Marketing Research Methods in SASsupport.sas.com/techsup/technote/mr2010title.pdfMarketing Research Methods in SAS Experimental Design, Choice, Conjoint, and Graphical Techniques Warren

Marketing Research

Methods in SAS

Experimental Design, Choice,

Conjoint, and Graphical Techniques

Warren F. Kuhfeld

October 1, 2010SAS 9.2 Edition

MR-2010

Copyright c© 2010 by SAS Institute Inc., Cary, NC, USA

This information is provided by SAS as a service to its users. The text, macros, and code are provided“as is.” There are no warranties, expressed or implied, as to merchantability or fitness for a particularpurpose regarding the accuracy of the materials or code contained herein.

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Contents OverviewMarketing Research: Uncovering Competitive Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . 27–40This chapter is based on a SUGI (SAS Users Group International) paper and provides a basic intro-duction to perceptual mapping, biplots, multidimensional preference analysis (MDPREF), preferencemapping (PREFMAP or external unfolding), correspondence analysis, multidimensional scaling, andconjoint analysis.

Introducing the Market Research Analysis Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41–52This SUGI paper discusses a point-and-click interface for conjoint analysis, correspondence analysis,and multidimensional scaling.

Experimental Design: Efficiency, Coding, and Choice Designs . . . . . . . . . . . . . . . . . . . . . 53–241This chapter discusses experimental design including full-factorial designs, fractional-factorial designs,orthogonal arrays, nonorthogonal designs, choice designs, conjoint designs, design efficiency, orthogon-ality, balance, and coding. If you are interested in choice modeling, read this chapter first.

Efficient Experimental Design with Marketing Research Applications . . . . . . . . . . . 243–265This chapter is based on a Journal of Marketing Research paper and discusses D-efficient experimentaldesigns for conjoint and discrete-choice studies, orthogonal arrays, nonorthogonal designs, relativeefficiency, and nonorthogonal design algorithms.

A General Method for Constructing Efficient Choice Designs . . . . . . . . . . . . . . . . . . . . 265–283This chapter discusses efficient designs for choice experiments.

Discrete Choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285–663This chapter discusses the multinomial logit model and discrete choice experiments. This is the longestchapter in the book, and it contains numerous examples covering a wide range of choice experimentsand choice designs. Study the chapter Experimental Design: Efficiency, Coding, and ChoiceDesigns before tackling this chapter.

Multinomial Logit Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665–680This SUGI paper discusses the multinomial logit model. A travel example is discussed.

Conjoint Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 681–801This chapter discusses conjoint analysis. Examples range from simple to complicated. Topics includedesign, data collection, analysis, and simulation. PROC TRANSREG documentation that describesjust those options that are most likely to be used in a conjoint analysis is included.

The Macros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803–1211This chapter provides examples and documentation for all of the autocall macros used in this book.

Linear Models and Conjoint Analysis with Nonlinear Spline Transformations 1213–1230This chapter is based on an AMA ART (American Marketing Association Advanced Research Tech-niques) Forum paper and discusses splines, which are nonlinear functions that can be useful in regressionand conjoint analysis.

Graphical Scatter Plots of Labeled Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1231–1261This chapter is based on a paper that appeared in the SAS journal Observations that discusses a macrofor graphical scatter plots of labeled points. ODS Graphics is also mentioned.

Graphical Methods for Marketing Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1263–1274This chapter is based on a National Computer Graphics Association Conference presentation anddiscusses the mathematics of biplots, correspondence analysis, PREFMAP, and MDPREF.

Contents

Preface 19

About this Edition 21

Getting Help and Contacting Technical Support 25

Marketing Research: Uncovering Competitive Advantages 27

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Perceptual Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Conjoint Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Introducing the Market Research Analysis Application 41

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41


Discrete Choice Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Correspondence Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Multidimensional Preference Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Multidimensional Scaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

5

6 CONTENTS

Experimental Design: Efficiency, Coding, and Choice Designs 53

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

The Basic Conjoint Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

The Basic Choice Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Chapter Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Experimental Design Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Orthogonal Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Eigenvalues, Means, and Footballs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Experimental Design Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Experimental Design: Rafts, Rulers, Alligators, and Stones . . . . . . . . . . . . . . . . 63

Conjoint, Linear Model, and Choice Designs . . . . . . . . . . . . . . . . . . . . . . . . . 67

Blocking the Choice Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Efficiency of a Choice Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Coding, Efficiency, Balance, and Orthogonality . . . . . . . . . . . . . . . . . . . . . . . 73

Coding and Reference Levels: The ZERO= Option . . . . . . . . . . . . . . . . . . . . . 78

Coding and the Efficiency of a Choice Design . . . . . . . . . . . . . . . . . . . . . . . . 81

Orthogonal Coding and the ZERO=’ ’ Option . . . . . . . . . . . . . . . . . . . . . . . . 89

Orthogonally Coding Price and Other Quantitative Attributes . . . . . . . . . . . . . . 91

The Number of Factor Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Randomization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Random Number Seeds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Duplicates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

Orthogonal Arrays and Difference Schemes . . . . . . . . . . . . . . . . . . . . . . . . . 95

Canonical Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Optimal Generic Choice Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

Block Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

The Process of Designing a Choice Experiment . . . . . . . . . . . . . . . . . . . . . . . 123

Overview of the Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Example 1: Orthogonal and Balanced Factors, the Linear Arrangement Approach . . . . 127

Example 2: The Linear Arrangement Approach with Restrictions . . . . . . . . . . . . . 156

Example 3, Searching a Candidate Set of Alternatives . . . . . . . . . . . . . . . . . . . 166

CONTENTS 7

Example 4, Searching a Candidate Set of Alternatives with Restrictions . . . . . . . . . 177

Example 5, Searching a Candidate Set of Choice Sets . . . . . . . . . . . . . . . . . . . . 188

Example 6, A Generic Choice Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

Example 7, A Partial-Profile Choice Experiment . . . . . . . . . . . . . . . . . . . . . . 207

Example 8, A MaxDiff Choice Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . 225

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233

Choice Design Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233

Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238

Efficient Experimental Design with Marketing Research Applications 243

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243

Design of Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245

Design Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249

Design Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251

Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260

A General Method for Constructing Efficient Choice Designs 265

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265

Criteria For Choice Design Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266

A General Method For Efficient Choice Designs . . . . . . . . . . . . . . . . . . . . . . . 268

Choice Design Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280

Discrete Choice 285

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285

Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287

Customizing the Multinomial Logit Output . . . . . . . . . . . . . . . . . . . . . . . . . 287

8 CONTENTS

Candy Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289

The Multinomial Logit Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289

The Input Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292

Choice and Survival Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294

Fitting the Multinomial Logit Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295

Multinomial Logit Model Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296

Fitting the Multinomial Logit Model, All Levels . . . . . . . . . . . . . . . . . . . . . . . 298

Probability of Choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300

Fabric Softener Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302

Set Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302

Designing the Choice Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304

Examining the Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306

The Randomized Design and Postprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 309

From the Linear Arrangement to a Choice Design . . . . . . . . . . . . . . . . . . . . . . 311

Testing the Design Before Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . 313

Evaluating the Design Relative to the Optimal Design . . . . . . . . . . . . . . . . . . . 319

Generating the Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323

Entering the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324

Processing the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325

Binary Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327

Fitting the Multinomial Logit Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329


Probability of Choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331

Custom Questionnaires . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333

Processing the Data for Custom Questionnaires . . . . . . . . . . . . . . . . . . . . . . . 337

Vacation Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339

Set Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340


The %MktEx Macro Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347


From a Linear Arrangement to a Choice Design . . . . . . . . . . . . . . . . . . . . . . . 356


CONTENTS 9


Entering and Processing the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371

Binary Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372

Quantitative Price Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377

Quadratic Price Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380

Effects Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382

Alternative-Specific Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386

Vacation Example and Artificial Data Generation . . . . . . . . . . . . . . . . . . . . 393

Vacation Example with Alternative-Specific Attributes . . . . . . . . . . . . . . . . . 410

Choosing the Number of Choice Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411


Ensuring that Certain Key Interactions are Estimable . . . . . . . . . . . . . . . . . . . 415


Blocking an Existing Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426



Generating Artificial Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 436

Reading, Processing, and Analyzing the Data . . . . . . . . . . . . . . . . . . . . . . . . 437

Aggregating the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442

Brand Choice Example with Aggregate Data . . . . . . . . . . . . . . . . . . . . . . . 444


Simple Price Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447

Alternative-Specific Price Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449

Mother Logit Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452

Aggregating the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460

Choice and Breslow Likelihood Comparison . . . . . . . . . . . . . . . . . . . . . . . . . 466

Food Product Example with Asymmetry and Availability Cross-Effects . . . . . . 468

The Multinomial Logit Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 468

Set Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469


Restrictions Formulated Using Actual Attribute Names and Levels . . . . . . . . . . . . 475

When You Have a Long Time to Search for an Efficient Design . . . . . . . . . . . . . . 477

10 CONTENTS


Designing the Choice Experiment, More Choice Sets . . . . . . . . . . . . . . . . . . . . 482

Examining the Subdesigns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493

Examining the Aliasing Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495

Blocking the Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497

The Final Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499


Generating Artificial Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 520


Cross-Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523


Modeling Subject Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 529

Allocation of Prescription Drugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535

Designing the Allocation Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535


Coding and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 550


Analyzing Proportions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552

Chair Design with Generic Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 556

Generic Attributes, Alternative Swapping, Large Candidate Set . . . . . . . . . . . . . . 557

Generic Attributes, Alternative Swapping, Small Candidate Set . . . . . . . . . . . . . . 564

Generic Attributes, a Constant Alternative, and Alternative Swapping . . . . . . . . . . 570

Generic Attributes, a Constant Alternative, and Choice Set Swapping . . . . . . . . . . 574

Design Algorithm Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579

Initial Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 580

Improving an Existing Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 580

When Some Choice Sets are Fixed in Advance . . . . . . . . . . . . . . . . . . . . . . . 583

Partial Profiles and Restrictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595

Pairwise Partial-Profile Choice Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595

Linear Partial-Profile Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 602

Choice from Triples; Partial Profiles Constructed Using Restrictions . . . . . . . . . . . 604

Six Alternatives; Partial Profiles Constructed Using Restrictions . . . . . . . . . . . . . 610

CONTENTS 11

Five-Level Factors; Partial Profiles Constructed Using Restrictions . . . . . . . . . . . . 626

Partial Profiles from Block Designs and Orthogonal Arrays . . . . . . . . . . . . . . 640

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663

Multinomial Logit Models 665

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665

Modeling Discrete Choice Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667

Fitting Discrete Choice Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 668

Cross-Alternative Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 674

Final Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 679

Conjoint Analysis 681

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 681

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 681

Conjoint Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 681


Choice-Based Conjoint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683

Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683

The Output Delivery System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683

Chocolate Candy Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 687

Metric Conjoint Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 687

Nonmetric Conjoint Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 690

Frozen Diet Entrees Example (Basic) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695

Choosing the Number of Stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695

Generating the Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 697

Evaluating and Preparing the Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 698

Printing the Stimuli and Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . 701

Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703


Frozen Diet Entrees Example (Advanced) . . . . . . . . . . . . . . . . . . . . . . . . . . 709

Creating a Design with the %MktEx Macro . . . . . . . . . . . . . . . . . . . . . . . . . 709

12 CONTENTS

Designing Holdouts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 711

Print the Stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 717

Data Collection, Entry, and Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . 718


Analyzing Holdouts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737

Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 739

Summarizing Results Across Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743

Spaghetti Sauce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751

Create an Efficient Experimental Design with the %MktEx Macro . . . . . . . . . . . . 751


Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764


Simulating Market Share . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 769

Simulating Market Share, Maximum Utility Model . . . . . . . . . . . . . . . . . . . . . 772

Simulating Market Share, Bradley-Terry-Luce and Logit Models . . . . . . . . . . . . . 778

Change in Market Share . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 780

PROC TRANSREG Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 789

PROC TRANSREG Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 789

Algorithm Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 790

Output Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 791

Transformations and Expansions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 792

Transformation Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 794

BY Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795

ID Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 796

WEIGHT Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 796

Monotone, Spline, and Monotone Spline Comparisons . . . . . . . . . . . . . . . . . . . 796

Samples of PROC TRANSREG Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . 799

Metric Conjoint Analysis with Rating-Scale Data . . . . . . . . . . . . . . . . . . . . . . 799


Monotone Splines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 800

Constraints on the Utilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 800

A Discontinuous Price Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 801

CONTENTS 13

Experimental Design and Choice Modeling Macros 803

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803

Changes and Enhancements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 804

Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 804

%ChoicEff Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 806

Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 808

Making the Candidate Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 916

Initial Designs and Evaluating a Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 925

Partial-Profile Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 930

Other Uses of the RSCALE=PARTIAL= Option . . . . . . . . . . . . . . . . . . . . . . 931

Optimal Alternative-Specific Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 937

%ChoicEff Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 946

%ChoicEff Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 955

%MktAllo Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 956

%MktAllo Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 957

%MktAllo Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 958

%MktBal Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 959

%MktBal Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 960

%MktBal Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 962

%MktBIBD Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 963

BIBD Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 971

%MktBIBD Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 973

Evaluating an Existing Block Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 976

%MktBIBD Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 978

%MktBlock Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 979

%MktBlock Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 984

%MktBlock Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 988

%MktBSize Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 989

%MktBSize Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 992

%MktBSize Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 994

%MktDes Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 995

14 CONTENTS

PROC FACTEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 995

%MktDes Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 997

%MktDes Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1003

%MktDups Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1004

%MktDups Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1009

%MktDups Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1011

%MktEval Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1012

%MktEval Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1014

%MktEval Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1016

%MktEx Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1017

Orthogonal Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1018

Randomization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1026

Latin Squares and Graeco-Latin Square Designs . . . . . . . . . . . . . . . . . . . . . . . 1026

Split-Plot Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1031

Candidate Set Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1045

Coordinate Exchange . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1045

Aliasing Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1047

%MktEx Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1051

%MktEx Macro Iteration History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1053

