In the MaxDiff Model report, the Parameter Estimates section provides model estimates, standard errors and fit criteria.
Term
Lists the terms in the model.
Estimate
An estimate of the parameter associated with the corresponding term. In discrete choice experiments, parameter estimates are sometimes referred to as part-worths. Each part-worth is the coefficient of utility associated with the given term. By default, these estimates are based on the Firth bias-corrected maximum likelihood estimators and therefore are considered to be more accurate than MLEs without bias correction.
Std Error
An estimate of the standard deviation of the parameter estimate.
The AICc (corrected Akaike’s Information Criterion), BIC (Bayesian Information Criterion), −2Loglikelihood, and −2Firth Loglikelihood fit statistics are shown as part of the report and can be used to compare models. See Likelihood, AICc, and BIC in Fitting Linear Models for more information about the first three of these measures.
The −2Firth Loglikelihood value is included in the report only when the Firth Bias-adjusted Estimates check box is checked in the launch window. This option is checked by default.
For each of these statistics, a smaller value indicates a better fit.