Overdispersion is a very common phenomenon that occurs when binomial responses exhibit larger variance than that permitted by the binomial distribution. When binary responses are clustered within a subject the independence of Bernoulli trials cannot be assumed because the observations within a cluster are positively correlated with each other in general. This fact may cause the extra variation of binomial distribution as discussed by Kupper, Haseman (1978). In this paper we suggest various overdispersion models for clustered binomial responses in the presence of covariate variables. The quasi-likelihood method, the zero-inflated binomial model, the beta-binomial model, and the random effects model are the available approaches. We also discuss the goodness-of-fit statistics assessing the overdispersion models. The goodness-of-fit of overdispersion models will be compared through several examples.