%MktEx Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1055

Advanced Restrictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1079

%MktKey Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1090

%MktKey Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1091

%MktLab Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1093

%MktLab Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1101

%MktLab Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1104

%MktMDiff Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1105

Experimental Design for a MaxDiff Study . . . . . . . . . . . . . . . . . . . . . . . . . . 1111

%MktMDiff Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1119

%MktMDiff Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1124

%MktMerge Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1125

%MktMerge Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1125

CONTENTS 15

%MktMerge Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1127

%MktOrth Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1128

%MktOrth Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1132

%MktOrth Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1135

The Orthogonal Array Catalog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1135

%MktPPro Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1145

%MktPPro Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1151

%MktPPro Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1152

%MktRoll Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1153

%MktRoll Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1157

%MktRoll Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1158

%MktRuns Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1159

%MktRuns Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1164

%MktRuns Macro Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1168

%Paint Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1169

%Paint Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1169

%PHChoice Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1173

%PHChoice Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1177

%PlotIt Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1178

%PlotIt Macro Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1187

Macro Error Messages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1211

Linear Models and Conjoint Analysis with Nonlinear Spline Transformations 1213

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1213

Why Use Nonlinear Transformations? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1213

Background and History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1214

The General Linear Univariate Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1214

Polynomial Splines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1215

Splines with Knots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1216

Derivatives of a Polynomial Spline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1218

Discontinuous Spline Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1219

Monotone Splines and B-Splines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1221

16 CONTENTS

Transformation Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1222

Degrees of Freedom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1223

Dependent Variable Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1223

Scales of Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1224


Curve Fitting Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1225

Spline Functions of Price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1227

Benefits of Splines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1227

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1230

Graphical Scatter Plots of Labeled Points 1231

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1231

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1231

An Overview of the %PlotIt Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1232

Changes and Enhancements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1233

Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1233

Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1245

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1246

Appendix: ODS Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1247

Graphical Methods for Marketing Research 1263

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1263

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1263

Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1264

Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1274

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1274

CONTENTS 17

Concluding Remarks 1275

References 1277

Index 1285

Preface

Marketing Research Methods in SAS discusses experimental design, discrete choice, conjointanalysis, and graphical and perceptual mapping techniques. The book has grown and evolved overmany years and many revisions. For example, the section on choice models grew from a two-pagehandout written by Dave DeLong in 1992. This edition was written for SAS 9.2 and subsequent SASreleases.

This book was written for SAS macros that are virtually identical to those shipped with the SAS 9.22release in 2010. All of the macros and most of the code used in this book should work in SAS 9.0,9.1, and SAS 9.2. However, some features, such as the standardized orthogonal contrast coding in the%ChoicEff macro, require SAS 9.2 or a later release. To be absolutely sure that you have the macrosthat correspond to this book, you should get the latest macros from the Web. All other macros areobsolete. Copies of this book and all of the macros are available on the Web (reports beginning with“MR-2010” at http://support.sas.com/resources/papers/tnote/tnote_marketresearch.html).This book is the October 1, 2010 edition, and it uses the macros that are dated July 25, 2010.

I hope that this book and tool set will help you do better research, do it quickly, and do it more easily.I would like to hear what you think. Many of my examples and enhancements to the software are basedon feedback from people like you. If you would like to be added to a mailing list to receive periodice-mail updates on SAS marketing research tools (probably no more than once every few months), e-mailWarren.Kuhfeld at sas.com. This list will not be sold or used for any other purpose.

Finishing a 1309-page book causes one to pause and reflect. As always, I am proud of this edition ofthe book and tools, however it is clear that I have stood on the shoulders of giants. The followingpeople contributed to writing portions of this book: Mark Garratt, Joel Huber, Ying So, Randy Tobias,Wayne Watson, and Klaus Zwerina. My parts could not have been written without the help of manypeople. I would like to thank Joel Huber, Ying So, Randy Tobias, and John Wurst. My involvementin the area of experimental design and choice modeling can be traced to several conversations withMark Garratt in the early 1990’s and then to the influence of Don Anderson, Joel Huber, JordanLouviere, and Randy Tobias. I first learned about choice modeling at a tutorial taught by JordanLouviere at the ART Forum. Later, as I got into this area, Jordan was very helpful at key times inmy professional development. Don Anderson has been a great friend and influence over the years. Dondid so much of the pioneering work on choice designs. There is no doubt that his name should bereferenced in this book way more than it is. Joel Huber got me started on the work that became the%ChoicEff macro. Randy Tobias has been a great colleague and a huge help to me over the years inall areas of experimental design, and many components of the %MktEx macro and other design macrosare based on his ideas and his work. Randy wrote PROC OPTEX and PROC FACTEX which providethe foundation for my design work. My work on balanced incomplete block designs can be traced toconversations with John Wurst.

Don Anderson, Warwick de Launey, Nam-Ky Nguyen, Shanqi Pang, Neil Sloane, Chung-yi Suen, RandyTobias, J.C. Wang, and Yingshan Zhang kindly helped me with some of the orthogonal arrays in the%MktEx macro. Brad Jones advised me on coordinate exchange. Much of our current success withcreating highly restricted designs is due to the difficult and very interesting design problems broughtto me by Johnny Kwan. I have also learned a great deal from the interesting and challenging problemsbrought to me by Ziad Elmously.

19

There are a few other people that I would like to acknowledge. Without these people, I would have neverbeen in the position to write a book such as this. From my undergraduate days at Kent State, I wouldlike to thank Roy Lilly∗, Larry Melamed, Steve Simnick and especially my adviser Ben Newberry.From graduate school at UNC, I would like to thank Ron Helms, Keith Muller, and especially myadviser Forrest Young∗. From SAS, I would like to thank Bob Rodriguez, Warren Sarle, and all of mycolleagues in SAS/STAT Research and Development. It is great to work with such a smart, talented,productive, and helpful group of people.

On a more personal note, I was diagnosed with prostate cancer in 2008. Most prostate cancers arenot very aggressive. Someone forgot to tell mine that. My Gleason Score was 9. A Gleason Score isa measure of prostate cancer aggressiveness that ranges from 2 to 10. A 9 is almost as scary as theycome. Thanks to modern medicine, early detection, and a brilliant and gifted surgeon using the latesttechnology, I am doing very well. Advocates of early testing and screening are trying to catch cases likemine early, while there is still time for a cure. In my case, every indication is that they were successfuland surgery alone got it all. I get my PSA checked every three months now, and PSA since the surgeryhas consistently been undetectable, which is perfect. I have been cancer free for over two years nowand am in the best shape of my life. I hope that all of you, men and women, get your regular physicalexams and health screenings and see your health care provider if you notice any changes in your bodyand how it functions. Yes, I know it’s not fun. Do it anyways! It saved my life; it might save yours too.I would like to thank a few of my friends who helped me through this period and the other difficulttimes that I went through in that year: Woody, Mike, Sara, Benny, Deborah, Gina, and Peg. You aremy guardian angels. You gave me hope, help, and support, and you were there when I needed you themost.

Finally, I would like to thank my mother∗, my father∗, my sister, and my stepfather Ed∗, for being sogood to my Mom and for being such a wonderful grandfather to my children. I dedicate this edition ofthe book to my children, Megan and Rusty, and to Donna, who helped me learn how to live and loveagain.

Warren F. Kuhfeld, Ph.D.Manager, Multivariate Models R&DSAS Institute Inc.October 1, 2010

∗It is sad that so many people that I acknowledge have passed away since I started working on this book. I wish Icould thank all of these people for their role in helping me to get to where I am today.

20

About this Edition

The 2010 edition of Marketing Research Methods in SAS is a partial revision of the 2009 book. Idid not have time to rewrite everything that I would have liked to rewrite. I do many different thingsprofessionally, way more than most readers of this book know. Those other things take most of mytime, and it is hard to find the large block of time that I need to completely modify a piece of work thissize every time there is an enhancement or innovation in the design macros. In this edition, I addednew material and also added some guidance in the ensuing paragraphs about how to navigate throughthis book.

This edition has explicit instructions about how to contact Technical Support when you have questionsor problems. See page 25 for more information. While I have never minded getting your questions,they really need to go to Technical Support first. I am not always in the office. Sometimes I am outbackpacking without any contact with the outside world. Contacting Technical Support will ensurethat your question is seen and addressed in a timely manner.

This edition contains some major new features that were not in the 2005 edition and one major newfeature that was not in the 2009 edition. With this 2010 edition, the %ChoicEff macro now allowsyou to specify a restrictions macro. You can use it to specify within alternative restrictions, withinchoice set (and across alternative) restrictions, and even restrictions across choice sets. You can specifyrestrictions directly with the alternative-swapping algorithm. You no longer need to make a choicedesign with the %MktEx macro or with the choice-set-swapping algorithm in the %ChoicEff macrowhen there are restrictions.

Most of this book is about experimental design. In particular, most of it is about designing choiceexperiments. This is a big topic with multiple tools and multiple approaches with multiple nuances, sohundreds of pages are devoted to it. This can be intimidating when you are first getting started. Thefollowing information can help you get started:

• If you are new to choice modeling and choice design, and you want to understand what you aredoing, you should start by reading the “Experimental Design: Efficiency, Coding, and ChoiceDesigns” chapter, which starts on page 53. It is a self-contained short course on basic choicedesign, complete with exercises at the end.

• If you just want to jump in and get started designing experiments, see the examples of the%ChoicEff macro starting on page 808. This section describes all of the tools that you need todesign almost any choice experiment. Many other tools and approaches exist and are describedin detail elsewhere in the book, but you almost certainly can get by with the subset describedstarting on page 808. However, if you are going to approach choice modeling intelligently, youneed to understand the coding and modeling issues discussed in the experimental design chapterand elsewhere throughout this book.

• If you want to understand the choice model and the classic approach to choice design, see the“Discrete Choice” chapter starting on page 285. While this chapter contains lots of great infor-mation on many topics related to choice modeling, and it uses an approach in most examplesthat is in many cases optimal or at least good, most of that chapter uses an approach that seemsto be less often used now days.

21

The process of designing an experiment for a linear model is generally straight-forward since software,such as the %MktEx macro, exists for finding an optimal (or at least efficient) design for the specifiedmodel. In contrast, the process of designing a choice experiment is guided more by heuristics than hardscience. You can only design an optimal experiment for a choice model if you know the parameters,and if you knew the parameters, there would be no reason to design the experiment. Much of the earlywork in choice design took a linear model design approach, which is discussed in detail in the designchapter starting on page 53 and the “Discrete Choice” chapter starting on page 285. In this approach,you make a design that is orthogonal and balanced (or at least nearly so) in all of the attributes ofall of the alternatives and rearrange that into a choice design. This approach has much to recommendit, particularly in the context of alternative-specific designs and designs with complicated effects suchas availability and cross effects. It is not the optimal approach for generic designs and simpler designproblems.

In previous editions, I referred to this approach to designing choice experiments as the “linear design”approach. With this edition, I have banished that phrase from this book. That phrase has alwaysbeen problematic and confusing. With this edition, I now use phrases like “linear model design” and“factorial design” interchangeably to refer to designs that will be used for a linear model such as aconjoint analysis. I no longer refer to a design constructed by the %MktEx macro that is converted to achoice design by the %MktRoll macro as a “linear design.” Instead, I use the term “linear arrangement”as a short-hand for “linear arrangement of a choice design” to refer to a design that will ultimatelybe used for a choice design, but is currently arranged with one row per choice set and one column forevery attribute of every alternative. The linear arrangement of a choice design can be constructed andevaluated by pretending that it will be used for a linear model with one factor for every attribute ofevery alternative. This is one way in which you can make a choice design, and it is discussed in detailin this book.

If you had to pick one approach to solve all of your design problems, and you did not have time tolearn about all of the other ways you could go about designing a choice experiment, here is whatI would recommend. Use the %MktEx macro to make a candidate set of alternatives, and use the%ChoicEff macro to create a choice design from it. If there are any restrictions on your design, use therestrictions= option in the %ChoicEff macro to impose the restrictions. The restrictions= optionin the %ChoicEff macro is new with this edition of the book and macros. Restrictions can be withinalternative, within choice set (and across alternative), or even across choice sets. You can imposerestrictions to prevent certain combinations of alternatives from occurring together, to minimize theburden on the subjects, to eliminate dominated alternatives, to make the design more realistic, or forany other reason. I have not eliminated the hundreds of pages of this book that are devoted to otherways to make choice designs, because those pages contain a lot of useful information. Rather, I simplypoint out that you can selectively devote your attention to different parts of the book and concentrateon using the %ChoicEff macro with a candidate set of alternatives for most of your choice design needs.

Each of the last few editions has relied much more heavily on the %ChoicEff macro than precedingeditions did. The %ChoicEff macro is heavily used both for design construction and for design evalua-tion. You should always use it to evaluate designs before data are collected. This has always been goodadvice, but with the addition of the standardized orthogonal contrast coding in PROC TRANSREG(which the macro calls) plus some new options and output, the %ChoicEff macro now provides a clearerpicture of choice design goodness for many choice designs. In particular, it provides a measure of designefficiency on a 0 to 100 scale for at least some choice designs. See page 81 for more information.

22

A big part of this book is about experimental design. Efficient experimental-design software, like someother search software, is notorious for not finding the exact same results if anything changes (operatingsystem, computer, SAS release, code version, compiler, math library, phase of the moon, and so on),and the %MktEx and %ChoicEff macros are no exception. They will find the same design if you specifya random number seed and run the same macro over and over again on the same machine, but ifyou change anything, they might find a different design. The algorithms are seeking to optimize anefficiency function. All it takes is one little difference, such as two numbers being almost identicalbut different in the last bit, and the algorithm can start down a different path. We expect as thingschange and the code is enhanced that the designs will be similar. Sometimes two designs might evenhave the exact same efficiency, but they will not be identical. The %MktEx and %ChoicEff macros, andother efficient design software take every step that increases efficiency. One can envision an alternativealgorithm that repeatedly evaluates every possible step and then takes only the largest one with fuzzingto ensure proper tie handling. Such an algorithm would be less likely to give different designs, but itwould be much slower. Hence, we take the standard approach of using a fast algorithm that makesgreat designs, but not always the same designs.

