Marketing Research Methods in SAS Experimental Design, Choice, Conjoint, and Graphical Techniques Warren F. Kuhfeld October 1, 2010 SAS 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 te xt, macros, and code are provided “as is.” There are no warranties, expressed or implied, as to merchantability or fitness for a particular purpose regarding the accuracy of the materials or code contained herein. SAS r  , SAS/AF r  , SAS/ETS r  , SAS/GRAPH r  , SAS/IML r  , SAS/QC r  , and SAS/STAT r  are trade- marks or registered trademarks of SAS in the USA and other countries. r  indicates USA registration. Contents Overview Marketing Research: Uncovering Competitive Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . 27–40 This 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), preference mapping (PREFMAP or external unfolding), correspondence analysis, multidimensional scaling, and conjoint analysis. Introducing the Market Research Analysis Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41–52 This SUGI paper discusses a point-and-click interface for conjoint analysis, correspondence analysis, and multidimensional scaling. Experimental Design: Efficiency, Coding, and Choice Designs . . . . . . . . . . . . . . . . . . . . . 53–241 This 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 co ding. If you are interested in choice modeling, read this chapter first. Efficient Exp eri mental Design with Marketing Research Applications . . . . . . . . . . . 243–265 This chapter is based on a Journal of Marketing Research paper and disc usse s D-efficient experimental designs for conjoint and discrete-choice studies, orthogonal arrays, nonorthogonal designs, relative efficiency, and nonorthogonal design algorithms. A General Method for Constructing Efficient Choice Designs . . . . . . . . . . . . . . . . . . . . 265–283 This chapter discusses efficient designs for choice experiments. Discrete Choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285–663 This chapter discusses the multinomial logit model and discrete choice experiments. This is the longest chapter in the book, and it contains numerous examples covering a wide range of choice experiments and choice designs. Study the chapter Experimental Design: Effici ency, Coding, and Choice Designs before tackling this chapter. Multinomial Logit Mo del s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665–680 This SUGI paper discusses the multinomial logit model. A travel example is discussed. Conjoint Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 681–801 This chapter discusses conjoint analysis. Examples range from simple to complicated. Topics include design, data collection, analysis, and simulation. PROC TRANSREG documentation that describes just those options that are most likely to be used in a conjoint analysis is included. The Macros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803–1211 This chapter provides e xamples and documentation for all of the autocall macros used in this book. Linear Models and Conjoint Analysis with Nonlinear Spline Transformations 1213–1230 This 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 regression and conjoint analysis. Graphical Scatter Plots of Labeled Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1231–1261 This chapter is based on a paper that appeared in the SAS journal Observations that discusses a macro for graphical scatter plots of labeled points. ODS Graphics is also mentioned. Graphical Methods for Marketing Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1263–1274 This chapter is based on a National Computer Graphics Association Conference presentation and discusses 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 Conjoint Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 Gener al 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 R elative 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 Multinomial Logit Model Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 Probability of Choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 Custom Questionnaires . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Processing the Data for Custom Questionnaires . . . . . . . . . . . . . . . . . . . . . . . 337 Vacation Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Set Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 Designing the Choice Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 The %MktEx Macro Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 Examining the Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 From a Linear Arrangement to a Choice Design . . . . . . . . . . . . . . . . . . . . . . . 356 Testing the Design Before Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . 360 CONTENTS 9 Generating the Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 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 A rtifici al Data Generation . . . . . . . . . . . . . . . . . . . . 393 Vacation Example with Alternative-Speci fic Attributes . . . . . . . . . . . . . . . . . 410 Choosing the Numb e r of Choice Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411 Designing the Choice Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 Ensuring that Certain Key Interactions are Estimable . . . . . . . . . . . . . . . . . . . 415 Examining the Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423 Blocking an Existing Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426 Testing the Design Before Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . 430 Generating the Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 Generating Artificial Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 436 Reading, Processing, and Analyzing the Data . . . . . . . . . . . . . . . . . . . . . . . . 437 Aggregating the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442 Brand Choice Example with Aggregate Data . . . . . . . . . . . . . . . . . . . . . . . 