Marketing research an applied orientation (6th edition)

A BRIEF GUIDE TO USING THIS TEXT The sixth edition of Marketing Research offers several features that will help you throughout this course FEATURE BENEFIT/DESCRIPTION PAGE REFERENCE SPSS and SAS computerized demonstration movies, screen captures, step-by-step instruction, and technology manual This edition offers the most extensive help available anywhere for learning these programs (1) From the Web site for this book you can download computerized demonstration movies illustrating step-by-step instructions for every data analysis technique covered in the chapters (2) You can also download screen captures with notes illustrating these step-by-step instructions (3) You can study detailed step-by-step instructions in the chapters (4) You can refer to the Study Guide and Technology Manual, a supplement that accompanies this book We provide the most extensive help available anywhere for learning SPSS and SAS You can learn these software tools on your own e.g., 419, 439 Stimulating critical-thinking practice Socratic questioning, critical reading and writing, and higher-order thinking and assessment are built into three comprehensive critical-thinking cases (2.1 American Idol, 2.2 Baskin-Robbins, and 2.3 Akron Children's Hospital), end-of-chapter review questions, applied problems, and group discussions These materials have been designed along guidelines from the Foundation for Critical Thinking Develop critical-thinking skills while you learn marketing research 780-787 Concept maps These maps will help you visualize interrelationships among concepts These are one or more concept maps per chapter based on the findings of the Institute for Human and Machine Cognition This tool has been proven to enhance learning e.g., 28 A focus on international issues Every chapter has a section entitled “International Marketing Research” or a Real Research example illustrating a data analysis technique in an international setting A capstone chapter discusses some advanced concepts in international marketing research (Chapter 24) Be better prepared to meet global challenges! e.g., 23 A focus on ethics Every chapter has a section entitled “Ethics in Marketing Research,” which takes the perspectives of the four stakeholders (the client, the marketing research firm, respondents, and the general public) or a Real Research example illustrating data analysis techniques with ethical implications You will develop an ethical mindset e.g., 25 A new running case A running case on Dell features real data with questions in every chapter This case is another way for you to see the linkages among chapters and trace the entire marketing research process throughout the book e.g., 30 Two comprehensive cases These cases feature actual questionnaires and real data collected by prominent marketing research firms These cases are 4.1 JPMorgan Chase (new) and 4.2 Wendy’s (updated); they include questions for all the chapters Three additional data analysis cases offer actual questionnaires and real data (3.1 AT&T, 3.2 IBM, and 3.3 Kimberly-Clark) See how professionals research 788–820 New video cases New video cases for each chapter contain questions pertaining to that chapter as well as the preceding chapters You can study the cases with or without the videos, but a picture is worth a thousand words e.g., 31 A live marketing research project Each chapter lets you complete one or more live marketing research projects that are sponsored by outside firms Learn research by doing it! e.g., 29 Extensive end-of-chapter exercises These exercises help you learn, apply, and practice concepts through review questions, applied problems, and group discussion The data analysis chapters include several datasets whose files are provided in both SPSS and SAS Practice makes perfect e.g., 29-30 This page intentionally left blank Marketing Research This page intentionally left blank Marketing Research An Applied Orientation Sixth Edition Naresh K Malhotra Georgia Institute of Technology Prentice Hall Boston Amsterdam Delhi Columbus Cape Town Mexico City Indianapolis New York Dubai Sao Paulo London Sydney Madrid San Francisco Milan Hong Kong Munich Seoul Upper Saddle River Paris Montreal Singapore Taipei Toronto Tokyo Editorial Director: Sally Yagan Acquisitions Editor: James Heine Product Development Manager: Ashley Santora Director of Marketing: Patrice Lumumba Jones Senior Marketing Manager: Anne Fahlgren Marketing Assistant: Susan Osterlitz Senior Managing Editor: Judy Leale Production Project Manager: Kelly Warsak Permissions Project Manger: Charles Morris Senior Operations Supervisor: Arnold Vila Operations Specialist: Ben Smith Art Director: Steven Frim Cover Designer: Steven Frim Cover Art: Steven Frim Interior Text: Jill Little Creative Director: Christy Mahon IRC Manager, Rights and Permissions: Zina Arabia Manager, Visual Research: Beth Brenzel Photo Researcher: Kathy Ringrose Image Permission Coordinator: Angelique Sharps Manager, Cover Visual Research & Permissions: Karen Sanatar Media Director: Lisa Rinaldi Lead Media Project Manager: Denise Vaughn Full-Service Project Management: Jennifer Welsch/BookMasters, Inc Composition: Integra Software Services Printer/Binder: Edwards Brothers Cover Printer: Lehigh-Phoenix Color Text Font: 10/12 Times Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook appear on appropriate page within text SAS logo is provided courtesy of SAS Institute Copyright © SAS Institute, Inc., Cary NC All Rights Reserved Used with permission SAS and all other SAS Institute Inc product or service names are registered trademarks or trademarks of SAS Institute Inc in the USA and other countires ® indicates USA registration Microsoft® and Windows® are registered trademarks of the Microsoft Corporation in the U.S.A and other countries Screen shots and icons reprinted with permission from the Microsoft Corporation This book is not sponsored or endorsed by or affiliated with the Microsoft Corporation Copyright © 2010, 2007, 2004, 1999, 1996 Pearson Education, Inc., publishing as Prentice Hall, One Lake Street, Upper Saddle River, New Jersey 07458 All rights reserved Manufactured in the United States of America This publication is protected by Copyright, and permission should be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise To obtain permission(s) to use material from this work, please submit a written request to Pearson Education, Inc., Permissions Department, One Lake Street, Upper Saddle River, New Jersey 07458 Many of the designations by manufacturers and seller to distinguish their products are claimed as trademarks Where those designations appear in this book, and the publisher was aware of a trademark claim, the designations have been printed in initial caps or all caps Library of Congress Cataloging-in-Publication Data Malhotra, Naresh K Marketing research : an applied orientation / Naresh K Malhotra—6th ed p cm Includes bibliographical references and index ISBN 978-0-13-608543-0 (casebound : alk paper) Marketing research Marketing research—Methodology I Title HF5415.2.M29 2010 658.8'3—dc22 2009011023 10 ISBN 10: 0-13-608543-1 ISBN 13: 978-0-13-608543-0 To the memory of my father, Mr H N Malhotra and To my mother, Mrs Satya Malhotra and To my wife Veena and children Ruth and Paul The love, encouragement, and support of my parents, wife, and children have been exemplary “The greatest of these is love.” I Corinthians 13:13 “But God showed how much He loved us by having Christ die for us, even though we were sinful.” Romans 5:8 The Holy Bible This page intentionally left blank Brief Contents Foreword xxi Preface xxiii Acknowledgments xxix Author Biography xxxi PART I Introduction and Early Phases of Marketing Research Chapter Chapter PART II Introduction to Marketing Research Defining the Marketing Research Problem and Developing an Approach 34 Research Design Formulation Chapter Chapter Chapter Chapter Chapter Chapter Chapter Chapter 10 Chapter 11 Chapter 12 67 Research Design 68 Exploratory Research Design: Secondary Data 98 Exploratory Research Design: Qualitative Research 136 Descriptive Research Design: Survey and Observation 176 Causal Research Design: Experimentation 213 Measurement and Scaling: Fundamentals and Comparative Scaling 248 Measurement and Scaling: Noncomparative Scaling Techniques 272 Questionnaire and Form Design 300 Sampling: Design and Procedures 336 Sampling: Final and Initial Sample Size Determination 370 PART III Data Collection, Preparation, Analysis, and Reporting Chapter 13 Chapter 14 Chapter 15 Chapter 16 Chapter 17 Chapter 18 Chapter 19 Chapter 20 Chapter 21 Chapter 22 Chapter 23 Chapter 24 399 Fieldwork 400 Data Preparation 418 Frequency Distribution, Cross-Tabulation, and Hypothesis Testing 448 Analysis of Variance and Covariance 496 Correlation and Regression 528 Discriminant and Logit Analysis 568 Factor Analysis 602 Cluster Analysis 628 Multidimensional Scaling and Conjoint Analysis 656 Structural Equation Modeling and Path Analysis 690 Report Preparation and Presentation 726 International Marketing Research 752 CASES Case 1.1 Case 2.1 Case 2.2 Case 2.3 Case 3.1 Case 3.2 Case 3.3 Dell Direct 774 American Idol: A Big Hit for Marketing Research? 