Essentials of marketing research 3rd hair celsi and bush

www.downloadslide.com Essentials of Marketing Research Third Edition Joseph F Hair, Jr Kennesaw State University Mary Wolfinbarger Celsi California State University–Long Beach David J Ortinau University of South Florida Robert P Bush Louisiana State University at Alexandria ESSENTIALS OF MARKETING RESEARCH, THIRD EDITION Published by McGraw-Hill, a business unit of The McGraw-Hill Companies, Inc., 1221 Avenue of the Americas, New York, NY 10020 Copyright © 2013 by The McGraw-Hill Companies, Inc All rights reserved Printed in the United States of America Previous editions © 2010 and 2008 No part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written consent of The McGraw-Hill Companies, Inc., including, but not limited to, in any network or other electronic storage or transmission, or broadcast for distance learning Some ancillaries, including electronic and print components, may not be available to customers outside the United States This book is printed on acid-free paper DOW/DOW ISBN 978–0–07–802881–6 MHID 0–07–802881–7 Vice President & General Manager: Brent Gordon Managing Director: Paul Ducham Sponsoring Editor: Sankha Basu Developmental Editor: Sean M Pankuch Marketing Manager: Donielle Xu Project Manager: Mary Jane Lampe Buyer: Nicole Baumgartner Media Project Manager: Prashanthi Nadipalli Cover Designer: Studio Montage, St Louis, MO Cover Image: Purestock/SuperStock Typeface: 10/12 Minion Pro Compositor: S4Carlisle Publishing Services Printer: R R Donnelley Library of Congress Cataloging-in-Publication Data Essentials of marketing research / Joseph F Hair, Jr [et al.].—3rd ed p cm Includes bibliographical references and indexes ISBN 978–0–07–802881–6 — ISBN 0–07–802881–7 Marketing research I Hair, Joseph F HF5415.2.E894 658.8'3—dc23 2013 2012019098 www.mhhe.com Dedication To my wife Dale, and our son Joe III, wife Kerrie, and grandsons Joe IV and Declan —Joseph F Hair, Jr., Kennesaw, GA To my father and mother, William and Carol Finley —Mary Wolfinbarger Celsi, Long Beach, CA To all my nieces and nephews, who will be society’s future leaders, and to all my past, present, and future students for enriching my life experiences as an educator and mentor on a daily basis —David J Ortinau, Tampa, FL To my two boys, Robert Jr and Michael —Robert P Bush, Sr., Alexandria, LA iii About the Authors Joseph Hair is professor of marketing at Kennesaw State University, and director of the DBA degree program He formerly held the Copeland Endowed Chair of Entrepreneurship at Louisiana State University He has published more than 40 books, including market leaders Multivariate Data Analysis, 7th edition, Prentice Hall, 2010, which has been cited more than 22,500 times; Marketing Research, 4th edition, McGraw-Hill/Irwin, 2009; Principles of Marketing, 12th edition, Thomson Learning, 2012, used at over 500 universities globally; and Essentials of Business Research Methods, 2nd edition, M E Sharpe, 2011 In addition to publishing numerous referred manuscripts in academic journals such as Journal of Marketing Research, Journal of Academy of Marketing Science, Journal of Business/Chicago, Journal of Advertising Research, and Journal of Retailing, he has presented executive education and management training programs for numerous companies, has been retained as consultant and expert witness for a wide variety of firms, and is frequently an invited speaker on research methods and mulivariate analysis He is a Distinguished Fellow of the Academy of Marketing Science, the Society for Marketing Advances, and has served as president of the Academy of Marketing Sciences, the Society for Marketing Advances, the Southern Marketing Association, the Association for Healthcare Research, the Southwestern Marketing Association, and the American Institute for Decision Sciences, Southeast Section Professor Hair was recognized by the Academy of Marketing Science with its Outstanding Marketing Teaching Excellence Award, and the Louisiana State University Entrepreneurship Institute under his leadership was recognized nationally by Entrepreneurship Magazine as one of the top 12 programs in the United States Mary Wolfinbarger Celsi earned a BS in English from Vanderbilt University and a Masters in Business and Public Administration and a PhD in Marketing from the University of California, Irvine Her specialties include digital marketing, online consumer behavior, internal marketing, and consumer and organizational identity She has been teaching at California State University, Long Beach, since 1990 Dr Celsi possesses expertise in both qualitative and quantitative research methodologies She received grants from the Center for Research on Information Technology in Organizations (CRITO), which enabled her to coauthor several articles about consumer behavior on the Internet Dr Celsi’s interest in e-commerce and technology extends to the classroom; she developed and taught the first Internet marketing course at CSULB in 1999 She also has written articles on the impact of technology and e-commerce on the classroom and on the business school curriculum Professor Celsi has collaborated on research about internal marketing, receiving two Marketing Science Institute grants and conducting studies at several Fortune 500 companies She has published articles in Journal of Marketing, Journal of Consumer Research, Journal of Retailing, California Management Review, Journal of the Academy of Marketing, Journal of Consumer Research, and Earthquake Spectra David J Ortinau earned his PhD in marketing from the Louisiana State University He began his teaching career at Illinois State University and after completing his degree moved iv About the Authors v to the University of South Florida, Tampa, where he continues to be recognized for both outstanding research and excellence in teaching at the undergraduate, graduate, and doctorate levels His research interests range from research methodologies and scale measurement development, attitude formation, and perceptual differences in retailing and services marketing environments to interactive electronic marketing technologies and their impact on information research problems He consults for a variety of corporations and small businesses, with specialties in customer satisfaction, customer service quality, service value, retail loyalty, and imagery Dr Ortinau has presented numerous papers at national and international academic meetings and continues to be a regular contributor to such prestigious publications as the Journal of the Academy of Marketing Science (JAMS), Journal of Retailing (JR), Journal of Business Research (JBR), Journal of Marketing Education (JME), Journal of Services Marketing (JSM), Journal of Health Care Marketing (JHCM), and others Professor Ortinau served as a member of the editorial review board for the Journal of the Academy of Marketing Science (JAMS) from 1988 through 2006 and continues to serve on the review board and serves as coassociate editor of marketing for the Journal of Business Research (JBR) He was coeditor of Marketing: Moving Toward the 21st Century (SMA Press, 1996) He remains an active leader in the marketing discipline He has held many leadership positions in the Society for Marketing Advances (SMA), and served as co-chair of the 1998 SMA Doctoral Consortium in New Orleans and the 1999 SMA Doctoral Consortium in Atlanta Dr Ortinau is a past president of SMA and was recognized as the 2001 SMA Fellow and nominated for the 2007 AMS Fellow He served as the 2004 Academy of Marketing Science Conference Program co-chair and the 2007 SMA Retailing Symposium co-chair Robert P Bush is a professor of marketing and holds the Alumni and Friends Endowed Chair of Business at Louisiana State University at Alexandria Dr Bush has published numerous articles in such journals as Journal of Retailing, Journal of Advertising, Journal of Marketing Education, Journal of Consumer Marketing, Journal of Customer Relationship Marketing, and others Preface We live in a world that is global, highly competitive, and increasingly influenced by information technology, particularly the Internet The first edition of our text Essentials of Marketing Research became a premier source for new and essential marketing research knowledge Many of you, our customers, provided feedback on the first and second editions of this book as well as the earlier editions of our longer text, Marketing Research Some of you like to applied research projects while others emphasize case studies or exercises at the end of the chapters Others have requested additional coverage of qualitative methods Students and professors alike are concerned about the price of textbooks This