Ibm Spss Conjoint

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i IBM SPSS Conjoint 20

Transcript of Ibm Spss Conjoint

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IBM SPSS Conjoint 20

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Note: Before using this information and the product it supports, read the general informationunder Notices on p. 39.

This edition applies to IBM® SPSS® Statistics 20 and to all subsequent releases and modificationsuntil otherwise indicated in new editions.

Adobe product screenshot(s) reprinted with permission from Adobe Systems Incorporated.

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Licensed Materials - Property of IBM

© Copyright IBM Corporation 1989, 2011.

U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADPSchedule Contract with IBM Corp.

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PrefaceIBM® SPSS® Statistics is a comprehensive system for analyzing data. The Conjoint optionaladd-on module provides the additional analytic techniques described in this manual. The Conjointadd-on module must be used with the SPSS Statistics Core system and is completely integratedinto that system.

About IBM Business Analytics

IBM Business Analytics software delivers complete, consistent and accurate information thatdecision-makers trust to improve business performance. A comprehensive portfolio of businessintelligence, predictive analytics, financial performance and strategy management, and analyticapplications provides clear, immediate and actionable insights into current performance and theability to predict future outcomes. Combined with rich industry solutions, proven practices andprofessional services, organizations of every size can drive the highest productivity, confidentlyautomate decisions and deliver better results.

As part of this portfolio, IBM SPSS Predictive Analytics software helps organizations predictfuture events and proactively act upon that insight to drive better business outcomes. Commercial,government and academic customers worldwide rely on IBM SPSS technology as a competitiveadvantage in attracting, retaining and growing customers, while reducing fraud and mitigatingrisk. By incorporating IBM SPSS software into their daily operations, organizations becomepredictive enterprises – able to direct and automate decisions to meet business goals and achievemeasurable competitive advantage. For further information or to reach a representative visithttp://www.ibm.com/spss.

Technical support

Technical support is available to maintenance customers. Customers may contact TechnicalSupport for assistance in using IBM Corp. products or for installation help for one of thesupported hardware environments. To reach Technical Support, see the IBM Corp. web siteat http://www.ibm.com/support. Be prepared to identify yourself, your organization, and yoursupport agreement when requesting assistance.

Technical Support for Students

If you’re a student using a student, academic or grad pack version of any IBMSPSS software product, please see our special online Solutions for Education(http://www.ibm.com/spss/rd/students/) pages for students. If you’re a student using auniversity-supplied copy of the IBM SPSS software, please contact the IBM SPSS productcoordinator at your university.

Customer Service

If you have any questions concerning your shipment or account, contact your local office. Pleasehave your serial number ready for identification.

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Training Seminars

IBM Corp. provides both public and onsite training seminars. All seminars feature hands-onworkshops. Seminars will be offered in major cities on a regular basis. For more information onthese seminars, go to http://www.ibm.com/software/analytics/spss/training.

Additional Publications

The SPSS Statistics: Guide to Data Analysis, SPSS Statistics: Statistical Procedures Companion,and SPSS Statistics: Advanced Statistical Procedures Companion, written by Marija Norušis andpublished by Prentice Hall, are available as suggested supplemental material. These publicationscover statistical procedures in the SPSS Statistics Base module, Advanced Statistics moduleand Regression module. Whether you are just getting starting in data analysis or are ready foradvanced applications, these books will help you make best use of the capabilities found withinthe IBM® SPSS® Statistics offering. For additional information including publication contentsand sample chapters, please see the author’s website: http://www.norusis.com

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Contents1 Introduction to Conjoint Analysis 1

The Full-Profile Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2An Orthogonal Array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2The Experimental Stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2Collecting and Analyzing the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Part I: User’s Guide

2 Generating an Orthogonal Design 5

Defining Values for an Orthogonal Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Orthogonal Design Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7ORTHOPLAN Command Additional Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3 Displaying a Design 9

Display Design Titles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10PLANCARDS Command Additional Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4 Running a Conjoint Analysis 11

Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Specifying the Plan File and the Data File. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Specifying How Data Were Recorded . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Optional Subcommands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Part II: Examples

5 Using Conjoint Analysis to Model Carpet-Cleaner Preference17

Generating an Orthogonal Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Creating the Experimental Stimuli: Displaying the Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

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Running the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23Utility Scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Relative Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Reversals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Running Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Preference Probabilities of Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Appendices

A Sample Files 30

B Notices 39

Bibliography 42

Index 43

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Chapter

1Introduction to Conjoint Analysis

Conjoint analysis is a market research tool for developing effective product design. Using conjointanalysis, the researcher can answer questions such as: What product attributes are important orunimportant to the consumer? What levels of product attributes are the most or least desirable inthe consumer’s mind? What is the market share of preference for leading competitors’ productsversus our existing or proposed product?The virtue of conjoint analysis is that it asks the respondent to make choices in the same fashion

as the consumer presumably does—by trading off features, one against another.For example, suppose that you want to book an airline flight. You have the choice of sitting in a

cramped seat or a spacious seat. If this were the only consideration, your choice would be clear.You would probably prefer a spacious seat. Or suppose you have a choice of ticket prices: $225 or$800. On price alone, taking nothing else into consideration, the lower price would be preferable.Finally, suppose you can take either a direct flight, which takes two hours, or a flight with onelayover, which takes five hours. Most people would choose the direct flight.The drawback to the above approach is that choice alternatives are presented on single attributes

alone, one at a time. Conjoint analysis presents choice alternatives between products defined bysets of attributes. This is illustrated by the following choice: would you prefer a flight that iscramped, costs $225, and has one layover, or a flight that is spacious, costs $800, and is direct? Ifcomfort, price, and duration are the relevant attributes, there are potentially eight products:

Product Comfort Price Duration

1 cramped $225 2 hours

2 cramped $225 5 hours

3 cramped $800 2 hours

4 cramped $800 5 hours

5 spacious $225 2 hours

6 spacious $225 5 hours

7 spacious $800 2 hours

8 spacious $800 5 hours

Given the above alternatives, product 4 is probably the least preferred, while product 5 is probablythe most preferred. The preferences of respondents for the other product offerings are implicitlydetermined by what is important to the respondent.Using conjoint analysis, you can determine both the relative importance of each attribute as

well as which levels of each attribute are most preferred. If the most preferable product is notfeasible for some reason, such as cost, you would know the next most preferred alternative. Ifyou have other information on the respondents, such as background demographics, you might beable to identify market segments for which distinct products can be packaged. For example, thebusiness traveler and the student traveler might have different preferences that could be met bydistinct product offerings.

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Chapter 1

The Full-Profile Approach

Conjoint uses the full-profile (also known as full-concept) approach, where respondents rank,order, or score a set of profiles, or cards, according to preference. Each profile describes acomplete product or service and consists of a different combination of factor levels for all factors(attributes) of interest.

An Orthogonal Array

A potential problem with the full-profile approach soon becomes obvious if more than a fewfactors are involved and each factor has more than a couple of levels. The total number of profilesresulting from all possible combinations of the levels becomes too great for respondents to rank orscore in a meaningful way. To solve this problem, the full-profile approach uses what is termed afractional factorial design, which presents a suitable fraction of all possible combinations ofthe factor levels. The resulting set, called an orthogonal array, is designed to capture the maineffects for each factor level. Interactions between levels of one factor with levels of anotherfactor are assumed to be negligible.The Generate Orthogonal Design procedure is used to generate an orthogonal array and is

typically the starting point of a conjoint analysis. It also allows you to generate factor-levelcombinations, known as holdout cases, which are rated by the subjects but are not used to buildthe preference model. Instead, they are used as a check on the validity of the model.

The Experimental Stimuli

Each set of factor levels in an orthogonal design represents a different version of the product understudy and should be presented to the subjects in the form of an individual product profile. Thishelps the respondent to focus on only the one product currently under evaluation. The stimulishould be standardized by making sure that the profiles are all similar in physical appearanceexcept for the different combinations of features.Creation of the product profiles is facilitated with the Display Design procedure. It takes a

design generated by the Generate Orthogonal Design procedure, or entered by the user, andproduces a set of product profiles in a ready-to-use format.