780 Baskin-Robbins: Can It Bask in the Good ‘Ole Days? 783 Kid Stuff? Determining the Best Positioning Strategy for Akron Children’s Hospital 786 AT&T Wireless: Ma Bell Becomes Ma Again 788 IBM: The World’s Top Provider of Computer Hardware, Software, and Services 793 Kimberly-Clark: Competing Through Innovation 801 vii 888 INDEX Solomon four-group design, 230 Spatial maps, 659, 664–665 See also Multidimensional scaling (MDS) Spearman’s rho (rs), 536 Special-purpose databases, 113 Specification search, 703 Specific components, 49 Split ballot technique, 313 Split-half reliability, 287, 612 SPSS ALSCAL, 683 AMOS, 720–721 analysis of variance and covariance, 520, 521 bivariate regression, 560 clustering of variables, 650 common factor analysis, 623 COMPARE MEANS, 485, 521 computerized demonstration movies, 439 instructions for running, 440 conjoint analysis, 684 consistency checks, 429 CORRELATE, 560 CROSSTABS, 484 cross-tabulation, 485 Data Entry, 265, 293, 329, 439 Data Entry (DE) Builder, 412 Data Entry Enterprise Server (DEES), 412 Data Entry Station (DES), 412 data file development, 424–427 Decision Time, 27, 59 DESCRIPTIVES, 484 DISCRIMINANT, 595 discriminant functions, determining significance of, 577–578 EXPLORE, 484 FREQUENCIES, 484 GENERAL LINEAR MODEL, 521 HIERARCHICAL CLUSTER, 649 hierarchical cluster analysis, 649–650 K-MEANS CLUSTER, 649 K-Means clustering method, 644 Kruskal-Wallis one-way analysis of variance, 521 k-sample median test, 521 leave-one-out cross-validation option, 580 logit analysis, 595 MANOVA, 521 Maps, 130 Missing Values Analysis, 439 multidimensional scaling program, 683–684 multiple regression, 561 nonmetric analysis of variance, 521 nonparametric tests, 485–486 n-way analysis of variance, 521 one-sample test, 485 one-way ANOVA, 521 for out-of-range values, 429, 439 Overall Evaluation (Overall) variable, 439, 440 paired samples t test, 485 parametric tests, 485 principal components analysis, 623 Recoded Income variable, 440–441 REGRESSION, 560 repeated measures ANOVA, 521 reporting procedures, 744–745 SamplePower, 391 screen captures with notes, 439 structural equation modeling, 720–721 Syntax Editor, 130 TABLES, 745 Text Smart, 439 three-group discriminant analysis with, 595 two-group discriminant analysis with Resort Visit, 595 two-independent-samples t test, 485 TwoStep cluster analysis, 644, 650 What If?, 22, 27, 59 Squared correlation index, 659, 665 Squared multiple correlations, 694, 703, 710 SSbetween (SSx), 501 SSwithin (SSerror), 501 SSy, 501 Stakeholders in marketing research, 25 Standard deviation, 455–456 Standard Directory of Advertisers, 109 Standard error, 374, 537 Standard error of estimate, 537 Standardization, 433, 541 Standardized discriminant function coefficients, 573 Standardized regression coefficient, 537, 555 Standardized residuals, 703 Standardized root mean residual (SRMR), 700, 715 Standard & Poor’s Statistical Service, 110 Standard Rate and Data Service, 110 Standard test market, 237–238 Stapel scales, 279 Static group, 228 Statistical Abstract of the United States, 110 Statistical approaches, 664 Statistical control, 225–226 Statistical design, 226 Statistical experimental designs, 231, 232 factorial design, 234–235 Latin square design, 233–234 randomized block design, 233 Statistical hypotheses, 54 Statistical inference, 373 Statistical regression (SR), 224 Statistical significance of relative importance of predictor variables, 555 Statistic, defined, 372 Statisticians, 19, 20 Statistics, 339 Stepwise discriminant analysis, 575, 588 Stepwise regression, 553–554, 555 Stepwise solution, 553 Story completion, 159–160 Stratification variables, 352 Stratified sampling, 352, 357 Stratum chart, 735 Stress, 659, 663 Structural environments, international, 758 Structural equation modeling (SEM), 691–725 application of (See First-order factor model; Second-order factor model) basic concept of, 692–693 concept map for, 723 conducting, 697 defined, 691 in department store patronage project, 716 foundations of, 694 dependence and correlational relationships in, 696 exogenous vs endogenous constructs, 695–696 model fit, 696 model identification, 696–697 theory, model, and path diagram, 694–695 individual constructs, defining, 697 in international marketing research, 718–719 vs other multivariate techniques, 707–708 path analysis and, 694, 716–718 software for, 719–721 statistics associated with, 693–694 See also SEM measurement model; SEM structural model Structural error, 694 Structural model, 694 Structural relationship Structure correlations, 573 Structured data collection, 179 Structured observation, 198 Structured questions dichotomous questions, 312–313 multiple-choice questions, 312 vs scales, 313 Stubs, 734 Student-Newman-Keuls, 516 Subjective estimates, 388 Substitution, 388 Sugging, 209 Summated scale, 277 Sum of squared errors, 537 Surrogate information error, 86 Surrogate variables, 614–615 Survey methods, 32 advantages/disadvantages of, 179 classified by mode of administration, 180 electronic interviewing, 186–188 ethics in, 209–211 ethnographic research, 206 international marketing research, 206–209 mail interviewing, 185–186 mystery shopping, 206 other, 196 personal interviewing, 180, 182–184 selection of, 197–198 structured-direct survey, 179 telephone interviewing, 180–181 See also Observation methods Survey methods, comparative evaluation of, 189, 190 for international marketing research, 208 respondent factors, 195–196 low incidence rate, 195–196 perceived anonymity, 195 INDEX respondent control, 196 social desirability/sensitive information, 195 situational factors, 194–195 cost, 195 data collection environment, control of, 194 field force, control of, 194 interviewer bias, potential for, 194 speed, 194–195 task factors, 189, 191–193 physical stimuli, use of, 189, 191 quantity of data, 192–193 questions and flexibility, diversity of, 189 response rate, 193 sample control, 191–192 Survey methods in international marketing research comparative evaluation of, 208 electronic surveys, 762–763 in-home personal interviews, 761 mail and scanner panels, 762 mail interviews, 762 mall intercept, 761–762 quantitative descriptive data, 206–208 telephone interviewing, 207, 760–761 Survey of Buying Power, 126 Survey of Current Business, 110 Surveys, 113 advantages and disadvantages of, 118 advertising evaluation, 116–117 general, 117 industry, 124 overview of, 115 psychographics and lifestyles, 114 syndicated panel, 114 uses of, 117 Symbolic analysis, 154–155 Symmetric lambda, 470 Syndicated data, 113 classification of, 114 concept map for, 114 from households electronic scanner services, 121–122 purchase and media panels, 118–120 surveys, 114–118 from institutions industry services, 124 retailer and wholesale audits, 122–123 overview of, 115 Syndicated panel surveys, 114 Syndicated services, 15 Systematic error, 286 Systematic sampling, 351–352, 357 T Table of