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(BQ) Part 2 book “Marketing research - An applied a pproach” has contents: Survey fieldwork, social media research, mobile research, data integrity, frequency distribution, cross-tabulation and hypothesis testing, analysis of variance and covariance, factor analysis,… and other contents.
www.downloadslide.net 14 Stage Problem definition Stage Research approach developed Stage Research design developed Stage Fieldwork or data collection Stage Data integrity and analysis Stage Communicating research findings Sampling: design and procedures There is no hope of making scientific statements about a population based on the knowledge obtained from a sample, unless we are circumspect in choosing a sampling method www.downloadslide.net 410 Marketing Research Objectives After reading this chapter, you should be able to: differentiate a sample from a census and identify the conditions that favour the use of a sample versus a census; discuss the sampling design process: definition of the target population, determination of the sampling frame, selection of sampling technique(s), determination of sample size, execution of the sampling process and validating the sample; classify sampling techniques as non-probability and probability sampling techniques; describe the non-probability sampling techniques of convenience, judgemental, quota and snowball sampling; describe the probability sampling techniques of simple random, systematic, stratified and cluster sampling; identify the conditions that favour the use of non-probability sampling versus probability sampling; understand the sampling design process and the use of sampling techniques across countries; appreciate how the growth in online panels is shaping the manner in which sampling may be designed and executed Overview Sampling is a key component of any research design Sampling design involves several basic questions: Should a sample be taken? If so, what process should be followed? What kind of sample should be taken? How large should it be? What can be done to control and adjust for non-response errors? This chapter introduces the fundamental concepts of sampling and the qualitative considerations necessary to answer these questions We address the question of whether or not to sample and describe the steps involved in sampling Included in questions of the steps involved in sampling are the use, benefits and limitations of the access panel in sample design We present the nature of non-probability and probability sampling and related sampling techniques We discuss the use of sampling techniques in international marketing research and identify the relevant ethical issues We conclude by examining the issues around designing and executing well-focused samples in the context of conducting online surveys (Statistical determination of sample size, and the causes for control of and adjustments for non-response error, are discussed in Chapter 15.) We begin with two examples The first illustrates the choice of a sampling method in a complex international study, with hard-to-access participants The second illustrates a key debate that challenges many researchers Given the demand for researchers to sample ‘willing’ participants to match specific profiles, the use of the access panel has grown enormously in the research industry As you progress through questions of the nature, purpose and techniques of sampling, the key debates in this example should be addressed www.downloadslide.net Chapter 14 Sampling: design and procedures Real research 411 Measuring the impact of empowerment1 Research by the United Nations has demonstrated that in most economies, women are the linchpin to the advancement of many indicators of prosperity In the West, it is often believed that greater financial prosperity always equates to greater happiness In those countries where women appear to be doing well financially, are these women really happier? In societies where women’s pursuit of prosperity and happiness is not supported, research has a role to play both in providing them with a voice to let their hopes and dreams be heard and in public policy designed to support them To address some of these issues, D3 Systems (www.d3systems.com) launched the Women in Muslim Countries study (WIMC) WIMC consisted of annually repeated, nationally representative quantitative research in 22 Muslim-majority countries of the globe The questions used for the WIMC were designed to measure women’s empowerment in actual daily practice, providing a deep look into the gap between current public policy and empowerment initiatives and actual practice on the personal and local level In some cases, WIMC got at the issues indirectly, as in many Muslim countries asking with direct wording would not yield honest answers Individual country surveys were conducted, either face to face or via CATI as appropriate Each country’s sampling frame was designed to provide the best possible representation of the attitudes and experience of that country’s women In all cases, the sample was two-stage, stratified random In the case of Egypt, the sampling frame was limited to urban areas only At its launch WIMC focused upon the following 10 countries: Real research Country Mode Afghanistan Bangladesh Egypt Iran Iraq Jordan Kosovo Pakistan Saudi Arabia Turkey Face-to-face nationwide Face-to-face nationwide Face-to-face nationwide, seven main cities and suburbs CATI nationwide Face-to-face nationwide Face-to-face nationwide Face-to-face nationwide Face-to-face nationwide CATI nationwide CATI nationwide Women only, n 1175 753 500 1003 1093 500 538 960 514 490 Down with random sampling Peter Kellner, President of YouGov (www.yougov.co.uk), the online political polling company, presented these contentious views on the challenges of conducting random sampling:2 We know that perfection