Collecting and Analyzing the Data

Since there is typically a great deal of between-subject variation in preferences, much of conjointanalysis focuses on the single subject. To generalize the results, a random sample of subjects fromthe target population is selected so that group results can be examined.The size of the sample in conjoint studies varies greatly. In one report (Cattin and Wittink,

1982), the authors state that the sample size in commercial conjoint studies usually ranges from100 to 1,000, with 300 to 550 the most typical range. In another study (Akaah and Korgaonkar,1988), it is found that smaller sample sizes (less than 100) are typical. As always, the sample sizeshould be large enough to ensure reliability.

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Introduction to Conjoint Analysis

Once the sample is chosen, the researcher administers the set of profiles, or cards, to eachrespondent. The Conjoint procedure allows for three methods of data recording. In the firstmethod, subjects are asked to assign a preference score to each profile. This type of method istypical when a Likert scale is used or when the subjects are asked to assign a number from 1 to100 to indicate preference. In the second method, subjects are asked to assign a rank to eachprofile ranging from 1 to the total number of profiles. In the third method, subjects are asked tosort the profiles in terms of preference. With this last method, the researcher records the profilenumbers in the order given by each subject.Analysis of the data is done with the Conjoint procedure (available only through command

syntax) and results in a utility score, called a part-worth, for each factor level. These utilityscores, analogous to regression coefficients, provide a quantitative measure of the preferencefor each factor level, with larger values corresponding to greater preference. Part-worths areexpressed in a common unit, allowing them to be added together to give the total utility, or overallpreference, for any combination of factor levels. The part-worths then constitute a model forpredicting the preference of any product profile, including profiles, referred to as simulationcases, that were not actually presented in the experiment.The information obtained from a conjoint analysis can be applied to a wide variety of market

research questions. It can be used to investigate areas such as product design, market share,strategic advertising, cost-benefit analysis, and market segmentation.Although the focus of this manual is on market research applications, conjoint analysis can

be useful in almost any scientific or business field in which measuring people’s perceptionsor judgments is important.

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Part I:User’s Guide

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Chapter

2Generating an Orthogonal Design

Generate Orthogonal Design generates a data file containing an orthogonal main-effects designthat permits the statistical testing of several factors without testing every combination of factorlevels. This design can be displayed with the Display Design procedure, and the data file can beused by other procedures, such as Conjoint.

Example. A low-fare airline startup is interested in determining the relative importance to potentialcustomers of the various factors that comprise its product offering. Price is clearly a primaryfactor, but how important are other factors, such as seat size, number of layovers, and whether ornot a beverage/snack service is included? A survey asking respondents to rank product profilesrepresenting all possible factor combinations is unreasonable given the large number of profiles.The Generate Orthogonal Design procedure creates a reduced set of product profiles that is smallenough to include in a survey but large enough to assess the relative importance of each factor.

To Generate an Orthogonal Design

E From the menus choose:Data > Orthogonal Design > Generate...

Figure 2-1Generate Orthogonal Design dialog box

E Define at least one factor. Enter a name in the Factor Name text box. Factor names can be anyvalid variable name, except status_ or card_. You can also assign an optional factor label.

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Chapter 2

E Click Add to add the factor name and an optional label. To delete a factor, select it in the list andclick Remove. To modify a factor name or label, select it in the list, modify the name or label,and click Change.

E Define values for each factor by selecting the factor and clicking Define Values.

Data File. Allows you to control the destination of the orthogonal design. You can save the designto a new dataset in the current session or to an external data file.

Create a new dataset. Creates a new dataset in the current session containing the factorsand cases generated by the plan.

Create new data file. Creates an external data file containing the factors and cases generated bythe plan. By default, this data file is named ortho.sav, and it is saved to the current directory.Click File to specify a different name and destination for the file.

Reset random number seed to. Resets the random number seed to the specified value. The seed canbe any integer value from 0 through 2,000,000,000. Within a session, a different seed is used eachtime you generate a set of random numbers, producing different results. If you want to duplicatethe same random numbers, you should set the seed value before you generate your first design andreset the seed to the same value each subsequent time you generate the design.

Optionally, you can:

Click Options to specify the minimum number of cases in the orthogonal design and to selectholdout cases.

Defining Values for an Orthogonal DesignFigure 2-2Generate Design Define Values dialog box

You must assign values to each level of the selected factor or factors. The factor name will bedisplayed after Values and Labels for.

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Enter each value of the factor. You can elect to give the values descriptive labels. If you do notassign labels to the values, labels that correspond to the values are automatically assigned (that is,a value of 1 is assigned a label of 1, a value of 3 is assigned a label of 3, and so on).

Auto-Fill. Allows you to automatically fill the Value boxes with consecutive values beginning with1. Enter the maximum value and click Fill to fill in the values.

Orthogonal Design OptionsFigure 2-3Generate Orthogonal Design Options dialog box

Minimum number of cases to generate. Specifies a minimum number of cases for the plan. Select apositive integer less than or equal to the total number of cases that can be formed from all possiblecombinations of the factor levels. If you do not explicitly specify the minimum number of cases togenerate, the minimum number of cases necessary for the orthogonal plan is generated. If theOrthoplan procedure cannot generate at least the number of profiles requested for the minimum,it will generate the largest number it can that fits the specified factors and levels. Note that thedesign does not necessarily include exactly the number of specified cases but rather the smallestpossible number of cases in the orthogonal design using this value as a minimum.

Holdout Cases. You can define holdout cases that are rated by subjects but are not included inthe conjoint analysis.

Number of holdout cases. Creates holdout cases in addition to the regular plan cases. Holdoutcases are judged by the subjects but are not used when the Conjoint procedure estimatesutilities. You can specify any positive integer less than or equal to the total number of casesthat can be formed from all possible combinations of factor levels. Holdout cases are generatedfrom another random plan, not the main-effects experimental plan. The holdout cases do notduplicate the experimental profiles or each other. By default, no holdout cases are produced.

Randomly mix with other cases. Randomly mixes holdout cases with the experimental cases.When this option is deselected, holdout cases appear separately, following the experimentalcases.

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Chapter 2

ORTHOPLAN Command Additional Features

The command syntax language also allows you to:

Append the orthogonal design to the active dataset rather than creating a new one.

Specify simulation cases before generating the orthogonal design rather than after the designhas been created.

See the Command Syntax Reference for complete syntax information.

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Chapter

3Displaying a Design

The Display Design procedure allows you to print an experimental design. You can print thedesign in either a rough-draft listing format or as profiles that you can present to subjects in aconjoint study. This procedure can display designs created with the Generate Orthogonal Designprocedure or any designs displayed in an active dataset.

To Display an Orthogonal Design

E From the menus choose:Data > Orthogonal Design > Display...

Figure 3-1Display Design dialog box

E Move one or more factors into the Factors list.

E Select a format for displaying the profiles in the output.

Format. You can choose one or more of the following format options:

Listing for experimenter. Displays the design in a draft format that differentiates holdoutprofiles from experimental profiles and lists simulation profiles separately following theexperimental and holdout profiles.

Profiles for subjects. Produces profiles that can be presented to subjects. This format does notdifferentiate holdout profiles and does not produce simulation profiles.

Optionally, you can:

Click Titles to define headers and footers for the profiles.

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Display Design TitlesFigure 3-2Display Design Titles dialog box

Profile Title. Enter a profile title up to 80 characters long. Titles appear at the top of the output ifyou have selected Listing for experimenter and at the top of each new profile if you have selectedProfiles for subjects in the main dialog box. For Profiles for subjects, if the special charactersequence )CARD is specified anywhere in the title, the procedure will replace it with the sequentialprofile number. This character sequence is not translated for Listing for experimenter.

Profile Footer. Enter a profile footer up to 80 characters long. Footers appear at the bottom ofthe output if you have selected Listing for experimenter and at the bottom of each profile if youhave selected Profiles for subjects in the main dialog box. For Profiles for subjects, if the specialcharacter sequence )CARD is specified anywhere in the footer, the procedure will replace it withthe sequential profile number. This character sequence is not translated for Listing for experimenter.

PLANCARDS Command Additional Features

The command syntax language also allows you to:

Write profiles for subjects to an external file (using the OUTFILE subcommand).

See the Command Syntax Reference for complete syntax information.

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Chapter

4Running a Conjoint Analysis

A graphical user interface is not yet available for the Conjoint procedure. To obtain a conjointanalysis, you must enter command syntax for a CONJOINT command into a syntax windowand then run it.