contents, 731 Tables, guidelines for arrangement of data items, 734 basis of measurement, 734 data sources, 734 explanations and comments, 734 leaders, rulings, and spaces, 734 title and number, 733 Tangibility (TANG), 692, 710, 712, 713–714, 715 Target population, 340, 341 Taste testing, 258–259 Tau b, 470 Tau c, 470 T distribution, 472 Technical and analytical services, 18 Technological environments, international, 758 Technology acceptance model (TAM), 708 See also First-order factor model Telephone interviewing computer-assisted telephone interviewing, 180–181, 305, 760–761, 768 ethics in, 209–210 international, 207 traditional telephone interviews, 180 Telescoping, 308 Telesession groups, 149 Telesessions, 149 Television advertising, 756 Tell ’Em principle, 739 10 S’s, 149 Territorial map, 585, 586 Test marketing, 237 controlled, 239 electronic, virtual, and Web-enabled, 239 simulated, 239 standard, 237–239 Test markets, 237 selected by cluster analysis, 631 Test-retest reliability, 286–287 Test statistics, 459 Test units, 221 Thematic Apperception Test (TAT), 160 Theory, 51 Thermometer scales, 283 Third-person technique, 162 Thomas Register of American Manufacturers, 109 Three-group discriminant analysis, 583–584 See also Multiple discriminant analysis Time series design, 230–231 Title page, 730 Total correlation matrix, 573 Total error, 85 Trace analysis, 203 Trade-off model, 678–679 Transitivity of preference, 258 Treatment in ANOVA, 499 Treatments (independent variables), 221 Tree graph, 632 Trend analysis, 388–389 True experimental designs, 226 posttest-only control group design, 229–230 pretest-posttest control group design, 228–229 True panels, 78, 114 True score model, 286 Trust (TRU) in IUIPC, 706 T statistic, 472, 537 T test, 472–475 concept map for conducting, 490 paired, 476, 480 Tucker Lewis Index (TLI), 700, 701 Tukey’s alternate procedure, 516 Turnover table, 79, 80 Turnstiles, 199, 204 889 Two-group discriminant analysis, 570, 572, 576–577 See also Multiple discriminant analysis Two-phase sampling, 356 Two-sample median test, 479 TwoStep clustering, 644, 645 Two-tailed test, 458 Two-way focus group, 148 Type I error (a), 459 Type II error (b), 459 U Unfolding, 660, 667 Unidimensionality, 694, 702 Unipolar rating scales, 279 U.S Census 2000 Test, 302 U.S Government Manual, 111 Univariate techniques, 434, 435 Universal product code (UPC), 121, 199 Unstructured observation, 198 Unstructured questions, 311–312 Unwillingness error, 87 Usability research, 81–82 U statistic, 573 V Validation sample, 573–578 Validity in experimental designs external, 223 internal validity, 222–223 invalidity, sources of, 232 threats to, 223, 225 Validity of scales, 288 Variability, measures of, 455 Variable respecification, 431 Variance, 455 Variance methods, 635 Varimax procedure, 613 Verbal models, 51 Vice president of marketing research, 20 Voice pitch analysis, 200, 201 Volume tracking data, 121 W Wall Street Journal Index, 109 Ward’s procedure, 635, 636–638 Weighting, 389, 430–431 Wilcoxon matched-pairs signed-ranks test, 471, 479, 480, 481, 487 Wilks’ l, 573 Wireless phone interviews, 196 Within-people variation, 517 Within-subjects design, 516 Word association, 41, 158 X X (exposure), 222 Xeroxing, 290 X variables, 695 Y Yea-saying (acquiescence bias), 316 Y variables, 696 Z Zaltman’s Metaphor Elicitation Technique, 160 Z test, 473 Z values, 374 890 INDEX Company/Product Index A A.B Volvo, 680 ABC, 125, 128, 178 ABI/Inform, 112 Abt Associates Inc., 16 Abt SRBI Inc., 16 Accenture, 61, 64–65 accutips.com, 738 Acer, 774 Acxiom Corporation, 238 Adaptec, 797 Adaptive Choice-Based Conjoint (ACBC), 683 Adaptive Conjoint Analysis (ACA), 683 ADC, 797 ADIC, 797 Adidas, 63, 251, 264 Adobe, 797 Adtran, 797 Advertising Research Foundation (ARF), 26, 740 AFLAC, 93, 132, 244, 245–246 Agency for International Development, 126–127 Aim, 221, 260, 268, 660–661, 665, 666, 667 Air Jordan, 174 AirTouch Cellular, 788 Air Transport Association (ATA), 162 Akron Children’s Hospital, 29, 61, 93, 131, 171, 212, 244, 267, 295, 330, 365, 392, 414, 445, 491, 524, 563, 599, 625, 653, 687, 745, 768, 786–787 AkzoNobel, 679 Alcatel, 797 Alfa Romeo, 680 Allegro, 201 AllTel Inc., 788, 791 ALSCAL, 682 AltaVista, 26 Altria Group, Inc., 744 Amazon.com, 361 American Airlines, 63, 104–105, 117, 155, 600, 648 American Association for Public Opinion Research (AAPOR), 26 American Business Information, 107 American Chicle Group, 317 American Express, 154 American Family Life Assurance Company See AFLAC American Idol, 29, 61, 93, 131, 171, 212, 244, 267, 295, 330, 365, 392, 414, 445, 491, 524, 563, 599, 625, 653, 687, 745, 768, 780–781 American Marketing Association (AMA), 7, 26, 108, 743 American Professional Football Association, 96 American Statistics Index, 112 American Tourister®, 26 AMOS, 719, 720, 725 Andersen Consulting, 64–65 Anheuser-Busch, 218 AnswerNet, 353 ANZ Bank, 184 AOL, 372, 475 AOL Search, 26 APM (Adaptive Perceptual Mapping), 683 Apple, 249, 307, 333, 774, 795, 797 Aqua-Fresh, 660–661, 664–665, 666, 667 Arbitron Inc., 16, 119, 386 Arby’s, 100, 814, 815, 820 Ariba, 179–180, 797 Arizona jeans, 39, 241, 242 Arthur Andersen, 64 Ashford.com, 123 Asia Plus, 558 ASI Market Research, 372 Ask Jeeves, 26 Association of Asia Pacific Airlines, 558 Atlanta Bread Company, 820 ATLAS.ti, 166 AT&T, 48, 119, 128, 183, 312, 445, 491, 524, 563, 599, 619–620, 625, 653, 687, 746, 768, 788–792 Attack, 681 ATTIK, Audi, 668 Autos.msn.com, 352 Avon Products, 529–530 A&W, 820 B BackOffice Small Business Server, 210 Baja Fresh, 820 Baked Lays, 54 Ball Group, 337 Banana Republic, 438 Banc One Corp., 808 Bank of America Corp., 14, 63, 808, 811 Barbie Doll, 283 Baskin-Robbins, 61, 93, 131, 171, 172, 212, 244, 267, 295, 330, 365, 392, 414, 445, 491, 524, 563, 599, 625, 653, 687, 745, 768, 783–784 Bates Asia, 762 Battleground, 178 Bauer, 174 BBDO Worldwide, 19, 202, 648 B2B software, 179 Beaumont Organization Ltd., 683 Beavis & Butthead, 363 Behavioral Risk Factor Surveillance System, 405–406 BehaviorScan, 121, 239 Beiersdorf, 772 Bélisle Marketing, 167 Belkin, 797 Bell Atlantic Mobile, 788 Bell & Howell, 178 Bellomy Research Inc., 17 BellSouth, 183, 788, 789 Berkshire Hathaway, 249 Berrier Associates, 400 Best Buy, 361, 511–512 Best Western International, Inc., 754 BevComm, 791 Big Classic burger, 814 BizRate.com, 414, 653, 688, 724, 746, 769, 821 Black Box, 797 Blimpie, 820 BMW, 5, 249, 291, 680 Boeing 737, 4, Boeing Commercial Airplanes, 3–4 Boeing Company, 3–4, 6, 23, 340, 389 Bold Breezes, 783 Boston Market, 99–100, 121, 593, 820 Bottom’s Dry, 804 BrandPulse, 197 BrandPulse Insight, 197 breakfast menu, 814 Breeze Ultra, 681 Brisk Iced Tea, 206 broadridge.com, 46 Buick, 668 Bureau of Economic Analysis, 111 Bureau of Labor Statistics, 30, 111, 358–359 Burger King, 38, 198, 394, 567, 814, 815, 820 Burke, Inc., 2, 15, 16, 19, 31–33, 151, 176, 248, 418, 420, 656, 690, 776 BuzzBack Market Research, 140 C Cabletron/Enterasys, 797 Cadbury Schweppes, 722 Cadillac, 668 California Milk Processor Board, 45 CALIS, 719 Calvin Klein, 242 Camay, 492 Camel, 744 Campbell Soup Company, 115–116, 124–125 Camry, 743 Canadian Tire, 338 Canon Cameras, 178, 179, 198, 199, 235, 290 Cappuccino Blast, 783 Captain D’s, 820 CARAVAN®, 781–782 Carl’s Jr., 820 Carnival Cruise Lines, 94, 429 Caterpillar, 107–108 Cathay Pacific, 558 CATPACII, 166, 172 CBC Hierarchical Bayes Module (CBC/HB), 683 CBS, 125, 128, 178 Celebrities Asia, 327 Cellular One, 791 Census of Population, 104 Centers for Disease Control and Prevention, 405–406 Chaps, 766 Charmin, 390–391 Chase Manhattan Corp., 808 Checker’s Drive In, 820 Chemical Banking Corp., 808 Cherry Coke, 41 Chesebrough-Ponds, 239 Chevrolet, 5, 668 Chick-Fil-A, 820 Chili’s, 117 Chipotle Mexican Grill, 820 Choice-Based Conjoint (CBC), 683 INDEX Chrysler, 14, 107, 667–668, 734, 735–737 Chrysler Personal Information Centers, 107 Chunky Soup, 116 Church’s, 820 CIA World Factbook, 127 CIGNA, 352, 353 CIGNATrade, 353 Cingular Wireless, 788 CI Resource Index, 109 Cisco, 795, 797 CIT Group/Commercial Services, 129 