Monte Carlo simulation is a method of evaluating substantive hypotheses and statistical estimators by developing a computer algorithm to simulate a population, drawing multiple samples from this pseudo-population, and evaluating estimates obtained from these samples.

The author explains the logic behind the method and demonstrates its uses for social and behavioural research in: conducting inference using statistics with only weak mathematical theory; testing null hypotheses under a variety of plausible conditions; assessing the robustness of parametric inference to violations of its assumptions; assessing the quality of inferential methods; and comparing the properties of two or more estimators. In addition, Christopher Z Mooney carefully demonstrates how to prepare computer algorithms using GAUSS code and uses several research examples to demonstrate these principles.

This volume will enable researchers to execute Monte Carlo simulation effectively and to interpret the estimated sampling distribution generated from its use.

Introduction
Generating Individual Samples from a Pseudo-Population
Using the Pseudo-Population in Monte Carlo Simulation
Using Monte Carlo Simulation in the Social Sciences
Conclusion