Most researchers using analysis of variance (ANOVA) use a fixed-effects model. However, a random- or mixed-effects model may be a more appropriate fit for many research designs. One benefit of the random- and mixed-effects models is that they yield more generalizable results. This paper focuses on the similarities and differences between the various ANOVA models and the factors that should be considered when determining which model to use. An explanation of the "Rules of Thumb" for deriving the correct formulas for computing "F" statistics is also given. (Contains 2 tables and 15 references.) (SLD)