For an example of command syntax for a CONJOINT command in the context of a completeconjoint analysis—including generating and displaying an orthogonal design—see Chapter 5.

For complete command syntax information about the CONJOINT command, see the CommandSyntax Reference.

To Run a Command from a Syntax Window

From the menus choose:File > New > Syntax...

This opens a syntax window.

E Enter the command syntax for the CONJOINT command.

E Highlight the command in the syntax window, and click the Run button (the right-pointingtriangle) on the Syntax Editor toolbar.

See the Core System User’s Guide for more information about running commands in syntaxwindows.

Requirements

The Conjoint procedure requires two files—a data file and a plan file—and the specification ofhow data were recorded (for example, each data point is a preference score from 1 to 100). Theplan file consists of the set of product profiles to be rated by the subjects and should be generatedusing the Generate Orthogonal Design procedure. The data file contains the preference scoresor rankings of those profiles collected from the subjects. The plan and data files are specifiedwith the PLAN and DATA subcommands, respectively. The method of data recording is specifiedwith the SEQUENCE, RANK, or SCORE subcommands. The following command syntax shows aminimal specification:

CONJOINT PLAN='CPLAN.SAV' /DATA='RUGRANKS.SAV'/SEQUENCE=PREF1 TO PREF22.

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Chapter 4

Specifying the Plan File and the Data File

The CONJOINT command provides a number of options for specifying the plan file and the datafile.

You can explicitly specify the filenames for the two files. For example:

CONJOINT PLAN='CPLAN.SAV' /DATA='RUGRANKS.SAV'

If only a plan file or data file is specified, the CONJOINT command reads the specified fileand uses the active dataset as the other. For example, if you specify a data file but omita plan file (you cannot omit both), the active dataset is used as the plan, as shown in thefollowing example:

CONJOINT DATA='RUGRANKS.SAV'

You can use the asterisk (*) in place of a filename to indicate the active dataset, as shownin the following example:

CONJOINT PLAN='CPLAN.SAV' /DATA=*

The active dataset is used as the preference data. Note that you cannot use the asterisk (*) forboth the plan file and the data file.

Specifying How Data Were Recorded

You must specify the way in which preference data were recorded. Data can be recorded in one ofthree ways: sequentially, as rankings, or as preference scores. These three methods are indicatedby the SEQUENCE, RANK, and SCORE subcommands. You must specify one, and only one, of thesesubcommands as part of a CONJOINT command.

SEQUENCE Subcommand

The SEQUENCE subcommand indicates that data were recorded sequentially so that each data pointin the data file is a profile number, starting with the most preferred profile and ending with theleast preferred profile. This is how data are recorded if the subject is asked to order the profilesfrom the most to the least preferred. The researcher records which profile number was first,which profile number was second, and so on.

CONJOINT PLAN=* /DATA='RUGRANKS.SAV'/SEQUENCE=PREF1 TO PREF22.

The variable PREF1 contains the profile number for the most preferred profile out of 22profiles in the orthogonal plan. The variable PREF22 contains the profile number for theleast preferred profile in the plan.

RANK Subcommand

The RANK subcommand indicates that each data point is a ranking, starting with the ranking ofprofile 1, then the ranking of profile 2, and so on. This is how the data are recorded if the subjectis asked to assign a rank to each profile, ranging from 1 to n, where n is the number of profiles. Alower rank implies greater preference.

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CONJOINT PLAN=* /DATA='RUGRANKS.SAV'/RANK=RANK1 TO RANK22.

The variable RANK1 contains the ranking of profile 1, out of a total of 22 profiles in theorthogonal plan. The variable RANK22 contains the ranking of profile 22.

SCORE Subcommand

The SCORE subcommand indicates that each data point is a preference score assigned to theprofiles, starting with the score of profile 1, then the score of profile 2, and so on. This type ofdata might be generated, for example, by asking subjects to assign a number from 1 to 100 toshow how much they liked the profile. A higher score implies greater preference.

CONJOINT PLAN=* /DATA='RUGRANKS.SAV'/SCORE=SCORE1 TO SCORE22.

The variable SCORE1 contains the score for profile 1, and SCORE22 contains the scorefor profile 22.

Optional SubcommandsThe CONJOINT command offers a number of optional subcommands that provide additionalcontrol 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 anidentifier for the subjects. If you do not specify a subject variable, the CONJOINT commandassumes that all of the cases in the data file come from one subject. The following examplespecifies 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 relationshipbetween factors and the rankings or scores. If you do not specify a model for a factor, CONJOINTassumes a discrete model. You can specify one of four models:

DISCRETE. The DISCRETE model indicates that the factor levels are categorical and that noassumption is made about the relationship between the factor and the scores or ranks. This isthe default.

LINEAR. The LINEAR model indicates an expected linear relationship between the factor andthe scores or ranks. You can specify the expected direction of the linear relationship with thekeywords MORE and LESS. MORE indicates that higher levels of a factor are expected to bepreferred, 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 identifysubjects whose estimates do not match the expected direction.

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IDEAL. The IDEAL model indicates an expected quadratic relationship between the scores or ranksand the factor. It is assumed that there is an ideal level for the factor, and distance from thisideal point (in either direction) is associated with decreasing preference. Factors described withthis model should have at least three levels.

ANTIIDEAL. The ANTIIDEAL model indicates an expected quadratic relationship between thescores or ranks and the factor. It is assumed that there is a worst level for the factor, and distancefrom this point (in either direction) is associated with increasing preference. Factors describedwith 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, ifyou 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 forany simulation cases included in the plan file, both, or none. Simulation cases are not rated bythe subjects but represent product profiles of interest to you. The Conjoint procedure uses theanalysis of the experimental data to make predictions about the relative preference for each of thesimulation profiles. In the following example, detailed output for each subject is suppressed, andthe 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 orincludes 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.

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UTILITY Subcommand

The UTILITY subcommand writes a data file in IBM® SPSS® Statistics format containingdetailed information for each subject. It includes the utilities for DISCRETE factors, the slopeand 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 formaking additional plots with other procedures. The following example creates a utility file namedrugutil.sav:

CONJOINT PLAN=* /DATA='RUGRANKS.SAV'/RANK=RANK1 TO RANK22 /SUBJECT=ID/UTILITY='RUGUTIL.SAV'.

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5Using Conjoint Analysis to ModelCarpet-Cleaner Preference

In a popular example of conjoint analysis (Green and Wind, 1973), a company interested inmarketing a new carpet cleaner wants to examine the influence of five factors on consumerpreference—package design, brand name, price, a Good Housekeeping seal, and a money-backguarantee. There are three factor levels for package design, each one differing in the location ofthe applicator brush; three brand names (K2R, Glory, and Bissell); three price levels; and twolevels (either no or yes) for each of the last two factors. The following table displays the variablesused in the carpet-cleaner study, with their variable labels and values.

Table 5-1Variables in the carpet-cleaner study

Variable name Variable label Value label

package package design A*, B*, C*

brand brand name K2R, Glory, Bissellprice price $1.19, $1.39, $1.59

seal Good Housekeeping seal no, yes

money money-back guarantee no, yes

There could be other factors and factor levels that characterize carpet cleaners, but these are theonly ones of interest to management. This is an important point in conjoint analysis. You wantto choose only those factors (independent variables) that you think most influence the subject’spreference (the dependent variable). Using conjoint analysis, you will develop a model forcustomer preference based on these five factors.This example makes use of the information in the following data files: carpet_prefs.sav

contains the data collected from the subjects, carpet_plan.sav contains the product profiles beingsurveyed, and conjoint.sps contains the command syntax necessary to run the analysis. For moreinformation, see the topic Sample Files in Appendix A in IBM SPSS Conjoint 20.

Generating an Orthogonal Design

The first step in a conjoint analysis is to create the combinations of factor levels that are presentedas product profiles to the subjects. Since even a small number of factors and a few levels for eachfactor will lead to an unmanageable number of potential product profiles, you need to generate arepresentative subset known as an orthogonal array.The Generate Orthogonal Design procedure creates an orthogonal array—also referred to as an

orthogonal design—and stores the information in a data file. Unlike most procedures, an activedataset is not required before running the Generate Orthogonal Design procedure. If you do nothave an active dataset, you have the option of creating one, generating variable names, variablelabels, and value labels from the options that you select in the dialog boxes. If you already have anactive dataset, you can either replace it or save the orthogonal design as a separate data file.