does not exist Pure random samples are too expensive Besides 100% response rates belong to the world of fantasy So we are told to the best we can There are two separate phenomena to address: the quality of the ‘designed’ sample and the quality of the ‘achieved’ sample When we deliver results to our clients, what matters is the second, not the first If the achieved sample is badly skewed, it is no defence to say that we used impeccably random samples to obtain it Our aim should be to present our clients with ‘representative achieved samples’ This means developing a much more www.downloadslide.net 412 Marketing Research purposive approach to sampling and weighting At YouGov we have been forced into this approach by the very nature of our business Our samples are drawn from a panel of more than 150,000 people throughout Great Britain By definition, we don’t approach the remaining 45 million adults who have not joined our panel Random sample purists look at our methods with scorn Yet our record demonstrates an overall accuracy that our rivals envy We draw on existing knowledge of a population in question to construct samples that are representative of that population We also apply weights that are relevant to each group, not simply all purpose demographic weights Of course, the non-response problem can never be completely eliminated We can never be sure of the views of people who never respond to pollsters of any kind In response, Harmut Schffler of TNS Infratest (www.tns-infratest.com) commented:3 Whilst we need to develop our expertise, and refusal rates are a growing problem, to say random sampling is obsolete and then present modulated access panels as the solution is astounding Yes, Peter Kellner will want to defend his business model He at least hints that access panels can produce enormous distortion with respect to who does and does not participate Instead of declaring the death of random sampling, we should improve its quality through better promoting our industry to the public and finding more intelligent ways to address potential participants so we can increase response rates We need random methods And so does Peter Kellner’s model or he will find no solution to his own recruitment distortion problem Andrew Zelin and Patten Smith of Ipsos MORI (www.ipsos-mori.com) added: We agree that a high quality of designed sample does not guarantee a high quality of achieved sample, that poor response rates coupled with differences between responders and non-responders lead to non-response bias and that demographic weighting may be a poor tool for removing this bias However, his arguments depend upon the implication that random probability samples produce unacceptable levels of non-response bias For some samples and some variables this will be true, but often it will not Unless we are certain that the alternatives to random probability sampling are superior, we should investigate non-response bias on a variable-by-variable survey-by-survey basis This example infers that the ‘best’ form of sampling is the probability random sample It may be an ideal that researchers would prefer to administer However, researchers have long recognised the balance between what may be seen as the scientific ideal of sampling and the administrative constraints in achieving that ideal This balance will be addressed throughout this chapter Before we discuss these issues in detail, we address the question of whether the researcher should sample or take a census Sample or census Population The aggregate of all the elements, sharing some common set of characteristics, that comprise the universe for the purpose of the marketing research problem The objective of most marketing research projects is to obtain information about the characteristics or parameters of a population A population is the aggregate of all the elements that share some common set of characteristics and that comprise the universe for the purpose of the marketing research problem The population parameters are typically numbers, such as the proportion of consumers who are loyal to a particular fashion brand Information about www.downloadslide.net Chapter 14 Sampling: design and procedures Census A complete enumeration of the elements of a population or study objects Sample A subgroup of the elements of the population selected for participation in the study 413 population parameters may be obtained by taking a census or a sample A census involves a complete enumeration of the elements of a population The population parameters can be calculated directly in a straightforward way after the census is enumerated A sample, on the other hand, is a subgroup of the population selected for participation in the study Sample characteristics, called statistics, are then used to make inferences about the population parameters The inferences that link sample characteristics and population parameters are estimation procedures and tests of hypotheses (These inference procedures are considered in Chapters 20 to 26.) Table 14.1 summarises the conditions favouring the use of a sample versus a census Budget and time limits are obvious constraints favouring the use of a sample A census is both costly and time-consuming to conduct A census is unrealistic if the population is large, as it is for most consumer products In the case of many industrial products, however, the population is small, making a census feasible as well as desirable For example, in investigating the use of certain machine tools by Italian car manufacturers, a census would be preferred to a sample Another reason for preferring a census in this case is that variance in the characteristic of interest is large For example, machine-tool usage of Fiat may vary greatly from the usage of Ferrari Small population sizes as well as high variance in the characteristic to be measured favour a census Table 14.1 Sample versus