(BQ) Part 2 book Essentials of marketing research has contents: Qualitative data analysis, preparing data for quantitative analysis, basic data analysis for quantitative research; examining relationships in quantitative research, communicating marketing research findings.
Part Data Preparation, Analysis, and Reporting the Results Qualitative Data Analysis Chapter Learning Objectives After reading this chapter, you will be able to: Contrast qualitative and quantitative data analyses Explain the steps in qualitative data analysis Describe the processes of categorizing and coding data and developing theory Clarify how credibility is established in qualitative data analysis Discuss the steps involved in writing a qualitative research report The Impact of Wireless Communication on Social Behavior Mobile phones were once all business But today they are all in the family A recent survey of Americans between the ages of 18 and 64 conducted by Knowledge Networks, a market research firm in Cranford, New Jersey, revealed that most respondents underscore “family” as the top reason to go wireless Young respondents, more so than older ones, cite “reaching friends” as their second leading reason to go wireless, with “work-related calls” being the overall third most important reason for having a wireless phone The survey also reported some interesting descriptive information For example, men tend to make more calls on mobile phones per day (8.3 calls) than women (5.5 calls) Although both put family first, women were more partial to calling friends, whereas men were three times as likely to use their phones for work In addition, 65 percent of African Americans have mobile phones, compared to 62 percent of Caucasians Hispanics remain well behind in mobile phone usage, with just 54 percent penetration While this describes the type of information that results from conducting traditional surveys, the findings are limited to aggregate descriptive interpretations and meaning In contrast, qualitative research on wireless phone usage offers greater opportunities to gain in-depth understanding of what lies beyond those descriptive numbers For example, with more than 190 million Americans owning mobile phones, the phones reach far into our lives They are beginning to create a deeper impression on the American psyche Robbie Blinkoof, principal anthropologist and managing partner at Context-Based Research Group in Baltimore, Maryland, and other ethnographers believe that wireless communication is beginning to have a notable impact on Americans’ social behavior—one that could have a long-lasting effect on society and the world around us For instance, recent ethnographic studies have yielded significant clues about cell phone users’ communication habits In general, observed changes relate to how mobile phone customers form relationships and define a sense of time and place In one study, researchers watched newly wired users at work and play, finding that one of the biggest differences is these users become more accessible to their social network 216 Part Data Preparation, Analysis, and Reporting the Results Mobile phones thus enable ongoing communication within social networks Mobile phone owners were more flexible in how they arranged their schedules and gradually became more willing to speak on a mobile phone in public, sustaining social ties for purely psychological and emotional value In another ethnographic study, Context researchers observed changes in how the subjects related to mobile life Participants were far more likely to see wireless as an enabler rather than as a toy They learned to use the wireless features they needed while ignoring those they didn’t Other interpretive findings reveal that wireless phones give people new opportunities for spontaneity because people can change their plans at the last minute more easily, or call friends and colleagues to tell them they are running behind schedule Also, wireless phones create flexibility by loosening time parameters, enabling people merely to suggest a time and place to meet and then pin down a specific location as they approach the meeting time Nature of Qualitative Data Analysis In this chapter, you will learn the processes used by researchers to interpret qualitative data and form insights about their meaning We often think of data analysis as involving numbers But the data qualitative researchers analyze consists of text (and sometimes images) rather than numbers Some researchers criticize qualitative research as “soft,” lacking rigor and being inferior But measurement and statistical analysis not ensure that research is useful or accurate What increases the likelihood of good research is a deliberate, thoughtful, knowledgeable approach whether qualitative or quantitative research methods are used While the reliability and validity of quantitative analysis can be evaluated numerically, the trustworthiness of qualitative analysis depends fundamentally on the rigor of the process used for collecting and analyzing the data As we explained in Chapter 4, when magnitude of response and statistical projectability are important, quantitative research should be used to verify and extend qualitative findings But when the purpose of a research project is to better understand psychoanalytical or cultural phenomena, quantitative research may not offer a great deal of insight or depth For these