Launch the Choice platform by selecting Analyze > Consumer Research > Choice.
Example of Profile Data Table and Dialog Window
Identifier for each row of choice combinations. If the Profile ID column does not uniquely identify each row in the profile data table, you need to add Grouping columns. Add Grouping columns until the combination of Grouping and Profile ID columns uniquely identify the row, or profile.
With the Profile ID, unique identifier of each row, or profile. For example, if Profile ID = 1 for Survey = A, and a different Profile ID = 1 for Survey = B, then Survey would be used as a Grouping column.
Response data contain the experimental results and have the choice set IDs for each trial as well as the actual choice selected by the subject. Each subject usually has several trials, or choice sets, to cover several choice possibilities. There can be more than one row of data for each subject. For example, an experiment might have 100 subjects with each subject making 12 choice decisions, resulting in 1200 rows in this data table. The Response data are linked to the Profile data through the choice set columns and the actual choice response column. Choice set refers to the set of alternatives from which the subject makes a choice. Grouping variables are sometimes used to align choice indices when more than one group is contained within the data.
Example of Response Data Table and Launch Window
The Profile ID from the Profile data table of the choice the subject selected.
With the Profile ID Choices, unique identifier of each choice profile.
The Profile IDs of the set of possible choices.
The Subject ID from the Subject data table.
The frequency of the observations. If n is the value of the Freq variable for a given row, then that row is used in computations n times. If it is less than 1 or missing, then JMP does not use it to calculate any analyses.
Example of Subject Data Table and Launch Window
See the JMP Scripting Index in the Help menu for an example.
The resulting parameter estimates are sometimes referred to as part-worths. Each part-worth is the coefficient of utility associated with that attribute. 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.
where k is the number of estimated parameters in the model and n is the number of observations in the data set. The BIC formula is:  2 LogLikelihood + k  ln(n), where k parameters is fitted to data with n observations and LogLikelihood is the maximized log-likelihood. Note that the  2 Firth Loglikelihood result is included only in the report when the Firth Bias-adjusted Estimates check box is checked in the launch window. (See Example of Profile Data Table and Dialog Window.) This option is checked by default. The decision to use or not use the Firth Bias-adjusted Estimates does not affect the AICc score or the  2 LogLikelihood results.
Choice Model Results with No Subject Data for Pizza Example