McFadden (1974) suggests a likelihood ratio index that is analogous to the Rsquare in the linear regression model:

where is the maximum of the loglikelihood function and is the maximum of the loglikelihood function when all coefficients, except for an intercept term, are zero. McFadden’s likelihood ratio index is bounded by 0 and 1.
Estrella (1998) proposes the following requirements for a goodnessoffit measure to be desirable in discrete choice modeling:
The measure must take values in , where 0 represents no fit and 1 corresponds to perfect fit.
The measure should be directly related to the valid test statistic for the significance of all slope coefficients.
The derivative of the measure with respect to the test statistic should comply with corresponding derivatives in a linear regression.
Estrella’s measure is written as

Estrella suggests an alternative measure,

where is computed with null parameter values, is the number of observations used, and represents the number of estimated parameters.
Other goodnessoffit measures are summarized as follows:












The AIC and SBC are computed as follows:


where is the loglikelihood value for the model, is the number of parameters estimated, and is the number of observations (that is, the number of respondents).