topics, qualitative research and analysis often is superior to quantitative research in providing useful knowledge for decision makers This chapter details a process that can be followed to ensure qualitative data analyses are careful and rigorous In this chapter we first compare qualitative and quantitative analyses Next we describe the steps involved in qualitative data analysis We explain categorization, coding, and assessing trustworthiness or credibility The chapter concludes by providing guidelines on writing a qualitative research report Qualitative versus Quantitative Analyses All marketing researchers construct stories that are based on the data they have collected The goal of these stories, whether they are based on qualitative or quantitative data, is to provide actionable answers to research questions Yet, there are many differences between the processes of analyzing and interpreting qualitative and quantitative data The most apparent difference stems from the nature of the data itself Qualitative data is textual Chapter Member checking Asking key informants to read the researcher’s report to verify that the analysis is accurate Qualitative Data Analysis 217 (and occasionally visual), rather than numerical While the goal of quantitative analysis is quantifying the magnitude of variables and relationships, or explaining causal relationships, understanding is the goal of qualitative analysis A second contrast between the two kinds of analysis is that qualitative analyses tend to be ongoing and iterative This means the data is analyzed as it is collected, which may affect further data collection efforts in terms of who is sampled and what questions are asked Another difference between the methods is that quantitative analyses are guided entirely by the researchers, while good qualitative researchers employ member checking Member checking involves asking key informants to read the researchers’ report to verify that the story they are telling about the focal problem or situation is accurate Qualitative data analysis is largely inductive The categories, themes, and patterns analysts describe in their reports emerge from the data, rather than being defined prior to data collection, as in quantitative analyses Because an inductive process is used, the theory that emerges is often called grounded theory.1 The categories and corresponding codes for categories are developed as researchers work through the texts and images and find what is there Of course, rarely is the development of categories and theory completely inductive Researchers bring with them knowledge, theory, and training that suggests categories, themes, and theories that might exist in the data they have collected There is no one process for analyzing qualitative data, although the three-step process described in this chapter has been useful to the thinking of many qualitative researchers Some researchers prefer a more impressionistic approach to qualitative analysis and not go through transcripts and other documents with the degree of care that we suggest here Nevertheless, “careful and deliberate analysis remains crucial to sound qualitative research.”2 Qualitative researchers differ in their beliefs about the use of quantifying their data Some feel that quantification is completely useless and likely misleading But others find that quantification can be useful in both counting responses and in model development.3 We discuss tabulation (counting) later in this chapter Qualitative researchers use different techniques for data collection These differences affect the kinds of analyses that can be performed with the data Analysts use the collected and transcribed textual data to develop themes, categories, and relationships between variables Categories are usually developed as the transcripts (and images) are reviewed by researchers Codes are attached to the categories, which are then used to mark the portions of text (or images) where the category is mentioned In this chapter, we review the process of analyzing qualitative data We explain the process of data reduction, data display, and conclusion making/verification We also explain how qualitative researchers develop analyses that are credible, which means the analyses are authentic and believable Finally, we explain how to write a qualitative research report The Process of Analyzing Qualitative Data After data are collected, researchers engage in a three-step process of analysis: data reduction, data display, and conclusion drawing/verification.4 The three steps and relationships between the steps and data collection efforts are pictured in Exhibit 9.1 Managing the Data Collection Effort Whether the collection method is focus groups or in-depth interviews, the data will be transcribed for further analysis Data from online focus groups, marketing research online communities (MROCs), and social media sites are collected in one database to facilitate 218 Exhibit 9.1 Part Data Preparation, Analysis, and Reporting the Results Components of Data Analysis: An Interactive Model Data collection Data display Data reduction Conclusion drawing/verifying Source: Matthew B Miles and A Michael Huberman, Qualitative Data Analysis: An Expanded Sourcebook (Thousand Oaks, CA: Sage Publications, 1994), p 12 Reprinted with permission from Sage Publications via Copyright Clearance Center analysis Occasionally, participants are asked to write