Modeling and Multivariate Methods - SAS

414 Performing Choice Modeling Chapter 16
Platform Options
Platform Options
The Choice Modeling platform has many available options. To access these options, click on the platform
drop-down menu.
Likelihood Ratio Tests tests the significance of each effect in the model. These are done by default if the
estimate of cpu time is less than five seconds.
Joint Factor Tests tests each factor in the model by constructing a likelihood ratio test for all the effects
involving that factor.
Confidence Intervals produces a 95% confidence interval for each parameter (by default), using the
profile-likelihood method. Shift-click on the platform drop-down menu and select Confidence
Intervals to input alpha values other than 0.05.
Correlation of Estimates
shows the correlations of the parameter estimates.
Effect Marginals shows the fitted utility values for different levels in the effects, with neutral values used
for unrelated factors.
Profiler
time.
produces a response surface viewer that takes vertical cross-sections across each factor, one at a
Save Utility Formula makes a new column with a formula for the utility, or linear model, that is
estimated. This is in the profile data table, except if there are subject effects. In that case, it makes a new
data table for the formula. This formula can be used with various profilers with subsequent analyses.
Save Gradients by Subject constructs a new table that has a row for each subject containing the average
(Hessian-scaled-gradient) steps on each parameter. This corresponds to using a Lagrangian multiplier
test for separating that subject from the remaining subjects. These values can later be clustered, using the
built-in-script, to indicate unique market segments represented in the data.
Model Dialog shows the Choice dialog box, which can be used to modify and re-fit the model. You can
specify new data sets, new IDs, and new model effects.
Example: Valuing Trade-offs
The Choice Modeling platform is also useful for determining the relative importance of product attributes.
Even if the attributes of a particular product that are important to the consumer are known, information
about preference trade-offs with regard to these attributes might be unknown. By gaining such information,
a market researcher or product designer is able to incorporate product features that represent the optimal
trade-off from the perspective of the consumer.
The advantages of this approach to product design can be found in the following example. It is already
known that four attributes are important for laptop design--hard-disk size, processor speed, battery life, and
selling price. The data gathered for this study are used to determine which of four laptop attributes (Hard
Disk, Speed, Battery Life, and Price) are most important. It also assesses whether or not there are Gender
or Job differences seen with these attributes.