third edition of Essentials of Marketing Research was written to meet the needs of you, our customers The text is concise, highly readable, and value-priced, yet it delivers the basic knowledge needed for an introductory text We provide you and your students with an exciting, up-to-date text and an extensive supplement package In the following section we summarize what you will find when you examine, and we hope, adopt, the third edition of Essentials Innovative Features of this Book First, in the last few years, data collection has migrated quickly to online approaches, and by 2011 reached about 60 percent of all collection methods This movement to online methods of data collection necessitated the addition of substantial new material on this topic The chapters on sampling, measurement and scaling, design of questionnaires, and preparation for data analysis all required new guidelines on how to deal with online related issues Social media monitoring and marketing research online communities are expanding research methods and are addressed in our chapter on qualitative and observational research Second, to enhance student analytical skills we expanded the continuing case on the Santa Fe Grill by adding a competitive restaurant—Jose’s Southwestern Café The addition of a competitor for the continuing case enables students to make comparisons of customer experiences in each of the two restaurants and to apply their research findings in devising the most effective marketing strategies for the Santa Fe Grill The exercises for the continuing case demonstrate practical considerations in sampling, qualitative and observational design, questionnaire design, data analysis and interpretation, and report preparation, to mention a few issues Social media monitoring and marketing research online communities are expanding research methods and are addressed in our chapter on qualitative and observational research Third, we have updated the Marketing Research Dashboards in each chapter to include new features that focus on timely, thought-provoking issues in marketing research Examples of topics covered include ethics, privacy and online data collection, particularly clickstream analysis, the role of Twitter and Linked-In in marketing research, and improving students’ critical thinking skills Fourth, other texts include little coverage of the task of conducting a literature review to find background information on the research problem Our text has a chapter that vi Preface vii includes substantial material on literature reviews, including guidelines on how to conduct a literature review and the sources to search Because students rely so heavily on the Internet, the emphasis is on using Google, Yahoo!, Bing, and other search engines to execute the background research In our effort to make the book more concise, we integrated secondary sources of information with digital media searches This material is in Chapter Fifth, our text is the only one that includes a separate chapter on qualitative data analysis Other texts discuss qualitative data collection, such as focus groups and in-depth interviews, but then say little about what to with this kind of data In contrast, we dedicate an entire chapter to the topic, referencing the seminal work in this area by Miles and Huberman, and enabling professors to provide a more balanced approach in their classes We also explain important tasks such as coding qualitative data and identifying themes and patterns An important practical feature in Chapter of the third edition is a sample report on a qualitative research project to help students better understand the differences between quantitative and qualitative reports We also have an engaging qualitative research assignment on product dissatisfaction as a new MRIA at the end of the chapter to help students more fully understand how to analyze qualitative research We think you and your students will find this assignment to be an interesting and relevant introduction to qualitative analysis Sixth, as part of the “applied” emphasis of our text, Essentials has two pedagogical features that are very helpful to students’ practical understanding of the issues One is the boxed material mentioned above entitled the Marketing Research Dashboard that summarizes an applied research example and poses questions for discussion Then at the end of every chapter, we feature a Marketing Research in Action (MRIA) exercise that enables students to apply what was covered in the chapter to a real-world situation Seventh, as noted above, our text has an excellent continuing case study throughout the book that enables the professor to illustrate applied concepts using a realistic example Our continuing case study, the Santa Fe Grill Mexican Restaurant, is a fun example students can relate to given the popularity of Mexican restaurant business themes As mentioned above, for this edition we added a competitor—Jose’s Southwestern Café—so students can complete a competitive analysis, including application of importance-performance concepts Because it is a continuing case, the professor does not have to familiarize students with a new case in every chapter, but instead can build on what has been covered earlier The Santa Fe Grill case is doubly engaging because the story/setting is about two college student entrepreneurs who start their own business, a goal of many students Finally, when the continuing case is used in later chapters on quantitative data analysis, a data set is provided that can be used with SPSS to teach data analysis and interpretation skills Thus, students can truly see how marketing research information can be used to improve decision making Eighth, in addition to the Santa Fe Grill case, there are five other data sets in SPSS format The data sets can be used to assign research projects or as additional exercises throughout the book These databases cover a wide variety of topics that all students can identify with and offer an excellent approach to enhance teaching of concepts An overview of these cases is provided below: Deli Depot is an expanded version of the Deli Depot case included in previous editions An overview of this case is provided as part of the MRIA (Marketing Research in Action) feature in Chapter 10 The sample size is 200 Remington’s Steak House is introduced as the MRIA in Chapter 11 Remington’s Steak House competes with Outback and Longhorn The focus of the case is analyzing data to identify restaurant images and prepare perceptual maps to facilitate strategy development The sample size is 200 viii Preface QualKote is a business-to-business application of marketing research based on an employee survey It is introduced as the MRIA in Chapter 12 The case examines the implementation of a quality improvement program and its impact on customer satisfaction The sample size is 57 Consumer Electronics is based on the rapid growth of the digital recorder/player market and focuses on the concept of innovators and early adopters The case overview and variables as well as some data analysis examples are provided in the MRIA for Chapter 13 The sample size is 200 Backyard Burgers is based on a nationwide survey of customers The database is rich with potential data analysis comparisons and covers topics with which students can easily identify The sample size is 300 Ninth, the text’s coverage of quantitative data analysis is more extensive and much easier to understand than other books’ Specific step-by-step instructions are included on how to use SPSS to execute data analysis for all statistical techniques This enables instructors to spend much less time teaching students how to use the software the first time It also saves time later by providing a handy reference for students when they forget how to use the software, which they often For instructors who want to cover more advanced statistical techniques our book is the only one that includes this topic In the third edition, we have added additional material on selecting the appropriate statistical technique and much more extensive coverage of how to interpret data analysis findings Tenth, as noted earlier, online marketing research techniques are rapidly changing the face of marketing, and the authors have experience with and a strong interest in the issues associated with online data collection For the most part other texts’ material covering online research is an “add-on” that does not fully integrate online research considerations and their impact In contrast, our text has extensive new coverage of these issues that is comprehensive and timely because it was written in the last year when many of these trends are now evident and information is available to document them New to the Third