stories or respond to open-ended questions, and their written responses become the data set The project in the Marketing Research in Action at the end of this chapter makes use of this technique Qualitative researchers often enter their interim thoughts in the database Field notes, which are observations written down during the data collection effort, also become part of the data set Finally, key participants may be asked to evaluate researchers’ initial research draft Their feedback becomes part of the official data set as well Step 1: Data Reduction Data reduction The categorization and coding of data that is part of the theory development process in qualitative data analysis Categorization Placing portions of transcripts into similar groups based on their content The amount of data collected in a qualitative study can be extensive Researchers must make decisions about how to categorize and represent the data This results in data reduction The most systematic method of analysis is to read through transcripts and develop categories to represent the data When similar topics are encountered, they are coded similarly Researchers may simply write codes in the margins of their transcripts But increasingly, software such as QSR NVIVO and Atlas/ti is used to track the passages that are coded Computer coding enables researchers to view all similarly coded passages at the same time, which facilitates comparison and deeper coding Computer coding also makes it easier to study relationships in the data Data reduction consists of several interrelated processes: categorization and coding; theory development; and iteration and negative case analysis Data Reduction: Categorization and Coding The first step in data reduction is categorization Researchers categorize sections of the transcript and label the categories with names and sometimes code numbers There may be some categories that are determined before the study because of existing researcher knowledge and experience However, Chapter Code sheet A document that lists the different themes or categories for a particular study Codes Labels or numbers that are used to track categories in a qualitative study Qualitative Data Analysis 219 most often the codes are developed inductively as researchers read through transcripts and discover new themes of interest and code new instances of categories that have already been discovered The sections that are coded can be one word long or several pages The same sections of data can be categorized in multiple ways If a passage refers to several different themes that have been identified by researchers, the passage will be coded for all the different relevant themes Some portions of the transcripts will not contain information that is relevant to the analysis and will not be coded at all.5 A code sheet is a piece of paper with all the codes (see Exhibit 9.2 for an example from a senior Internet adoption study) The coded data may be entered into a computer, but the first round of coding usually occurs in the margins (Exhibit 9.3) The codes can be words or numbers that refer to categories on the coding sheet An example of the process of data coding comes from an online shopping study based on data collected from both online and offline focus groups One theme that emerged from the data was the importance of freedom and control as desirable outcomes when shopping online.6 The following are some examples of passages that were coded as representing the freedom and control theme: • • • • “You’re not as committed [online] You haven’t driven over there and parked and walked around so you have a little more flexibility and can get around a lot faster.” “ when I go to a store and a salesperson’s helping me for a long time and it’s not really what I wanted I’ll oblige them, they spent all this time with me but online, I know I will get to the point and be ready to order, but I know I don’t have to, I can come back anytime I want to.” “You can sit on your arse and eat while you shop You kin even shop nekked!” “For me, online browsing is similar [to offline browsing], but I have more of a sense of freedom I’ll browse stores I might not go into offline Victoria’s Secret comes to mind also I’ll go into swank stores that I might feel intimidated in going into offline when you’re a 51-year-old chubby gramma, online Victoria’s Secret just feels a bit more comfortable.” Categories may be modified and combined as data analysis continues The researcher’s understanding evolves during the data analysis phase and often results in revisiting, recoding, and recategorizing data Comparison The process of developing and refining theory and constructs by analyzing the differences and similarities in passages, themes, or types of participants Data Reduction: Comparison Comparison of differences and similarities is a fundamental process in qualitative data analysis There is an analogy to experimental design, in which various conditions or manipulations (for instance, price levels, advertising appeals) are compared to each other or to a control group Comparison first occurs as researchers identify categories Each potential new instance of a category or theme is compared to already coded instances to determine if the new instance belongs in the existing category When all transcripts have been coded and important categories and themes identified, instances within a category will be scrutinized