For many editions, I regenerated every design, every sample data set, every bit of output, and thenmade changes all over the text to refer to the new output. Many times I had to do this more thanonce when a particularly attractive enhancement that changed the results occurred to me late in thewriting cycle. It was difficult, tedious, annoying, error prone, and time consuming, and it really didnot contribute much to the book since you would very likely be running under a different configurationthan me and not get exactly the same answers as me, no matter what either you or I did. Startingwith the January 2004 edition, I said enough is enough! For many versions now, in the accompanyingsample code, I have hard-coded in the actual example design after the code so you can run the sampleand reproduce my results. I am continuing to do that, however I have not redone every example.Expect to get similar but different results, and use the sample code if you want to get the exact samedesign that was in the book. I would rather spend my time giving you new capabilities than rewritingold examples that have not changed in any important way.

In this and every other edition, all of the data sets in the discrete choice and conjoint examples areartificial. As a software developer, I do not have access to real data. Even if I did, it would be hard touse them since most of those chapters are about design. Of course the data need to come from subjectswho make judgments based on the actual design. If I had real data in an example, I would no longer beable to change and enhance the design strategy for that example. Many of the examples have changedmany times over the years as better design software and strategies became available. In this edition,like all previous editions, the emphasis is on showing design strategies not on illustrating the analysisof the data.

The orthogonal array catalog is essentially complete up through 143 runs,∗ with pretty good coveragefrom 144 to 513 runs, and spotty coverage beyond 513 runs. New arrays are being discovered regularly.If you know of any orthogonal arrays that are not in my catalog, please e-mail Warren.Kuhfeld atsas.com. I would particularly like to hear from you if you know how to make any of the arrays thatare missing. Also, if you know how to construct any of these difference schemes, I would appreciatehearing from you: D(60, 36, 3); D(102, 51, 3); D(60, 21, 4); D(112, 64, 4); D(30, 15, 5); D(35, 17, 5);D(40, 25, 5); D(55, 17, 5); D(60, 25, 5); D(65, 25, 5); D(85, 35, 5); D(60, 11, 6); D(84, 16, 6); D(35,11, 7); D(63, 28, 7); D(40, 8, 10); and D(30, 7, 15). The notation D(r, c, s) refers to an r× c matrix oforder s. You can always go to http://support.sas.com/techsup/technote/ts723.html to see thecurrent state of the orthogonal array catalog.

∗There are a few missing designs in 108 runs. I would welcome help in making them.

23

ODS Graphics is used throughout the book. With ODS Graphics and SAS 9.2, statistical proceduresproduce graphs as automatically as they produce tables, and graphs are now integrated with tables inthe ODS output. See 1247 for the section of the book that says the most about ODS Graphics. Alsosee “Chapter 21, Statistical Graphics Using ODS” in SAS/STAT documentation for more on ODSGraphics: http://support.sas.com/documentation/. You can learn more about ODS Graphicsin my new book, Statistical Graphics in SAS: An Introduction to the Graph TemplateLanguage and the Statistical Graphics Procedures. You can learn more about the book athttp://support.sas.com/publishing/authors/kuhfeld.html.

I hope you like this edition. Feedback is welcome. Your feedback can help make these tools better.

24

Getting Help and

Contacting Technical Support

SAS Technical Support can help you if you encounter a problem or issue while working with the marketresearch design macros or procedures in this book. However, you can help Technical Support greatlyby providing certain details of your problem.

A new track will be initiated when you contact Technical Support about a specific problem, and notesadded to that track as you work through the problem with your support specialist. For this reason,you should avoid starting multiple tracks on the same topic.

You can expect to hear back from a support specialist within one business day, but this does notnecessarily mean that your question will be resolved by then. You might be asked to provide additionalinformation to help solve your problem.

Opening a Track via the Web

You can contact Technical Support at the Technical Support Web site, which can be opened by usingthe link below. Working through a problem with your technical support specialist via Web and emailis recommended for usage questions relating to this book.

http://support.sas.com/ctx/supportform/index.jsp

Opening a Track via the Phone

You can contact SAS Technical support via phone. We recommend this approach for short questionsonly. Please consult the SAS Technical Support Web site by clicking on the link below to obtain theappropriate Technical Support phone numbers for US and international users.

SAS Support Phone Numbers919.677.8008 (US)http://www.sas.com/offices/intro.html (International Support via Worldwide SAS Offices)

Important Information to Provide SAS Technical Support

Providing the following pieces of information to Technical Support can significantly shorten the timenecessary to understand and solve your problem:

• Your Contact Information. Provide your full contact information: name, phone number, emailaddress, and site number.

• Information about your SAS Version and Market Design Macros. Please include informationabout the version of SAS that you have installed and are using. You can find this information underHelp → About SAS.

Please include information about the version of the macros that you have installed and are using. Youcan find this information by submitting the following statement before running any of the macros:%let mktopts = version;.

25

Example:

1? %let mktopts = version;2? %mktex(2 ** 3, n=4)

Produces:MktEx macro version 25Jul2010MktRuns macro version 25Jul2010Seed = 4247959MktOrth macro version 25Jul2010

Note that some macros call other macros, and all must be the same version.

• Information about your Design. Please describe your design fully:

1. identify the type of design you want to generate (for example, choice, MaxDiff, conjoint, partialprofile)

2. the number of factors, the number of levels associated with each factors

3. the number of runs (or choice sets) in the final design

4. the number of alternatives in a choice design

5. the model you want to estimate

6. if your model has constraints, define the desired constraints

• Details about your Problem. Include the program statements that you have tried to generatethe design. Did you see an warning or error message in connection with your problem? If so, pleaseattach a copy of the message to your technical support inquiry, and include a copy of the SAS .log filefor the analysis.

26

Concluding Remarks

I hope you like this book, the new options, the new presentation style, and the new and improvedmacros. In particular, I hope you find the %MktEx and %ChoicEff macros to be very powerful anduseful. My goal in writing this book and tool set is to help you do better research and do it morequickly and more easily. I would like to hear what you think. Many of my examples and enhancementsto the software are based on feedback from people like you. If you have comments or suggestions forfuture revisions, write Warren F. Kuhfeld, (Warren.Kuhfeld at sas.com) at SAS Institute Inc. My goalto provide you with enough examples so that you can easily adapt aspects of one or more examplesto fit your particular needs. When I do not succeed, tell me about it and I will try to add a newexample to the next revision. Please direct questions to the Technical Support Division (see page 25)and suggestions to me. Please email me. I would like to hear from you!

1275

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Index

bbad (IML restrictions matrix) 158 165pbad (IML restrictions matrix) 165

+- (options=, MktEx macro) 10623 (options=, MktEx macro) 1062512 (options=, MktEx macro) 1062512 (options=, MktOrth macro) 1134512 (options=, MktRuns macro) 1167@@ 293A-efficiency 62 246abbreviating option names 789accept option 1052 1059 1062 1066 1086-1088accept (options=, MktEx macro) 1059active observations 796Addelman, S. 254 265 269 1018 1277adjust1= (PlotIt macro) 1206adjust2= (PlotIt macro) 1206adjust3= (PlotIt macro) 1206adjust4= (PlotIt macro) 1206adjust5= (PlotIt macro) 1207Age variable 530aggregate data 442-445 460-464 522-524 546 550-

552 679Agresti, A. 665 1277algorithm options, PROC TRANSREG 790-791aliased 58 245aliased, defined 233aliasing structure 495all function 821allcode (options=, MktDes macro) 1000allocation study 535-550allocation study, defined 233Allocs data set 958Alt variable 333 982 985Alt variable 1157alt= 134 311-312 985 1157alt= (MktBlock macro) 985alt= (MktRoll macro) 1157alternatives 53alternatives, defined 234alternative-specific attribute, defined 234alternative-specific effects 55 70 386-388 438 449

454 459 470 510 515 667-674 859 884altnum variable 840 953AltType variable 909Anderson, D.A. 19 53 102 243 249 257 265 278

470 493 645 1277 1280

Ann iteration history table entry 1054anneal= (MktEx macro) 1070annealfun= (MktEx macro) 1078annealing 348-349 422 1045-1046 1070 1078anniter= (MktEx macro) 1070antiidea= (PlotIt macro) 1199anti-ideal points 34 1269Arabie, P. 1277arithmetic mean 62array statement 155 219 228-229 300 323 335-

337 369 433 436 444 461 520 544 575arrays 669 672 675artificial data 285 393 436-437 520-521asymmetric design, defined 234asymmetry 112 410 468 884-894 1101 1104attribute levels, order 790attributes, defined 234attributes, design 53 681-683attrlist variable 1109attrortr= 977attrs= (MktMDiff macro) 1122augmenting an existing design 583autocall macros 803availability cross-effects 468-471 493 510 517-518

524availability cross-effects, defined 234available, not 1095average importance 745-746b= 122 1148 1151b= (MktBIBD macro) 974b= (MktBSize macro) 993b= (MktMDiff macro) 1121bad (IML restrictions matrix) 157-158 165 180

471-472 612 820-822 830 953 1064-1065badness 158 1065balance 58 63 303 427 504balance, defined 234balance= 158 487-488 491-492 591 1059 1062

1065balance= (MktEx macro) 1057balanced and orthogonal 59 63 76-78 303balanced incomplete block designs 115 640 963-

966 971-975 978 989-994balanced incomplete block designs, defined 234Benzecri, J.P. 34 1269bestcov= (ChoicEff macro) 949

1285

bestout= 170 950bestout= (ChoicEff macro) 949bestworst 1109beta= 314 361 430 509 560 809 878-879 947-949

952beta= (ChoicEff macro) 949beta=zero 137 170Bibby, J.M. 1281BIBD, see balanced incomplete block designsbig designs 410big= 1001big= (MktDes macro) 998big= (MktEx macro) 1073bin of attributes 68-69 311 369binary coding 73 327 372-373 382 438 447binary coding, defined 234biplot 28 1264block designs 115 963-966 971-975 978 989-994Block variable 357 537 550 979 985 1098block= 985block= (MktBlock macro) 985Blocked data set 987blocking 351-353 426-427 483 492 497-499 536

979-988 1015 1098-1099 1125-1126blocking, defined 234blocks= 371 1125blocks= (MktEval macro) 1015blocks= (MktMerge macro) 1126blue bus 469blue= (PlotIt macro) 1205Booth, K.H.V. 247 1277border (options=, PlotIt macro) 1194Bose, R.C. 1018 1277Bradley, R.A. 44 751 770-773 776-780Bradley-Terry-Luce model

compared to other simulators 771defined 770market share 778

brand choice (aggregate data) example 444Brand variable 81 89 134 311-313 326-327 447

452-453 504 510 513-517 530 544 547 550858-859 865 882 886-887 890 908 956 985-986 1153-1157

branded design, defined 234branded (options=, MktDups macro) 1010bratwurst, see bestworstBreiman, L. 1214 1277Breslow likelihood 467Breslow, N. 467 666-670 679 1277

brief 153-154 329-331 375 534bright= (PlotIt macro) 1203britypes= (PlotIt macro) 1195B-splines 1221Bunch, D.S. 102 265 269 1277bundles of attributes 556 604Burgess, L. 102 1282Burt table, correspondence analysis 1272bus 469Butson, A.H. 1018 1278bw 1107-1109 1120bwalt 1108 1121bwaltpos 1108 1121bwpos 1107 1121by statement, syntax 795c = 2 - (i eq choice) 294c variable 292-295 326 331 336 371 452-453 523

548 956 1125-1126c*c(2) 295 329c*c(3) 295Can iteration history table entry 421 475 1054cand= 1000cand= (MktDes macro) 998candidate set 347 470 477 558 564 570 574 604

806-807 819 859 1045 1086-1089canditer= (MktEx macro) 1070candy example (choice) 289candy example (conjoint) 687canonical correlation 101-102 307 700canonical correlation, defined 235canonical initialization 790cards, printing in a DATA step 701 717 760Carmone, F.J. 243 1277Carroll, J.D. 30-31 1264-1266 1269 1273 1277Carson, R.T. 245 249 275 278 285 680 1277cat= 924 1036 1134cat= (MktEx macro) 1066CATMOD procedure, see PROC CATMODCattin, P. 243 1283CB data set 1015ceil function 408 747cfill= (MktLab macro) 1101cframe= (PlotIt macro) 1203chair (generic attributes) example 556Chakravarti, I.M. 249 252 261 1277change, market share 780 788check the data entry 297check option 351check (options=, MktDes macro) 1000

1286

check (options=, MktEx macro) 1059chi-square metric, correspondence analysis 1270Chi-Square statistic 297chocolate candy example (choice) 289chocolate candy example (conjoint) 687choice design

67-72 102 311-313 325 1153basic 55blocking 71candidate set of alternatives 85-87 166 170

177-179 203 559 564 567 570 809 814 829833-835 838 842 851 855-859 887 891 908919 924 927 935 942 1006

candidate set of choice sets 188 193 576 819-820 827 847 878-883 899-901 913 917 921926 929-932 944-945 982 1005 1145 1150

coding 81 91defined 235efficiency 71-72 102 430 508-519 557-561 564

567 570 576 608 806 816 860-862 878-883evaluation 81-83 103-105 109 112 137 140-142