Edition The following list highlights many of the changes you will appreciate in the third edition In this edition we have: • • • • • • • • Substantially updated the sources and data throughout the text Rewritten the introductory material in Chapter to be more engaging Clarified the difference between research problems and research questions in Chapter Added material on developing good hypotheses in Chapter Provided the latest available information on social media monitoring and marketing online research communities (MROC’s) in Chapter Expanded Chapter to include new survey types, such as SurveyGizmo and Qualtrics, and updated methods There are also more in-depth explanations of key concepts with more information on validity and test marketing Included more material on sampling terminology, sample size determination approaches, and the central limit theorem in Chapter Expanded the materal on validity in Chapter and added additional information on ordinal scales, scale development, adapting existing scales/constructs, and negatively worded statements Five of the exhibits were revised to include more examples Preface • • • • • • • ix Added material on online questionnaires and expanded the discussion of questionnaire layout in Chapter Added a small-scale group project to help instructors teach qualitative analysis in Chapter Revised all SPSS exhibits throughout Chapters 10 to 13 using Version 20 Made several changes to the interactive material in Chapter 10, including Exhibit 10.2 The Employee Questionnaire for the Santa Fe Grill, the Hands-On exercise for Deli Depot MRIA, and SPSS discussion questions Thoroughly revised the hypothesis development section in Chapter 11 Added paragraphs and exhibits to Chapter 12 to illustrate homoscedasticity and normality Created three new exhibits for Chapter 13 and heavily revised the material to include the use of DVR’s We have also added material on oral presentation Pedagogy Many marketing research texts are readable But a more important question is, Can students comprehend what they are reading? This book offers a wealth of pedagogical features, all aimed at answering the question positively Below is a list of the major pedagogical elements: Learning Objectives Each chapter begins with clear Learning Objectives that students can use to assess their expectations for and understanding of the chapter in view of the nature and importance of the chapter material Real-World Chapter Openers Each chapter opens with an interesting, relevant example of a real-world business situation that illustrates the focus and significance of the chapter material For example, Chapter illustrates the emerging role of social networking sites such as Twitter in enhancing marketing research activities Marketing Research Dashboards The text includes a boxed feature in all chapters that acts like a dashboard for the student to communicate emerging issues in marketing research decision making Key Terms and Concepts These are bold-faced in the text and defined in the page margins They also are listed at the end of the chapters along with page numbers to make reviewing easier, and they are included in the comprehensive marketing research Glossary at the end of the book Ethics Ethical issues are treated in the first chapter to provide students with a basic understanding of ethical challenges in marketing research Coverage of increasingly important ethical issues has been updated and expanded from the second edition, and includes online data collection ethical issues Chapter Summaries The detailed chapter Summaries are organized by the Learning Objectives presented at the beginning of the chapters This approach to organizing summaries helps students to remember the key facts, concepts, and issues The Summaries serve as an excellent study guide to prepare for in-class exercises and for exams Questions for Review and Discussion The Review and Discussion Questions are carefully designed to enhance the self-learning process and to encourage application of the concepts learned in the chapter to real business decision-making situations There are two or three questions in each chapter directly related to the Internet and designed to provide students with opportunities to enhance their digital data gathering and interpretative skills 400 Subject Index Information research process (Cont.) determining the research problem, 31–36 final report, 41 gatekeeper technologies, 26 for hotel-choice criteria study, 30–31 identify and clarify information needs, 32–33 literature review, 34–35 managerial decisions, 27–29 need determination, 27–29 overview, 29–31 phases and steps of, 29–30, 31 primary data sources, 26 questionnaire design and pretest, 39 research design execution, 39–40 research design selection, 36–38 research proposal, 41 sampling as part of, 136–137 sampling design and size development, 38 scientific method in, 30 secondary data sources, 26, 53–54 situation analysis, 32 situations not needed, 27, 28 specify research objectives, 36 time constraints, 28–29 transforming data into knowledge, 30 unit of analysis, 34 value of measurement in, 158 variable identification, 34 In-home interviews, 111, 112 In-store observation, 81 Integrated marketing communications, Integration, 221 Interaction effect, 294 Internal consistency, scale reliability, 166 Internal research providers, 10 Internal secondary data common sources of, 54, 55 defined, 50 Internal validity, 125–126 International marketing research challenges of, 4–5, Internet See also Online surveys blogs on, 56–57 clickstream analysis, 14 customer privacy and, 14–15 de-anonymizing data, 14–15 ethical issues with, 12, 14–15 “found” data, 78, 79 impact on marketing research, 26 in-depth interviews, 81 literature reviews and, 51, 56 media panel measuring use on, 61 microblogging, mobile searches, 135–136 netnography, 98 observational research on, 95–96 popular secondary sources on, 56 scholarly sources on, 57 search engine marketing (SEM), 135–136 senior adoption of Internet study, 219, 220, 223, 224, 230–231 social media monitoring, 97–98 tracking use of, 61 visual stimuli during telephone interviewing using, 113 wireless web surveys, 114 Internet Advertising Bureau (IAB), 51, 56–57 Internet-based poll, challenges with, Internet surveys See Online surveys Interpretive skills, 82 Interval data mean for, 270 t-Test, 288 Interval scales appropriate statistic and, 278–279 defined, 163 examples of, 164 mean, 268 measures of central tendency, 170, 171 measures of dispersion, 170, 171 overview, 163–164 Pearson correlation coefficient, 317 statistical technique and, 278 Interviewer instructions, 203 Interviewers curbstoning by, 13 skills of in-depth, 82 Interviews See also In-depth interviews call records for, 205 curbstoning, 13 deliberate falsification, 13 in-home interviews, 111 interviewer instructions, 203 mail-intercept interviews, 112 quotas for, 204–205 screening questions, 203–204 subject debriefing, 14 sugging/frugging, 14 taping without respondent consent, 14 word association tests, 92 Introduction defined, 194, 347 elements of, 347–348 introductory section of questionnaire, 194 IPhone, 135 IRS (Internal Revenue Service), 267 Iteration during data reduction phase, 222–223 defined, 222 memoing, 222 negative case analysis, 222–223 verification phase, 225 J Jamming, 29 J.D Power and Associates, 1, 77 Jeep Wrangler campaign, 75–76 Johnson Properties, Inc., 42 Judgment sampling, 145–146 advantages, 145–146 defined, 145 disadvantages, 145–146 401 Subject Index K Keywords, used in search engines, 56 KISS (Keep It Simple and Short) test, 201 Knowledge defined, 30 interpretation of data for, 40 transforming data into, 30 Knowledge level, of participants, 121 Knowledge Metrics/SRI, 62 Knowledge Networks, 215 Kodak, 10 Kraft Foods, 10, 90 L Laboratory (lab) experiments, 126 Latin America, emerging market in, Leading questions, 193 Least squares procedure, 323, 324 Lee Apparel Company, 128–129 Lexus/Nexus, 56 Likert scales, 117 defined, 171 example of, 172 overview, 171–172 Limitations defined, 362 in marketing research report, 362 noted in qualitative research report, 230 of observation methods, 96 Linear relationship bivariate regression analysis, 322 correlation coefficient and, 319 curvilinear relationship and, 313, 316 defined, 312 multiple regression analysis, 327 Pearson correlation coefficient, 317 regression analysis, 322 Listening platforms/posts, 97 Literature review, 34–35, 51–54 constructs and, 63, 64 defined, 51 developing a conceptual model, 63–64 evaluation of secondary data sources, 51–52 for “market maven” construct, 63 reasons for conducting, 51 Santa Fe Grill Mexican Restaurant case study, 69 synthesizing secondary research for, 63 uses, 51 Loaded