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To create an orthogonal design:

E From the menus choose:Data > Orthogonal Design > Generate...

Figure 5-1Generate Orthogonal Design dialog box

E Enter package in the Factor Name text box, and enter package design in the Factor Label text box.

E Click Add.

This creates an item labeled package ‘package design’ (?). Select this item.

E Click Define Values.

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Figure 5-2Generate Design Define Values dialog box

E Enter the values 1, 2, and 3 to represent the package designs A*, B*, and C*. Enter the labelsA*, B*, and C* as well.

E Click Continue.

You’ll now want to repeat this process for the remaining factors, brand, price, seal, and money.Use the values and labels from the following table, which includes the values you’ve alreadyentered for package.

Factor name Factor label Values Labels

package package design 1, 2, 3 A*, B*, C*

brand brand name 1, 2, 3 K2R, Glory, Bissell

price price 1.19, 1.39, 1.59 $1.19, $1.39, $1.59

seal Good Housekeeping seal 1, 2 no, yes

money money-back guarantee 1, 2 no, yes

Once you have completed the factor specifications:

E In the Data File group, leave the default of Create a new dataset and enter a dataset name. Thegenerated design will be saved to a new dataset, in the current session, with the specified name.

E Select Reset random number seed to and enter the value 2000000.

Generating an orthogonal design requires a set of random numbers. If you want to duplicate adesign—in this case, the design used for the present case study—you need to set the seed valuebefore you generate the design and reset it to the same value each subsequent time you generatethe design. The design used for this case study was generated with a seed value of 2000000.

E Click Options.

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Figure 5-3Generate Orthogonal Design Options dialog box

E In the Minimum number of cases to generate text box, type 18.

By default, the minimum number of cases necessary for an orthogonal array is generated. Theprocedure determines the number of cases that need to be administered to allow estimation ofthe utilities. You can also specify a minimum number of cases to generate, as you’ve done here.You might want to do this because the default number of minimum cases is too small to beuseful or because you have experimental design considerations that require a certain minimumnumber of cases.

E Select Number of holdout cases and type 4.

Holdout cases are judged by the subjects but are not used by the conjoint analysis to estimateutilities. They are used as a check on the validity of the estimated utilities. The holdout cases aregenerated from another random plan, not the experimental orthogonal plan.

E Click Continue in the Generate Orthogonal Design Options dialog box.

E Click OK in the Generate Orthogonal Design dialog box.

Figure 5-4Orthogonal design for the carpet-cleaner example

The orthogonal design is displayed in the Data Editor and is best viewed by displaying valuelabels rather than the actual data values. This is accomplished by choosing Value Labels fromthe View menu.The variables in the data file are the factors used to specify the design. Each case represents

one product profile in the design. Notice that two additional variables, CARD_ and STATUS_,appear in the data file. CARD_ assigns a sequential number to each profile that is used to identifythe profile. STATUS_ indicates whether a profile is part of the experimental design (the first 18

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cases), a holdout case (the last 4 cases), or a simulation case (to be discussed in a later topic inthis case study).The orthogonal design is a required input to the analysis of the data. Therefore, you will

want to save your design to a data file. For convenience, the current design has been saved incarpet_plan.sav (orthogonal designs are also referred to as plans).

Creating the Experimental Stimuli: Displaying the Design

Once you have created an orthogonal design, you’ll want to use it to create the product profiles tobe rated by the subjects. You can obtain a listing of the profiles in a single table or display eachprofile in a separate table.

To display an orthogonal design:

E From the menus choose:Data > Orthogonal Design > Display...

Figure 5-5Display Design dialog box

E Select package, brand, price, seal, and money for the factors.

The information contained in the variables STATUS_ and CARD_ is automatically included inthe output, so they don’t need to be selected.

E Select Listing for experimenter in the Format group. This results in displaying the entire orthogonaldesign in a single table.

E Click OK.

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Figure 5-6Display of orthogonal design: Single table layout

The output resembles the look of the orthogonal design as shown in the Data Editor—one rowfor each profile, with the factors as columns. Notice, however, that the column headers are thevariable labels rather than the variable names that you see in the Data Editor. Also notice thatthe holdout cases are identified with a footnote. This is of interest to the experimenter, but youcertainly don’t want the subjects to know which, if any, cases are holdouts.Depending on how you create and deliver your final product profiles, you may want to save this

table as an HTML, Word/RTF, Excel, or PowerPoint file. This is easily accomplished by selectingthe table in the Viewer, right clicking, and selecting Export. Also, if you’re using the exportedversion to create the final product profiles, be sure to edit out the footnotes for the holdout cases.Perhaps the needs for your survey are better served by generating a separate table for each

product profile. This choice lends itself nicely to exporting to PowerPoint, since each table(product profile) is placed on a separate PowerPoint slide.

To display each profile in a separate table:

E Click the Dialog Recall button and select Display Design.

E Deselect Listing for experimenter and select Profiles for subjects.

E Click OK.

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Figure 5-7Display of orthogonal design: Multitable layout

The information for each product profile is displayed in a separate table. In addition, holdoutcases are indistinguishable from the rest of the cases, so there is no issue of removing identifiersfor holdouts as with the single table layout.

Running the AnalysisYou’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 5-8Preference data for the carpet-cleaner example

The preference data collected from the subjects is stored in carpet_prefs.sav. The data consist ofresponses from 10 subjects, each identified by a unique value of the variable ID. Subjects wereasked to rank the 22 product profiles from the most to the least preferred. The variables PREF1through PREF22 contain the IDs of the associated product profiles, that is, the card IDs fromcarpet_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)

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/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 thefile specification with an asterisk (*), without the quotation marks.

The SEQUENCE subcommand specifies that each data point in the preference data is a profilenumber, 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 betweenthe preference data and the factor levels. The specified factors refer to variables defined in theplan file named on the PLAN subcommand.

The keyword DISCRETE is used when the factor levels are categorical and no assumption ismade about the relationship between the levels and the data. This is the case for the factorspackage and brand that represent package design and brand name, respectively. DISCRETEis 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 tobe linearly related to the factor. For example, preference is usually expected to be linearlyrelated to price. You can also specify quadratic models (not used in this example) with thekeywords IDEAL and ANTIIDEAL.

The keywords MORE and LESS, following LINEAR, indicate an expected direction for therelationship. Since we expect higher preference for lower prices, the keyword LESS is usedfor price. However, we expect higher preference for either a Good Housekeeping seal ofapproval or a money-back guarantee, so the keyword MORE is used for seal and money (recallthat 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 estimatesof the utilities. These keywords are used simply to identify subjects whose estimates do notmatch the expected direction. Similarly, choosing IDEAL instead of ANTIIDEAL, or viceversa, does not affect coefficients or utilities.

The PRINT subcommand specifies that the output contains information for the group ofsubjects 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 tocarpet_prefs.sav and carpet_plan.sav. For a complete description of all options, see theCONJOINT command in the Command Syntax Reference.

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Utility ScoresFigure 5-9Utility scores

This table shows the utility (part-worth) scores and their standard errors for each factor level.Higher utility values indicate greater preference. As expected, there is an inverse relationshipbetween price and utility, with higher prices corresponding to lower utility (larger negative valuesmean lower utility). The presence of a seal of approval or money-back guarantee corresponds to ahigher utility, as anticipated.Since the utilities are all expressed in a common unit, they can be added together to give the

total utility of any combination. For example, the total utility of a cleaner with package designB*, brand K2R, price $1.19, and no seal of approval or money-back guarantee is:

utility(package B*) + utility(K2R) + utility($1.19)

+ utility(no seal) + utility(no money-back) + constant

or

1.867 + 0.367 + (−6.595) + 2.000 + 1.250 + 12.870 = 11.759

If the cleaner had package design C*, brand Bissell, price $1.59, a seal of approval, and amoney-back guarantee, the total utility would be:

0.367 + (−0.017) + (−8.811) + 4.000 + 2.500 + 12.870 = 10.909

CoefficientsFigure 5-10Coefficients

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This table shows the linear regression coefficients for those factors specified as LINEAR (forIDEAL and ANTIIDEAL models, there would also be a quadratic term). The utility for a particularfactor level is determined by multiplying the level by the coefficient. For example, the predictedutility for a price of $1.19 was listed as −6.595 in the utilities table. This is simply the value of theprice level, 1.19, multiplied by the price coefficient, −5.542.