Ci3, 181 Citibank, 83–84, 811 Citigroup, 83 Citrosuco Paulista, 56 Claritas, Inc., 112, 126 Clausthaler, 546 Clinique, 164, 406 Close Up, 260, 660–661, 665, 666, 667 Club Monaco, 766 CMI, 272, 628 CMT, 369 Coach, 256 Coca-Cola Classic, 63, 245, 657–658 Coca-Cola Company, 14, 19, 40–41, 56–57, 89, 172, 245, 258, 268, 345, 346, 414, 546, 653, 658, 666, 722, 735, 748, 761, 770, 780–781 Cognos, 793 Coke, 257, 259, 268, 303, 307, 784, 821 Cold Brew Blend, 206 Cold Stone Creamery, 783 Coldwater Creek, 475 Colgate, 258, 260, 268, 316, 483, 660–661, 665, 666, 667, 681 Colgate Palmolive, 14, 681, 801 Colombian Coffee Federation, 70 Comcast, 475 Comedy Central, 363 Commission of the European Union to the United States, 127 Commonwealth of Independent States, 290–291 Compaq Computer, 414, 653, 688, 724, 746, 769, 794–795, 821 Compaq/Hewlett-Packard, 797 Compass™, 126 Compete Inc., 17 Computer Associates, 797 ComScore, Inc., 16, 103, 151, 338 Consumer Reports, 789 Continental Airlines, 162, 667–668 Convergence Audit™, 126 Converse, 174 Coolpix, 647 Corel Corporation, 797 Corporate Marketing Services (CMS), 11 CORRESPONDENCE ANALYSIS, 683 Cossette Communication Group, 161–162 Council for Marketing and Opinion Research (CASRO), 25, 26, 209–210, 401 Council of American Survey Research Organizations, 26 Country Music Television, 369 Courtyard by Marriott, 749 CoverGirl, 42 Covington and Burling, 210 Cranberry Lift, 259 Cranergy™, 259 CreateSurvey, 188, 325, 402 Crest, 42, 258, 260, 268, 390, 660–661, 662, 664–665, 666, 667 Crest White Strips, 270 CrossWorlds Software, 793 C&R Research Services Inc., 17 Curves Cereal, 481, 482 Curves International, 481–482 CyberDialogue, 152 D Data Development Worldwide, 17 Datalink, 797 Data Recognition Corporation, 738 D&B, 20, 46, 107, 124 Decision Analyst, Inc., 726 Decision Sciences, Burke, Inc., 568 Decision Time, 27, 59 Dell Computer, 30, 61, 63, 95, 131, 133, 171, 173, 212, 213, 244, 245, 267, 269, 295, 297, 307, 330, 333, 365, 367, 393, 396, 414, 415, 445, 447, 491, 495, 524, 527, 531, 563, 567, 570, 575, 599, 601, 625, 633, 653, 655, 687, 689, 724, 725, 745, 748, 768, 771, 774–779, 797 Dell notebooks, 570 Del Monte International, 483 Delta Air Lines, 62, 104, 155, 182, 200, 206, 328–329, 389 Del Taco, 820 Delta Hotels, 338 Dentsu, 759 Dialog Corporation, 112 Diesel, 242 Diet Black Cherry Vanilla Coke, 722 Diet Cherry Coke, 40, 41 Diet Coke, 63, 280, 657–658, 722 Diet Coke Plus, 722 Diet Pepsi, 657–658 Diet Slice, 657–658 Diet 7-Up, 657–658 Digital Marketing Services, 372 di Paris sac, 217–218, 221 Directions Research Inc., 16 Discovery Research Group, 381 Disney World, 203 Domino’s Pizza, 820 Donnelley Marketing, 106 Don’t Count Us Out, 102 Doritos, 238–239 DoubleClick, 200 Dove, 369 Dow Jones Industrial Average, 808 Dr Pepper, 657–658 Duck stamps, 337–338 Dunkin’ Decaf, 334 Dunkin’ Donuts, 61, 171, 212, 267, 330, 334–335 DuPont, 159–160 891 E EarthLink, 475 Eastman Kodak, 116 eCareers, 292 EcommercePulse, 103 Economic Information Systems, Inc., 112 EDGAR Database of Corporate Information, 111 EFM Feedback, 188, 325 e-FocusGroups, 153 eGO, 61, 93, 132 eGO clothing, 299 eGO Cycle, 298–299 eGO Cycle2, 299 eGO Vehicles, 298–299 eJobs, 292 Electrolux, 128 El Pollo Loco, 820 Eli Lilly, 184 EMC, 795, 797 Emerge Marketing, 43 eNation, 114 Encyclopedia Britannica, 108 E.piphany, 797 EQS, 719, 725 eServer Z-Series, 753 Estee Lauder, 138, 314 Ethics Resource Center, 483 Ethnograph, 166 European Society for Opinion and Marketing Research (ESOMAR), 26 European Union, 756, 767 Everyday Lives Ltd., 42 Exabyte, 797 EXCEL, 27, 63, 213, 245, 394, 424–425, 427, 429, 439, 446, 483–484, 520, 559, 622, 649, 683, 744 Exhibitions and Trade Fairs (ETF), 184 Expedia, 203 Experian Simmons, 117 Export-Import Bank of the United States, 127 Extreme, 797 F Fab Power Plus, 681 Fairfield Inn and Suites, 749 Fairfield Inns, 749 Fairway Forum, 78 FAST search, 26 Federal Aviation Administration, 105 Federal Communications Commission, 789 Federal Express, 22, 155, 249, 296, 317, 428 Federal Trade Commission, 221 Federation Internationale de Football Association, 255–256 FedStats, 110 Fidelity Investments, 762, 811 Field Facts, Inc., 18 Field Work Chicago, Inc., 18 Finesse, 258 First Chicago Corp., 808 First Chicago NBD, 808 First Choice Power, 448 First Commerce Corp., 808 First Data, 154 Fisher-Price, 44 Flake-Wilkerson Market Insights, 16 892 INDEX Florida Department of Tourism, 343 Fluke Networks, 797 FocusVision Network, Inc., 142 Folger’s Flakes, 239 Foote, Cone & Belding Worldwide, 21 Ford Motor Company, 5, 14, 90, 122, 136, 138, 296, 339, 545, 734, 735–737, 743, 781 Ford of Europe Inc., 262 Forrester Research, 103 Fortune, 64, 109, 118, 124, 249, 271, 292, 479, 793 Fossil, 680 Fox, 128 Fox News, 230 Frito-Lay, 54, 238–239 Frosty, 682, 815 Fuji Heavy Industries, 397 Fusilade, 679 G Gain, 159 Gallup, 82, 105, 388, 411, 744, 761 Gallup China, 756 Gallup & Robinson, Inc., 116 Galvin Manufacturing Corporation, 368 Gap, Inc., 39, 242, 438, 765 Gap jeans, 39 Gateway, 795, 797 Gatorade, 150 General Electric, 249, 738, 748 General Foods, 193 General Hospital, 125 General Mills, 121, 239, 481–482, 630 General Motors, 14, 19, 116, 117, 410, 477, 680, 734, 735–737, 770 Beiersdorf, 772 GfK, 23, 89, 317 GfK AG USA, 16 GfK Audits & Surveys, 123 GfK Custom Research, Inc., 116, 372 Gfk NOP World, 352, 353 GfK-Nurnberg, 762 Gleem, 260, 660–661, 665, 666, 667 Global Financial Data, 112 Global Home Products LLC, 201 Global Mapping International, 528 Global Opinion Panels, 114 Goldman Sachs, 811 Gongos Research Inc., 17 Goodby, Silverstein & Partners, 45 Google, 26, 716 Grandy’s, 820 Grapentine Company, Inc., 496 Great Plains, 797 Greenacre, 683 greenbook.org, 19 Green Burrito, 820 Greenfield Consulting, 15, 187 Greenfield Online Research Center Inc., 15, 94, 333, 492 Groups Plus, Inc., 729 GS Capital Partners, 788 GTE Wireless, 788 Gucci, 117 Guideline Inc., 16 Guiding Light, 125 H H J Heinz Co., 56, 120 Häagen-Dazs, 436, 629–630 Hallmark Fresh Ink, 181 Hallmark Inc., 181 Hardee’s, 820 Hard Rock Café, 327 Harley-Davidson, 35–36, 42, 46–47, 52 Harris InfoSource, 108 Harris Interactive Inc., 3, 4, 6, 15, 16, 76, 117, 151, 177–178, 187, 388, 644 The Harris Poll, Harris Poll Online, 361 Head and Shoulders, 258 Heinz ketchup, 56, 120 Henley Center, 410 Herbal Essences, 359 Hewlett-Packard (HP), 307, 333, 507, 594, 774–775, 794–795 Hickory Tech, 791 Hills Bros., 239 Hills Bros High Yield Coffee, 239 Hispanic Consumer Research, 18 Hitachi, 797 Hitwise, 17, 120 Holiday Inn, 72 Holiday Inn Family Suites Resorts, 72 Holiday Inn Hotels and Resorts, 72 Holiday Inn Select, 72 Holiday Inn SunSpree Resorts, 72 Home Depot, 105, 334 Homescan, 121 Home Shopping Budapest, 535–536 Honda, 5, 137–138, 206, 339, 667–668, 734, 735–737 Honda Pilot SUV, 137–138 Hotel Resorts & Suites, 749 HotJobs, 292 House of Mao, 327 HP PC, 333 Huggies, 801, 804 Huggies Supreme Gentle Care, 801 Huggies Supreme Natural Fit, 801 Hyperion, 797 Hyundai, 668 I IBM, 445, 491, 524, 563, 599, 625, 687, 724, 746, 753–754, 768, 774–775, 793–796, 797 IBM Enterprise Server, 753 IBM System Z10, 753–754 ichotelsgroup.com, 72 ICI Americas Agricultural Products, 679–680 ICR/Int’l Communications Research, 17 IHOP, IMS Health Inc., 16, 23 INDSCAL, 683 Infiniti, 680 InfoPrint Solutions Company, 793 Informa Research Services Inc., 17 Information Resources, Inc., 23, 124, 239 Informix, 793 InfoScan, 121 InfoUSA, 107, 112 Infratest, 23 In-N-Out Burger, 820 Innovation Focus, 73 Institutional Investor, 809 Intage Inc., 24 Intel Corporation, 21, 61, 93, 132, 171, 212, 330, 365, 393, 414, 416–417, 795, 797, 821 InterActive Research, 185–186 Interbrand, 260, 761 International Air Transport Association, 328 International Chambers of Commerce, 127 International Monetary Fund, 127 International Olympic Committee, 345–346 Interuniversity Consortium for Political and Social Research at the University of Michigan, 325 INTERVIEWER, 768 Investor Communication Solutions, 46 Ipsos Group SA, 16, 742 IRI, 16 Ivory shampoo, 239 J J Walter Thompson, 19 Jack in the