Homoscedasticity is an important assumption of linear regression. This paper explains what it is and why it is important to the researcher. Graphical and mathematical methods for testing the homoscedasticity assumption are demonstrated. Sources of homoscedasticity and types of homoscedasticity are discussed, and methods for correction are demonstrated. Graphs are used to illustrate different patterns that may be caused by heteroscedasticity. An extensive example for using Weighted Least Squares regression is provided using both the Statistical Package for the Social Sciences (SPSS) and a step-by-step manual process. SPSS code for reproducing all examples is included. Finally, examples are used to highlight the interactive relationship between good experimental design and sound statistical practice. Appendixes contain the listing for residual plot examples and the listing for correction examples. (Contains 4 tables, 17 figures, and 22 references.) (Author/SLD)