Relative Importance

The range of the utility values (highest to lowest) for each factor provides a measure of howimportant the factor was to overall preference. Factors with greater utility ranges play a moresignificant role than those with smaller ranges.

Figure 5-11Importance values

This table provides a measure of the relative importance of each factor known as an importancescore or value. The values are computed by taking the utility range for each factor separately anddividing by the sum of the utility ranges for all factors. The values thus represent percentages andhave the property that they sum to 100. The calculations, it should be noted, are done separatelyfor each subject, and the results are then averaged over all of the subjects.Note that while overall or summary utilities and regression coefficients from orthogonal

designs are the same with or without a SUBJECT subcommand, importances will generallydiffer. For summary results without a SUBJECT subcommand, the importances can be computeddirectly from the summary utilities, just as one can do with individual subjects. However, whena SUBJECT subcommand is used, the importances for the individual subjects are averaged, andthese averaged importances will not in general match those computed using the summary utilities.The results show that package design has the most influence on overall preference. This

means that there is a large difference in preference between product profiles containing the mostdesired packaging and those containing the least desired packaging. The results also show thata money-back guarantee plays the least important role in determining overall preference. Priceplays a significant role but not as significant as package design. Perhaps this is because the rangeof prices is not that large.

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CorrelationsFigure 5-12Correlation coefficients

This table displays two statistics, Pearson’s R and Kendall’s tau, which provide measures of thecorrelation between the observed and estimated preferences.The table also displays Kendall’s tau for just the holdout profiles. Remember that the holdout

profiles (four in the present example) were rated by the subjects but not used by the Conjointprocedure for estimating utilities. Instead, the Conjoint procedure computes correlations betweenthe observed and predicted rank orders for these profiles as a check on the validity of the utilities.In many conjoint analyses, the number of parameters is close to the number of profiles rated,

which will artificially inflate the correlation between observed and estimated scores. In thesecases, the correlations for the holdout profiles may give a better indication of the fit of the model.Keep in mind, however, that holdouts will always produce lower correlation coefficients.

Reversals

When specifying LINEAR models for price, seal, and money, we chose an expected direction(LESS or MORE) for the linear relationship between the value of the variable and the preferencefor that value. The Conjoint procedure keeps track of the number of subjects whose preferenceshowed the opposite of the expected relationship—for example, a greater preference for higherprices, or a lower preference for a money-back guarantee. These cases are referred to as reversals.

Figure 5-13Number of reversals by factor and subject

This table displays the number of reversals for each factor and for each subject. For example, threesubjects showed a reversal for price. That is, they preferred product profiles with higher prices.

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Running SimulationsThe real power of conjoint analysis is the ability to predict preference for product profiles thatweren’t rated by the subjects. These are referred to as simulation cases. Simulation cases areincluded as part of the plan, along with the profiles from the orthogonal design and any holdoutprofiles.The simplest way to enter simulation cases is from the Data Editor, using the value labels

created when you generated the experimental design.

To enter a simulation case in the plan file:

E On a new row in the Data Editor window, select a cell and select the desired value from the list(value labels can be displayed by choosing Value Labels from the View menu). Repeat for allof the variables (factors).

E Select Simulation for the value of the STATUS_ variable.

E Enter an integer value, to be used as an identifier, for the CARD_ variable. Simulation casesshould be numbered separately from the other cases.

Figure 5-14Carpet-cleaner data including simulation cases

The figure shows a part of the plan file for the carpet-cleaner study, with two simulation casesadded. For convenience, these have been included in carpet_plan.sav.The analysis of the simulation cases is accomplished with the same command syntax used

earlier, that is, the syntax in the file conjoint.sps. In fact, if you ran the syntax described earlier,you would have noticed that the output also includes results for the simulation cases, since theyare included in carpet_plan.sav.You can choose to run simulations along with your initial analysis—as done here—or

run simulations at any later point simply by including simulation cases in your plan file andrerunning CONJOINT. For more information, see the CONJOINT command in the CommandSyntax Reference.

Preference Probabilities of SimulationsFigure 5-15Simulation results

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This table gives the predicted probabilities of choosing each of the simulation cases as the mostpreferred one, under three different probability-of-choice models. The maximum utility modeldetermines the probability as the number of respondents predicted to choose the profile divided bythe total number of respondents. For each respondent, the predicted choice is simply the profilewith the largest total utility. The BTL (Bradley-Terry-Luce) model determines the probability asthe ratio of a profile’s utility to that for all simulation profiles, averaged across all respondents. Thelogit model is similar to BTL but uses the natural log of the utilities instead of the utilities. Acrossthe 10 subjects in this study, all three models indicated that simulation profile 2 would be preferred.

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ASample Files

The sample files installed with the product can be found in the Samples subdirectory of theinstallation directory. There is a separate folder within the Samples subdirectory for each ofthe following languages: English, French, German, Italian, Japanese, Korean, Polish, Russian,Simplified Chinese, Spanish, and Traditional Chinese.

Not all sample files are available in all languages. If a sample file is not available in a language,that language folder contains an English version of the sample file.

Descriptions

Following are brief descriptions of the sample files used in various examples throughout thedocumentation.

accidents.sav. This is a hypothetical data file that concerns an insurance company that isstudying age and gender risk factors for automobile accidents in a given region. Each casecorresponds to a cross-classification of age category and gender.

adl.sav. This is a hypothetical data file that concerns efforts to determine the benefits of aproposed type of therapy for stroke patients. Physicians randomly assigned female strokepatients to one of two groups. The first received the standard physical therapy, and the secondreceived an additional emotional therapy. Three months following the treatments, eachpatient’s abilities to perform common activities of daily life were scored as ordinal variables.

advert.sav. This is a hypothetical data file that concerns a retailer’s efforts to examine therelationship between money spent on advertising and the resulting sales. To this end, theyhave collected past sales figures and the associated advertising costs..

aflatoxin.sav. This is a hypothetical data file that concerns the testing of corn crops foraflatoxin, a poison whose concentration varies widely between and within crop yields.A grain processor has received 16 samples from each of 8 crop yields and measured thealfatoxin levels in parts per billion (PPB).

anorectic.sav. While working toward a standardized symptomatology of anorectic/bulimicbehavior, researchers (Van der Ham, Meulman, Van Strien, and Van Engeland, 1997) made astudy of 55 adolescents with known eating disorders. Each patient was seen four times overfour years, for a total of 220 observations. At each observation, the patients were scored foreach of 16 symptoms. Symptom scores are missing for patient 71 at time 2, patient 76 at time2, and patient 47 at time 3, leaving 217 valid observations.

bankloan.sav. This is a hypothetical data file that concerns a bank’s efforts to reduce therate of loan defaults. The file contains financial and demographic information on 850 pastand prospective customers. The first 700 cases are customers who were previously givenloans. The last 150 cases are prospective customers that the bank needs to classify as goodor bad credit risks.

bankloan_binning.sav. This is a hypothetical data file containing financial and demographicinformation on 5,000 past customers.