Box, 820 Jackson Associates, 18 Jacksonville Symphony Orchestra, 383 Jaguar, 668 Japanese External Trade Organization, 127 Java, 793 JCPenney, 22, 39, 119, 159, 242, 253, 254, 257, 277, 305–306, 488, 685 JCPenney’s Arizona brand jeans, 39, 241, 242 J.D Power and Associates, 16 JD Edwards, 797 Jhirmack, 258 Johnson & Johnson, 12, 249, 763, 801 Johnson & Johnson baby aspirin, 12 Journal of Marketing Research, 100 Journal of Retailing, 100 Journal of the Academy of Marketing Science, 100 Jovan Musk for Men, 281 Joy liquid detergent, 221 JP Morgan Chase, 14, 61, 93, 131, 171, 212, 244, 267, 295, 330, 365, 393, 414, 445, 491, 524, 563, 599, 603–604, 625, 653, 687, 724, 746, 769, 808–813 Juice Spritzers, 259 Jupiter Research, 15, 103 Just The Facts, Inc., 163–164 K Kaiser Permanente, 76 Kantar Group, 16, 19, 23 Kao Corp., 681 Kaplan Thaler Group, 246 Kellogg Company, 9, 58–59, 119, 139–140, 150, 338 Kellogg’s Corn Flakes, 221 Kellogg’s Nutri-Grain Cereal Bar Blackberry, Kellogg’s Pop-Tarts Yogurt Blasts, 139–140 KFC/Kentucky Fried Chicken, 820 INDEX Kforce, 292 Kimberly-Clark, 445, 491, 524, 563, 599, 625, 653, 687, 724, 746, 768, 801–802 King Louie International, 78 Kmart, 22, 217–218, 253, 305–306 Knowledge Networks Inc., 16 KnowThis, 26 Kodak, 346, 770 Kohl’s, 22, 253, 254, 305–306, 433 Kool-Aid, 148, 150 Kraft Foods, 148, 214 Kroger, 335, 627 KS&R Inc., 17 KYST, 682 L Lange Uhren GmbH, 240 La Salsa, 820 Lauren, 766 Lay’s, 238–239 Lee, 39 Legally Blonde, 622 Lenovo, 333, 554, 793 Lenovo computers, 554 LeSportsac, Inc., 217–218, 221 Lever Brothers, 681 Levi’s, 513 Levi Strauss & Co., 39, 241–242, 262, 513 Lexmark, 775 Lexus, 261, 433, 668, 680 LHK Partners Inc., 182 Lieberman Research Group, 17 Lieberman Research Worldwide, 16 The Link Group, 17 Linksys, 797 LINMAP, 683 Linux, 793 Lion Corp., 681 Lipton, 206 LISREL, 719–720, 725 Little Caesars, 820 Logo, 369 Long John Silvers, 820 L’Oréal, 470 Lotus Development Corporation, 20, 169, 793 Louis Harris Data Center at the University of North Carolina, Chapel Hill, 325 LPGA, 78 LPGA-licensed clothing, 78 Lucent, 795, 797 Lufthansa German Airlines, 155 Luvs, 801 Lycos, 26 M Macy’s, 159, 253, 254, 256, 305–306, 685, 689 Major League Baseball (MLB), 50, 344, 707 Malaysian Airlines, 558 Mall of Atlanta, 146–147 Mall of Georgia, 147 Management Contents, 112 Management Institute, 814 Manufacturers Hanover Corp., 808 M/A/R/C Research, 45, 372, 387, 402 Marcus Thomas, 786–787 Maritz Research, 16 MarketCast, 17 Marketing Analysts Inc., 17 MarketingPower, Marketing Research Association (MRA), 18, 26, 415 Marketing Research Services, Inc., 18 Marketing Strategies International, 16, 34 Market Probe Inc., 17 marketresearchcareers.com, 20 Market Research Society (MRS), 26 Market Research Society of Australia (MRSA), 26 MarketTools, 108, 325, 402 MarketVision Research Inc., 17 Marriott Courtyard, 11 Marriott Fairfield Inn, 11 Marriott International, Inc., 10–11, 61, 93, 132, 171, 200, 212, 214, 244, 267, 295, 330, 365, 393, 414, 445, 491, 517–518, 524, 563, 599, 625, 653, 687, 724, 746, 749–751, 769 Marriott Ramada International, 11 Marriott Renaissance, 11 Marriott Residence Inn, 11 Marriott Springhill Suites, 11 Marriott Towneplace Suites, 11 Marshalls, 253, 268, 305–306 MasterCard, 154 Max chips, 238 Maxtor, 797 Maybelline, 154 Mayo Clinic, 93, 134–135 Mazda North America, 5, 110 McAfee, 797 McCann Erickson Worldwide, 202 McDonald’s, 6, 38–39, 89, 116, 133, 161, 275–276, 345, 346, 761, 786, 814, 815, 820 McDonald’s hamburgers, 283 McKinsey & Co., 100, 593 MDPREF, 683 MDSCAL, 682 Mediamark Research, Inc (MRI), 124, 182 Mediamark Research & Intelligence, 117 Mercedes, 667–668 Message Factors, 555–556 Metaphase, 201 MetaSolv, 797 Metromail, 106 Microsoft, 81–82, 199, 210, 245, 249, 288, 793, 794–795, 797 Microsoft Office, 82 Microsoft Office 2003, 81–82 Microsoft Office 2007, 81–82 Microsoft Office XP, 81–82 Microsoft Small Business Council, 210 Microsoft Small Business Technology Partnership Board, 210 Microsoft Usability Group, 81 Microsoft Word, 763 Microsoft XP, 428 MINITAB, 27, 429, 439, 446, 483–484, 520, 559, 578, 594, 600, 622, 649, 683, 744 Minolta Camera Co., 178, 290 893 Mintel International, 264 Mint Museum of Art, 185–186 Mirro, 201 Mitsubishi Electric Corp., 290 M&M/Mars, 240, 246 M&M’s, 246 Moist Mates, 390–391 MONANOVA, 683 Money magazine, 104–105 Monster, 292–293 Moody’s, 108 Morpace Inc., 17 Moto, 368–369 MOTOROKR T505 Bluetooth, 368 Motorola, 61, 128, 171, 212, 244, 330, 365, 368–369 MSInteractive, 275 MSN Search, 26 MTV, 363 MTV Networks, 363 Multicultural Insights, 18 Murano, 411 MVL Group Inc., 16 N National Analysts Worldwide, 17 National Association of Stock Car Auto Racing (NASCAR), 91–92 National Cattlemen’s Beef Association, 205–206 National Council on Published Polls, 178 National Football League (NFL), 9, 61, 93, 96–97, 116, 160, 707 National Highway and Transportation Safety Agency, 298 National Hockey League (NHL), 707 National Motor Carriers, 107 National Research Corp., 16 National Scan Track, 121 National Trade Data Bank, 127 National Transportation Safety Board, 105 NBC, 5, 6, 102, 128, 178 NBD Bancorp., 808 Neiman-Marcus, 22, 74, 253, 255, 305–306, 421, 488 Nestlé, 630 Netgear, 797 Netscape Search, 26 New Balance Athletic Shoe, Inc., 264–265 New Coke, 258, 784 New England Patriots, 427 New Tastes Menu, 161 New York City Transit, 273 New York Life, 811 New York Yankees, 344 NFO World Group, 119, 186 Nick at Nite, 363 Nickelodeon, 363 Nielsen BASES, 231, 239 Nielsen BuzzMetrics, 5, 197 Nielsen Claritas, 112, 126, 630 Nielsen Company, 15, 16, 19, 121, 133, 239, 292, 762 Nielsen Homevideo Index®, 119 Nielsen Media Research, 15, 102, 119, 120, 123, 126, 128 Nielsen Online’s EcommercePulse, 103, 120 894 INDEX Nielsen People Meter, 102, 119 Nielsen Television Index (NTI), 15, 119 Nike, 61, 63, 101, 171, 174–175, 241, 251, 264, 492, 526, 566, 627, 689 Nike Golf, 174 Nike Pro, 174 Nikon, 647 99-cent Crispy Chicken Sandwich, 815 99-cent Double Stack cheeseburger, 815 99-cent Junior Bacon Cheeseburger, 815 99-cent Super Value Menu, 814 Nine West, 140 Nissan Micra, 262–263 Nissan Motor Co., 411–412, 734, 735–737 Nissan North America, 152–153, 262–263 Nissan Z sports car, 411 Nivea, 61, 93, 132, 171, 212, 295, 365, 769, 772–773 Nivea Styling, 772 Nivea Vital, 772, 773 Nokia, 615 Nordstrom, 646 Nortel, 797 North American Free Trade Agreement (NAFTA), 755 North American Industry Classification System, 649 Northwest Airlines, 104, 328 Novell, 797 NPD Group, 6, 15, 16, 39, 118–119, 593 NVivo, 166 O Ocean Spray, 259 Office of Scales Research, 296 Ogilvy & Mather Worldwide, 202, 368 Ohio Lottery, 787 Old Navy, 246, 307, 438 Olean, 238 Olestra, 238 Omo, 681 Onyx, 797 Opinion Research Corporation, 16, 117, 781, 782 Opinion Research/Guideline Group, 16 Opinion Suites, 18 Oracle, 794, 797 Organization for Economic Cooperation and Development, 127 OTX, 16 Outback, 186 P P Robert and Partners, 328 Pampers, 42, 270–271, 390, 801 Panasonic, 346 Panda Express, 820 Panera Bread, 820 Pao Hand Force, 681 Pao M Wash, 681 Papa John’s, 820 Parisian, 253, 305–306 PC-MDS, 683 PC’s Limited, 774 Pentium, 416 PeopleSoft, 794, 797 Pepperidge Farm, 161 Pepsi, 246, 257, 259, 303, 657–658, 821 PepsiCo, 116, 214, 246, 414, 653, 722, 735, 761, 780–781 Pepsi/Frito-Lay, 218 Pepsodent, 260, 268, 660–661, 662, 665, 666, 667 Perception Analyzer, 275 Peregrine Systems, 797 PerfectSurveys, 325 Performance Research, 345–346 Perrier, 546 Pert, 258 Peterbilt, 107 Pew Research, 178 Pfizer Company, 218, 317 Phoenix Marketing International, 17 Pick Up Stix, 820 Pillsbury, 119 Pizza Hut, 100, 820 Planet Hollywood, 327 Plural, 774 Plus White, 260, 660–661, 665, 666, 667 PM USA, 744 Point of Purchase Advertising International, 218 Polo, 766 Polo by Ralph Lauren, 766 Polo Jeans Co, 766 Polo Ralph Lauren Corporation, 349, 766–767 Polo Sport, 766 Popeye’s, 820 Pop-Tarts, 140, 150 Porsche, 667–668 Portable People Meter, 119 Port Authority of New York and New Jersey, 127 PortiCo Research, 206 Power Decisions, 13 Predicasts Terminal System, 112 PREFMAP, 683 Presto