213 313 317 322 360 365-366 396 430 508-509 542 597-600 816-817 862-865 921 926-932 937 940 943 952

generic 559 564 567 570linear arrangement 127 156 878 881 1005MaxDiff 225optimal generic 98-102 198 809partial profile 207reference levels 89restrictions 156 177 188terminology 57

choice model285 289-291 300 466-468coding 89-92 150 176 187 327 372 378-380 383

388 438 447-449 452 460-464 523 528-530550 876

fitting 152 176 187 221 295 298 329 375 378-380 385 390 440-442 447-449 457 462-464524 528 532 550-552 668-678

choice probabilities 300choice set 53choice set, defined 235choice sets, minimum number 556choice simulators

Bradley-Terry-Luce model 770compared 771defined 769example 776-778

logit model 770macro 773maximum utility model 770-772

Choice variable 294choice-based conjoint 285 683%ChoicEff macro

19-23 69 78 81-88 95 103-106 109 112 123-127137-142 166 170 177-182 188 193-195 198-199 203 213-214 240 274 280 286-287 313-322 360-361 364-366 394-397 407 430 504508-510 513 524 541-542 556-561 564-567570 574-576 579 597-599 604 607-608 618621 624-625 628-632 636 645-647 650-656659 662-663 803-821 827-829 833-835 838842-847 851 855-866 869-871 878-884 887891 899-901 904-905 908 911-913 916-932935-937 940-948 952 979 982 985 1005-10061012 1102 1126 1145 1148-1150 1275

alternative swapping 570 579documentation 806-955set swapping 574 579versus the MktEx macro 579

choose 1073Choose variable 337chosen alternative 294Chrzan, K. 56 121 207 595 806 963 1062 1145

1277chunks= (ChoicEff macro) 949cirsegs= (PlotIt macro) 1207class 79-80 89-90 150 234-236 326-327 373 377

383 387 438-439 447-449 453 493 507 510523-524 547 556 559 687 690 704 722 765769 783-785 799-801 862 876-878 953 1177

class PROC TRANSREG syntax 793class statement 493 998 1002classopts= 232 1110 1123-1124classopts= (MktDes macro) 998classopts= (MktMDiff macro) 1122Client variable 1095close (options=, PlotIt macro) 1194cluster analysis 748coded (options=, ChoicEff macro) 951coding down 419 999 1073coding

binary 73 327 372-373 438 447choice model 89-92 150 176 187 327 372 378-

380 383 388 438 447-449 452 460-464 523528-530 550 876

effects 73 382-383

1287

orthogonal 73 91-92price 91-92reference cell 73

coding= (MktDes macro) 998coefficients 722 765 769 783-785coefficients PROC TRANSREG syntax 792Colbourn, C.J. 1018 1278Color variable 1154-1156color= (PlotIt macro) 1203colors= (Paint macro) 1170colors= (PlotIt macro) 1196column profiles, correspondence analysis 1270column (PHChoice macro) 1177column statement 686 1175combinations

printing in a DATA step 701 717 760unrealistic 754

Conforms iteration history table entry 617 1054confounded 58 245confounded, defined 235conjoint analysis

37 41 681-801 1224defined 682discontinuous functions 801identity attribute 800metric conjoint analysis 799model 689monotone attribute 800monotone spline 800nonmetric conjoint analysis 799price 801rank data 799typical options 799

conjoint designexample 697 709 754

conjoint measurement 37 681constant alternative 302 333 444constrained part-worth utilities 800contacting technical support 25CONTENTS procedure, see PROC CONTENTSconverge= 861converge= (ChoicEff macro) 949converge= PROC TRANSREG syntax 790convergence criterion 790Cook, R.D. 248 253 268 347 473 559 1045 1278Coolen, H. 1214 1278coordinate-exchange algorithm 347 1045Corr data set 1015correlations 737

CORRESP procedure, see PROC CORRESPcorrespondence analysis 34 46 1269-1272Count variable 157 180 544-546 956cov= 952cov= (ChoicEff macro) 949covariance matrix 62 67 81-88 111-112 115 139-

140 144-146 173 183 197-198 206 318-319809-811 900 934-940

covariance matrix, defined 235Cox, D.R. 247 1277cprefix= 142 203 314 361 431cross-effects 446 452-460 468-471 493 510 515-516

522-524 528cross-effects, defined 235cursegs= (PlotIt macro) 1207curve fitting 1225curvecol= (PlotIt macro) 1203customizing

multinomial logit output 287PROC PHREG output 287 1173-1177TRANSREG output 684

D-efficiency 62 246D-efficiency, 0 to 100 scale 63 73 76-78 493D-optimality 1017data collection

rank data 718data entry

checking 297choice 148 174 186 220 292-293 324-325 337

371 437 444 460 521 529 543 666MaxDiff 229rank data 703 718rating-scale data 687 764simulation 781 785

datacollection, rank data 718generating artificial 393 436 520processing 338 383 575 672 675 687 703 718

764-765validation, rank data 718

data= 137 149-150 170 295 314 325-326 331 361430 509 537 548 560 722 765 769 783-785799-801 809 814-816 925 950 957 986 10101095-1096 1101 1124-1126

data= (ChoicEff macro) 950data= (MktAllo macro) 957data= (MktBlock macro) 985data= (MktDups macro) 1011data= (MktEval macro) 1015

1288

data= (MktLab macro) 1101data= (MktMDiff macro) 1122data= (MktMerge macro) 1126data= (Paint macro) 1170data= (PlotIt macro) 1191data= PROC TRANSREG syntax 789datatype= (PlotIt macro) 1192Davey, K.S. 243 253 1278Dawson, J.E. 1018 1278Day, N.E. 666 1277de Boor, C. 1214 1221 1277De Cock, D. 1018 1278de Launey, W. 19 1018 1278de Leeuw, J. 1214 1224 1277-1281 1284debug= (Paint macro) 1172debug= (PlotIt macro) 1207define statement 686degree, splines 794degree= 380degree= PROC TRANSREG syntax 794demographic information 529DenDF variable 768depend variable 736 791depvar variable 736-739 743-745 767 783-785

derivativessplines 1218

DeSarbo, W.S. 252 1282design

attributes 53 681-683differences 697 954 962 975 987 1001 1066efficient choice 71-72 102 430 508-519 557-561

564 567 570 576 608 806 816 860-862 878-883

efficient linear 62-63 67-68 245-246 697 709-712 721 751 754-756 1017

evaluating efficiency 352 425 1059evaluation 306 349 353 413 423 480 485 488

491-493 538 698 709 754 959factors 53 245fractional-factorial 57 245full-factorial 57 245-246 347-348 470 721 807generation 304 333 343 352 413-417 425 472

477-479 485 488 491 536 556-558 570 575580 583 587 808-809 819 858 878 881 979982 1005-1009 1017-1018 1026 1093 1096-1099 1102-1103 1154-1155

holdouts 711key 133 192 311-312 357 386 428 504 546 575

819 878 881 982 1005 1008 1091 1153-1158

levels 53 245 254methods compared 579nonorthogonal 711 754-756runs 53 244 302 340 411 415 482-483 535 557

695 752-753 1159saturated 78shifted 102-105size 302 340 411 415 482-483 535 557 695 752-

753 1057 1159-1162testing 313 360 430 504

design 150 327 372 438 447 876Design data set 961 1001 1026 1068Design variable 342 563design= 149 312 325-326 357 977 1122 1125-1126

1153 1157-1158design= (MktMDiff macro) 1121design= (MktMerge macro) 1126design= (MktPPro macro) 1151design= (MktRoll macro) 1157Dest variable 369detail option 951-952detail (options=, ChoicEff macro) 951detfuzz= (MktEx macro) 1078deviations from means coding, defined 235deviations 89Dey, A. 1018 1278diag (options=, PlotIt macro) 1194difference scheme 95-97differences (machine) in designs 954 962 975 987

1001 1066diminishing returns on iterations 478Dinitz, J.H. 1278 1282discontinuous functions

conjoint analysis 801splines 1219

dog analogy 612dolist= (MktLab macro) 1101dollar format 537drop= 240 368 862-863 950-951drop= (ChoicEff macro) 950&droplist variable 767-769 781 785dropping variables 328 373Duan, L. 1018 1284dummy variables 69dummy PROC TRANSREG syntax 790DuMouchell, W. 259 1278Duncan, G.J. 674 1279duplicates 95 319 368 519 564 567 570 576 597 607

617-618 625 628 632 636 645 650 654-656

1289

659 662 809 816-819 833-835 840 899-901917-918 982 1005-1010 1052

dups option 924 1034 1066dups (options=, MktOrth macro) 1134Dykstra, O. 248 1278Eckart, C. 29 1264 1278edit statement 686 1175effects coding 73 382-383 878-880 948effects coding, defined 235effects 89 383 878efficiency

choice design 71-72 102 430 508-519 557-561564 567 570 576 608 806 816 860-862 878-883

defined 235evaluating for an existing design 352 425 1059linear model design 62-63 67-68 245-246 697

709-712 721 751 754-756 1017Efficiency variable 563Ehrlich, H. 1018 1278eigenvalues 60-62Elliott, J.E.H. 1018 1278Elrod, T. 56 121 207 243 253 595 806 963 1062

1145 1277-1278errors in running macros 1211errors, contacting technical support 25eval function 214%EvalEff macro

493evaluation, design 698 709 754evenly spaced knots 795evenly PROC TRANSREG syntax 795examine= 307 351 700 998 1047 1058examine= (MktDes macro) 998examine= (MktEx macro) 1058examining the design 306 349 353 413 423 480

485 488 491-493 538 959example

Bradley-Terry-Luce model 778brand choice (aggregate data) 444candy (choice) 289candy (conjoint) 687cereal bar, candidate set of alternatives

(choice) 166cereal bar, candidate set of alternatives

(choice) with restrictions 177cereal bar, candidate set of choice sets (choice)

188cereal bar, generic design (choice) 198

cereal bar, linear arrangement of a choice de-sign 127

cereal bar, linear arrangement of a choice de-sign with restrictions 156

cereal bar, MaxDiff design (choice) 225cereal bar, partial profile design (choice) 207chair (generic attributes) 556chocolate candy (choice) 289chocolate candy (conjoint) 687conjoint design 697 709 754fabric softener 302food product (availability) 468frozen diet entrees (advanced) 709frozen diet entrees (basic) 695logit model 778market share 772 778-780maximum utility model 772metric conjoint analysis 687 722 765new products 780 787nonmetric conjoint analysis 690 704nonorthogonal design 751partial profiles 595prescription drugs (allocation) 535simulation 772 778-780spaghetti sauce 751stimulus creation 701 717 760vacation 339 393vacation (alternative-specific) 410

exchange= 158 634 1062-1064 1071 1088exchange= (MktEx macro) 1073excolors= (PlotIt macro) 1203existing design, improving 580expand (options=, PlotIt macro) 1194expansion

class 793polynomial spline 794

experimental design53-283defined 235evaluation 306 349 353 413 423 480 485 488

491-493 538 959generation 304 333 343 352 413-417 425 472

477-479 485 488 491 536 556-558 570 575580 583 587 808-809 819 858 878 881 979982 1005-1009 1017-1018 1026 1093 1096-1099 1102-1103 1154-1155

saturated 78shifted 102-105size 302 340 411 415 482-483 535 557 695 752-

1290

753 1057 1159-1162testing 313 360 430 504

extend= (PlotIt macro) 1199external attributes 529external unfolding 1266extraobs= (PlotIt macro) 1198extreme value type I distribution 468exttypes= (PlotIt macro) 1196f variable 583f1 variable 168-170 178 858 886f2 variable 168-170 178 858 886f3 variable 168-170 178f4 variable 168-170 178fabric softener example 302facopts= (MktDes macro) 998FACTEX procedure, see PROC FACTEXfactor, defined 235factors, design 53 245factors statement 1000factors= 147 174 998-1000 1006factors= (MktBlock macro) 985factors= (MktDes macro) 998factors= (MktDups macro) 1011factors= (MktEval macro) 1015failed initialization 1052FASTCLUS procedure, see PROC FASTCLUSFederer, W.T. 267 1282Fedorov, modified 347 807 1045Fedorov, V.V. 248-249 254 259 268 347-348 473

484-486 559-561 807 1000 1045 1278Fiedler, J.A. 1280file option 1061file statement 323file (options=, MktEx macro) 1059filepref= (PlotIt macro) 1191filter= (MktOrth macro) 1133Final data set 1103Finkbeiner, C.T. 771 1278Finn, A. 57 121 225 963 1105 1278Fisher, R. 1224 1279fitting the choice model 152 176 187 221 295 298

329 375 378-380 385 390 440-442 447-449457 462-464 524 528 532 550-552 668-678

fixed choice sets 583fixed= 583 950fixed= (ChoicEff macro) 950fixed= (MktEx macro) 1074flag variables 813flags= 170 193 560 576 809 813 847 909 947-950

flags= (ChoicEff macro) 948font= (PlotIt macro) 1199food product (availability) example 468football 61Form variable 333 371 442FORMAT procedure, see PROC FORMATformat statement 387 1126format= (MktEval macro) 1015format= (Paint macro) 1171formats 93 111 136 139 162 168 173 177 183 192

197 201 206 301 304 310 325 335 358 428438 444 453 460 500 507 514 520 698 709-711 721 754 796

formatting a weight variable 796&forms variable 333fractional-factorial design 57 245fractional-factorial design, defined 235framecol= (PlotIt macro) 1203Franke, G.R. 34 1269 1279FREQ procedure, see PROC FREQFreq data set 1015freq statement 442 462-464 524 550-552Freq variable 462FREQ variable 442 523-524freq= 548 957-958freq= (MktAllo macro) 957freqs= 1015freqs= (MktEval macro) 1015frequencies, n-way 1015frequency variable 442-444 460-464Friedman, J.H. 1214 1277frozen diet entrees (advanced) example 709frozen diet entrees (basic) example 695FSum data set 1016full-factorial design 57 245-246 347-348 470 721