© Copyright IBM Corporation 1989, 2011. 30

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behavior.sav. In a classic example (Price and Bouffard, 1974), 52 students were asked torate the combinations of 15 situations and 15 behaviors on a 10-point scale ranging from0=“extremely appropriate” to 9=“extremely inappropriate.” Averaged over individuals, thevalues are taken as dissimilarities.

behavior_ini.sav. This data file contains an initial configuration for a two-dimensional solutionfor behavior.sav.

brakes.sav. This is a hypothetical data file that concerns quality control at a factory thatproduces disc brakes for high-performance automobiles. The data file contains diametermeasurements of 16 discs from each of 8 production machines. The target diameter for thebrakes is 322 millimeters.

breakfast.sav. In a classic study (Green and Rao, 1972), 21 Wharton School MBA studentsand their spouses were asked to rank 15 breakfast items in order of preference with 1=“mostpreferred” to 15=“least preferred.” Their preferences were recorded under six differentscenarios, from “Overall preference” to “Snack, with beverage only.”

breakfast-overall.sav. This data file contains the breakfast item preferences for the firstscenario, “Overall preference,” only.

broadband_1.sav. This is a hypothetical data file containing the number of subscribers, byregion, to a national broadband service. The data file contains monthly subscriber numbersfor 85 regions over a four-year period.

broadband_2.sav. This data file is identical to broadband_1.sav but contains data for threeadditional months.

car_insurance_claims.sav. A dataset presented and analyzed elsewhere (McCullagh andNelder, 1989) concerns damage claims for cars. The average claim amount can be modeledas having a gamma distribution, using an inverse link function to relate the mean of thedependent variable to a linear combination of the policyholder age, vehicle type, and vehicleage. The number of claims filed can be used as a scaling weight.

car_sales.sav. This data file contains hypothetical sales estimates, list prices, and physicalspecifications for various makes and models of vehicles. The list prices and physicalspecifications were obtained alternately from edmunds.com and manufacturer sites.

car_sales_uprepared.sav. This is a modified version of car_sales.sav that does not include anytransformed versions of the fields.

carpet.sav. In a popular example (Green and Wind, 1973), a company interested inmarketing a new carpet cleaner wants to examine the influence of five factors on consumerpreference—package design, brand name, price, a Good Housekeeping seal, and amoney-back guarantee. There are three factor levels for package design, each one differingin the location of the applicator brush; three brand names (K2R, Glory, and Bissell); threeprice levels; and two levels (either no or yes) for each of the last two factors. Ten consumersrank 22 profiles defined by these factors. The variable Preference contains the rank of theaverage rankings for each profile. Low rankings correspond to high preference. This variablereflects an overall measure of preference for each profile.

carpet_prefs.sav. This data file is based on the same example as described for carpet.sav, but itcontains the actual rankings collected from each of the 10 consumers. The consumers wereasked to rank the 22 product profiles from the most to the least preferred. The variablesPREF1 through PREF22 contain the identifiers of the associated profiles, as defined incarpet_plan.sav.

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catalog.sav. This data file contains hypothetical monthly sales figures for three products soldby a catalog company. Data for five possible predictor variables are also included.

catalog_seasfac.sav. This data file is the same as catalog.sav except for the addition of a setof seasonal factors calculated from the Seasonal Decomposition procedure along with theaccompanying date variables.

cellular.sav. This is a hypothetical data file that concerns a cellular phone company’s effortsto reduce churn. Churn propensity scores are applied to accounts, ranging from 0 to 100.Accounts scoring 50 or above may be looking to change providers.

ceramics.sav. This is a hypothetical data file that concerns a manufacturer’s efforts todetermine whether a new premium alloy has a greater heat resistance than a standard alloy.Each case represents a separate test of one of the alloys; the heat at which the bearing failed isrecorded.

cereal.sav. This is a hypothetical data file that concerns a poll of 880 people about theirbreakfast preferences, also noting their age, gender, marital status, and whether or not theyhave an active lifestyle (based on whether they exercise at least twice a week). Each caserepresents a separate respondent.

clothing_defects.sav. This is a hypothetical data file that concerns the quality control processat a clothing factory. From each lot produced at the factory, the inspectors take a sample ofclothes and count the number of clothes that are unacceptable.

coffee.sav. This data file pertains to perceived images of six iced-coffee brands (Kennedy,Riquier, and Sharp, 1996) . For each of 23 iced-coffee image attributes, people selected allbrands that were described by the attribute. The six brands are denoted AA, BB, CC, DD, EE,and FF to preserve confidentiality.

contacts.sav. This is a hypothetical data file that concerns the contact lists for a group ofcorporate computer sales representatives. Each contact is categorized by the department ofthe company in which they work and their company ranks. Also recorded are the amount ofthe last sale made, the time since the last sale, and the size of the contact’s company.

creditpromo.sav. This is a hypothetical data file that concerns a department store’s efforts toevaluate the effectiveness of a recent credit card promotion. To this end, 500 cardholders wererandomly selected. Half received an ad promoting a reduced interest rate on purchases madeover the next three months. Half received a standard seasonal ad.

customer_dbase.sav. This is a hypothetical data file that concerns a company’s efforts to usethe information in its data warehouse to make special offers to customers who are mostlikely to reply. A subset of the customer base was selected at random and given the specialoffers, and their responses were recorded.

customer_information.sav. A hypothetical data file containing customer mailing information,such as name and address.

customer_subset.sav. A subset of 80 cases from customer_dbase.sav.

debate.sav. This is a hypothetical data file that concerns paired responses to a survey fromattendees of a political debate before and after the debate. Each case corresponds to a separaterespondent.

debate_aggregate.sav. This is a hypothetical data file that aggregates the responses indebate.sav. Each case corresponds to a cross-classification of preference before and afterthe debate.

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demo.sav. This is a hypothetical data file that concerns a purchased customer database, forthe purpose of mailing monthly offers. Whether or not the customer responded to the offeris recorded, along with various demographic information.

demo_cs_1.sav. This is a hypothetical data file that concerns the first step of a company’sefforts to compile a database of survey information. Each case corresponds to a different city,and the region, province, district, and city identification are recorded.

demo_cs_2.sav. This is a hypothetical data file that concerns the second step of a company’sefforts to compile a database of survey information. Each case corresponds to a differenthousehold unit from cities selected in the first step, and the region, province, district, city,subdivision, and unit identification are recorded. The sampling information from the firsttwo stages of the design is also included.

demo_cs.sav. This is a hypothetical data file that contains survey information collected using acomplex sampling design. Each case corresponds to a different household unit, and variousdemographic and sampling information is recorded.

dmdata.sav. This is a hypothetical data file that contains demographic and purchasinginformation for a direct marketing company. dmdata2.sav contains information for a subset ofcontacts that received a test mailing, and dmdata3.sav contains information on the remainingcontacts who did not receive the test mailing.

dietstudy.sav. This hypothetical data file contains the results of a study of the “Stillman diet”(Rickman, Mitchell, Dingman, and Dalen, 1974). Each case corresponds to a separatesubject and records his or her pre- and post-diet weights in pounds and triglyceride levelsin mg/100 ml.

dvdplayer.sav. This is a hypothetical data file that concerns the development of a new DVDplayer. Using a prototype, the marketing team has collected focus group data. Each casecorresponds to a separate surveyed user and records some demographic information aboutthem and their responses to questions about the prototype.

german_credit.sav. This data file is taken from the “German credit” dataset in the Repository ofMachine Learning Databases (Blake and Merz, 1998) at the University of California, Irvine.

grocery_1month.sav. This hypothetical data file is the grocery_coupons.sav data file with theweekly purchases “rolled-up” so that each case corresponds to a separate customer. Some ofthe variables that changed weekly disappear as a result, and the amount spent recorded is nowthe sum of the amounts spent during the four weeks of the study.

grocery_coupons.sav. This is a hypothetical data file that contains survey data collected bya grocery store chain interested in the purchasing habits of their customers. Each customeris followed for four weeks, and each case corresponds to a separate customer-week andrecords information about where and how the customer shops, including how much wasspent on groceries during that week.

guttman.sav. Bell (Bell, 1961) presented a table to illustrate possible social groups. Guttman(Guttman, 1968) used a portion of this table, in which five variables describing such thingsas social interaction, feelings of belonging to a group, physical proximity of members, andformality of the relationship were crossed with seven theoretical social groups, includingcrowds (for example, people at a football game), audiences (for example, people at a theateror classroom lecture), public (for example, newspaper or television audiences), mobs (like acrowd but with much more intense interaction), primary groups (intimate), secondary groups

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(voluntary), and the modern community (loose confederation resulting from close physicalproximity and a need for specialized services).

health_funding.sav. This is a hypothetical data file that contains data on health care funding(amount per 100 population), disease rates (rate per 10,000 population), and visits to healthcare providers (rate per 10,000 population). Each case represents a different city.

hivassay.sav. This is a hypothetical data file that concerns the efforts of a pharmaceuticallab to develop a rapid assay for detecting HIV infection. The results of the assay are eightdeepening shades of red, with deeper shades indicating greater likelihood of infection. Alaboratory trial was conducted on 2,000 blood samples, half of which were infected withHIV and half of which were clean.

