Optional Subcommands for a Conjoint Analysis
The CONJOINT command offers
a number of optional subcommands that provide additional control and
functionality beyond what is required.
SUBJECT Subcommand
The SUBJECT subcommand allows
you to specify a variable from the data file to be used as an identifier
for the subjects. If you do not specify a subject variable, the CONJOINT command assumes that all of the
cases in the data file come from one subject. The following example
specifies that the variable ID,
from the file rugranks.sav, is
to be used as a subject identifier.
CONJOINT PLAN=* /DATA='RUGRANKS.SAV'
/SCORE=SCORE1 TO SCORE22 /SUBJECT=ID.FACTORS Subcommand
The FACTORS subcommand allows
you to specify the model describing the expected relationship between
factors and the rankings or scores. If you do not specify a model
for a factor, CONJOINT assumes
a discrete model. You can specify one of four models:
DISCRETE. The DISCRETE model indicates
that the factor levels are categorical and that no assumption is made
about the relationship between the factor and the scores or ranks.
This is the default.
LINEAR. The LINEAR model indicates
an expected linear relationship between the factor and the scores
or ranks. You can specify the expected direction of the linear relationship
with the keywords MORE and LESS. MORE indicates that higher levels of a factor are expected to be preferred,
while LESS indicates that lower
levels of a factor are expected to be preferred. Specifying MORE or LESS will not affect estimates of
utilities. They are used simply to identify subjects whose estimates
do not match the expected direction.
IDEAL. The IDEAL model indicates an
expected quadratic relationship between the scores or ranks and the
factor. It is assumed that there is an ideal level for the factor,
and distance from this ideal point (in either direction) is associated
with decreasing preference. Factors described with this model should
have at least three levels.
ANTIIDEAL. The ANTIIDEAL model indicates
an expected quadratic relationship between the scores or ranks and
the factor. It is assumed that there is a worst level for the factor,
and distance from this point (in either direction) is associated with
increasing preference. Factors described with this model should have
at least three levels.
The following command syntax provides an example using the FACTORS subcommand:
CONJOINT PLAN=* /DATA='RUGRANKS.SAV'
/RANK=RANK1 TO RANK22 /SUBJECT=ID
/FACTORS=PACKAGE BRAND (DISCRETE) PRICE (LINEAR LESS)
SEAL (LINEAR MORE) MONEY (LINEAR MORE).- Note that both package and brand are modeled as discrete.
PRINT Subcommand
The PRINT subcommand allows
you to control the content of the tabular output. For example, if
you have a large number of subjects, you can choose to limit the output
to summary results only, omitting detailed output for each subject,
as shown in the following example:
CONJOINT PLAN=* /DATA='RUGRANKS.SAV'
/RANK=RANK1 TO RANK22 /SUBJECT=ID
/PRINT=SUMMARYONLY.You can also choose whether the output includes analysis of the experimental data, results for any simulation cases included in the plan file, both, or none. Simulation cases are not rated by the subjects but represent product profiles of interest to you. The Conjoint procedure uses the analysis of the experimental data to make predictions about the relative preference for each of the simulation profiles. In the following example, detailed output for each subject is suppressed, and the output is limited to results of the simulations:
CONJOINT PLAN=* /DATA='RUGRANKS.SAV'
/RANK=RANK1 TO RANK22 /SUBJECT=ID
/PRINT=SIMULATION SUMMARYONLY.PLOT Subcommand
The PLOT subcommand controls
whether plots are included in the output. Like tabular output (PRINT subcommand), you can control whether
the output is limited to summary results or includes results for each
subject. By default, no plots are produced. In the following example,
output includes all available plots:
CONJOINT PLAN=* /DATA='RUGRANKS.SAV'
/RANK=RANK1 TO RANK22 /SUBJECT=ID
/PLOT=ALL.UTILITY Subcommand
The UTILITY subcommand writes
a data file in IBM® SPSS® Statistics format containing
detailed information for each subject. It includes the utilities for DISCRETE factors, the slope and quadratic
functions for LINEAR, IDEAL, and ANTIIDEAL factors, the regression constant, and the estimated preference scores.
These values can then be used in further analyses or for making additional
plots with other procedures. The following example creates a utility
file named rugutil.sav:
CONJOINT PLAN=* /DATA='RUGRANKS.SAV'
/RANK=RANK1 TO RANK22 /SUBJECT=ID
/UTILITY='RUGUTIL.SAV'.