line, 175 PreTesting Company, 199 PriceWaterhouseCoopers, 380, 793 PrimeCo, 788 Princess Cruises, 429 Printronix, 658–659 PrivaSys, 154 PRIZM, 126 Procter & Gamble, 14, 19, 31, 42, 57, 61, 93, 108, 116, 132, 133, 159, 171, 197–198, 206, 212, 218, 238–239, 244, 245, 249, 267, 270–271, 334–335, 359–360, 389–391, 519, 616, 737, 748, 770, 801 Procter & Gamble’s Ariel, 57 Product 19, 258 Professional Marketing Research Society of Canada (PMRS), 26, 327 Profit Impact of Market Strategies (PIMS), 113 Public Opinion Strategies, 17 Pursuant, Inc., 780 Q Q Research Solutions Inc., 17 Qualitative Research Consultants Association (QRCA), 26, 172 Qualtrics, 325 Quantum, 797 Quest, 312 quicken.com, 46 Quick-Track®, 6, 15 Quiznos, 820 Qwest, 789, 791 R R L Polk, 106 Rally’s, 820 RALPH, 766 Ralph Lauren, 766 Ralph Lauren Purple Label, 766 Ralston-Purina, 218 Ramada, 749 Rasmussen, 178 Raspberry Cranberry Lift, 259 Rational Software, 793 RCA, 128, 512 RC Cola, 257 RDA Group Inc., 17 The Real World, 363 Reebok, 63, 106, 208, 251, 264, 477 Regal, 201 Renaissance, 749 Renault, 411, 680 Research Industry Coalition (RIC), 26 Research Info, 26 Research International, 410 Residence Inn, 749 RFID printers, 659 Rice Krispies, 240–241 Ricoh Co., 290, 793 Rite Aid Drug Co., 218, 221, 235 R.J Reynolds Tobacco Holdings, Inc., 744 RL, 766 Rockport, 283 Rolling Stone, 744 RONIN Corporation, 753 Roper Center at the University of Connecticut, 325 Roundup, 680 Roundup Ready soybeans, 680 RTi Market Research & Brand Strategy, 17 Rubio’s, 820 Ruffles, 238–239 S Saatchi & Saatchi Advertising, 743 Saatchi & Saatchi Worldwide, 202, 536 Sage Communications, 780 Saks Fifth Avenue, 22, 74, 253, 305–306, 421 SamplePower, 27, 391 Samsonite, 26–27 Samsonite luggage®, 26–27 Samsung, 21, 320 Sandelman & Associates, 6, 15 SAP, 794, 797 Sara Lee, 593 SAS, 27, 419, 429, 439, 440, 441, 446, 449, 483, 484, 485–488, 520, 521, 559–560, 561, 578, 594–596, 600, 622, 623, 649, 650–651, 682, 683, 684–685, 721–722, 744, 745 Satmetrix, 4–5, Saturn, 668 INDEX Savitz Research Companies, 17 Sawtooth Software, 683 Sawtooth Technologies, 18, 181 Scarborough Research, 98 SDR Consulting, 18, 446, 602 Seagate, 797 Sears, 23, 47, 49–50, 52–53, 83, 116, 159, 161, 164, 205, 227–229, 237, 253, 256, 257, 275, 276–277, 305–306, 433, 492, 562, 651, 685, 716, 760 Sea World, 147 SecondaryData.com, 108 SEM, 690–708, 710, 711, 716, 717, 719–720, 722, 725 Sensodyne, 260, 660–661, 665, 666, 667 Service Management Group, 17 Sethburg Communications, 68 7-11, 334 7-Up, 657–658 Sighthound Solutions, Inc., 216 SIMCA, 683 Simmons Market Research Bureau, 119 Simmons National Consumer Survey (NCS), 120 Singapore International Airlines, 558 Slice, 657–658 Small Business Administration, 127, 210 Small Business Edition of Microsoft Works, 210 Smarte Carte, Inc., 43–44 Smart Start, 481 SMC, 797 Smile Internet Bank, 594 Society of Competitive Intelligence Professionals (SCIP), 14 Sonic, 820 Sony BMG Music Entertainment, Inc., 644 Sony Computer Entertainment America, Inc., 644 Sony Corporation, 22, 157, 188, 644 Sony Electronics, Inc., 644, 646 Sony Pictures Entertainment, 644 Sony PlayStation 3, 157, 188 Specialty Coffee Association of America, 70 Spencer Trask Software Group, 20 Spike TV, 369 Sports Illustrated, 346, 744 Sprint Nextel, 312, 788, 789, 791 Sprite, 303 SPSS, 27, 59, 130, 265, 293, 329, 391, 412, 419, 425, 427, 429, 439–440, 443, 446, 449, 483, 484–485, 520–521, 522, 559–560, 578, 580, 594–595, 600, 622–623, 649–650, 682, 683–684, 720, 744–745 SRI Consulting, 116 SRI International, 116 SSI Web, 325 Standard and Poor’s, 108 Standard Research Systems, 336 Stanford Research Institute, 116 Starbucks, 24, 70, 171, 212, 214–215, 334, 780 Starbucks Frappuccino, 214 Starch Readership Survey, 117 Stat-USA, 110 Stop & Shop, 334 Strategic Planning Institute, 113 Strategic Resource Group/Flickinger Consulting, 438 Stripe, 260 Subaru, 61, 93, 132, 171, 212, 244, 267, 295, 330, 365, 393, 397–398, 738–739 Subaru Forester, 397 Subaru Impreza, 397 Subaru Legacy, 397 Subaru Outback, 397 Subaru Tribeca, 397 Subway, 38, 820 Sun Microsystems, 793, 797 SunTrust Bank, 811 SuperBar, 814 SurveyPro, 188, 325 Survey Research Center, 405 Survey Research Library at Florida State University, 325 Survey Samples, Inc., 361 Survey Sampling International, 18, 187, 370 Survey System, 381 SurveyTime, 325 Surveyz, 325 Sweet Cookies, 94 Swiffer, 271 Sybase, 797 Symantec, 797 Synovate, 15, 16, 114, 186 Synovate Global Opinion Panels, 114 T T Rowe Price, 811 Tab, 657–658 Taco Bell, 82, 100, 820 Taco Bueno, 820 Target, 117, 249 Taurus, 138 Team Marketing Reports, 552 Team Starter, 174 TeleNation, 114 Tennis, 587 Thai Airways International, 558 ThomasNet, 108 3Com, 797 Tide, 42, 159, 197–198, 221, 245, 390, 519 Tiffany & Co., 621–622 Tiffany Heart Tag, 621–622 Timberland, 581 Time magazine, 87 TimeOut, 78 Timex, 338 Titan, 411 TiVo, 414, 653, 688, 724, 746, 769, 821 Tivoli Systems, 793, 797 T-Mobile, 378, 788, 791 TNS Global, 15, 19, 740, 752 TNS U.S., 16 Tommy Hilfiger, 129, 242 Toronto-Dominion Bank, 338 Toshiba, 774, 797 Tostitos, 239 Total cereal, 121, 257, 481, 499–500 Touche Ross, 483 TouchScreen Research, 184 895 Tovili, 794 TownePlace Suites, 749 Toyota Camry, 245 Toyota Celica, Toyota Echo, Toyota Motor, 5–6, 249, 667–668, 734, 735–737, 743 Toyota MR2 Spyder, Toyota Scion, 5–6 TPG Capital, 788 TRADEOFF, 683 Trade-Off Research Services, 658 Triarc Companies, Inc., 681, 815 Trigo Technologies, 793 TRU, 39 Truth campaign, 355 TRW, 107 TV BehaviorGraphics™, 119 TV Land, 369 2020research.com, 153 Two Twelve Associates, 302 U Ultra Brite, 260, 268, 660–661, 665, 666, 667 Unilever, 57, 119, 770 Unilever’s Persil, 57 Unilever Surf Superconcentrate, 57 United Airlines, 155, 214, 254, 727–728 United Nations, 127 U.S Bureau of the Census, 101, 110, 111, 192, 301–302, 315, 366 U.S Commerce Department, 103, 110, 126 U.S Department of Agriculture, 127 U.S Department of Commerce, 110 U.S Department of Labor, 100, 110, 127 U.S Department of State, 127 U.S Department of Tourism and Convention Administration, 497 U.S Fish and Wildlife Service, 337–338 United Way, 96 Universal Studios, 147, 759 Universal Studios Japan, 759 University of Colorado at Boulder, 453 University of Nevada–Las Vegas, 497 UPS, 296, 428 USAData.com., 108 US Airways, 155 USA Today, 781 US Cellular, 791 V Vail Cascade Resort, Colorado, 381 Vanguard, 811 Vaseline Intensive Care lotion, 239 V8 juice, 125 V8 Splash Juice Drinks, 125 V8 100% Vegetable Juices, 125 V8 V-Fusion Juices, 125 Verizon Communications Inc., 125–126, 312, 475, 619, 788, 789, 791 VH1, 363 Viacom, 363 Vidal Sassoon, 258 Virgin Mobile, 789 Visa, 90, 157, 160, 346 VNU, 23 896 INDEX Vodafone, 788 Volkswagen, 5, 620, 667–668 Volkswagen Beetle, 620, 821 Vovici, 763 Vovici v4 Enterprise Edition, 763 Voxco of Montreal, 768 Vu/Text Information Systems, Inc., 112 W Walker Information, 17 Wall Street Journal, 87 Wal-Mart, 22, 41, 117, 253, 254, 255, 256, 268, 305–306, 334, 335, 475 Walt Disney Company, 276, 292, 438 Washington Mutual, 604, 808 Waterpik Technologies, 73–74 WearEver Company, 201 WebCrawler, 26 Web Online Surveys, 188 Wells Fargo, 84, 381, 811 Wendy’s, 6, 38, 61, 93, 131, 171, 212, 244, 267, 295, 330, 365, 393, 414, 445, 457, 491, 524, 563, 599, 617, 625, 653, 681–682, 687, 724, 746, 769, 814–820 Wendy’s/Arby’s Group, Inc., 681–682, 814–820 Westat Inc., 16 West Michigan Whitecaps, 555–556 Wet ‘b Gone, 804 Whataburger, 820 What If?, 22, 27, 59 Whirlpool, 138, 446 Whirlpool Appliances, 446 White Cranberry, 259 Williams-Sonoma, 475 Wm Wrigley Jr Company, 240 Women’s Sports Foundation, 78 Wondra, 239 World Bank, 127 WOW!, 238–239 WPP Group, 762 Wrangler, 39 X Anderson, Richard, 328, 329 Arras, Richard, 593 Athaide, Ken, 34 B Bacon, Lynd, 216 Baker-Prewitt, Jamie, 568 Bakken, David, Ballmer, Steve, 210 Barstys, Joe, 397–398 Baumgardner, Michael, Bélisle, Pierre, 167 Bernanke, Ben, 103 Berrier, Robert J., 400 Blythe, Bruce, 262 Bottoli, Marcello, 27 Bowers, Diane, 300 Browne, Joe, 96, 97 Burke, Alberta, 31–33 Burnett, Leo, 438 Bush, George H W., 168–169 