so that the theme can be defined and explained in more detail For example, in a study of employee reactions to their own employers’ advertising, the category “effectiveness of advertising with consumers” was a recurring theme Because of the importance of advertising effectiveness in determining employees’ reactions to the ad, employees’ views of what made ads effective were compared and contrasted Employees most often associated the following qualities with effective organizational ads to consumers: (1) likely to result in short-term sales, (2) appealing to the target audience (3) attention grabbing, (4) easily understandable, and (5) authentically portraying the organization and its products.7 220 Exhibit 9.2 Part Data Preparation, Analysis, and Reporting the Results Senior Adoption of the Internet Study Initial Code Sheet I Antecedents A Observability Seeing others use the Internet Having an “a-ha” experience Marketing influences B Trialability Family Community centers Friends Work C Complexity Physical challenges Learning challenges Initial fear D Relative Advantage Cultural currency Ability to engage in hobbies Finding information Communication Creativity E Compatibility Openness to experience/life involvement Technology optimism Self-efficacy/proactive coping Financial resources Time in retirement Previous experience w/computing II Processes A Attend formal classes B Consult published sources C Mentors D Bricolage (learning by doing) E Ancillary systems (e.g., handwritten notes) F Flow G Multitasking III Uses A Communication (e-mail, jokes, support groups) B Gather info health hobbies places news financial product travel C Banking D Shop selectively E Later life uses (Keeper of the meaning, generativity, integrity) F Intended uses G Acting as an intermediary/proxy H Entertainment I Word processing, etc J Creativity IV Outcomes Connectedness a Companionship b Social support c Linkages to places visited, lived Self-efficacy/independence Cultural Currency a Computer skills b Increased knowledge Excitement Evangelism Fun Self-extension V Coping Strategies A Security–personal info B Protecting privacy C Flow/limiting flow D Ease E Satisficing Codes for Senior Characteristics B ϭ Broadband M ϭ Modem OO ϭ Old Old 75ϩ Y ϭ Young Old 65–74 S ϭ Self-adopted O ϭ Other Adopted SA ϭ Self-Assisted Chapter Exhibit 9.3 221 Coding Transcripts in the Margins Moderator: III III 2 C D Qualitative Data Analysis What’s a typical session like? You sit down at the computer and III Nisreen: I sit down at the computer and then I go into my emails I check my emails and I make the replies Then I want to find out about certain things, then I find out about those things and then I go to World News Then I go to the different countries I’m interested in, then I go to the newspapers I get the news about Pakistan right now I go into Asia and then I go into Pakistan and then I get the news right there before I think my relatives know in Pakistan I know the news before that So isn’t it wonderful? IV D Moderator: Yes It really is It’s amazing IV A Nisreen: My cousin in Australia before he thought he was leaving Australia from Sydney and I knew all about it, it’s faster than telegram It’s so wonderful I almost feel like I’m sitting on a magic carpet and I press IV D a button and boom, I’m there IV C IV F Moderator: That’s interesting Just reading the paper makes you feel like you are there III 2D Nisreen: And then I want to read the viewpoint of different newspapers, so I go into different III 2C countries like India, Bangladesh or Pakistan or the Middle East In the Middle East I used to be a voluntary assistant of the, Perspective, which is the only women’s magazine in the Middle East At that time, Jordan was a very peaceful place The rest of the world was up in arms and that kind of thing So you see, I feel like IV A I’m in touch with the whole world It’s such a wonderful feeling at my age to be in touch with the world I wish more and more because I think in the near future, that would be the order of the day IV C2 Comparison processes are also used to better understand the differences and similarities between two constructs of interest In the study of online shopping, two types of shopping motivations emerged from analyses of transcripts: goal-oriented behavior (shopping to buy or find information about specific products) and experiential behavior (shopping to shop) Comparison of shopper motivations, descriptions, and desired outcomes from each type of behavior reveals that consumers’ online shopping behavior is different depending on whether or not the shopping trip is goal-oriented or experiential.8 Comparisons can also be made between different kinds of informants In a study of high-risk leisure behavior, skydivers with different levels of experience were interviewed As a result of comparing more and less experienced skydivers, the researchers were able to show that motivations changed and evolved, for example, from thrill, to pleasure, to flow, as skydivers continued their participation in the sport.9 Similarly, in a study of post-socialist Eastern European women who were newly exposed to cosmetics and cosmetics brands, researchers compared women who embraced cosmetics to those who were either ambivalent about cosmetics or who rejected them entirely.10 Integration The process of moving from the identification of themes and categories to the development