807full-factorial design, defined 235G-efficiency 63 246Gabriel, K.R. 28-29 1264 1279Gail, M.H. 679 1279GANNO procedure, see PROC GANNOGarratt, M. 19 67 243 266 471 1213 1280gdesc= (PlotIt macro) 1191Gellat, C.D. 1046 1280general linear univariate model 1214generate statement 999generate= (MktDes macro) 999generic attribute 55 375generic attribute, defined 235

1291

generic design 102 556-563 570-578generic design, defined 235generic (options=, MktDups macro) 1010geometric mean 62 246Gifi, A. 34 683 1214 1269 1279GLM procedure, see PROC GLMglossary 233gname= (PlotIt macro) 1191gopplot= (PlotIt macro) 1190gopprint= (PlotIt macro) 1190gopts2= (PlotIt macro) 1190gopts= (PlotIt macro) 1190gout= (PlotIt macro) 1191GPLOT procedure, see PROC GPLOTGraeco-Latin Square design 1026 1030-1031graphical scatter plots 1178-1210 1231-1274Green, P.E. 37 243 249 262 265 681 1224 1273

1277-1280green= (PlotIt macro) 1205Greenacre, M.J. 34 1269-1270 1273 1279Group variable 1121group= (MktBIBD macro) 974group= (MktMDiff macro) 1123groups= 1107-1108Hadamard matrices 1018 1061 1093-1094Hadamard, J. 96 1018 1279Hastie, T. 34 1214 1269-1270 1279Hayashi, C. 34 1269header statement 686Hedayat, A.S. 267 1018 1279 1282help, contacting technical support 25Hensher, D.A. 1277hminor= (PlotIt macro) 1199hnobs= (PlotIt macro) 1206Hoffman, D.L. 34 1269 1279Hoffman, S.D. 674 1279holdouts

design 711validation 737

holdouts= 583 587holdouts= (MktEx macro) 1074host differences 697 954 962 975 987 1001 1066hpos= (PlotIt macro) 1207href= (PlotIt macro) 1200HRLowerCL 1177HRUpperCL 1177hsize= (PlotIt macro) 1208Huber, J. 19 243 265-268 272 277-278 559 1279-

1280

(i eq choice) 294i (IML restrictions matrix) 157 165 1064 1080

1088ibd= (MktPPro macro) 1151id 150id statement 328 373 439 447ID statement, syntax 796id= 985-986id= (MktBlock macro) 986ideal point model 33 1268identity attribute, conjoint analysis 800identity 327 378 447-449 453 510 524 687 722

765 769 783-785 797-801 1177identity PROC TRANSREG syntax 793IIA 452 459 468 674-676 679IIA, defined 235IML procedure, see PROC IMLimlopts= (MktEx macro) 1078importance

average 745-746defined 690inflated 690outtest= 789

improving an existing design 580in= 909inc= (PlotIt macro) 1200include= (Paint macro) 1171Income variable 530incomplete block designs 963-966 971-975 978

989-994incomplete block designs, defined 236independence from irrelevant alternatives 452

459 468Index variable 563 862 925indicator variables 69 73indicator variables, defined 236individual R-square 744-745 767-768 783-786inertia, correspondence analysis 1270infile statement 887infinite, see recursioninflated, importance 690information matrix 62 246information matrix, defined 236informats 780Ini iteration history table entry 1054init= 137 314 351 361 430 509 580 583 597 816

862 925 950 1059 1074 1088init= (ChoicEff macro) 950init= (MktBal macro) 961

1292

init= (MktEx macro) 1067initblock= (MktBlock macro) 986initialization failed 1052initialization switching 1052initvars= 950initvars= (ChoicEff macro) 950input data 292input function 326 358-359input statement 293 887int (options=, MktEx macro) 1059int= 201 813int= (MktLab macro) 1101interact= 1000interact= (MktDes macro) 999interact= (MktEx macro) 1058interact= (MktRuns macro) 1165interactions 57 386 415 441 469-470 495-496 504interactions, defined 236interpol= (PlotIt macro) 1200interval scale of measurement 246 681 793 1215

1224intiter= 137-138 314 361 430 509 816 925 950-

951intiter= (ChoicEff macro) 950invalid page errors 1211invalue statement 780ireplace 704 722 765 769 783-785 799-801ireplace PROC TRANSREG syntax 792iter= 999iter= (ChoicEff macro) 950iter= (MktBal macro) 961iter= (MktBlock macro) 986iter= (MktDes macro) 999iter= (MktEx macro) 1071iteration

history suppressed 791history, %MktEx 1053-1054history, MktEx 1053maximum number of 790metric conjoint analysis 689nonmetric conjoint analysis 691

iterative algorithm 790j (IML restrictions matrix) 165j1 (IML restrictions matrix) 158 165 1064 1075

1080 1086-1088j2 (IML restrictions matrix) 158 165 1064 1075

1080 1087-1088j3 (IML restrictions matrix) 158 165 1064 1080

1087-1088

Johnson, R.M 265 1277Jones, B. 19 259 1278justinit option 1060justinit (options=, MktEx macro) 1060justparse (options=, MktRuns macro) 1166k (IML restrictions matrix) 165k= 122 974 993k= (MktBIBD macro) 974k= (MktBSize macro) 993k= (MktMDiff macro) 1122keep= 504 881 999keep= (MktDes macro) 999keep= (MktRoll macro) 1158keep=set 137 816Kendall Tau 737Kent, J.T. 1281Key data set

%MktLab 501 538 1094-1099 1102%MktRoll 133 192 311-312 357 386 428 504

546 575 819 878 881 982 1005 1008 10911153-1158

key= 312 500 504 538 803 819 1091 1094-10961099 1103 1153-1158

key= (MktLab macro) 1102key= (MktRoll macro) 1158key=key 192Kharaghania, H. 1018 1280Kirkpatrick, S. 1046 1280knots

defined 1215evenly spaced 795in splines 1215-1230number of 795specifications 795

knots= 380 801knots= PROC TRANSREG syntax 795Krieger, A.B. 265 1280Krishnamurthi, L. 690 1283Kruskal, J.B. 36 1214 1224 1280Kuhfeld, W.F. 1 20 27 40-41 52-53 67 243 265-

268 274 280 285 471 665 680-681 803 10181213-1214 1222 1231 1263 1275-1277 12801283

labcol= (PlotIt macro) 1196label prefix option 790label separator characters 791label, variable 295 298 310 314 326 373 378-380

383 439 447-449 452-457 460-464 500 510523 528-530 1099 1102 1175-1177

1293

label statement 1126label= (Paint macro) 1171label= (PlotIt macro) 1200labelcol= (PlotIt macro) 1203labels= 1099labels= (MktLab macro) 1102labelvar= (PlotIt macro) 1193labfont= (PlotIt macro) 1196labsize= (PlotIt macro) 1196large data sets 442 460largedesign option 239 611 1071 1075largedesign (options=, MktEx macro) 1060%Latin macro

1027Latin Square design 1026-1031layout (MktMDiff macro) 1120Lazari, A.G. 243 249 257 265 278 470 493 680

1280Lebart, L. 34 1269 1280level= (Paint macro) 1170levels

defined 236design 53 245 254order 790

levels= 1093 1096levels= (MktEx macro) 1074libname 310libref 310likelihood 287 291 294-295 329 392 443 452 464-

467 666-670 678-679lineage option 923 1032-1034 1066lineage (options=, MktEx macro) 1060lineage (options=, MktOrth macro) 1134linear arrangement 68-69 72 304 311-313 341 356

413 428 500 504-505 579 604 1153linear arrangement, defined 236linear design, see linear arrangementlinear (options=, MktDups macro) 1010linesleft= 323List data set 1015list (MktBal macro) 960list (MktEx macro) 1056list (MktKey macro) 1092list (MktRuns macro) 1165list= 307list= (MktBlock macro) 986list= (MktEval macro) 1015Liu, S.Y. 1018 1281Lodge variable 357 373 388 428 438

–2 LOG L 297 452 464-466 679LOGISTIC procedure, see PROC LOGISTIClogit model

compared to other simulators 771defined 770market share 778

Louviere, J.J. 19 57 102 121 225 243 253 265 285681 963 1105 1277-1278 1281

lprefix= 142 150 203 314 328 361 373 431 439447-449 510 765 769 783-785

lprefix= PROC TRANSREG syntax 790ls= (PlotIt macro) 1200lsinc= (PlotIt macro) 1201lsizes= (PlotIt macro) 1201Lu, Y. 1018 1284Lubin, J.H. 679 1279Luce, R.D. 44 751 770-773 776-780machine differences 697 954 962 975 987 1001

1066macro

autocall 803documentation 803-1211errors 1211variables 304 333

macro= (Paint macro) 1170macros

%ChoicEff 19-23 69 78 81-88 95 103-106 109112 123-127 137-142 166 170 177-182 188193-195 198-199 203 213-214 240 274 280286-287 313-322 360-361 364-366 394-397407 430 504 508-510 513 524 541-542 556-561 564-567 570 574-576 579 597-599 604607-608 618 621 624-625 628-632 636 645-647 650-656 659 662-663 803-955 979 982985 1005-1006 1012 1102 1126 1145 1148-1150 1275

%EvalEff 493%Latin 1027%MktAllo 286 548-549 803 956-958%MktBal 286 492 803 959-962 1057%MktBIBD 117-122 209-210 226 234 287 641-

642 645 650 654-656 659 662 803-804 930963-978 989-992 1105-1106 1111-1115 11181121-1123 1145 1150-1151

%MktBlock 217 287 426 492 497-498 803 979-988 1098-1099

%MktBSize 121-122 207 226 641-642 660 803971 989-994 1105 1111 1117 1150-1151

%MktDes 803-804 995-1003 1069-1070

1294

%MktDups 95 126 147 174 185 198 206 287 319368 519 564 567 570 576 597 604 607 617628 636 645 649-650 653-659 662 701 803809 812 816-819 951 1004-1011

%MktEval 124 130 147 253 286 306-309 349353-354 413 423 480 485-486 489-493 538588 591 698-701 709-712 754-755 758 803904 959 981 987 1012-1016 1042 1073 1099

%MktEx 19-23 40 60 69 72-73 81 85 93-100 103-105 109 112 115-118 124-130 139 156-159162-166 177 190-191 199-201 213 239-243248 255 264 286-287 304-311 314-316 320333 342-344 347-353 360 393 413-418 422425 471-472 475-476 479 485-492 536-537556-561 564 567 570 574-575 579-580 583587-588 591 595-596 602-605 611-617 621624-628 634-635 639-641 645-647 650 654-656 659 662-663 696-701 709-712 721 751754-756 803-808 815-820 827-829 833-835838-840 844-846 850 858 861 865-867 878881 884 896 903-907 916-931 935-937 940-943 951-952 959-961 979 985 995-998 10021005-1009 1012 1017-1089 1093-1103 11281132-1137 1142-1146 1150-1155 1160-11661211 1275

%MktKey 124 133 191-192 286 356 546 556 575607 617 628 636 803 845-846 897 937 9401085 1090-1092 1153 1158

%MktLab 81 85 103-105 109 112 125 166 177201 287 326 333 353-354 499-501 537-538564 567 596-597 602 697-698 709-712 754-755 803 813-817 858 865 921 926 931 943952 959 979 1006 1026 1062 1074 1093-1104

%MktMDiff 230-234 287 803-804 963 1105-1124%MktMerge 126 149 176 187 221 286 293-294

325 371 387 437 521-522 529 803 1125-1127%MktOrth 106 287 342 660 803-804 922-924

1018 1034-1036 1049 1066-1067 1128-1146%MktPPro 213 234 287 645-647 650 654-656

659 662 803 930 963 1145-1152%MktRoll 22 69 124 134 162 191-192 240-241

286 311-314 320 357 387 429 500 504-505546 556 563 574-575 607 617 628 636 803806 819 845 878 881 898 917 929 937 940943 979 982 985 1005 1008 1012 1085 10911125-1126 1153-1158

%MktRuns 78 124-125 128-130 188-190 199-200247 286 302 340 411 415 471 482-483 535557 695-696 751-753 803-804 865 895 905-

907 916 919 960 1000 1057 1132-1135 11461159-1168 1211

%Paint 803 1169-1172%PHChoice 126 152-153 176 187 221 230 286-

288 296 329 375 378 440 447 524 550 8031108-1109 1173 1177 1211

%PhChoice 1173-1177 1211%PlotIt 40 803 1178-1210 1231-1247 1256

1274%SIM 773 778 784-786

Mahajan, V. 252 1282main effects 57 469-470 495main effects, defined 236&main variable 1087-1088makefit= (PlotIt macro) 1208Manski, C.F. 285 1281Mardia, K.V. 280 1281Market Research Analysis Application 41market share

Bradley-Terry-Luce model 770 778change 780 788example 772logit model 770 778maximum utility model 770-772simulation 772

mass, correspondence analysis 1269mautosource 804max= 415 1161 1166max= (MktRuns macro) 1166maxdesigns= 1073maxdesigns= (MktEx macro) 1071MaxDiff, defined 236maximum number of iterations 790maximum utility model

compared to other simulators 771defined 770example 772

maxinititer= (MktBal macro) 961maxiter= 479 560 608 799-801 809 821-823 838

844 909 950-952 959-961 995 1071maxiter= (ChoicEff macro) 950maxiter= (MktBal macro) 961maxiter= (MktBlock macro) 986maxiter= (MktDes macro) 999maxiter= (MktEx macro) 1071maxiter= (PlotIt macro) 1201maxiter= PROC TRANSREG syntax 790maxlev= 1128maxlev= (MktOrth macro) 1133