questions, 193 Long-term MROCs, 90 Lotame Solutions, Inc., 36, 37 Lowe’s Home Improvement, Inc., 35 M Macy’s department store, 122–123 Magnum Hotel customer satisfaction survey, 30 loyalty program, 107 Preferred Guest Card Program, 42–44 Mail-order forms, as source of secondary data, 55 Mail surveys, 116 Main panel surveys, 116 Mall-intercept interviews, 112 Mapping, perceptual See Perceptual mapping Marketing blogs, 56 Marketing maven construct, 64 Marketing research assessing usefulness of, 29 defined, distribution decisions, 7–8 growing complexity of, 4–5 international, 4–5 product decisions, promotion decisions, role and value of, 6–10 situations when not needed, 27, 28 Marketing Research Association (MRA), 14–15 Marketing researchers, management decision makers vs., 28 Marketing research ethics See Ethics Marketing research industry careers in, 22–23 changing skills for, 11 emerging trends in, 16–17 types of firms, 10–11 Marketing research online communities (MROCs), 89–90 percentage of research providers using, 81 social media monitoring vs., 97 used with Hispanics, 99 Marketing research process See also Information research process changing view of, 26–27 decision maker responsibilities for, 27–29 management vs marketing researcher characteristics and roles, 27–29 phases and steps in, 29–30, 31 secondary data and, 53 size and diversity of methods, transforming data into knowledge, 30 universal use of techniques, use of multiple methods, value of, 26 Marketing research report See Research report Marketing Research Society, 16 Marketing research studies, as source of secondary data, 55 Marketing research suppliers, 10 Marketing research tools, Marketing Resource Group (MRG), 43, 107 Marketing Scales Handbook (Bruner), 170 Marketing Science Institute (MSI.org), Marketing theory applicable to other countries, examples of, 9–10 importance of, Market segmentation research, 8–9 benefits and lifestyle studies, 8–9 Market Truths, Marriott Hotels, 26 Matrix, in qualitative data display, 225 Maxwell House, 278 Mazda Motor Corporation, 137 402 Subject Index McDonald’s, 26 Mean ANOVA, 291, 292–293 appropriate statistic and, 278 comparing means, 287–288 defined, 268 dialog boxes for calculating, 271 distortion, 268–269 example, 269, 270 for interval or ratio data, 270 interval scale, 164 measures of central tendency, 170 n-way ANOVA, 296 one-way tabulation and, 255 relationship between scale levels and, 171 semantic differential scale, 173 t-Test used to compare, 288–289 univariate statistical analysis, 280 Measurement See also Construct; Scale measurement defined, 158 overview of process, 158–159 research value of, 158 Measures of central tendency appropriateness of use for each, 270 appropriate statistic and, 278 defined, 170 example, 268, 269 interpretation of results, 270 mean See Mean median See Median mode See Mode outliers, 270 overview, 268 relationship between scale levels and, 170, 171 scale measurement development, 170 in scale measurement development, 170 SPSS applications, 270 Measures of dispersion, 170, 171, 271–273 appropriate statistic and, 278 defined, 170 examples, 274 overview, 271 range, 271–272 relationship between scale levels and, 170, 171 in scale measurement development, 170 SPSS application, 272–273 standard deviation, 273 variance, 272 Measures of location See Measures of central tendency Measures Toolchest (Academy of Management), 170 Median appropriate statistic and, 278 defined, 269 dialog boxes for calculating, 271 example, 269, 270 interval and ratio data using, 170 interval scale, 163 measures of central tendency, 170 for ordinal data, 270 ordinal scales analyzed using, 170 overview, 269 relationship between scale levels and, 171 use of, 278 Median rankings, 321 Media panels, 61–62 Member checking, 217 Memoing, 222 Methodology, secondary data evaluation and, 52 Methods-and-procedures section defined, 348 issues addressed in, 348 presentation slide example, 348 Metropolitan statistical areas (MSAs), 144 Microblogging, Midas Auto Systems, 174 Middle East, emerging market in, Missing data assigning a coded value to, 251–252 data entry and, 253–254 one-way frequency tables showing, 256 Mobile devices, explosion of, 135–136 Mobile phones See also Wireless phone surveys impact on social behavior, 215–216 mobile phone households, 114 used while shopping, 49–50 with web interactions, 135–136 Mode appropriate statistic, 278 defined, 269 dialog boxes for calculating, 271 example, 269, 270 interval and ratio data using, 170 interval scale, 163 measures of central tendency, 170 for nominal data, 270 ordinal scales, 162, 170 overview, 269–270 relationship between scale levels and, 171 Model F statistic, 328 Moderate relationship, 312–313, 317 Moderators See Focus group moderators Moderator’s guide, 86 MPC Consulting Group, 187 MROCs See Marketing research online communities (MROCs) MSN, 56 Multicollinearity, 332 Multiple-item scale choosing between single-item scale and, 178–179 defined, 178 Multiple regression, 324 Multiple regression analysis, 327–333 assumptions, 329 beta coefficient, 327–328 defined, 327 dependent-independent variable relationship, 327 interpretation of results, 330–332 Model F statistic, 328 multicollinearity, 332 SPSS application, 329–333 statistical significance, 328 403 Subject Index MyStarbucksIdea.com, 89 Mystery shopping, Mysurvey.com, 61 N NAICS (North American Industry Classification System) codes, 59–60 Namestormers, 7, 10 National Eating Trends (NET), 61 National Hardwood Lumber Association, 52 Natural language processing (NLP), 97 Negative case analysis, 222–223 during data reduction phase, 223 defined, 222 iterative process, 222 Negative relationship covariation, 314, 315–316 defined, 65 Netnographic research, Netnography defined, 98 process, 98 Neuromarketing, New-product development new product planning, perceptual mapping, 298 Newspaper advertisements, 86 Newspaper, as secondary data source, 56 The New York Times, 56 NFO (National Family Opinion), 26 Nielsen Media Research, 61 Nielsen’s Buzzmetrics, 98 Nike brand, 173 Nokia, 268 Nominal data, mode for, 270 Nominal scales appropriate statistic and, 279 defined, 162 examples of, 162 measures of central tendency and dispersion, 170, 171 overview, 162 statistical technique and, 278 Non-bipolar descriptors, 173–174 Noncomparative rating scales defined, 175 graphic rating scale, 176–177 Nonforced choice scale, 169 Nonparametric statistics, 278–279 Nonprobability sampling design convenience sampling, 145 defined, 140 judgment sampling, 145–146 mail-intercept interviews, 112 quota sampling, 146 in research design development, 38 sample size determination, 147 sampling errors, 140 sampling unit selection, 140 snowball sampling, 146 types of methods, 140 Nonresponse bias mail surveys, 116 respondent participation, 122 Nonresponse error, 110 convenience sampling, 145 defined, 110 reasons for, 110 Nonsampling errors, 109 characteristics of, 110 data accuracy and, 140 defined, 110, 139 nonresponse error, 110 occurrence of, 139–140 respondent errors, 110 response error, 110 in survey research, 110 Normal curve, 329 defined, 329 example, 330 Normal distribution, assumption of, 317 North American Industry Classification System (NAICS), 59–60 Novartis, 90 NPD Group, 60–61 N-Series wireless phone, 268 Null hypothesis for ANOVA, 291 Chi-square analysis, 284 comparing means, 287 correlation coefficient, 316–317 defined, 67 model F statistic, 328 notation of, 68 n-way ANOVA, 295 for Pearson correlation coefficient, 316 rejection of, 68 testing, 276–277 univariate statistical analysis, 279–280 Numerical value, as a code, 151 N-way ANOVA defined, 294 interaction effect, 294 interpretation of results, 295–296 SPSS application, 295–297 uses of, 294–295 O Objectives, research See Research objectives Objects construct development and, 161 examples of concrete features and abstract constructs of, 160 rank-order scales, 176 Observation in descriptive designs, 109 four characteristics of, 94 questioning vs., 39 technology-mediated observation, 95 Observation methods, 93–98 benefits and limitations of, 96 listening platform/post, 97 404 Subject Index Observation methods (Cont.) netnography, 98 scanner technology, 95 selecting, 95–96 sentiment analysis, 97–98 social media monitoring, 97 technology-mediated observation, 95 types of, 94–96 Observation research defined, 93 on the Internet, 95–96 netnography, 98 overview, 93–94 uses of, 93 Odd-point, nonforced scales, 169 Odd-point scale descriptors, 169 Offline tracking information, 95 Olson Zaltman Associates, 93 One-on-one interviews See In-depth interviews One-step approach to cluster sampling, 144 One-way ANOVA problem, 290, 304, 305 One-way frequency table, 255–257 One-way tabulation