of theory Data Reduction: Theory Building Integration is the process through which researchers build theory that is grounded, or based on the data collected The idea is to move from the identification of themes and categories to the development of theory In qualitative research, relationships may or may not be conceptualized and pictured in a way that looks like the traditional causal model employed by quantitative researchers 222 Part Recursive A relationship in which a variable can both cause and be caused by the same variable For instance, relationships may be portrayed as circular or recursive In recursive relationships, variables may both cause and be caused by the same variable A good example is the relationship between job satisfaction and financial compensation Job satisfaction tends to increase performance and thus compensation earned on the job, which in turn increases job satisfaction Qualitative researchers may look for one core category or theme to build their storyline around, a process referred to as selective coding All other categories will be related to or subsumed to this central category or theme Selective coding is evident in the following studies that all have an overarching viewpoint or frame: Selective coding Building a storyline around one core category or theme; the other categories will be related to or subsumed to this central overarching category • • • Data Preparation, Analysis, and Reporting the Results A study of personal websites finds that posting a website is an imaginary digital extension of self A study of an online Newton (a discontinued Apple PDA) user group finds several elements of religious devotion in the community A study of Hispanic consumer behavior in the United States uses the metaphor of boundary crossing to explore Hispanic purchase and consumption.11 Given its role as an integrating concept, it is not surprising that selective coding generally occurs in the later stages of data analysis Once the overarching theme is developed, researchers review all their codes and cases to better understand how they relate to the larger category, or central storyline, that has emerged from their data Iteration Working through the data several times in order to modify early ideas Memoing Writing down thoughts as soon as possible after each interview, focus group, or site visit Negative case analysis Deliberately looking for cases and instances that contradict the ideas and theories that researchers have been developing Data Reduction: Iteration and Negative Case Analysis Iteration means working through the data in a way that permits early ideas and analyses to be modified by choosing cases and issues in the data that will permit deeper analyses The iterative process may uncover issues that the already collected data not address In this case, the researcher will collect data from more informants, or may choose specific types of informants that he or she believes will answer questions that have arisen during the iterative process The iterative procedure may also take place after an original attempt at integration Each of the interviews (or texts or images) may be reviewed to see whether it supports the larger theory that has been developed This iterative process can result in revising and deepening constructs as well as the larger theory based on relationships between constructs An important element of iterative analysis is note taking or memoing Researchers should write down their thoughts and reactions as soon after each interview, focus group, or site visit that time will allow Researchers may want to write down not only what participants say they feel, but whether or not what they say is credible Perhaps most important, during the iterative process researchers use negative case analysis, which means that they deliberately look for cases and instances that contradict the ideas and theories that they have been developing Negative case analysis helps to establish boundaries and conditions for the theory that is being developed by the qualitative researcher The general stance of qualitative researchers should be skepticism toward the ideas and theory they have created based on the data they have collected.12 Otherwise they are likely to look for evidence that confirms their preexisting biases and early analysis Doing so may result in important alternative conceptualizations that are legitimately present in the data being completely overlooked Iterative and negative case analyses begin in the data reduction stage But they continue through the data display and conclusion drawing/verification stages As analysis continues in the project, data displays are altered Late in the life of the project, iterative analysis and 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 ... Categorization 21 8 Codes 21 9 Code sheet 21 9 Comparison 21 9 Credibility 22 7 Cross-researcher reliability 22 6 Data reduction 21 8 Emic validity 22 6 Integration 22 1 23 5 Iteration 22 2 Member checking 21 7 Memoing... Iteration 22 2 Member checking 21 7 Memoing 22 2 Negative case analysis 22 2 Peer review 22 9 Recursive 22 2 Selective coding 22 2 Triangulation 22 7 Verbatims 23 1 23 6 Part Data Preparation, Analysis, and... collection effort, also become part of the data set Finally, key participants may be asked to evaluate researchers’ initial research draft Their feedback becomes part of the official data set as well