Consumer Research • MaxDiff • Launch the MaxDiff Platform • Launch Window for One Table, Stacked
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Launch Window for One Table, Stacked
Figure 6.9 shows the one-table launch window populated using Potato Chip Combined.jmp.
Figure 6.9 Launch Window for One Table, Stacked Data Format
Select Data Table
Select or open the data table that contains the combined data. Select Other to open a file that is not already open.
Response Indicator
A column containing the preference data. Use two of the values 1, -1, and 0 for the Best and Worst choices, and the third value for profiles that are not Best or Worst. Unless you specify a different coding using the menus next to Best and Worst in the lower left portion of the window, a 1 will indicate the Best choice and a -1 the Worst choice.
Subject ID
An identifier for the study participant.
Choice Set ID
An identifier for the set of profiles presented to the subject for a given preference determination.
Grouping
A column which, when used with the Choice Set ID, uniquely designates each choice set. For example, if a choice set has Choice Set ID = 1 for Survey = A, and another choice set has Choice Set ID = 1 for Survey = B, then Survey should be used as a Grouping column.
Construct Profile Effects
Add effects constructed from the attributes for the profiles.
For information about the Construct Profile Effects panel, see Construct Model Effects in the Fitting Linear Models book.
Construct Subject Effects (Optional)
Add effects constructed from subject-related factors.
For information about the Construct Subject Effects panel, see Construct Model Effects in the Fitting Linear Models book.
Firth Bias-adjusted Estimates
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.
Hierarchical Bayes
Uses a Bayesian approach to estimate subject-specific parameters. See Bayesian Parameter Estimates.
Number of Bayesian Iterations
(Applicable only if Hierarchical Bayes is selected.) The total number of iterations of the adaptive Bayes algorithm used to estimate subject effects. This number includes a burn-in period of iterations that are discarded. The number of burn-in iterations is equal to half of the Number of Bayesian Iterations specified on the launch window.

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Help created on 7/12/2018