defined, 254 determining valid percentages, 257 example, 255 indications of missing data, 256 one-way frequency table, 255–257 summary statistics, 257 uses of, 254, 255 Online focus groups advantages, 84 bulletin board format, 84 characteristics of, 84 content analysis, 89 data collection, 217 disadvantages, 84 percentage of research providers using, 81 Online research See also Online surveys online technology supporting offline tracking, 95 retailing research, Online shopping comparison and, 221 data coding, 219 Online social networks, purposed communities, 89–90 Online surveys, 116–118 advantages, 116–117 coding, 249 data entry, 252 data validation, 243, 244 defined, 116 design issues, 201 editing of data, 245 evaluation of questionnaire for, 200–201 hard-to-reach samples, 116 missing data, 253–254 propensity scoring, 118 recruitment of participants, 200–201 response rate metric calculation, 200–201 sampling and, 150 screening questions, 248 time needed to complete, 193–194, 201 Open-ended questions coding, 245, 249–250 editing responses to, 249 range, 272 responses to, 249 unstructured questions as, 190 Opinion mining, 97–98 Optical-scanner technology, 60, 95 Oral presentations, 363–364 Ordinal data Chi-square, 277, 278, 283 median, 269, 270, 278 Ordinal scales, 162–163 appropriate statistic and, 278, 279 defined, 162 examples of, 163 measures of central tendency and dispersion, 170, 171 overview, 162–163 percentile, 278 Spearman rank order correlation scale, 320 statistical technique and, 278 Ordinary least squares, 324, 329 Outliers, 270 P Paired-comparison scales, 177 Paired sample defined, 288 t-Test, 289–290 Panel-based purchasing data, 60–6160 Panel data consumer, 60–61 media, 61–62 Parameter, 68 Parametric statistics, 278–279 Participant observation, 91 Past marketing research studies, as source of secondary data, 55 Pearson correlation coefficient assumptions for calculating, 317 defined, 316 null hypothesis for, 316 SPSS application, 317–319 Peer review defined, 229 qualitative research, 229 People for the Ethical Treatment of Animals (PETA), 52 People Meter, 61 People Meter technology, 61, 62, 95 Percentage distribution, 258 Percentile, use of, 278 Perceptual image profile, 173 Perceptual mapping applications in marketing research, 298 approaches used to develop, 298 defined, 7, 298 of fast-food restaurants, 298, 299 Remington Steak House example, 300–306 405 Subject Index Person-administered survey methods, 111 advantages of, 112 defined, 111 disadvantages of, 112 in-home interviews, 111 mall-intercept interviews, 112 Personal interview, structured questions in, 191 Petroshius, Susan M., 389 Pew American and Internet Life, 49 Phone surveys See Telephone-administered surveys Picture tests, 91 Pie charts, 352–353 Pilot studies, 36 Place, marketing research applied to, 6, 7, 8–9 PlayStation Underground, 27 PlayStation website, 26 Point-of-purchase (POP) displays, 62 Popular sources of secondary data, 56–57 Population See also Defined target population census data, 136 defined, 137 in sampling theory, 137 variance in, sample size determination and, 147 world’s online, 138 Population parameter, 277 Portable People Meter (PPM), 62 Positioning, Positive relationship, 65 covariation, 315–316 defined, 65 scatter diagram, 314 Post, 97 PowerPoint presentations, 346, 347 Precision defined, 147 sample size determination, 147–148 Precision, data, 119 Predictably Irrational (Ariely), Present, relationship as, 312 Pretesting questionnaires, 39, 202 Pricing Amazon experiment, e-tailing, unethical, 12 Pricing decisions, marketing research applied to, 6, Primary data census, 136 defined, 26 descriptive/causal research, 108 as “field research,” 50 qualitative research, 76 research design selection, 37–38 Primary research sampling design and, 38 secondary research subordinate to, 53 Privacy issues ethical challenges, 12, 14–15 gatekeeper technologies and, 26 GPS technology used as research tool, 15 Probability sampling design cluster sampling, 143–145 defined, 140 in research design development, 38 sampling error, 140 sampling methods, 140–141, 143–145 sampling unit selection, 140 simple random sampling procedure, 140–141 systematic random sampling, 141–142 types of methods, 140 Probing questions, 81–82 Problem identification process, 32 Procedure, data validation, 244 Procter & Gamble, 10, 90, 145, 311–312 Product decisions, marketing research applied to, 6, Product dissatisfaction, qualitative approach to understanding, 233–234 Progressive Insurance, 15 Projective hypothesis, 93 Projective techniques, 91–93 defined, 91 disadvantage of, 92 sentence completion tests, 92 word association tests, 92 Zaltman Metaphor Elicitation Technique (ZMET), 93 Project Planet, Promotional decisions, marketing research for, Promotion, marketing research applied to, 6, Propensity scoring, 118 Proportionately stratified sampling, 143, 144 Psychogalvanometers, 95 Pupilometers, 95 Purchase behavior, scanner data and, 2411 Purpose cross-tabulation, 281 data validation, 243 of research request, 32 screening questions, 194, 203–204 of secondary data, 52 Purposed communities, 89–90 Purposive sampling, 86, 145–146 Q QSR NVIVO software, 218 Quaker Oats, online survey, 120 Qualitative data analysis characteristics of data, 78 conclusion drawing, 225–229 credibility of, 225–229 data display, 225 grounded theory, 217 managing data collection effort, 217–218 member checking, 217 nature of, 216 process of, 217–229 quantifying data, 217 quantitative analysis vs., 216–217 research reports, 229–231 Santa Fe Grill case study, 232 triangulation, 227, 229 406 Subject Index Qualitative data analysis (Cont.) understanding product dissatisfaction through, 233–234 verification, 226–229 Qualitative data analysis, data reduction categorization, 218–219 code sheets, 219, 220 comparison, 219, 221 defined, 218 integration, 221–222 iterative process, 222 negative case analysis, 222–223 recursive relationship, 212 software for, 218 tabulation, 223–224 theory building, 221–222 Qualitative research See also Exploratory research advantages of, 79–80 case study, 91 credibility, 225–229 data collection methods, 80, 81 defined, 78 as descriptive, 109 disadvantages of, 79, 80 ethnography, 91 focus group interviews, 82, 84–89 goals and objectives, 78 grounded theory, 217 in-depth interviews, 81–82 Jeep Wrangler campaign, 75–76 marketing research online communities (MROCs), 89–90 online conversations, 98 overview, 78–80 projective techniques, 91–93 purposed communities, 89 quantitative research vs., 78 reaching Hispanics through, 99–100 sample size, 80 Santa Fe Grill case study, 232 sentence completion tests, 92 small samples used in, 38 triangulation, 227, 229 universally applicable, uses of, 78–79 value of, 76 word association tests, 92 Zaltman Metaphor Elicitation Technique (ZMET), 93 QualKote Manufacturing, 334–335, 336 Qualtrics, 117 QualVu, 84, 94 Quantitative data analysis, 242–263 See also Statistical analysis applied to qualitative data, 77 coding, 249–252 data entry, 252–254 data tabulation, 254–259 data validation, 243–245 Deli Depot examples, 260–261 descriptive statistics, 257–259 editing, 245–249 graphical illustration of data, 257 qualitative data analysis vs., 216–217 value of preparing data for, 242–243 Quantitative research See also Quantitative data analysis; Survey research designs advantages, 109 credibility in, 225 defined, 77 as descriptive, 109 disadvantages, 109 main goals of, 78 observation methods, 93–98 overview, 77–78 qualitative methods and, 78 sentiment analysis, 97–98 social media monitoring, 97–98 techniques as universally applicable, used to verify and extend qualitative findings, 216 Zaltman Metpahor Elicitation Technique (ZMET), 91, 93 Quarterly sales reports, as source of secondary data, 55 Questioning, advantage over observation, 39 Questionnaire design, 39, 188–202 American Bank example, 188–202 bad questions, 192–193 call records, 205 client approval, 201 coding and, 249–250 common methods variance (CMV), 199 considerations in, 200 correct screening questions, 246–248 cover letter, 202, 203 data collection methods, 189–190 Deli Depot, 262–263 evaluating, 199–201 example of banking survey, 194, 195–198 framing questions, 193 general-to-specific order of questions, 193 implementation of survey, 202 interviewer instructions, 203 introductory section, 194 layout, determining, 194 online survey considerations, 200–201 pretesting, 39 pretest/revise/finalize, 202 question format, 190 question/scale format, 192–194 quotas, 204–205 research questions section, 194 response order bias avoidance, 194 samples used for, 136–137 Santa Fe Grill Mexican Restaurant, 206–211 screening questions, 194 sensitive questions in, 192 skip questions, 193 “smart” questionnaires, 199 steps involved in, 188, 189 supervisor instructions, 202–203, 204 time for completion given, 193–194 wording, 190–192 Questionnaires electronic products opinion survey, 366–367 scanner technology and, 241 407 Subject Index university life residence plans, 187–188 value in marketing research, 188 Questions bad, 192–193 branching, 193 for causal vs exploratory and research, 122–123 closed-ended, 190 cross-tabulation, 282 defining research, 34–35 demographic, 190, 194 double-barreled, 193 editing of data and, 245, 246–248 ethnic origin, 190 evaluating adequacy of scale, 178 in focus group interviews, 87 focus group participant screening, 85–86 “framing,” 193 general-to-specific order of, 193 leading/loaded, 193 open-ended See Open-ended questions open-ended, editing responses to, 249 probing, 81–82 proper wording, 190–192 in qualitative research, 79 screening, 194, 203–204 sensitive, 192 skip, 193, 244 structured, 190, 191 unanswerable, 192–193 unstructured, 190 using Chi-square analysis, 284 Quotas defined, 204 questionnaire design, 204–205 Quota sampling advantages, 146 defined, 146 disadvantages, 146 R Radian6, 98 Radical Clarity Group, Random sample/sampling univariate statistical analysis, 280 using SPSS to select, 150 Random telephone screening, 86 Range defined, 170, 271 examples, 271–272 interval and ratio data using, 170 ordinal scales analyzed using, 170 relationship between scale levels and, 171 SPSS application, 273 Rank-order scales, 176 defined, 176 example, 177 uses, 176 Rating cards, 203, 204 Rating scales comparative rating scale, 175, 177 constant-sum scales, 176–177 graphic rating scale, 175–176 noncomparative, 175 rank-order scales, 176 semantic differential scale, 172–174 Ratio data mean for, 270 t-Test, 288 Ratio scales appropriate statistic and, 278–279 defined, 164 examples of, 165 measures of central tendency and dispersion, 170, 171 overview, 164 Pearson correlation coefficient, 317 statistical technique and, 278 true natural zero, 164 Realism, in field experiments, 126 Recommendations illustration of, 361 in research report, 360–361 Recursive relationship, 222 Referral sampling, 146 Regression analysis, 322–333 assumptions, 322 beta coefficient, 327–328 bivariate regression analysis, 322, 324–327 fundamentals of, 322–324 independent and dependent variable relationship, 323 least squares procedure, 323, 324 multicollinearity, 332 multiple, 327–333 need for, 321–322 ordinary least squares, 324 regression coefficients, 324 SPSS application, 324–326, 329–333 statistical significance, 326–327 substantive significance, 327, 329 unexplained variance, 323 Regression coefficient beta coefficient, 327–328 Coefficients table, 325–326 defined, 324 multiple regression analysis, 327 statistical significance, 328 substantive significance, 328 Regression line, 323, 329 Related samples, 287 Relationships conceptualization, 66 correlation analysis, 316–321 covariation, 313–316 curvilinear, 212, 312, 315–316 defined, 63 developing a conceptual model, 63–64 direction of, 312 linear, 312–313 literature review helping to conceptualize, 63–66 moderate, 312–313 negative, 65, 314, 315–316 Pearson correlation coefficient, 316–317 positive, 65, 315–316 408 Subject Index Relationships (Cont.) recursive, 222 regression analysis, 321–333 scatter diagram showing, 313 strength of association, 312 systematic, 312 type of, 312–313 Reliability cross-researcher, 226 qualitative research, 226–227 scale measurement, 165–166 Reporting approaches, 205 Research See also Information research process nonuseful information from, not meeting professional standards, 13 Research design development, 36–40 causal research design, 37 confirmation of research objectives, 189 data collection and, 39 data sources, 37–38 descriptive research design, 36–37 execution of, 39–40 exploratory research design, 36 overview of, 76–77 questionnaire design and pretest, 39 sampling design and size, 38 selection of, 36–39 Research designs See Causal research design; Descriptive research; Exploratory research Research firms, ethical issues with, 12–13 Research information providers, ethical issues with, 11 Research methods, in research report, 348 Research objectives causal research, 77 confirmed in questionnaire development, 189 defined target population and, 137 descriptive research, 77 exploratory research, 36, 76 questionnaire development, 189 sampling design selection, 147 specifying, 36 Research problem determination, 31–36 define research questions, 34–35 determine relevant variables, 34 evaluate expected value of information, 36 iceberg principle, 32, 33 identify and clarify information needs, 32–34 identify and separate out symptoms, 32–33 purpose of research request, 32 situation analysis, 32 specify research objectives, 36 three interrelated activities of, 31 unit analysis, 34 Research proposal defined, 40 example, 42–44 final research report vs., 41 general outline of, 40 Research question section, in questionnaire design, 194 Research questions, redefined, 35 Research report appendix in, 362 believability in, 344 common problems in preparing, 362–363 conclusions and recommendations section in, 360–361 correlations, 358–360 credibility of, 344 critical thinking in, 344, 345 data analysis and findings section, 349–360 executive summary, 346–347 format of, 345–362 introduction in, 347–348 limitations noted in, 362 methods-and-procedures section in, 348 objectives, 342–345 oral presentations, guidelines for preparing, 363–364 presenting results in easy-to-understand manner, 342–343 in quantitative research vs qualitative research, 324 regressions reported in, 360 required topics in, 342 table of contents in, 346 title page for, 346 used as future reference, 344–345 value of, 342 visual presentations, guidelines for preparing, 364 Research report, in qualitative research analysis of data/findings, 230–231 conclusion and recommendations, 231 introductory section, 230 limitations section, 230 methodology section, 230 quantitative research report compared with, 324 research and objectives explained in, 229–230 three sections of, 230 verbatims, 231 Respondent abuse, 13–15 Respondent errors, 110 Respondents ability to participate, 121 diversity of, 120 ethical issues with, 11–12, 13–15 incentives for, 122 incidence rate of, 120–121 knowledge level of, 121 unethical activities by, 15–16 willingness to participate, 121 Response error, 110, 242 Response order bias, 194 Response rate, 202 Response rate metric, 200–201 Retailing research, Review of the literature, 34–35 Revision of questionnaire, 202 Riders jeans, 128 Role-playing activities, projective techniques, 91 S Sales activity reports, as source of secondary data, 55 Sales invoices, as source of secondary data, 55 Salesperson expense reports, as source of secondary data, 55 409 Subject Index Sales tracking, Sample defined, 38 focus group interviews, 86 independent, 287, 288–289 Internet, 117–118 paired, 287–288 related, 287 sampling design/size development, 38 SPSS software used to select, 150 stratified purposive, 86 tools used to assess quality of, 139–140 used for designing questionnaires, 136–137 Sample size, 38 determining, 147–150 determining for sampling plan, 151 in-depth interviews, 80 nonprobability, 149 probability, 147–149 qualitative research, 80 for qualitative research, 80 sampling from small population, 149 Sample size determination, 147–150 business-to-business studies, 149 confidence level, 147 formula, 148 informal approaches, 149–150 nonprobability sample size, 149 population variance, 147 precision and, 147–148 probability designs, 147–149 small population, 149 Sample statistic defined, 68 uses, 277 Sampling defined, 136 nonprobability, 38 online surveys and, 150 as part of the research process, 136–137 probability, 38 purposive, 86 theoretical, 86 universally applicable, used instead of a census, 136 value of, 136–137 Sampling error defined, 109, 139 detecting, 139 increasing size of sample and, 139 nonprobability sampling, 140 nonsampling error, 110, 139–140 probability sampling, 140 random, 139 sampling units, 139 survey research, 109 Sampling frame common sources of, 138 defined, 138 identifying in sampling plan, 151 Sampling methods area sampling, 144 convenience sampling, 145 determining appropriate, 146, 147 judgment sampling, 145–146 nonprobability sampling design, 140, 145–146 probability sampling design, 140–141, 143–145 quota sampling, 146 referral sampling, 146 simple random sampling, 140–141 snowball sampling, 146 stratified purposive sampling, 86 stratified random sampling, 143, 144 systematic random sampling, 141–142 Sampling plan contact rate determination, 151 data collection method selection for, 151 defined, 151 for new menu