Bush, George W., 78, 169, 178 C Calhoun, Jack, 438 Chakrapani, Chuck, 336 Chubachi, Ryoji, 646 Clarkson, Kelly, 781 Clauss, Phil, 814 Clinton, Bill, 169 Cohen, Robert L., 98 Colleton, Beth, 97 Cowell, Simon, 780 D Davis, Jim, 264, 265 Davis, Marsha, 783–784 Dell, Michael, 774 DeVito, Danny, 45 Dichter, Ernest, 139 Drexler, Millard “Mickey,” 438 Drucker, Peter, 37 Dukakis, Michael, 168–169 Duyff, Roberta, 481 Xerox, 290–291, 346 XSight, 166 Xterra, 152–153 E Y F Yahoo!, 26, 46 Yankelovich and Partners, 546 Yankelovich Monitor, 115–116 Yankelovich Research and Consulting Services, 115–116 Young & Rubicam, 19, 183, 202 Z Zogby, 178 Zoomerang, 188, 325, 402 Name Index A Abdul, Paula, 780 Andersen, Jack, 658 Anderson, John, 241–242 Anderson, Kerrii, Eden, Charles, 690 Edwards, Trevor, 174 Finn, Jack, 620 Flickinger, Burt, 438 France, Brian, 91–92 Freeman, Mickey, 126 Freston, Tom, 363 G Gallup, George, 77 Gamba, Philip, 680 Gehring, Fred, 129 Getz, Patricia M., 359 Ghosn, Carlos, 411 Ginsburg, Seth, 68 Goldfarb, Jaime, 429 Gore, Al, 169, 178 Grapentine, Terry, 496 Greenbaum, Thomas, 729 Gupta, Kunal, 656 H Hamman, Jim, 298–299 Hansen, Marka, 438 Harrington, Laurie, 448 Harris, Louis, 77 Hatch, Denny, 292 Hodapp, Susan, 518 Hughes, Sarah, 116 I Iannuzzi, Sal, 292, 293 J Jackson, Randy, 780 Jarvis, Ian, 752 Jones, Damon, 418, 420 Jones, Janet, 786–787 Jordan, Michael, 174 K Kallfelz, Andrew, 298 Karam, J David, 682 Kent, Muhtar, 722 Kerry, John, 169 Kim, Eric, 20–21 Kimberlin, Kevin, 20–21 Kimmel, Ken, 783–785 Klupp, Mary, 136 Kroll, Bob, 483 L la Forgia, John, 134 Lafley, A G., 390, 391 Lai, Paul, 328 Lambourne, Gordon, 749–750 Lane, Scott, 555, 556 Laszlo, Joseph, 188 Likert, Rensis, 276 Litzenberger, Julie, 780–782 Lunden, Joan, 45 M Mackay, David, 58, 59 Marcello, Melissa, 780–782 Marriott, Alice S., 11, 517, 749 Marriott, J Willard, 517, 749 Martin, Ricky, 147 Mayo, Charles, 134 Mayo, William, 134 McCain, John, 169 McGee, Jim, 528 McGrath, Judy, 363 McNabb, Donovan, 116 Miller, Bob, 743 Miller, Jeff, 176 Meyers, Tom, 628 N Nader, Ralph, 178 Neal, William D., 602 Nightingale, Megan, 481 Norton, Mark, 786–787 O Obama, Barack, 169, 177–178, 388 Otellini, Paul, 21 P Paul, Gerry, 201 Payne, Stanley, 303 INDEX Pearson, Karl, 530 Peller, Clara, 814 Perlman, Rhea, 45 Pitts, Bob, 334 Politz, Alfred, 139 Polonsky, Leonara, 270 Powell, Aaron, 786–787 Pressler, Paul, 438 Q Quinn, James E., 622 R Redington, Michael, 402 Rhodin, Mike, 169 Riccitiello, John, 436 Romer, Richard, 129 Ronaldo, 174 Rosenberg, Bill, 334 S V Salama, Eric, 410 Sanger, Stephen W., 481 Schneider, Michele, 529 Schultz, Howard, 214 Seacrest, Ryan, 780 Spears, Britney, 147 Stapel, Jan, 279 Stengel, Jim, 270 Stirland, Richard, 558 Van Scoy, Greg, 248 T Tatehiro Tsuruta, 178 Taylor, Jeff, 292 Thomas, Dave, 681–682, 814 Thomas, Jerry, 726 Tiffany, Charles Lewis, 621 Tomohiko Ikeda, 738–739 Trebek, Alex, 45 W Warner, Kurt, 116 Weber, Alan, 107–108 Weiman, Beverly, 370 Weyforth, Frank, 107–108 Wheatly, Zack, 783–784 Willard, J., 11 Witherspoon, Reese, 622 Woods, Tiger, 64, 174 Wyrick, Debbi, 31 Y Yang, Thomas, 214 Z Zalesky, Chet, 272 897 This page intentionally left blank Page 378: Specifying precision in relative rather than absolute terms by specifying that the estimate be within plus or minus R percentage points of the mean: Selected Formulas D ϭ Rm Chapter 12: Sampling: Final and Initial Sample Size Determination In these cases, the sample size may be determined by s2z2 n = TABLE 12.1 Symbols for Population Parameters and Sample Statistics Variable Population Sample Mean Proportion m p X p s2 s s2 s N sx n Variance Standard deviation D R2 where the coefficient of variation CV ϭ (s/m) would have to be estimated Page 379: Determining the sample size using the formula for the standard error of the proportion: p(1 - p)z2 n = D2 sqx Page 383: Considering completion rates when calculating final sample size: In general, if there are c qualifying factors with an incidence of Q1, Q2, Q3, , Qc, each expressed as a proportion, sp sp Incidence rate = Q1 * Q2 * Q3 Á * Qc X - m X - X s s X Size Standard error of the mean q Standard error of the proportion Standardized variate (z) CV2z2 = s s m Coefficient of variation (CV) Final sample size Initial sample size = Incidence * Completion rate Response rates: Response Rate = Page 373: Mean: Number of Completed Interviews Number of Eligible Units in Sample n a a Xi b 1X = i=1 n Chapter 14: Data Preparation Page 433: Standardization: Standardized scores, zi, may be obtained as: Page 374: Standard error of the mean and the proportion: Mean s sxq = 1n zi = 1Xi - X2>s Proportion p(1 - p) sp = n A Chapter 15: Frequency Distribution, Cross-Tabulation, and Hypothesis Testing Estimate of the population standard deviation: n s = a (Xi - X) i=1 n i=1 i=1 n - In cases where s is estimated by s, the standard error of the mean becomes s sxq = 1n Estimate of the standard error of the proportion: est sp = Range ϭ Xlargest Ϫ Xsmallest Page 456: Coefficient of variation: CV = A fe = n nr nc n where X - m z = and sxq nr ϭ total number in the row nc ϭ total number in the column n ϭ total sample size p - p sp Finite population correction factor: Calculating the chi-square statistic: N - n AN - χ2 = a In this case sxq = s X Page 467: Calculating the expected cell frequency under the null hypothesis: p11 - p2 Computing z values: z = Page 455: Range: n s = T or n - Q n a Xi - a a Xi b all cells s N - n 1n A N - and sp = A p (1 - p) N - n n AN - s2z2 fo ϭ observed frequency fe ϭ expected frequency Page 468: Phi coefficient: D f = Applying the finite population correction: nc ϭ nN/(N ϩ n Ϫ 1) χ2 Cn Page 469: Contingency coefficient: where n ϭ sample size without fpc nc ϭ sample size with fpc fe where Page 377: Determining the sample size using the formula for the standard deviation of the mean: n = ( fo - fe)2 C = χ2 Cχ2 + n Relationship between Cramer’s V and the phi correlation coefficient for a table with r rows and c columns: V = φ2 V = or C min(r - 1), (c - 1) χ 2/n C min(r - 1), (c - 1) Page 473: Hypothesis test of means for two independent samples: Chapter 16: Analysis of Variance and Covariance Page 501: Total variation in Y, denoted by SSy: SSy ϭ SSbetween ϩ SSwithin Page 502: The total variation in Y may be decomposed as: SSy ϭ SSx ϩ SSerror H0: m1 = m2 H1: m1 Z m2 where Computing a pooled variance estimate from two sample variances: n1 s2 = n2 - N SSy = a (Yi - Y )2 - 2 a (Xi1 - X1) + a (Xi2 - X2 ) i=1 i=1 i=1 c or n1 + n2 - 2 SSx = a n(Yj - Y )2 j=1 c n (n1 - 1)s1 + (n2 - 1)s2 s = n1 + n2 - SSerror = a a (Yij - Yj)2 j sX = X2 A s2¢ Yj = mean for category j 1 + ≤ n1 n2 Y = mean over the whole sample, or grand mean Yij = ith observation in the jth category The appropriate value of t can be calculated as: t = (X1 - X2 ) - (m1 - m2) sX - X Page 503: Measuring the strength of the effects of X on Y: h2 = Page 474: Computing the F statistic: F(n1 - 1), (n2 - 1) = s12 s22 SSx SSy = (SSy - SSerror) SSy Using the F statistic to test whether SSx and SSerror come from the same source of variation: F= where n1 ϭ size of sample n2 ϭ size of sample n1 Ϫ ϭ degrees of freedom for sample n2 Ϫ ϭ degrees of freedom for sample s21 ϭ sample variance for sample s22 ϭ sample variance for sample i Yi = individual observation The standard deviation of the test statistic can be estimated as: SSx /(c - 1) MSx = SSerror /(N - c) MSerror Page 509: Measuring the strength of the joint effect of two factors: multiple h2 = (SSx1 + SSx2 + SSx1x2) SSy The significance of the overall effect may be tested by an F test, as follows: F= Page 475: Test statistic for two independent samples: p1 - p2 z = s p1 - p2 = (SSx1 + SSx2 + SSx1x2)/dfn SSerror /dfd SSx1, x2, x1x2 /dfn SSerror /dfd = MSx1, x2, x1x2 MSerror Standard error of the difference in the two proportions: sp1 - p2 = A p(1 - p)c 1 + d n1 n2 where dfn ϭ degrees of freedom for the numerator ϭ (c1 Ϫ 1) ϩ (c2 Ϫ 1) ϩ (c1 Ϫ 1)(c2 Ϫ 1) ϭ c1c2 – dfd ϭ degrees of freedom for the denominator ϭ N Ϫ c1c2 MS ϭ mean square where p = n1 p1 + n2 p2 n1 + n2 Page 476: Paired samples t test: Measuring the significance of the interaction effect: H0: m D = F= H1: m D Z tn - = D - mD sD dfn ϭ (c1 Ϫ 1)(c2 Ϫ 1) dfd ϭ N Ϫ c1c2 a Di i=1 F= n n sD = sD = a (Di - D) i=1 Q sD MSx1x2 MSerror Page 510: The significance of the main effect of each factor may be tested as follows for X1: n D = = where 1n where SSx1x2 /dfn SSerror /dfd Page 478: Kolmogorov-Smirnov test statistic: K = Max | Ai - Oi | = MSx1 MSerror where dfn ϭ c1 Ϫ dfd ϭ N Ϫ c1c2 n - 1n SSx1 /dfn SSerror /dfd Page 515: Calculating omega squared: v2x = SSx - (dfx * MSerror) SStotal + MSerror Selected Formulas (continued ) Page 541: Relationship between the standardized and nonstandardized regression coefficients: Byx ϭ byx(sx /sy) Chapter 17: Correlation and Regression Page 542: Decomposition of total variation: Page 530: Product moment correlation, r, can be calculated as: SSy ϭ SSreg ϩ SSres n a (Xi - X)(Yi - Y) where n i=1 r = n a A i=1 SSy = a (Yi - Y )2 n (Xi - X)2 a (Yi - Y )2 i=1 n i=1 SSreg = a (YNi - Y )2 Division of the numerator and denominator by n – gives: n a n n SSres = a (Yi - YNi)2 (Xi - X)(Yi - Y ) n - i=1 r = i=1 (Xi - X )2 n (Yi - Y a n - a n - A i=1 i=1 )2 = i=1 COVxy sx sy The strength of association may then be calculated as follows: Page 532: Expressing r in terms of the decomposition of the total variation: r2 = = Explained variation Total variation = SSreg r2 = SSy SSy Page 533: Testing the statistical significance of the relationship between two variables measured by using r: t = rc - r2 d SSy - SSres SSy Page 543: Test for the significance of the coefficient of determination: The hypotheses in this case are: SSx H0: R2pop ϭ SSy - SSerror Total variation - Error variation = Total variation SSy n - = H1: R2pop > The appropriate test statistic is the F statistic: F = 1/2 SSreg SSres /(n - 2) Page 544: Calculating the standard error of estimate, SEE: Page 534: Calculating the partial correlation coefficient: n N a (Yi - Y ) rxy - (rxz)(ryz) rxy.z = 2 21 - rxz 21 - ryz Page 535: Part correlation coefficient: ry(x.z) = SEE = i=1 Q SEE = or n - SSres An - or more generally, if there are k independent variables, rxy - rxz ryz SEE = 21 - rxz SSres An - k - Page 546: General form of the multiple regression model: Page 537: Bivariate regression analysis: Y = β + β 1X1 + β 2X2 + β 3X3 + Á + β kXk + e Bivariate regression model The basic regression equation is Yi ϭ b0 ϩ b1Xi ϩ ei, where Y ϭ dependent or criterion variable, X ϭ independent or predictor variable, b0 ϭ intercept of the line, b1 ϭ slope of the line, and ei is the error term associated with the ith observation which is estimated by the following equation: Estimated or predicted value The estimated or predicted value of Yi is YNi = a + bx, where YN i is the predicted value of Yi, and a and b are estimators of b0 and b1, respectively Page 548: Measuring the strength of association (also called the coefficient of multiple determination): YN = a + b1X1 + b2X2 + b3X3 + Á + bkXk t statistic A t statistic with n Ϫ degrees of freedom can be used to test the null hypothesis that no linear relationship exists between X and Y, or H0: b1 ϭ 0, where t = b SEb Page 540: Estimating the parameters: In most cases, b0 and b1 are unknown and are estimated from the sample observations using the equation YNi = a + bxi where YNi is the estimated or predicted value of Yi, and a and b are estimators of b0 and b1, respectively The slope, b, may be computed in terms of the covariance between X and Y, (COVxy), and the variance of X as: n b = COVxy s2x = n a (Xi - X)(Yi - Y) i=1 n a (Xi - X) i=1 The intercept, a, may then be calculated using: a = Y - bX = a XiYi - nXY i=1 n 2 a X i - nX SSreg R2 = SSy Page 549: R2 adjusted for the number of independent variables and the sample size: Adjusted R2 = R2 - k(1 - R2) n - k - Significance testing using an F statistic: F = = SSreg /k SSres /(n - k - 1) R2/k (1 - R )/(n - k - 1) This has an F distribution with k and (n Ϫ k Ϫ 1) degrees of freedom Page 550: Incremental F statistic: i=1 F = SSxi /1 SSres /(n - k - 1) This has an F distribution with and (n Ϫ k Ϫ 1) degrees of freedom Chapter 18: Discriminant and Logit Analysis Page 571: The discriminant analysis model involves linear combinations of the following form: D ϭ b0 ϩ b1X1 ϩ b2X2 ϩ b3X3 ϩ Á ϩ bkXk where The importance of an attribute, Ii, is defined in terms of the range of the part-worths, aij, across the levels of that attribute: Ij = 5max (αij ) - (αij )6, for each i The attribute’s importance is normalized to ascertain its importance relative to other attributes, Wi: D ϭ discriminant score b’s ϭ discriminant coefficient or weight X’s ϭ predictor or independent variable Page 589: Using the logit model to model the probability of success: a Ii so that m exp a a a i X i b a Wi = i=1 i=0 k + exp a a a i X i b i=0 m i=1 k p = Ii Wi = Chapter 22: Structural Equation Modeling and Path Analysis Page 696: Estimating model parameters: where p ϭ probability of success Xi ϭ independent variable i ϭ parameter to be estimated Estimation model when OLS regression is used: n p = a X i i=0 (p(p ϩ 1))/2 ϭ p(p Ϫ 1)/2 ϩ p If the actual number of estimated parameters, k, is less than (p(p ϩ 1))/2, the model is overidentified In that case we have positive degrees of freedom Conversely, if k is greater than (p(p ϩ 1))/2, the model is underidentified and a unique solution cannot be found Page 698: Representing the measurement model: X1 = lx1,1 j1 + d1 Chapter 19: Factor Analysis where j = latent factors Page 605: Representing the factor model: X = measured variables Xi = Ai1F1 + Ai2F2 + Ai3 F3 + Á + Aim Fm + Vi Ui lx = factor loadings where Xi ϭ ith standardized variable Aij ϭ standardized multiple regression coefficient of variable i on common factor j F ϭ common factor Vi ϭ standardized regression coefficient of variable i on unique factor i Ui ϭ the unique factor for variable i m ϭ number of common factors The unique factors are uncorrelated with each other and with the common factors The common factors themselves can be expressed as linear combinations of the observed variables d = errors Page 700: Chi-square: x2 = (n - 1) (S - © k) Page 700: Determining the degrees of freedom (df) for SEM: df ϭ 1/2[(p) (p ϩ 1)] Ϫ k Page 701: Composite reliability: p ( a li)2 i=1 CR = Fi = Wi1 X1 + Wi2 X2 + Wi3 X3 + Á + WikXk p p ( a li)2 + ( a di) i=1 where Fi ϭ estimate of ith factor Wi ϭ weight or factor score coefficient k ϭ number of variables where CR ϭ composite reliability l ϭ completely standardized factor loading d ϭ error variance p ϭ number of indicators or observed variables Page 614: Estimating factor scores: Factor scores for the ith factor may be estimated as follows: Fi = Wi1X1 + Wi2 X2 + Wi3 X3 + Á + WikXk i=1 Page 702: Average variance extracted: p AVE = Chapter 21: Multidimensional Scaling and Conjoint Analysis ki U(X) = a a αij xij p p i=1 i=1 where AVE ϭ average variance extracted l ϭ completely standardized factor loading d ϭ error variance p ϭ number of indicators or observed variables i=1 j=1 where U(X) ϭ overall utility of an alternative aij ϭ the part-worth contribution or utility associated with the jth level ( j ϭ 1, 2, ki) of the ith attribute (i ϭ 1, 2, m) ki ϭ number of levels of attribute i m ϭ number of attributes xij ϭ if the jth level of the ith attribute is present ϭ otherwise i=1 a li + a di Page 673: Representing the basic conjoint analysis model: m a li Page 705: Chi-square difference statistic (⌬x2): ¢x2¢df = x2df(M1) - x2df(M2) and ⌬df ϭ df(M1) Ϫ df(M2) ... Journal of Marketing Research, Journal of Marketing, Journal of Consumer Research, and Journal of the Academy of Marketing Science Marketing Research: An Applied Orientation has been translated... Discriminant and Logit Analysis 568 Objectives 568 Overview 569 Basic Concept of Discriminant Analysis 570 Relationship of and Logit Analysis to ANOVA and Regression 571 Discriminant Analysis... Cross-Tabulation, and Hypothesis Testing 448 Analysis of Variance and Covariance 496 Correlation and Regression 528 Discriminant and Logit Analysis 568 Factor Analysis 602 Cluster Analysis 628 Multidimensional