Fixed Effects Regression Models, by Paul D. Allison, is a useful handbook that concentrates on the application of fixed-effects methods for a variety of data situations, from linear regression to survival analysis. Fixed-effects models make less restrictive assumptions than their random-effects counterparts. For example, fixed-effects models allow unobservable variables to have whatever associations with the observed variables. As Allison points out, the individuals act as their own controls in a fixed-effects setting. That is not to say that fixed-effects models are not without their disadvantages. For example, the effects of covariates that are constant within individuals cannot be measured in this setting. However, such disadvantages should not dissuade one from using this powerful analysis technique that makes the most minimal of independence assumptions. In an appendix, Allison shows how to perform all the analyses by using Stata.