1295

maxlev= (MktRuns macro) 1166maxn= 1128maxn= (MktOrth macro) 1133maxoa= (MktRuns macro) 1166maxokpen= (PlotIt macro) 1201maxreps= 122 990maxreps= (MktBSize macro) 993maxstages= (MktEx macro) 1071maxstages=1 1060maxstarts= 959-961maxstarts= (MktBal macro) 961maxtime= 239 349 479 611 634 1071-1073maxtime= (MktEx macro) 1071maxtries= 959maxtries= (MktBal macro) 961MCA 34 48 1272-1273McFadden, D. 256 266 285 468-469 666 1281McKelvey, R.D. 665 1281MDPREF 30 49 1264-1266MDS 36 50MDS procedure, see PROC MDSMEANS procedure, see PROC MEANSMeixia, M. 1018 1284memory, running with less 442method= 722 765 769 783-785method= (MktDes macro) 1000method= (PlotIt macro) 1190method= PROC TRANSREG syntax 790metric conjoint analysis

37conjoint analysis 799defined 682example 687-688 722 765iteration 689versus nonmetric 682 799

Meyer, R.K. 248 255 347 1045 1281Micro variable 504 507 510 515Miller, F.L. 248 1281Miller, R. 1280minimum number of choice sets 556mintry= 487 591mintry= (MktEx macro) 1059missing 500missing statement 500 764missing= (Paint macro) 1172missover 887Mitchell, T.J. 248 1281%MktAllo macro

286 548-549 803 956-957

documentation 956-958%MktBal macro

286 492 803 959-962 1057documentation 959-962

%MktBIBD macro117-122 209-210 226 234 287 641-642 645

650 654-656 659 662 803-804 930 963-973976-978 989-992 1105-1106 1111-1115 11181121-1123 1145 1150-1151

documentation 963-978%MktBlock macro

217 287 426 492 497-498 803 979-984 987 1098-1099

documentation 979-988%MktBSize macro

121-122 207 226 641-642 660 803 971 989-9931105 1111 1117 1150-1151

documentation 989-994%MktDes macro

803-804 995-1002 1069-1070documentation 995-1003

MktDesCat data set 1134MktDesLev data set 1134%MktDups macro

95 126 147 174 185 198 206 287 319 368 519564 567 570 576 597 604 607 617 628 636645 649-650 653-659 662 701 803 809 812816-819 951 1004-1010

documentation 1004-1011%MktEval macro

124 130 147 253 286 306-309 349 353-354 413423 480 485-486 489-493 538 588 591 698-701 709-712 754-755 758 803 904 959 981987 1012-1014 1042 1073 1099

documentation 1012-1016%MktEx macro

19-23 40 60 69 72-73 81 85 93-100 103-105 109112 115-118 124-130 139 156-159 162-166177 190-191 199-201 213 239-243 248 255264 286-287 304-311 314-316 320 333 342-344 347-353 360 393 413-418 422 425 471-472 475-476 479 485-492 536-537 556-561564 567 570 574-575 579-580 583 587-588591 595-596 602-605 611-617 621 624-628634-635 639-641 645-647 650 654-656 659662-663 696-701 709-712 721 751 754-756803-808 815-820 827-829 833-835 838-840844-846 850 858 861 865-867 878 881 884896 903-907 916-931 935-937 940-943 951-

1296

952 959-961 979 985 995-998 1002 1005-1009 1012 1017-1019 1026-1039 1042 1045-1069 1073-1086 1093-1103 1128 1132-11371142-1146 1150-1155 1160-1166 1211 1275

algorithm 347-349 1045-1046documentation 158 1017-1089versus the ChoicEff macro 579common options explained 304 343 351

mktex (options=, MktOrth macro) 1134%MktKey macro

124 133 191-192 286 356 546 556 575 607617 628 636 803 845-846 897 937 940 10851090-1091 1153 1158

documentation 1090-1092%MktLab macro

81 85 103-105 109 112 125 166 177 201 287326 333 353-354 499-501 537-538 564 567596-597 602 697-698 709-712 754-755 803813-817 858 865 921 926 931 943 952 959979 1006 1026 1062 1074 1093-1103

documentation 1093-1104%MktMDiff macro

230-234 287 803-804 963 1105-1110 1119documentation 1105-1124

%MktMerge macro126 149 176 187 221 286 293-294 325 371 387

437 521-522 529 803 1125-1126documentation 1125-1127

%MktOrth macro106 287 342 660 803-804 922-924 1018 1034-

1036 1049 1066-1067 1128-1137 1142-1146documentation 1128-1144

%MktPPro macro213 234 287 645-647 650 654-656 659 662 803

930 963 1145 1150-1151documentation 1145-1152

%MktRoll macro22 69 124 134 162 191-192 240-241 286 311-314

320 357 387 429 500 504-505 546 556 563574-575 607 617 628 636 803 806 819 845878 881 898 917 929 937 940 943 979 982985 1005 1008 1012 1085 1091 1125-11261153-1158

documentation 1153-1158%MktRuns macro

78 124-125 128-130 188-190 199-200 247 286302 340 411 415 471 482-483 535 557 695-696 751-753 803-804 865 895 905-907 916919 960 1000 1057 1132-1135 1146 1159-

1167documentation 1159-1168errors 1211with interactions 415

mktruns (options=, MktOrth macro) 1134model comparisons 392 452 466 678-679model 150 948model statement 295 327-329 373-375 438 447-

449 453 493 574 687 690 704 722 765 769785 799-801 809 948 1000-1002

model statementoptions, PROC TRANSREG 790transformation options, PROC TRANSREG

794transformations 793

model= 82 109 140 170 560 809 859 863 947 950model= (ChoicEff macro) 948monochro= (PlotIt macro) 1204monotone spline

794 800-801 1221conjoint analysis 800

monotone 690 704 797-801monotone PROC TRANSREG syntax 793MORALS algorithm 790morevars= 951morevars= (ChoicEff macro) 951Morineau, A. 34 1269 1280mother logit 452 459 469 524 676-678mother logit model, defined 236bbad (IML restrictions matrix) 1065pbad (IML restrictions matrix) 158 1065

mspline 797 800-801mspline PROC TRANSREG syntax 794multidimensional preference analysis 30 49 1264-

1266multidimensional scaling 36 50multinomial logit 289 294-295 329 447-449 468

665-680multiple choices 535multiple correspondence analysis 34 48 1272-1273multiple (options=, MktRuns macro) 1167multiple2 (options=, MktRuns macro) 1167Mut iteration history table entry 1054mutate= 1072mutate= (MktEx macro) 1072mutations 348-349 1045-1046mutiter= (MktEx macro) 1072mutually orthogonal Latin Square 1026mutually orthogonal Latin Square, see Graeco-

1297

Latin Square design.N special missing value 1095n variable 342 563n= 304 333 416 470 808 879-881 906 918 951 1000

1028n= (ChoicEff macro) 951n= (MktBal macro) 960n= (MktDes macro) 1000n= (MktEx macro) 1057n= (MktRuns macro) 1166Nachtsheim, C.J. 248 253-255 268 347 473 559

1045 1278 1281nalts variable 840 953nalts= 170 193 314 325 361 371 430 509 522

548 576 819 847 947-950 957 985-988 10101125-1126

nalts= (ChoicEff macro) 949nalts= (MktAllo macro) 957nalts= (MktBlock macro) 986nalts= (MktDups macro) 1010nalts= (MktMerge macro) 1126nattrs= 122 963 993 1107-1109 1148nattrs= (MktBIBD macro) 976nattrs= (MktBSize macro) 994nattrs= (MktMDiff macro) 1121nblocks= (MktBlock macro) 986neighbor option 969 975neighbor (options=, MktBIBD macro) 975new products example 780 787next= (MktBlock macro) 986nfill= 597nfill= (MktLab macro) 1102Nguyen, M.V.M. 1018 1281Nguyen, N.K. 19 1018 1281Nishisato, S. 34 1269 1281nknots= 380 800nknots= (PlotIt macro) 1208nknots= PROC TRANSREG syntax 795nlev= 999-1001nlev= (MktDes macro) 1000noanalysis (options=, MktMDiff macro) 1123noback (options=, PlotIt macro) 1194nobeststar (options=, ChoicEff macro) 951noborder (options=, PlotIt macro) 1194nocenter option 1208-1209nocenter (options=, PlotIt macro) 1194nocheck (options=, MktBSize macro) 993noclip (options=, PlotIt macro) 1194nocode (options=, ChoicEff macro) 951

nocode (options=, MktDes macro) 1000nocode (options=, MktMDiff macro) 1123nocode (options=, PlotIt macro) 1194nodelete (options=, PlotIt macro) 1194nodrop (options=, MktMDiff macro) 1123nodups 95nodups option 94-95 308 701 712 1059-1060 1064

1070nodups (options=, ChoicEff macro) 951nodups (options=, MktEx macro) 1060nofinal option 1060-1061nofinal (options=, MktEx macro) 1060nohistory (options=, MktEx macro) 1060nohistory (options=, PlotIt macro) 1194nolegend (options=, PlotIt macro) 1194nominal scale of measurement 1224nominal variables 793None alternative 470 504 524 527 532-534nonlinear transformations 1213nonmetric conjoint analysis

37conjoint analysis 799defined 682example 690-691 704iteration 691versus metric 682 799

nonorthogonal design 711 754-756nooadups (options=, MktEx macro) 1060noprint 769 783-785noprint PROC TRANSREG syntax 790noprint (options=, MktBal macro) 961noprint (options=, MktDups macro) 1010noprint (options=, MktLab macro) 1103noprint (options=, MktRuns macro) 1167noprint (options=, PlotIt macro) 1194noqc (options=, MktEx macro) 1061norestoremissing 150 327 372-373 383 387 438

447 876nosat (options=, MktRuns macro) 1167nosort option 157 583 1032 1064 1068nosort (options=, MktBal macro) 961nosort (options=, MktBlock macro) 987nosort (options=, MktEx macro) 1061nosort (options=, MktMDiff macro) 1123not available 1095notests (options=, ChoicEff macro) 952notruncate 552nowarn option 357nowarn (options=, MktRoll macro) 1158

1298

nox option 477 606nox (options=, MktEx macro) 1061nozeroconstant 327nozeroconstant 327 372 438 447nsets variable 840 953nsets= 122 314 325 361 371 430 509 560 809 947

974 1109 1124-1126 1151nsets= (ChoicEff macro) 948nsets= (MktBIBD macro) 974nsets= (MktBSize macro) 993nsets= (MktMDiff macro) 1121nsets= (MktMerge macro) 1126nudge approach 624number of choice sets, minimum 556number of runs 53 244 302 340 411 415 482-483

535 557 695 752-753 1159number of stimuli 695NumDF variable 768Nums data set 1167n-way frequencies 1015oa (options=, MktBal macro) 962ODS 288 683 1173ods exclude statement 684 687 690 704 722 765

783-785 799-801ODS Graphics 1247-1261ods listing statement 737ods output statement 737offset= (PlotIt macro) 1201onoff (PHChoice macro) 1177OPTEX procedure, see PROC OPTEXoptimal generic choice design 102options, PROC TRANSREG

algorithm 790-791output (statement) 792transformation 794-795

options (MktDups macro) 1010options= (ChoicEff macro) 951options= (MktBal macro) 961options= (MktBIBD macro) 974options= (MktBlock macro) 986options= (MktBSize macro) 993options= (MktDes macro) 1000options= (MktEx macro) 1059options= (MktLab macro) 1103options= (MktMDiff macro) 1123options= (MktOrth macro) 1133options= (MktRoll macro) 1158options= (MktRuns macro) 1166options= (PlotIt macro) 1193

options=+- (MktEx macro) 1062options=3 (MktEx macro) 1062options=512 (MktEx macro) 1062options=512 (MktOrth macro) 1134options=512 (MktRuns macro) 1167options=accept 1052 1059 1062 1066 1086-1088options=accept (MktEx macro) 1059options=allcode (MktDes macro) 1000options=border (PlotIt macro) 1194options=branded (MktDups macro) 1010options=check 351options=check (MktDes macro) 1000options=check (MktEx macro) 1059options=close (PlotIt macro) 1194options=coded (ChoicEff macro) 951options=detail 951-952options=detail (ChoicEff macro) 951options=diag (PlotIt macro) 1194options=dups 924 1034 1066options=dups (MktOrth macro) 1134options=expand (PlotIt macro) 1194options=file 1061options=file (MktEx macro) 1059options=generic (MktDups macro) 1010options=int (MktEx macro) 1059options=justinit 1060options=justinit (MktEx macro) 1060options=justparse (MktRuns macro) 1166options=largedesign 239 611 1071 1075options=largedesign (MktEx macro) 1060options=lineage 923 1032-1034 1066options=lineage (MktEx macro) 1060options=lineage (MktOrth macro) 1134options=linear (MktDups macro) 1010options=mktex (MktOrth macro) 1134options=mktruns (MktOrth macro) 1134options=multiple (MktRuns macro) 1167options=multiple2 (MktRuns macro) 1167options=neighbor 969 975options=neighbor (MktBIBD macro) 975options=noanalysis (MktMDiff macro) 1123options=noback (PlotIt macro) 1194options=nobeststar (ChoicEff macro) 951options=noborder (PlotIt macro) 1194options=nocenter 1208-1209options=nocenter (PlotIt macro) 1194options=nocheck (MktBSize macro) 993options=noclip (PlotIt macro) 1194options=nocode (ChoicEff macro) 951

1299

options=nocode (MktDes macro) 1000options=nocode (MktMDiff macro) 1123options=nocode (PlotIt macro) 1194options=nodelete (PlotIt macro) 1194options=nodrop (MktMDiff macro) 1123options=nodups 94-95 308 701 712 1059-1060

