least-squares principle, which minimizes the sum of residual squares. Some examples of

these widely used quantitative methods are regression analysis, univariate and multivariate analysis of

variance, discriminant analysis, canonical correlation analysis, and covariance structure analysis (i.e.,

structural equation modeling).

Before we begin our detailed discussion about how to use the SAS System to conduct Monte Carlo

studies, we would like to take some time to discuss briefly a few more general but relevant topics.

More specifically, we want to discuss the following:

What is a Monte Carlo study?

Why are Monte Carlo studies often necessary?

What are some typical situations where Monte Carlo simulation is needed?

Why use the SAS System for conducting Monte Carlo studies?

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