Running the Analysis

You've generated an orthogonal design and learned how to display the associated product profiles. You're now ready to learn how to run a conjoint analysis.

Figure 1. Preference data for the carpet-cleaner example
Data editor displaying preference data for the carpet-cleaner example

The preference data collected from the subjects is stored in carpet_prefs.sav. The data consist of responses from 10 subjects, each identified by a unique value of the variable ID. Subjects were asked to rank the 22 product profiles from the most to the least preferred. The variables PREF1 through PREF22 contain the IDs of the associated product profiles, that is, the card IDs from carpet_plan.sav. Subject 1, for example, liked profile 13 most of all, so PREF1 has the value 13.

Analysis of the data is a task that requires the use of command syntax—specifically, the CONJOINT command. The necessary command syntax has been provided in the file conjoint.sps.

CONJOINT PLAN='file specification'
    /DATA='file specification'
    /SEQUENCE=PREF1 TO PREF22
    /SUBJECT=ID
    /FACTORS=PACKAGE BRAND (DISCRETE) 
     PRICE (LINEAR LESS)
     SEAL (LINEAR MORE) MONEY (LINEAR MORE)
    /PRINT=SUMMARYONLY.
  • The PLAN subcommand specifies the file containing the orthogonal design—in this example, carpet_plan.sav.
  • The DATA subcommand specifies the file containing the preference data—in this example, carpet_prefs.sav. If you choose the preference data as the active dataset, you can replace the file specification with an asterisk (*), without the quotation marks.
  • The SEQUENCE subcommand specifies that each data point in the preference data is a profile number, starting with the most-preferred profile and ending with the least-preferred profile.
  • The SUBJECT subcommand specifies that the variable ID identifies the subjects.
  • The FACTORS subcommand specifies a model describing the expected relationship between the preference data and the factor levels. The specified factors refer to variables defined in the plan file named on the PLAN subcommand.
  • The keyword DISCRETE is used when the factor levels are categorical and no assumption is made about the relationship between the levels and the data. This is the case for the factors package and brand that represent package design and brand name, respectively. DISCRETE is assumed if a factor is not labeled with one of the four alternatives (DISCRETE, LINEAR, IDEAL, ANTIIDEAL) or is not included on the FACTORS subcommand.
  • The keyword LINEAR, used for the remaining factors, indicates that the data are expected to be linearly related to the factor. For example, preference is usually expected to be linearly related to price. You can also specify quadratic models (not used in this example) with the keywords IDEAL and ANTIIDEAL.
  • The keywords MORE and LESS, following LINEAR, indicate an expected direction for the relationship. Since we expect higher preference for lower prices, the keyword LESS is used for price. However, we expect higher preference for either a Good Housekeeping seal of approval or a money-back guarantee, so the keyword MORE is used for seal and money (recall that the levels for both of these factors were set to 1 for no and 2 for yes).

    Specifying MORE or LESS does not change the signs of the coefficients or affect estimates of the utilities. These keywords are used simply to identify subjects whose estimates do not match the expected direction. Similarly, choosing IDEAL instead of ANTIIDEAL, or vice versa, does not affect coefficients or utilities.

  • The PRINT subcommand specifies that the output contains information for the group of subjects only as a whole (SUMMARYONLY keyword). Information for each subject, separately, is suppressed.

Try running this command syntax. Make sure that you have included valid paths to carpet_prefs.sav and carpet_plan.sav. For a complete description of all options, see the CONJOINT command in the Command Syntax Reference.

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