Fixed-effects models are a class of statistical models in which the levels (i.e., values) of independent variables are assumed to be fixed (i.e., constant), and only the dependent variable changes in response to the levels of independent variables. This class of models is fundamental to the general linear models that underpin fixed-effects regression analysis and fixed-effects analysis of variance, or ANOVA (fixed-effects ANOVA can be unified with fixed-effects regression analysis by using dummy variables to represent the levels of independent variables in a regression model; see the article by Andrew Gelman for more information); the generalized linear models, such as logistic regression for binary response variables and binomial counts; Poison regression for Poisson (count) response variables; as well as the analysis of categorical data using ...