1064 1070options=nodups (ChoicEff macro) 951options=nodups (MktEx macro) 1060options=nofinal 1060-1061options=nofinal (MktEx macro) 1060options=nohistory (MktEx macro) 1060options=nohistory (PlotIt macro) 1194options=nolegend (PlotIt macro) 1194options=nooadups (MktEx macro) 1060options=noprint (MktBal macro) 961options=noprint (MktDups macro) 1010options=noprint (MktLab macro) 1103options=noprint (MktRuns macro) 1167options=noprint (PlotIt macro) 1194options=noqc (MktEx macro) 1061options=nosat (MktRuns macro) 1167options=nosort 157 583 1032 1064 1068options=nosort (MktBal macro) 961options=nosort (MktBlock macro) 987options=nosort (MktEx macro) 1061options=nosort (MktMDiff macro) 1123options=notests (ChoicEff macro) 952options=nowarn 357options=nowarn (MktRoll macro) 1158options=nox 477 606options=nox (MktEx macro) 1061options=oa (MktBal macro) 962options=orthcan (ChoicEff macro) 952options=outputall (ChoicEff macro) 952options=parent (MktOrth macro) 1134options=position (MktBIBD macro) 974options=progress (MktBal macro) 962options=quick 906options=quick (MktEx macro) 1061options=quickr 165 239 422 606 611 846 1071

1075options=quickr (MktEx macro) 1061options=quickt 1061options=quickt (MktEx macro) 1061options=refine (MktEx macro) 1062options=relative 109 317 560 804 809 948 952-

954options=relative (ChoicEff macro) 952

options=render 1059options=render (MktEx macro) 1061options=rescale (MktMDiff macro) 1123options=resrep 159 165 181-182 611 821-823

842 846 951 1062-1064 1080-1082options=resrep (ChoicEff macro) 952options=resrep (MktEx macro) 1062options=sequential (MktBal macro) 962options=serial 968-969options=serial (MktBIBD macro) 975options=source 1162 1167options=source (MktRuns macro) 1167options=square (PlotIt macro) 1194options=textline (PlotIt macro) 1195options=ubd 207 991options=ubd (MktBSize macro) 993optiter= 606 974 1070-1072optiter= (MktBIBD macro) 974optiter= (MktEx macro) 1072order of the spline 801order= 438 704 846 991order= (MktBSize macro) 994order= (MktEx macro) 1075order= (Paint macro) 1171order= PROC TRANSREG syntax 790order=data 327 373order=matrix 417 422 485-488order=random 605 611 622 635 1062order=random=n 634ordering the attribute levels in the output 790ordinal scale of measurement 1224ordinal variables 793-794orthcan (options=, ChoicEff macro) 952orthogonal 58 63orthogonal and balanced 59 63 76-78 303orthogonal array 59orthogonal array, defined 237orthogonal coding 73 76-77 91-92orthogonal contrast coding, defined 237orthogonal, defined 236orthogonal 89otherfac= (MktDes macro) 1000otherint= (MktDes macro) 1000out=

149 170 216 312 325-327 373 438 447 538 548722 765 769 783-785 792 876 952-953 957-958 965 987 1006 1026 1059 1068 1095-10961101 1121 1125-1126 1146 1153-1158

predicted utilities 792

1300

syntax 792transformation 792

out= (ChoicEff macro) 953out= (MktAllo macro) 958out= (MktBal macro) 961out= (MktBIBD macro) 975out= (MktBlock macro) 987out= (MktBSize macro) 994out= (MktDes macro) 1001out= (MktDups macro) 1011out= (MktEx macro) 1068out= (MktLab macro) 1103out= (MktMDiff macro) 1122out= (MktMerge macro) 1126out= (MktPPro macro) 1151out= (MktRoll macro) 1158out= (MktRuns macro) 1167out= (Paint macro) 1170out= (PlotIt macro) 1191out=design 961outall= 1059 1134outall= (MktEx macro) 1068outall= (MktOrth macro) 1134outbest= 193outcat= (MktOrth macro) 1134outcb= (MktEval macro) 1015outcoded= (MktMDiff macro) 1122outcorr= (MktEval macro) 1015Outdups data set 1011outeff= (MktEx macro) 1069outest= 295outf= (MktBIBD macro) 975outfreq= (MktEval macro) 1015outfsum= (MktEval macro) 1016outi= 965outi= (MktBIBD macro) 975outlev= 1130 1134outlev= (MktOrth macro) 1134outlist= (MktDups macro) 1011outlist= (MktEval macro) 1015outorth= (MktRuns macro) 1167outparm= 1124outparm= (MktMDiff macro) 1122output delivery system 288 683 1173output (statement) options, PROC TRANSREG

792output statement 328 373 439 447 690 722 792

799-801outputall (options=, ChoicEff macro) 952

outr= 93 1026 1059 1068-1069 1095outr= (MktBlock macro) 987outr= (MktEx macro) 1069outs= (MktBIBD macro) 975outtest= 722 743 765 769 783-785outtest=

importance 789part-worth utilities 789R-square 789syntax 789utilities 789

outward= (PlotIt macro) 1208p 704 722 765 769 783-785 792 799-801p PROC TRANSREG syntax 792p1-p8 variable 887page errors 1211page, new 335%Paint macro

803 1169documentation 1169-1172

paint= (PlotIt macro) 1204Paley, R.E.A.C 1018 1281Pang, S. 19 1018 1281-1284param=effect 73param=glm 73param=ortheffect 73param=orthref 493param=reference 73parameters 289 295-297 300 380 383 466 469-470

666-667 671 676 679-680parent (options=, MktOrth macro) 1134partial profiles 595 602-640 1062partial= 158 596 602-605 1059 1062 1065 1069

1089partial= (MktEx macro) 1062partial-profile design, defined 237part-worth utilities

constrained 800defined 682displaying 791output option 792outputting predicted 792outtest= 789summing to zero 795

part-worth utility 37 289 300 382 437 441&pass variable 1087-1088Pattern variable 1155-1156p depend variable 736-739 791Pearson r 737

1301

perceptual mapping 28permanent SAS data set 309-311 698 754 792 949

987 1001 1011 1068-1069 1103Perreault, W.D. 1214 1224 1282%PHChoice macro

126 152-153 176 187 221 230 286-288 296 329375 378 440 447 524 550 803 1108-11091173 1177 1211

%PhChoice macrodocumentation 1173-1177errors 1211

PHREG procedure, see PROC PHREGPlace variable 357 373 387-388 428 438-439place= (PlotIt macro) 1201%PlotIt macro

40 803 1178-1187 1190 1194 1207 1231-12471256 1274

documentation 1178-1210plotopts= (PlotIt macro) 1202plotting the transformation 690plotvars= (PlotIt macro) 1195point labels, scatter plots 1178point= 294 336polynomial splines 794 1213-1230positer= 210 644 969positer= (MktBIBD macro) 975position (options=, MktBIBD macro) 974post= (PlotIt macro) 1191Pre iteration history table entry 1054predicted utilities

option 792out= 792variables 736

preference mapping 31 1266prefix, label option 790prefix= (MktLab macro) 1103PREFMAP 31 1266preproc= (PlotIt macro) 1198prescription drugs (allocation) example 535price

assigning actual 326 358 380 387 429 437coding 91-92conjoint analysis 801pricing study 302 884quadratic 91-92 380 384 470

Price variable 81 157 180 311-313 326-327 331357-359 373 377-378 388 428 438 447 452-453 469 500 504 510 513-517 550 882 887956 1153-1156

principal row coordinates, correspondence anal-ysis 1270

PRINCOMP procedure, see PROC PRINCOMPPRINQUAL procedure, see PROC PRINQUALprint= 308 701print= (MktBlock macro) 987print= (MktEval macro) 1016print= (MktPPro macro) 1152printing questionnaire 701 717 760Prob variable 563probability of choice 71 289-291 300-301 331-332

468 667-671problems, contacting technical support 25PROC CATMOD 670PROC CONTENTS 743PROC CORR 737PROC CORRESP 1180 1237-1239 1251-1252PROC DISCRIM 1183PROC FACTEX 347 995-1002 1045 1069 1086-

1087PROC FASTCLUS 748PROC FORMAT 93 111 136 139 162 168 173 177

183 192 197 201 206 301 304 325 358 428444 453 460 500 514 520 698 709-711 721754 780 796

PROC FREQ 115 365 900 903 910 914 1005PROC GANNO 1186PROC GLM 495 1138-1141PROC IML 75-76 115 561 1064 1145 1182PROC LOGISTIC 665PROC MDS 1260PROC MEANS 332 745 910 1069PROC OPTEX 347 477-478 493 995 998-1002

1045-1046 1051 1060 1069-1070 1073 1086-1087

PROC PHREG 152 176 187 221 287-288 294-295298 328-329 373-375 378-380 383-385 390440-444 447-449 457 462-464 524 528 532550-552 668-680 1173

PROC PHREG output, customizing 287 1173-1177

PROC PHREG, common options explained 295PROC PLAN 347 995 998-1001 1045 1069 1086-

1087PROC PLOT 1183-1185 1195 1198-1202 1206-

1209PROC PRINCOMP 1233-1235 1247-1249PROC PRINQUAL 1181-1182 1240-1242 1253-

1254

1302

PROC PROBIT 665PROC REG 364PROC SCORE 331PROC SGPLOT 290 478 771 1257PROC SGRENDER 1250PROC SORT 301 337 703 707 739 748 787PROC SUMMARY 442 522 546PROC TEMPLATE 288 683-686 1173-1176 1211

1250PROC TRANSPOSE 116 334 337 703 719 745

765-767 785PROC TRANSREG

89-90 150 176 187 327-329 372 375 378-380383 386-388 438 447-449 452 457 460-464523 528-530 550 574 687 690 704 722 765769 783-785 789-796 876 1173 1177 11811242-1243 1255-1258

advanced sample 800-801common options explained 327 438customizing output 684discontinuous price sample 801monotone spline sample 800nonmetric example 690nonmetric sample 799rank data sample 799samples 799-801simple example 687specifications 789syntax 789-796typical example 722

processingdata 338 383 575 672 675 687 703 718 764-765results 707 737-739 743-748 767 773 776-787

procopts= (MktDes macro) 1001procopts= (PlotIt macro) 1202progress (options=, MktBal macro) 962proportional hazards 287 294 464 668proportions, analyzing 552proximity data 36ps= (PlotIt macro) 1208pseudo-factors 999pspline 380pspline PROC TRANSREG syntax 794put 219put function 326 358put statement 437quadratic price effects 91-92 380 384 470quantitative attribute 91-92 331 377 380 441questionnaire

323-324 333-337 369 433printing 701 717 760

quick option 906quick (options=, MktEx macro) 1061quickr option 165 239 422 606 611 846 1071 1075quickr (options=, MktEx macro) 1061quickt option 1061quickt (options=, MktEx macro) 1061radii= (PlotIt macro) 1208Raktoe, B.L. 267 1282Ramsay, J.O. 1214 1222 1282Ran iteration history table entry 1054random mutations 348-349 422 1045-1046random number seeds 94 304 343 351 478 492 497

560 697 809 954 962 975 987 1001 1066random number seeds, defined 237randomization 57 93 309-310 333 433 504 698randomization, defined 237Randomized data set 1026 1069range= 1034 1128range= (MktOrth macro) 1134rank data

conjoint analysis 799data collection 718data entry 703 718data validation 718reflect 799versus rating-scale data 799

rank PROC TRANSREG syntax 794Rank variable 703-705 708Rao, C.R. 1018 1282Rao, V.R. 37 681 1279Rating variable 688rating-scale data

data entry 687 764versus rank data 799

recursion, see infinitered bus 469red= (PlotIt macro) 1205reference cell coding, defined 237reference level 73 300 377 382-383 470reference level, defined 237Reference variable 342reference-cell coding 79refine (options=, MktEx macro) 1062reflect

704 722 799-801rank data 799syntax 795

1303

reflection 705 795REG procedure, see PROC REGregdat= (PlotIt macro) 1198regfun= (PlotIt macro) 1208regopts= (PlotIt macro) 1208regprint= (PlotIt macro) 1209Reibstein D.J. 690 1283relative option 109 804 809 948 952-954relative (options=, ChoicEff macro) 952rename= 1039render option 1059render (options=, MktEx macro) 1061repeat= (MktEx macro) 1072replacing independent variables, ireplace 792rescale (options=, MktMDiff macro) 1123rescale= 1123-1124rescale= (MktMDiff macro) 1123residuals PROC TRANSREG syntax 792reslist= (MktEx macro) 1063resmac= (MktEx macro) 1063resolution 58 245resolution, defined 237resrep option 159 165 181-182 611 821-823 842

846 951 1062-1064 1080-1082resrep (options=, ChoicEff macro) 952resrep (options=, MktEx macro) 1062restrictions 471-472 475 479 485 488 491 595 602-

639 754 1064-1065 1086restrictions matrix

bbad 158 165pbad 165

bad 157-158 165 180 471-472 612 820-822 830953 1064-1065

i 157 165 1064 1080 1088j 165j1 158 165 1064 1075 1080 1086-1088j2 158 165 1064 1075 1080 1087-1088j3 158 165 1064 1080 1087-1088k 165bbad 1065pbad 158 1065

try 158 165 1064 1080 1088x 157-158 165 180 820-821 840 851 953 1064-

1065 1080 1087-1088x1 158 165 1064 1080 1141x2 1080 1141xmat 157-158 165 180 840 851 953 1064 1080

1087-1088restrictions not met 1052

restrictions, based on meaningful names and la-bels 475-477

restrictions= 157 180 472 476-479 485 488 491605 611-613 621 626 634 821 845-846 10591062-1066 1069

restrictions= (ChoicEff macro) 953restrictions= (MktEx macro) 1064results processing 707 737-739 743-748 767 773