hourlywagedata.sav. This is a hypothetical data file that concerns the hourly wages of nursesfrom office and hospital positions and with varying levels of experience.

insurance_claims.sav. This is a hypothetical data file that concerns an insurance companythat wants to build a model for flagging suspicious, potentially fraudulent claims. Eachcase represents a separate claim.

insure.sav. This is a hypothetical data file that concerns an insurance company that is studyingthe risk factors that indicate whether a client will have to make a claim on a 10-year termlife insurance contract. Each case in the data file represents a pair of contracts, one of whichrecorded a claim and the other didn’t, matched on age and gender.

judges.sav. This is a hypothetical data file that concerns the scores given by trained judges(plus one enthusiast) to 300 gymnastics performances. Each row represents a separateperformance; the judges viewed the same performances.

kinship_dat.sav. Rosenberg and Kim (Rosenberg and Kim, 1975) set out to analyze 15 kinshipterms (aunt, brother, cousin, daughter, father, granddaughter, grandfather, grandmother,grandson, mother, nephew, niece, sister, son, uncle). They asked four groups of collegestudents (two female, two male) to sort these terms on the basis of similarities. Two groups(one female, one male) were asked to sort twice, with the second sorting based on a differentcriterion from the first sort. Thus, a total of six “sources” were obtained. Each sourcecorresponds to a proximity matrix, whose cells are equal to the number of people in asource minus the number of times the objects were partitioned together in that source.

kinship_ini.sav. This data file contains an initial configuration for a three-dimensional solutionfor kinship_dat.sav.

kinship_var.sav. This data file contains independent variables gender, gener(ation), and degree(of separation) that can be used to interpret the dimensions of a solution for kinship_dat.sav.Specifically, they can be used to restrict the space of the solution to a linear combination ofthese variables.

marketvalues.sav. This data file concerns home sales in a new housing development inAlgonquin, Ill., during the years from 1999–2000. These sales are a matter of public record.

nhis2000_subset.sav. The National Health Interview Survey (NHIS) is a large, population-basedsurvey of the U.S. civilian population. Interviews are carried out face-to-face in a nationallyrepresentative sample of households. Demographic information and observations abouthealth behaviors and status are obtained for members of each household. This datafile contains a subset of information from the 2000 survey. National Center for HealthStatistics. National Health Interview Survey, 2000. Public-use data file and documentation.ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/. Accessed 2003.

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Sample Files

ozone.sav. The data include 330 observations on six meteorological variables for predictingozone concentration from the remaining variables. Previous researchers (Breiman andFriedman, 1985), (Hastie and Tibshirani, 1990), among others found nonlinearities amongthese variables, which hinder standard regression approaches.

pain_medication.sav. This hypothetical data file contains the results of a clinical trial foranti-inflammatory medication for treating chronic arthritic pain. Of particular interest is thetime it takes for the drug to take effect and how it compares to an existing medication.

patient_los.sav. This hypothetical data file contains the treatment records of patients who wereadmitted to the hospital for suspected myocardial infarction (MI, or “heart attack”). Each casecorresponds to a separate patient and records many variables related to their hospital stay.

patlos_sample.sav. This hypothetical data file contains the treatment records of a sampleof patients who received thrombolytics during treatment for myocardial infarction (MI, or“heart attack”). Each case corresponds to a separate patient and records many variablesrelated to their hospital stay.

poll_cs.sav. This is a hypothetical data file that concerns pollsters’ efforts to determine thelevel of public support for a bill before the legislature. The cases correspond to registeredvoters. Each case records the county, township, and neighborhood in which the voter lives.

poll_cs_sample.sav. This hypothetical data file contains a sample of the voters listed inpoll_cs.sav. The sample was taken according to the design specified in the poll.csplan planfile, and this data file records the inclusion probabilities and sample weights. Note, however,that because the sampling plan makes use of a probability-proportional-to-size (PPS) method,there is also a file containing the joint selection probabilities (poll_jointprob.sav). Theadditional variables corresponding to voter demographics and their opinion on the proposedbill were collected and added the data file after the sample as taken.

property_assess.sav. This is a hypothetical data file that concerns a county assessor’s efforts tokeep property value assessments up to date on limited resources. The cases correspond toproperties sold in the county in the past year. Each case in the data file records the townshipin which the property lies, the assessor who last visited the property, the time since thatassessment, the valuation made at that time, and the sale value of the property.

property_assess_cs.sav. This is a hypothetical data file that concerns a state assessor’s effortsto keep property value assessments up to date on limited resources. The cases correspondto properties in the state. Each case in the data file records the county, township, andneighborhood in which the property lies, the time since the last assessment, and the valuationmade at that time.

property_assess_cs_sample.sav. This hypothetical data file contains a sample of the propertieslisted in property_assess_cs.sav. The sample was taken according to the design specified inthe property_assess.csplan plan file, and this data file records the inclusion probabilitiesand sample weights. The additional variable Current value was collected and added to thedata file after the sample was taken.

recidivism.sav. This is a hypothetical data file that concerns a government law enforcementagency’s efforts to understand recidivism rates in their area of jurisdiction. Each casecorresponds to a previous offender and records their demographic information, some detailsof their first crime, and then the time until their second arrest, if it occurred within two yearsof the first arrest.

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Appendix A

recidivism_cs_sample.sav. This is a hypothetical data file that concerns a government lawenforcement agency’s efforts to understand recidivism rates in their area of jurisdiction. Eachcase corresponds to a previous offender, released from their first arrest during the month ofJune, 2003, and records their demographic information, some details of their first crime, andthe data of their second arrest, if it occurred by the end of June, 2006. Offenders were selectedfrom sampled departments according to the sampling plan specified in recidivism_cs.csplan;because it makes use of a probability-proportional-to-size (PPS) method, there is also a filecontaining the joint selection probabilities (recidivism_cs_jointprob.sav).

rfm_transactions.sav. A hypothetical data file containing purchase transaction data, includingdate of purchase, item(s) purchased, and monetary amount of each transaction.

salesperformance.sav. This is a hypothetical data file that concerns the evaluation of twonew sales training courses. Sixty employees, divided into three groups, all receive standardtraining. In addition, group 2 gets technical training; group 3, a hands-on tutorial. Eachemployee was tested at the end of the training course and their score recorded. Each case inthe data file represents a separate trainee and records the group to which they were assignedand the score they received on the exam.

satisf.sav. This is a hypothetical data file that concerns a satisfaction survey conducted bya retail company at 4 store locations. 582 customers were surveyed in all, and each caserepresents the responses from a single customer.

screws.sav. This data file contains information on the characteristics of screws, bolts, nuts,and tacks (Hartigan, 1975).

shampoo_ph.sav. This is a hypothetical data file that concerns the quality control at a factoryfor hair products. At regular time intervals, six separate output batches are measured and theirpH recorded. The target range is 4.5–5.5.

ships.sav. A dataset presented and analyzed elsewhere (McCullagh et al., 1989) that concernsdamage to cargo ships caused by waves. The incident counts can be modeled as occurring ata Poisson rate given the ship type, construction period, and service period. The aggregatemonths of service for each cell of the table formed by the cross-classification of factorsprovides values for the exposure to risk.

site.sav. This is a hypothetical data file that concerns a company’s efforts to choose newsites for their expanding business. They have hired two consultants to separately evaluatethe sites, who, in addition to an extended report, summarized each site as a “good,” “fair,”or “poor” prospect.

smokers.sav. This data file is abstracted from the 1998 National HouseholdSurvey of Drug Abuse and is a probability sample of American households.(http://dx.doi.org/10.3886/ICPSR02934) Thus, the first step in an analysis of this data fileshould be to weight the data to reflect population trends.

stocks.sav This hypothetical data file contains stocks prices and volume for one year.

stroke_clean.sav. This hypothetical data file contains the state of a medical database after ithas been cleaned using procedures in the Data Preparation option.

stroke_invalid.sav. This hypothetical data file contains the initial state of a medical databaseand contains several data entry errors.

