AIC, AICc, QAIC, and QAICc

The number of parameters in the model is K. The AIC depends on the number of parameters as

AIC = -2log Likelihood + 2K

and as does the QAIC (quasi-AIC)

QAIC = -2log Likelihood/c-hat + 2K

the AICc:

AICc = -2log Likelihood + 2K + 2K(K + 1)/(n-ess - K - 1)

and the QAICc:

QAICc = -2log Likelihood/c-hat + 2K + 2K(K + 1)/(n-ess - K - 1)

where n-ess is the effective sample size.

You can change the QAIC and QAICc value by changing c-hat with the Adjustments | c-hat menu options from the Results Browser.

An alternative model selection metric is BIC.