776-787Results data set 953resvars= 180 821 953retain statement 167 887 908revars= (ChoicEff macro) 953Reynolds, M.L. 36 1282rgbround= (Paint macro) 1171rgbround= (PlotIt macro) 1205rgbtypes= (PlotIt macro) 1196ridge= 861 1042ridge= (MktBlock macro) 987ridge= (MktEx macro) 1078ridging 861 987 1078rolled out data set 791row profiles, correspondence analysis 1270RowHeader 1175row-neighbor balance 966-968rscale 87rscale= 214 322 839 952-954rscale= (ChoicEff macro) 954rscale=20 648 1149rscale=partial= 931-933 954R-square

individual 744-745 767-768 783-786outtest= 789

Rubinstein, L.V. 679 1279Run variable 985 988 1098run= (MktDes macro) 1001runs, defined 237runs, number of 53 244 302 340 411 415 482-483

535 557 695 752-753 1159sasuser 310saturated design 78 303 341 413scales of measurement 1224scatter plots 1178Scene variable 357 373 388 428 438Schaffer, C.M. 1273 1277Schiffman, S.S. 36 1282SCORE procedure, see PROC SCOREscore= 331Seberry, J. 1018 1282

1304

second choice 292 295seed, see random number seedsseed= 94 304 560 697 809 954 962 975 987 1001

1066seed= (ChoicEff macro) 954seed= (MktBal macro) 962seed= (MktBIBD macro) 975seed= (MktBlock macro) 987seed= (MktDes macro) 1001seed= (MktEx macro) 1066seeds, defined 237select= (Paint macro) 1171separator characters 791separators= 361 431 439 449 453 510 687 690

704 722 765 769 783-785separators= PROC TRANSREG syntax 791sequential algorithm 493sequential (options=, MktBal macro) 962serial option 968-969serial (options=, MktBIBD macro) 975Set 988set statement 294 336 908Set variable 134 292 295-297 313-314 326 329

333-338 361 430 442-444 452-453 462 509547 563 597 816 925 985 1158

set variable 909set= 985set= (MktBlock macro) 988set= (MktRoll macro) 1158setnum variable 840 953setsize= 122 993 1107-1109 1148setsize= (MktBIBD macro) 974setsize= (MktBSize macro) 993setsize= (MktMDiff macro) 1122SetType variable 909setvars= 325 371 1125-1126setvars= (MktMerge macro) 1126SGPLOT procedure, see PROC SGPLOTSGRENDER procedure, see PROC SGRENDERShape variable 1154-1156shelf talker 468 499 504 528Shelf variable 504 507 510 515Shepard, R.N. 1214 1224 1280short 687 704 722 765 769 783-785 799-801short PROC TRANSREG syntax 791show= (Paint macro) 1171Side variable 428 439%SIM macro

773 778 784-786

simulated annealing 348-349 422 1045-1046 10701078

simulationdata entry 781 785example 772 778-780market share 772observations 721 739 743 796

simulators, conjointBradley-Terry-Luce model 770compared 771example 776logit model 770macro 773maximum utility model 770-772

Size variable 1154-1156size= (MktDes macro) 1001Sloane, N.J.A. 19 1018 1279 1282Smith, P.L. 1216 1282So, Y.C. 19 285 665SORT procedure, see PROC SORTsource option 1162 1167source statement 1175source stat.phreg statement 1173source stat.transreg statement 684source (options=, MktRuns macro) 1167spaghetti sauce example 751special missing value 500Spence, E. 1018 1282spline 797spline PROC TRANSREG syntax 794splines

797 1213-1230degree 794derivatives 1218discontinuous functions 1219monotone 794 800-801 1221order 801with knots 1216

split-plot design 1031-1044square (options=, PlotIt macro) 1194Srinivasan, V. 37 681 1224 1279sta 82 109 140 317 809 953standard column coordinates, correspondence

analysis 1270standardized orthogonal contrast coding, defined

237standorth 82 89 109 140 317 804 809 948statements= 712statements= (MktLab macro) 1103

1305

statements= (MktMerge macro) 1126Statistic variable 744Steckel, J.H. 252 1282Steinberg, D. 1277step= 1001-1002step= (MktDes macro) 1002stimuli, number of 695 751-753stimulus creation, DATA step 701 717 760Stinson, D.R. 1282stmts= 387stopearly= 1052stopearly= (MktEx macro) 1077stopping early 1052Stove variable 500strata 295-297 329-331 442-444 460 466-467 668-

670 679strata statement 295 329 444 462Street, D. 102 1282structural zeros 300 383 392Stufken, J. 1018 1278-1279style= (PlotIt macro) 1204Style=RowHeader 1175subdesign 470 493subdesign evaluation macro 493Subj variable 292 295-297 326 329 444-445 452-

453 523subject attributes 529submat= (ChoicEff macro) 954subsequent choice 292 295 371 444Suen, C.Y. 19 1018 1280-1283SUMMARY procedure, see PROC SUMMARYsummary table 296-297 464 527summing to zero, part-worth utilities 795support, contacting technical support 25survival analysis 287 295 668Swait, J. 1277switching initialization 1052symbols= (Paint macro) 1171symbols= (PlotIt macro) 1196symcol= (PlotIt macro) 1197symfont= (PlotIt macro) 1197symlen= (PlotIt macro) 1195symmetric design 112symmetric design, defined 237symsize= 1078symsize= (PlotIt macro) 1197symtype= (PlotIt macro) 1197symvar= (PlotIt macro) 1195sysevalf function 648 954 1149

t= 122 963 991-993t= (MktBIBD macro) 976t= (MktBSize macro) 994t= (MktMDiff macro) 1121Tab iteration history table entry 422 1054tabiter= 606tabiter= (MktEx macro) 1073tabsize= (MktEx macro) 1078Taguchi, G. 1018 1283Takane, Y. 1214 1224 1281 1284target= (MktEx macro) 1078Tayfeh-Rezaiea, B. 1018 1280t depend variable 736-739 791technical support, contacting 25tempdat1= (PlotIt macro) 1198tempdat2= (PlotIt macro) 1198tempdat3= (PlotIt macro) 1198tempdat4= (PlotIt macro) 1198tempdat5= (PlotIt macro) 1198tempdat6= (PlotIt macro) 1198TEMPLATE procedure, see PROC TEMPLATEtemplate, utilities table 684temporary 323 886

Tenenhaus, M. 34 1269 1283Terry, M.E. 44 751 770-773 776-780textline (options=, PlotIt macro) 1195Tibshirani, R. 1214 1279tickaxes= (PlotIt macro) 1202tickcol= (PlotIt macro) 1204tickfor= (PlotIt macro) 1202ticklen= (PlotIt macro) 1202ties=breslow 153 287 294-295 329 464time (computer), saving 442Timmermans, H. 1277titlecol= (PlotIt macro) 1204Tobias, R.D. 19 67 243 266 285 471 1018 1280toobig= (MktRuns macro) 1167trace of a matrix 62trade-offs 681TRank variable 708transformation

class 793identity 793monotone spline 794 800-801monotone 793mspline 794 800-801options, PROC TRANSREG 794-795out= 792plot 690

1306

polynomial spline 794pspline 794rank 794regression 1213 1222spline 794

TRANSPOSE procedure, see PROC TRANS-POSE

TRANSREG procedure, see PROC TRANSREGtranstrn function 1034& trgind variable 329-331 375 378-380 385 390

440-442 447-449 454 457 462-464 524 532550-552 736 739 747-748

try (IML restrictions matrix) 158 165 1064 10801088

tsize= (PlotIt macro) 1202Turyn, R.J. 1018 1283–2 LOG L 297 452 464-466 679types= 913 954-955types= (ChoicEff macro) 954types= (PlotIt macro) 1197typevar= 913 954-955typevar= (ChoicEff macro) 955typevar= (PlotIt macro) 1198typical options, conjoint analysis 799ubd option 207 991ubd (options=, MktBSize macro) 993Unb iteration history table entry 1054unbalanced block design, defined 237unbalanced= (MktEx macro) 1073unit= (PlotIt macro) 1209UNIVARIATE algorithm 790unrealistic combinations 754utilities

37constrained 800defined 682 689displaying 791outputting predicted 792outtest= 789predicted 736table, template 684

utilities 687 690 704 722 765 769 783-785 799-801

utilities PROC TRANSREG syntax 791vacation (alternative-specific) example 410vacation example 339 393validation, holdouts 737Value variable 744 768values= 1099-1104

values= (MktLab macro) 1103values= (Paint macro) 1170van der Burg, E. 1214 1277van Rijckevorsel, J. 1214 1278 1282var variable 1239var= (Paint macro) 1170variable label 295 298 310 314 326 373 378-380

383 439 447-449 452-457 460-464 500 510523 528-530 1099 1102 1175-1177

variableAge 530Alt 333 982 985Alt 1157altnum 840 953AltType 909attrlist 1109Block 357 537 550 979 985 1098Brand 81 89 134 311-313 326-327 447 452-453

504 510 513-517 530 544 547 550 858-859865 882 886-887 890 908 956 985-986 1153-1157

c 292-295 326 331 336 371 452-453 523 548956 1125-1126

Choice 294Choose 337Client 1095Color 1154-1156Count 157 180 544-546 956DenDF 768depend 736 791depvar 736-739 743-745 767 783-785Design 342 563Dest 369&droplist 767-769 781 785Efficiency 563f 583f1 168-170 178 858 886f2 168-170 178 858 886f3 168-170 178f4 168-170 178Form 333 371 442&forms 333Freq 462FREQ 442 523-524Group 1121Income 530Index 563 862 925Lodge 357 373 388 428 438&main 1087-1088

1307

Micro 504 507 510 515n 342 563nalts 840 953nsets 840 953NumDF 768p1-p8 887&pass 1087-1088Pattern 1155-1156p depend 736-739 791Place 357 373 387-388 428 438-439Price 81 157 180 311-313 326-327 331 357-

359 373 377-378 388 428 438 447 452-453469 500 504 510 513-517 550 882 887 9561153-1156

Prob 563Rank 703-705 708Rating 688Reference 342Run 985 988 1098Scene 357 373 388 428 438Set 134 292 295-297 313-314 326 329 333-338

361 430 442-444 452-453 462 509 547 563597 816 925 985 1158

set 909setnum 840 953SetType 909Shape 1154-1156Shelf 504 507 510 515Side 428 439Size 1154-1156Statistic 744Stove 500Subj 292 295-297 326 329 444-445 452-453 523t depend 736-739 791TRank 708& trgind 329-331 375 378-380 385 390 440-442

447-449 454 457 462-464 524 532 550-552736 739 747-748

Value 744 768var 1239w 507-509 522 712 736weight 723 781y 1138

variablesinterval 793nominal 793ordinal 793-794predicted utilities 736replacing in output data set 792

residuals 792variance matrix 62 246variance matrix, defined 237vars= 353 548 957vars= (MktAllo macro) 958vars= (MktBlock macro) 985vars= (MktDups macro) 1011vars= (MktEval macro) 1015vars= (MktLab macro) 1104vars= (MktMDiff macro) 1124Vecchi, M.P. 1046 1280vechead= (PlotIt macro) 1209vector model 32 1266view= 493Violations iteration history table entry 1054vminor= (PlotIt macro) 1202vnobs= (PlotIt macro) 1206vpos= (PlotIt macro) 1209vref= (PlotIt macro) 1203vsize= (PlotIt macro) 1209vtoh= (PlotIt macro) 1209w variable 507-509 522 712 736Wallis, W.D. 1018 1279Wang, J.C. 19 97 1018 1283Wang, Y. 1018 1284Warwick, K.M. 34 1269 1280Watson, W. 19 41wb 1107 1121wbalt 1107 1121wbaltpos 1108 1121wbpos 1108 1121weight format 721 796weight statement 722 783-785 799-801weight statement

conjoint analysis 800holdouts 796syntax 796

weight variable 723 781weight variable, formatting 721weight= 509 913weight= (ChoicEff macro) 955weighted loss function 796weights= (MktBIBD macro) 976Weiguo, L. 1018 1284whack approach 621 624 634where statement 493 522 550 924 997 1003 1137where= 331where= (MktDes macro) 1003Wiley, J.B. 265 1277

1308

MR-2010 — Index 1309

Williamson, J. 1018 1283Wind, Y. 37 243 249 262 681 1279Winsberg, S. 1214 1283Wish, M. 36 1280With Covariates 297 392 452Wittink, D.R. 243 690 1280 1283Woodworth, G. 243 265 285 1281worksize= 1078Wu, C.F.J. 97 1018 1283Wurst, J. 19x (IML restrictions matrix) 157-158 165 180 820-

821 840 851 953 1064-1065 1080 1087-1088x1 (IML restrictions matrix) 158 165 1064 1080

1141x2 (IML restrictions matrix) 1080 1141x= (MktPPro macro) 1152xmat (IML restrictions matrix) 157-158 165 180

840 851 953 1064 1080 1087-1088xmax= (PlotIt macro) 1210Xu, H. 1018 1283y variable 1138Yamada, M. 1018 1282-1283

ymax= (PlotIt macro) 1210Young, F.W. 20 34-36 683 1214 1224 1269 1281-

1284Young, G. 29 1264 1278Zavoina, W. 665 1281zero= 79-81 89 237 327 361 373 377-378 383 431

438-439 447-449 460 509-510 524 597 687690 704 722 765 769 783-785 799-801 862882 1123-1124

zero=syntax for choice models 78-81 89-91

zero=list 378 438-439zero= PROC TRANSREG syntax 795zero=’ ’ 89-91 438-439 859zero=first 79-80zero=last 79-80zero=none 79-80 89 859zero=sum 79Zhang, Y.S. 19 1018 1281-1284Zheng, Z. 1018 1284Zwerina, K. 19 265-268 272 277-278 559 1279

Documents

Marketing Research Methods in SASsupport.sas.com/techsup/technote/mr2010title.pdfMarketing Research Methods in SAS Experimental Design, Choice, Conjoint, and Graphical Techniques Warren