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Sample Files

stroke_survival. This hypothetical data file concerns survival times for patients exiting arehabilitation program post-ischemic stroke face a number of challenges. Post-stroke, theoccurrence of myocardial infarction, ischemic stroke, or hemorrhagic stroke is noted and thetime of the event recorded. The sample is left-truncated because it only includes patients whosurvived through the end of the rehabilitation program administered post-stroke.

stroke_valid.sav. This hypothetical data file contains the state of a medical database after thevalues have been checked using the Validate Data procedure. It still contains potentiallyanomalous cases.

survey_sample.sav. This data file contains survey data, including demographic data andvarious attitude measures. It is based on a subset of variables from the 1998 NORC GeneralSocial Survey, although some data values have been modified and additional fictitiousvariables have been added for demonstration purposes.

telco.sav. This is a hypothetical data file that concerns a telecommunications company’sefforts to reduce churn in their customer base. Each case corresponds to a separate customerand records various demographic and service usage information.

telco_extra.sav. This data file is similar to the telco.sav data file, but the “tenure” andlog-transformed customer spending variables have been removed and replaced bystandardized log-transformed customer spending variables.

telco_missing.sav. This data file is a subset of the telco.sav data file, but some of thedemographic data values have been replaced with missing values.

testmarket.sav. This hypothetical data file concerns a fast food chain’s plans to add a new itemto its menu. There are three possible campaigns for promoting the new product, so the newitem is introduced at locations in several randomly selected markets. A different promotionis used at each location, and the weekly sales of the new item are recorded for the first fourweeks. Each case corresponds to a separate location-week.

testmarket_1month.sav. This hypothetical data file is the testmarket.sav data file with theweekly sales “rolled-up” so that each case corresponds to a separate location. Some of thevariables that changed weekly disappear as a result, and the sales recorded is now the sum ofthe sales during the four weeks of the study.

tree_car.sav. This is a hypothetical data file containing demographic and vehicle purchaseprice data.

tree_credit.sav. This is a hypothetical data file containing demographic and bank loan historydata.

tree_missing_data.sav This is a hypothetical data file containing demographic and bank loanhistory data with a large number of missing values.

tree_score_car.sav. This is a hypothetical data file containing demographic and vehiclepurchase price data.

tree_textdata.sav. A simple data file with only two variables intended primarily to show thedefault state of variables prior to assignment of measurement level and value labels.

tv-survey.sav. This is a hypothetical data file that concerns a survey conducted by a TV studiothat is considering whether to extend the run of a successful program. 906 respondents wereasked whether they would watch the program under various conditions. Each row represents aseparate respondent; each column is a separate condition.

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Appendix A

ulcer_recurrence.sav. This file contains partial information from a study designed to comparethe efficacy of two therapies for preventing the recurrence of ulcers. It provides a goodexample of interval-censored data and has been presented and analyzed elsewhere (Collett,2003).

ulcer_recurrence_recoded.sav. This file reorganizes the information in ulcer_recurrence.savto allow you model the event probability for each interval of the study rather than simply theend-of-study event probability. It has been presented and analyzed elsewhere (Collett etal., 2003).

verd1985.sav. This data file concerns a survey (Verdegaal, 1985). The responses of 15 subjectsto 8 variables were recorded. The variables of interest are divided into three sets. Set 1includes age and marital, set 2 includes pet and news, and set 3 includes music and live.Pet is scaled as multiple nominal and age is scaled as ordinal; all of the other variables arescaled as single nominal.

virus.sav. This is a hypothetical data file that concerns the efforts of an Internet serviceprovider (ISP) to determine the effects of a virus on its networks. They have tracked the(approximate) percentage of infected e-mail traffic on its networks over time, from themoment of discovery until the threat was contained.

wheeze_steubenville.sav. This is a subset from a longitudinal study of the health effects ofair pollution on children (Ware, Dockery, Spiro III, Speizer, and Ferris Jr., 1984). The datacontain repeated binary measures of the wheezing status for children from Steubenville, Ohio,at ages 7, 8, 9 and 10 years, along with a fixed recording of whether or not the mother wasa smoker during the first year of the study.

workprog.sav. This is a hypothetical data file that concerns a government works programthat tries to place disadvantaged people into better jobs. A sample of potential programparticipants were followed, some of whom were randomly selected for enrollment in theprogram, while others were not. Each case represents a separate program participant.

worldsales.sav This hypothetical data file contains sales revenue by continent and product.

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Appendix

BNotices

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Appendix B

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Notices

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BibliographyAkaah, I. P., and P. K. Korgaonkar. 1988. A conjoint investigation of the relative importance ofrisk relievers in direct marketing. Journal of Advertising Research, 28:4, 38–44.

Bell, E. H. 1961. Social foundations of human behavior: Introduction to the study of sociology.New York: Harper & Row.

Blake, C. L., and C. J. Merz. 1998. "UCI Repository of machine learning databases." Available athttp://www.ics.uci.edu/~mlearn/MLRepository.html.

Breiman, L., and J. H. Friedman. 1985. Estimating optimal transformations for multipleregression and correlation. Journal of the American Statistical Association, 80, 580–598.

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Collett, D. 2003. Modelling survival data in medical research, 2 ed. Boca Raton: Chapman &Hall/CRC.

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Hartigan, J. A. 1975. Clustering algorithms. New York: John Wiley and Sons.

Hastie, T., and R. Tibshirani. 1990. Generalized additive models. London: Chapman and Hall.

Kennedy, R., C. Riquier, and B. Sharp. 1996. Practical applications of correspondence analysisto categorical data in market research. Journal of Targeting, Measurement, and Analysis forMarketing, 5, 56–70.

McCullagh, P., and J. A. Nelder. 1989. Generalized Linear Models, 2nd ed. London: Chapman &Hall.

Price, R. H., and D. L. Bouffard. 1974. Behavioral appropriateness and situational constraints asdimensions of social behavior. Journal of Personality and Social Psychology, 30, 579–586.

Rickman, R., N. Mitchell, J. Dingman, and J. E. Dalen. 1974. Changes in serum cholesterolduring the Stillman Diet. Journal of the American Medical Association, 228, 54–58.

Rosenberg, S., and M. P. Kim. 1975. The method of sorting as a data-gathering procedure inmultivariate research. Multivariate Behavioral Research, 10, 489–502.

Van der Ham, T., J. J. Meulman, D. C. Van Strien, and H. Van Engeland. 1997. Empirically basedsubgrouping of eating disorders in adolescents: A longitudinal perspective. British Journal ofPsychiatry, 170, 363–368.

Verdegaal, R. 1985. Meer sets analyse voor kwalitatieve gegevens (in Dutch). Leiden: Departmentof Data Theory, University of Leiden.

Ware, J. H., D. W. Dockery, A. Spiro III, F. E. Speizer, and B. G. Ferris Jr.. 1984. Passivesmoking, gas cooking, and respiratory health of children living in six cities. American Review ofRespiratory Diseases, 129, 366–374.

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Indexanti-ideal model, 24

BTL (Bradley-Terry-Luce) model, 28

)CARDin Display Design, 10

card_ variablein Generate Orthogonal Design, 5

coefficients, 25command syntaxCONJOINT command, 23

correlation coefficients, 27

data filesin Generate Orthogonal Design, 5

discrete model, 24Display Design, 2, 9, 21)CARD, 10footers, 10listing format, 9saving profiles, 10single-profile format, 9titles, 10

factor levels, 2, 17factors, 2, 17footersin Display Design, 10

full-profile approach, 2

Generate Orthogonal Design, 2, 5, 17data files, 5defining factor names, labels, and values, 6holdout cases, 7minimum cases, 7random number seed, 5simulation cases, 8

holdout cases, 2in Generate Orthogonal Design, 7

ideal model, 24importance scores, 26importance values, 26

Kendall’s tau, 27

legal notices, 39

linear model, 24listing formatin Display Design, 9

logit model, 28

max utility model, 28

orthogonal array, 2orthogonal designsdisplaying, 9, 21generating, 5, 17holdout cases, 7minimum cases, 7

part-worths, 3Pearson’s R, 27

random number seedin Generate Orthogonal Design, 5

reversals, 27

sample fileslocation, 30

sample size, 2simulation cases, 3, 14, 28in Generate Orthogonal Design, 8

simulation resultsBTL (Bradley-Terry-Luce) model, 28logit model, 28max utility model, 28

single-profile formatin Display Design, 9

status_ variablein Generate Orthogonal Design, 5

syntaxCONJOINT command, 23

titlesin Display Design, 10

total utility, 25trademarks, 40

utility scores, 3, 25

43