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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).
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