Figure 5.13 shows the launch window for Multiple Tables, using Pizza Profiles.jmp as the Profile table.
The profile data table describes the attributes associated with each choice. Each attribute defines a column in the data table. There is a row for each profile. A column in the table contains a unique identifier for each profile. Figure 5.14 shows the Pizza Profiles.jmp data table and a completed Profile Data panel.
Identifier for each row of attribute combinations (profile). 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.
A column which, when used with the Profile ID column, uniquely designates each choice set. 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.
For information about the Construct Profile Effects panel, see Construct Model Effects in the Fitting Linear Models book.
Computes bias-corrected MLEs that produce better estimates and tests than MLEs without bias correction. These estimates also improve separation problems that tend to occur in logistic-type models. Refer to Heinze and Schemper (2002) for a discussion of the separation problem in logistic regression.
The response data table includes a subject identifier column, columns that list the profile identifiers for the profiles in each choice set, and a column containing the preferred profile identifier. There is a row for each subject and choice set. Grouping variables can be used to distinguish choice sets when the data contain more than one group of choice sets. Figure 5.15 shows the Pizza Responses.jmp data table and a completed Response Data panel.
A column which, when used with the Profile ID Chosen column, uniquely designates each choice set.
A column containing frequencies. 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.
The subject data table is optional and depends on whether you want to model subject effects. The table contains a column with the subject identifier used in the response table, and columns for attributes or characteristics of the subjects. You can put subject data in the response data table, but you should specify the subject effects in the Subject Data outline. Figure 5.16 shows the Pizza Subjects.jmp data table and a completed Subject Data panel.
For information about the Construct Model Effects panel, see Construct Model Effects in the Fitting Linear Models book.