AIC, AICc, QAIC, and AICc
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
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.