initiative survey, 153 nonprobability, 38 operating plan for selecting sample units, 152 plan execution, 152 probability, 38 probability plan, 38 purpose of, 38 sampling frame identification, 151 sampling method selection, 151 steps in developing, 151–152 target population defined for, 151 Sampling theory, 137–140 central limit theorem (CLT), 138–139 factors underlying, 138–139 nonsampling error, 139–140 population, 137 sampling error, 139 sampling frame, 138 terminology, 137 tools for assessing quality of samples, 139–140 Sampling units cluster sampling, 143, 144, 145 defined, 137 nonprobability sampling, 140 probability sampling, 140 sampling error and, 139 sampling plan development, 151, 152 systematic sampling, 141 Santa Fe Grill Mexican Restaurant case study ANOVA, 291–292 bivariate regression analysis, 323–324, 324–325 Chi-square analysis, 285–287 customer loyalty, 157–158 employee questionnaire, 246–248 explained, 17 hypotheses, 67, 276 independent samples t-Test, 288–289 interval scales, 164 literature review, 69 measures of central tendency, 270 measures of dispersion, 273 new menu initiative survey, 153 410 Subject Index Santa Fe Grill Mexican Restaurant case study (Cont.) n-Way ANOVA, 295–297 paired samples t-Test, 289–290 Pearson coefficient correlation, 317–319 proportionately vs disproportionately stratified samples, 144 proposed variables, 92 qualitative research, 232 questionnaire design, 206–211 random sample selection, 150 research question development, 67 secondary data usage, 58 Spearman Rank Order correlation, 320–321 statistical analysis, 299 surveying of customers, 18–19, 139 systematic sampling, 142 t-Test, 288–289 univariate hypothesis test, 279–280 Scale, 159 Scale descriptors balanced scale, 168 behavioral intention scale, 174 bipolar descriptors, 173 defined, 161 discriminatory power of, 168 even-point, forced-choice, 169 forced-choice scale, 169 free-choice scales, 169 non-bipolar descriptors, 173–174 nonforced choice scale, 169 odd-point, nonforced, 169 unbalanced scale, 168 Scale measurement, 161–167 of attitudes and behaviors, 171–175 choosing appropriate statistical technique and, 277–279 clear wording for scales, 179 comparative rating scale, 176–177 defined, 161 evaluating, 178, 179 influence on correlation analysis, 320 interval scales, 163–164 multiple-item scale, 178 nominal scale, 162 ordinal scales, 162–163 ratio scales, 164–165 scale descriptors, 161 scale points, 161–162 selection of research design and, 38–39 single-item scale, 178 validity, 166–167 Scale measurement development, 167–171 adapting established scales, 170–171 balanced vs unbalanced scales, 168 behavioral intention scale, 174–175 criteria for, 167–170 discriminatory power of scale descriptors, 168 evaluating, 178 forced-choice vs nonforced scale descriptors, 169–170 measures of central tendency, 170, 171 measures of dispersion, 170, 171 negatively worded statements, 170 number of scale points, 168 questions, ability to understand, 167–168 requirements, 167 semantic differential scale, 172–174 steps in, 172 Scale points, 161–162 behavioral intention scale, 174–175 defined, 161 interval scales, 163 number of, 168 Scale reliability, 165–166 coefficient alpha, 166 defined, 165 equivalent form technique, 166 internal consistency, 166 of multiple-item vs single-item scales, 179 split-half tests, 166 test-retest approach, 165–166 Scale validity content validity, 167 convergent validity, 167 discriminant validity, 167 explained, 166–167 face validity, 167 Scaling, in online surveys, 117 Scanner-based panels, 95 Scanner technology check-out counter information, 95 data entry and, 252 data preparation and, 243 scanner-based panels, 95 understanding purchase behavior with, 241 Scatter diagram curvilinear, 315 defined, 313 negative relationship, 314, 315 positive relationship, 314 regression analysis, 322 showing relationships, 313 uses of, 316 Scheffé procedure, 293 Scholarly sources, 57 Scientific method defined, 30 research process and, 30 Scientific Telephone Samples, 138 Screening, data validation and, 243, 244 Screening questions defined, 194 editing for correct, 246–247 purpose of, 194, 203–204 Search engine marketing, 135–136 Search engines, 56 search.twitter.com, Secondary data defined, 26, 50 external See External secondary data increased emphasis on collecting, 16 internal, 50, 54, 55 NAICS codes, 59–60 research design selection, 37–38 411 Subject Index role of, 50 sources of, 38 study using, 49–50 Secondary data search, variables sought in, 53 Secondary data sources See also Literature review accuracy assessment, 52 bias assessment, 52 blogs, 56–57 census data, 58–59 consistency assessment, 52 credibility assessment, 52 databases for, 56 evaluating, 51–52 external, 54, 56–63 government documents, 58–59 internal, 54, 55 Internet, 56–57 media panels, 61–62 methodology assessment, 52 NAICS codes, 59–60 popular sources, 56–57 purpose assessment, 52 scholarly sources, 57 syndicated data, 60 triangulating, 62 Secondary research literature review and, 51 role in marketing research process, 53–54 sampling design and, 38 as subordinate to primary research, 53 synthesized for literature review, 63 weaknesses within, Second Life, Secure Customer Index* (SCI*), 180–181 Security cameras, ATM locations, 95 Segmentation studies, 8–9 Selective coding, 222 Self-administered surveys advantages of, 115 defined, 115 description of, 111 disadvantages of, 115 mail panel surveys, 116 mail surveys, 116 online surveys, 116–118 structured questions for, 191 Self-completion surveys, missing data and, 253 Semantic differential scale bipolar descriptors, 173 defined, 172 examples, 173, 174 non-bipolar descriptors, 173–174 overview, 172–173 perceptual image profile, 173 Sensitive questions, 192, 193, 194 Sentence completion tests, 91, 92 Services marketing research, 10 Shopper marketing, Shopping intention scale, 174–175 Short messaging (text messaging) format, 114 Short-term MROCs, 90 Simple random sampling advantages of, 141 defined, 140 disadvantages of, 141 overview, 140–141 Single-item scale choosing between multiple-item scale and, 178–179 defined, 178 Situation analysis defined, 32 research problem determination, 32 Skip interval, 141 Skip questions, 193, 244 “Smart” questionnaires, 199 Smiling faces, graphic rating scale using, 176 Snowball sampling, 86 defined, 146 situations used in, 146 Social behavior, impact of wireless communication on, 215–216 Social media netnography, 98 promotional activities on, qualitative data from, 95–96 Social media monitoring, 97 Social media research, netnography, 98 Social networks, purposed communities, 89–90 Social web 2.0, Society of Competitive Intelligence Professionals, 60 Software See also SPSS (Statistical Product and Service Solution) for coding, 218 data reduction, 218 online survey, 117 Sony, 27 Spearman rank order correlation coefficient defined, 320 SPSS application, 320–321 Split-half tests, 166 SPSS (Statistical Product and Service Solution), 242 ANOVA, 291–294 bivariate regression analysis, 289–290, 324–326 Chi-square analysis, 285–287 correlation coefficient, 320–321 independent samples t-Test, 288–289 measures of central tendency, 270 measures of dispersion, 272–273 multiple regression, 329–333 n-way ANOVA, 295–297 paired samples t-Test, 289–290 Pearson correlation, 317–319 preparation charts, 275 random sampling, 150 range, 273 regression analysis, 324–326, 329–333 sample selection, 150 Spearman rank order correlation coefficient, 320–321 univariate statistical analysis, 280–281 Standard deviation defined, 272 formula, 272 412 Subject Index Standard deviation (Cont.) interval scales and, 164 one-way tabulation and, 255 overview, 272 SPSS application, 273, 280–281 type of scale and appropriate use of, 278 uses, 272 Standardized regression coefficient, 327, 332 Standardized research firms, 11 Starbucks, 278, 291 Statistical analysis analyzing relationships of sample data, 277–294 ANOVA (analysis of variance), 291–294 bivariate statistical tests, 281–294 charts, 274, 275 Chi-square analysis, 284–287 choosing appropriate statistical technique, 277–279 comparing means, 287–288 cross-tabulation, 281–284 developing hypotheses, 275–277 facilitating smarter decisions, 267–268 measures of central tendency, 270 measures of dispersion, 272–273 n-way ANOVA, 294–297 parametric vs nonparametric statistics, 278–279 perceptual mapping, 298, 299 Remington’s Steak House example, 300–306 sample statistics, 277 scale of measurement and, 278 t-Test, 288–290 univariate statistical tests, 279–281 value of, 268 Statistical significance ANOVA and, 291, 292 bivariate regression analysis, 326–327 chi-square analysis, 284 comparison of means, 289 correlation coefficient, 317 F-test, 291 multiple regression