When applied to the study of research design, simulations can serve as a suitable substitute for constructing and understanding field research. As applied to the study of research design, simulations can serve as a tool to help the teacher, evaluator, and methodologist address the complex interaction of data construction and analysis, statistical theory, and the violation of key assumptions. In a simulation, the analyst first creates data according to a known model and then examines how well the model can be detected through data analysis. Teachers can show students that measurement, sampling, design, and analysis issues are dependent on the model that is assessed. Students can directly manipulate the simulation model and try things out to see immediately how results change and how analyses are affected.
Simulations are better for some purposes than is the analysis of real data.
With real data, the analyst never perfectly knows the real-world processes that caused the particular measured values to occur. In a simulation, the analyst controls all of the factors making up the data and can manipulate these systematically to see directly how specific problems and assumptions affect the analysis. Simulations also have some advantages over abstract theorizing about research issues. Most of this work has been confined to the more technical literature in these fields. Although the simulations described here can certainly be accomplished on mainframe computers, this workbook will illustrate their use in manual and microcomputer contexts.