444 Processing the 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 Designing the Choice Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471 Restrictions Formulated Using Actual Attribute Names and Levels . . . . . . . . . . . . 475 When You Have a Long Time to Search for an Efficient Design . . . . . . . . . . . . . . 477 10 CONTENTS Examining the Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 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 Testing the Design Before Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . 504 Generating Artificial Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 520 Processing the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521 Cross-Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523 Multinomial Logit Model Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524 Modeling Subject Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 529 Allocation of Prescription Drugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535 Designing the Allocation Expe riment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535 Processing the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 Coding and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 550 Multinomial Logit Model Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 Des ign . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595 Linear Partial-Profile Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 602 Choice from Triples; Partial Profiles Constructed Using Res trictions . . . . . . . . . . . 604 Six Alternatives; Partial Profiles Constructed Using Restrictions . . . . . . . . . . . . . 610 [...]... Index 1285 Preface Marketing Research Methods in SAS discusses experimental design, discrete choice, conjoint analysis, and graphical and perceptual mapping techniques The book has grown and evolved over many years and many revisions For example, the section on choice models grew from a two-page handout written by Dave DeLong in 1992 This edition was written for SAS 9.2 and subsequent SAS releases This... mapping, multidimensional preference analysis, and multidimensional scaling These methods allow you to analyze purchasing decision trade-offs, display product positioning, and examine differences in customer preferences They can help you gain insight into your products, your customers, and your competition This chapter discusses these methods and their implementation in SAS. ∗ Introduction Marketing research. .. Advantages Marketing researchers try to answer these questions using both standard data analysis methods, such as descriptive statistics and crosstabulations, and more specialized marketing research methods This chapter discusses two families of specialized marketing research methods, perceptual mapping and conjoint analysis Perceptual mapping methods produce plots that display product positioning, product... Contact Information Provide your full contact information: name, phone number, email address, and site number • Information about your SAS Version and Market Design Macros Please include information about the version of SAS that you have installed and are using You can find this information under Help → About SAS Please include information about the version of the macros that you have installed and are using... interpretable in MCA MR-2010A — Marketing Research: Uncovering Competitive Advantages Figure 6 Multiple Correspondence Analysis 35 36 MR-2010A — Marketing Research: Uncovering Competitive Advantages Figure 7 MDS and PREFMAP Multidimensional Scaling Multidimensional scaling (MDS) is a class of methods for estimating the coordinates of a set of objects in a space of specified dimensionality from data measuring... design Did you see an warning or error message in connection with your problem? If so, please attach a copy of the message to your technical support inquiry, and include a copy of the SAS log file for the analysis 26 Marketing Research: Uncovering Competitive Advantages Warren F Kuhfeld Abstract SAS provides a variety of methods for analyzing marketing data including conjoint analysis, correspondence... model and some pre- and post-processing The regression model uses the MDS or MDPREF coordinates as independent variables along with an additional independent variable that is the sum of squares of the coordinates The model is a constrained response-surface model 34 MR-2010A — Marketing Research: Uncovering Competitive Advantages The results in Figure 5 were modified from the raw results to eliminate... This can be intimidating when you are first getting started The following information can help you get started: • If you are new to choice modeling and choice design, and you want to understand what you are doing, you should start by reading the Experimental Design: Efficiency, Coding, and Choice Designs” chapter, which starts on page 53 It is a self-contained short course on basic choice design, complete... modeling intelligently, you need to understand the coding and modeling issues discussed in the experimental design chapter and 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 information on many topics related to choice modeling, and. .. marital/family status (single, married, single and living with children, and married living with children), and sex (male, female) The variables are all categorical The top-right quadrant of the plot suggests that the categories single, single with kids, one income, and renting a home are associated Proceeding clockwise, the categories sporty, small, and Japanese are associated In the bottom-left quadrant you . Marketing Research Methods in SAS Experimental Design, Choice, Conjoint, and Graphical Techniques Warren F. Kuhfeld October 1, 2010 SAS 9.2 Edition MR-2010 Copyright c . 17 Concluding Remarks 1275 References 1277 Index 1285 Preface Marketing Research Methods in SAS discusses experimental design, disc rete choice, conjoint analysis,