analysis, 328 relationship between variables and, 312 substantive significance compared with, 319, 327 Statistics nonparametric, 278–279 parametric, 278–279 Stealth marketing, 56 Store audits, 62 Storyline, selective coding, 222 Strata, 143 Stratified purposive sample, 86 Stratified random sampling, 143 advantages, 143 defined, 143 disadvantages, 143 disproportionately, 143, 144 drawing a random sample, 143 proportionately, 143, 144 Strong relationship, 312, 317 Structured questions, 190, 191 Subject debriefing, 14 Substantive significance of a regression equation, 327 statistical significance vs., 319, 327 Sugging, 14, 113 Summary statistics, 257 Summated rating, 159 Sum of the squared errors, 323 SunTrust Bank, 204 Supervisor instruction form, 202–203 Survey Gizmo, 117 SurveyMonkey.com, 117 Survey research See also Questionnaire design; Questionnaires central limit theorem (CLT), 138 sampling and online, 150 sampling plan for new menu initiative, 153 Santa Fe Grill customers, 139 used to develop university residence life plans, 187–188 Survey research designs advantages of, 109 causal research designs vs., 122 disadvantages of, 109 error types in, 109–110 Survey research errors, 109–110 nonsampling errors, 110 sampling errors, 109 Survey research methods defined, 109 drop-off surveys, 116 in-home interviews, 111 mail panel surveys, 116 main surveys, 116 mall-intercept interviews, 112 online surveys, 116–118 person-administered, 111–112 self-administered, 111, 115–118 telephone-administered, 111, 112–115 types of, 110–118 Survey research method selection, 118–122 ability to participate, 121 amount of information needed from respondents, 119–120 best practices to increase participation, 122 budget considerations, 118 completion time frame, 118 data completeness, 118–119 data generalizability, 119 data precision, 119 data quality, 118–119 diversity of respondents, 120 incidence rate, 120–121 knowledge level of respondents, 121 required stimuli, 119 respondent characteristics, 120–122 situational characteristics, 118–119 task characteristics, 119–120 task difficulty, 119 topic sensitivity, 120 willingness to participate, 121 Survey Sampling Inc., 10, 138 Survey Sampling International, 136 Syndicated business services, 11 413 Subject Index Syndicated (commercial) data, 60–63 consumer panels, 60–61 defined, 60 media panels, 61–62 store audits, 62–63 uses, 60 Synovate ViewsNet, 61 Systematic random sampling advantages, 141 defined, 141 disadvantages, 141 skip interval, 141 steps in drawing a, 142 Systematic variation, 110 T Table of contents, of research report, 346 Tables, in qualitative data display, 225 Tabulation, in qualitative data analysis controversy over, 223 co-occurences of themes, 224 descriptive statistics, 254, 255, 257–258 example, 223 fuzzy numerical qualifiers, 224 role of, 223–224 Tabulation, in quantitative data analysis, 254–259 cross-tabulation, 254 defined, 254 graphical illustration of data, 249 one-way tabulation, 254, 255–257 Target market for focus group interviews, 85 presence in a given virtual world, sampling design/size development, 38 Target population See also Defined target population central limit theorem, 138 defined, 38 defined for developing a sampling plan, 151 sample size determination and knowledge of, 147 Task characteristics, survey research method selection, 119–121 Technology complexity of marketing research and, gatekeeper technologies, 26 marketing research on early adopters of, 365–368 study on paradoxes in technology products, 225, 226 Technology-mediated observation, 95 Telephone-administered surveys, 111 advantages of, 113 computer-assisted telephone interview (CATI), 113–114 defined, 112 disadvantages of, 113 ethical issues with, 113 questionnaire development, 190 response order bias, 194 wireless phone surveys, 114–115 Telephone interview, structured questions for, 191 Television viewing, data collection on habits, 61 Test marketing applications in marketing research, 126–127 costs, 127 defined, 7, 126 Lee Apparel Company example, 128–129 uses, 126–127 Test-retest reliability technique, 165–166 Text-based focus groups, 84 Text-based wireless phone survey, 114 Text message surveys, 114 Thematic appreciation tests (TAT), 91 Themes co-occurence of, in tabulation, 224 freedom and control, 219 selective coding, 222 Theoretical sampling, 86 Theory building integration, 221 recursive relationships, 221–222 selective coding, 222 Theory, marketing, 9–10 Threadless.com, 117 ThreatTracker, 98 3Com, Thriving on Chaos (Peters), 267 Time Spent methodology, 36, 37 Title page of research report, 346 TNS Symphony, 98 Topic sensitivity, 120 Total variance, 291 Tracking approaches, 205 Trackur, 98 Traffic counters, 95 Triangulation defined, 227 in qualitative research, 227 of secondary data sources, 62 types of, 227 “True natural zero,” 164 True natural zero/true state of nothing, 164 “True state of nothing,” 164 T statistic, 328 T-test defined, 288 formula for calculating t value, 288 independent samples, 288–289 paired sample, 288, 289–290 SPSS application, 288–289 type of scale and, 278 univariate statistical analysis, 281 uses, 288 TweetDeck, Tweets, Twitter, 3, Two-step approach to cluster sampling, 144 U Unanswerable questions, 192–193 Unbalanced scales, 168 Uncontrollable variables, 125 Underground marketing, 56 Unexplained variance, 323 Uniscore, 10–11 414 Subject Index Unit of analysis, 34 Univariate statistical tests defined, 279 hypothesis testing, 279–281 propositions, examples of, 279 Santa Fe Grill example, 279–280 SPSS application, 280–281 univariate statistical analysis, 280–281 uses, 279 Universal product code (UPC), 95 Unstructured questions, 190 UpSNAP, 135 U.S Bureau of the Census, 58 U.S Census, 136 U.S Census data, 58–59 U.S Census Reports, 58 U.S Department of Commerce, 58 U.S Television Index (NTI), 95 V Validity See also Validation concerns of, in experimental research, 125–126 content, 167 defined, 125 emic validity, 226 external, 125–126 face, 167 internal, 125 qualitative research, 226–227 scale measurement, 166–167 Valid percentages, determining, 257 Variables See also Dependent variable; Independent variable ANOVA, 290–291 bivariate regression analysis, 323 causal research design, 122, 123–124 Chi-square analysis, 284 conceptualization and, 66 control, 124 covariation, 313–314, 315 cross-tabulation, 281 defined, 63, 123 determining the relevant, 34 developing a conceptual model, 63 experimental research, 123, 124, 125 extraneous, 124 indicator, 159, 160 investigated in marketing research, examples, 34 linear relationships, 312 list of, 34 negative relationship between, 65, 314, 315–316 number of , and statistical technique, 278 n-Way ANOVA, 294 295 parameter as actual value of, 68 positive relationship between, 65, 315–316 relationships between, 63–64, 313–316 sample statistic, 68 in secondary data search, 53 types used in experimental research designs, 124 uncontrollable, in experimental research, 125 used to measure a concept, 159 Variance, 272 See also Analysis of Variance (ANOVA) unexplained, 323 Verbatims, 231 Verification, qualitative research, 217, 225–226 Verizon, 90 Video-based focus groups, 84 Video cameras, 95 VideoDiary, 84 Visual display of data, 341 Visual presentation of research report, 364 Vocabulary, in questionnaires, 190 W The Wall Street Journal, 56 Walmart, 26, 90, 241 Warranty cards, as source of secondary data, 55 Waterston, Adriana, 99 Weak relationship, 312, 317, 327 Weather.com, Weave, 57 Web-based bookmarking tools, 57 Willingness to participate, 121 Wireless-only households, 114 Wireless phones See Mobile phones Wireless phone surveys, 114–115 challenges to, 115 defined, 114 described, 111 respondent participation, 114 text message surveys, 114 uses, 114 web-based format, 114 Wireless web surveys, 114 Within-group variance, 291 Word association tests, 91, 92 Wording, in questionnaire, 190–192 Word-of-mouth marketing, search terms for, 56 Worldwide, Inc., 26 www.ClickZ.com, 56 X Xmarks, 57 Y Yahoo!, 56 Youthbeat, 61 Z Zaltman Metpahor Elicitation Technique (ZMET), 91, 93 Zappos.com, Zeta Interactive, 98 Zoomerang.com, 117 ... MARKETING RESEARCH DASHBOARD: CONDUCTING INTERNATIONAL MARKETING RESEARCH The Role and Value of Marketing Research Marketing Research and Marketing Mix Variables Marketing Theory MARKETING RESEARCH. .. Journal of Marketing Research, Journal of Academy of Marketing Science, Journal of Business/Chicago, Journal of Advertising Research, and Journal of Retailing, he has presented executive education and. .. The Role and Value of Marketing Research Information The Growing Complexity of Marketing Research Technology and the growth of global business are increasing the complexity of marketing research