Enter a new value for the quasi-likelihood parameter (c-hat). Typically, this value is 1.0000, meaning that the data are not over-dispersed, i.e., no extrabinomial variation. However, you may want to correct for overdispersion by increasing the value. An estimator of c-hat is the deviance divided by its degrees of freedom, although this estimator generally is biased high for finite sample sizes.
The value for c cannot be <1, because there is no biologically reasonable model that would generate underdispersed data.
The value of c-hat is used to compute the QAIC (quasi-AIC) for a model with k parameters by the following formula:
QAIC = -2log Likelihood/c-hat + 2K
and the QAICc by the formula:
QAICc = -2log Likelihood/c-hat + 2K + 2K(K + 1)/(n-ess - K - 1)
where n-ess is the effective sample size.
Values of c-hat > 1 can also be used to compute profile likelihod confidense intervals.
More details on c-hat are provided on the WWW page http://www.cnr.colostate.edu/class_info/fw663/index.html.