census Factors Conditions favouring the use of Sample 1 Budget Time available Population size Variance in the characteristic Cost of sampling errors Cost of non-sampling errors Nature of measurement Attention to individual cases Small Short Large Small Low High Destructive Yes Census Large Long Small Large High Low Non-destructive No If the cost of sampling errors is high (e.g if the sample omitted a major manufacturer such as Ford, the results could be misleading), a census, which eliminates such errors, is desirable If the cost of non-sampling errors is high (e.g interviewers incorrectly questioning target participants) a sample, where fewer resources would have been spent, would be favoured A census can greatly increase non-sampling error to the point that these errors exceed the sampling errors of a sample Non-sampling errors are found to be the major contributor to total error, whereas random sampling errors have been relatively small in magnitude.4 Hence, in most cases, accuracy considerations would favour a sample over a census A sample may be preferred if the measurement process results in the destruction or contamination of the elements sampled For example, product usage tests result in the consumption of the product Therefore, taking a census in a study that requires households to use a new brand of toothpaste would not be feasible Sampling may also be necessary to focus attention on individual cases, as in the case of in-depth interviews Finally, other pragmatic considerations, such as the need to keep the study secret, may favour a sample over a census www.downloadslide.net 414 Marketing Research The sampling design process The sampling design process includes six steps, which are shown sequentially in Figure 14.1 These steps are closely interrelated and relevant to all aspects of the marketing research project, from problem definition to the presentation of the results Therefore, sample design decisions should be integrated with all other decisions in a research project.5 Figure 14.1 Define the target population The sampling design process Determine the sampling frame Select a sampling technique Determine the sample size Execute the sampling process Validate the sample Define the target population Target population The collection of elements or objects that possess the information sought by the researcher and about which inferences are to be made Element An object that possesses the information sought by the researcher and about which inferences are to be made Sampling unit An element, or a unit containing the element, that is available for selection at some stage of the sampling process Sampling design begins by specifying the target population This is the collection of elements or objects that possess the information sought by the researcher and about which inferences are to be made The target population must be defined precisely Imprecise definition of the target population will result in research that is ineffective at best and misleading at worst Defining the target population involves translating the problem definition into a precise statement of who should and should not be included in the sample The target population should be defined in terms of elements, sampling units, extent and time An element is the object about which, or from which, the information is desired In survey research, the element is usually the participant A sampling unit is an element, or a unit containing the element, that is available for selection at some stage of the sampling process Suppose that Clinique wanted to assess consumer response to a new line of lipsticks and wanted to sample females over 25 years of age It may be possible to sample females over 25 directly, in which case a sampling unit would be the same as an element Alternatively, the sampling unit might be households In the latter case, households would be sampled and all females over 25 in each selected household would be interviewed Here, the sampling unit and the population element are different Extent refers to the geographical boundaries of the research, and the time refers to the period under consideration www.downloadslide.net Chapter 14 Sampling: design and procedures 415 Defining the target population may not be as easy as it was in this example Consider a marketing research project assessing consumer response to a new brand of men’s moisturiser Who should be included in the target population? All men? Men who have used a moisturiser during the last month? Men of 17 years of age or older? Should females be included, because some women buy moisturiser for men whom they know? These and similar questions must be resolved before the target population can be appropriately defined.6 This challenge is further illustrated in the following example Real research Kiasma: the insightful museum7 Kiasma Museum of Contemporary Art (www.kiasma.fi) in Finland is dedicated to contemporary art Throughout its existence Kiasma has been the most visited museum in Finland Kiasma’s marketing and management team wanted to explore the museum’s marketing strategy, contextual development and changes in the external working environment Research was planned between Kiasma and the media agency Dagmar (www dagmar.fi), with whom it had been working for over 10 years One of the first challenges was to establish what the population for the research would be Would it be the total population for Finland? Kiasma had a public duty to serve the whole population, but it was unfeasible in the context of the research to segment the whole Finnish population, since the museum was located in Helsinki and just pure distance was a hindrance for visiting and/or visiting regularly The approach the researchers chose was to first gauge the interest in contemporary art in an online panel The question they posed was a simple ‘Are you interested in contemporary art – yes/no?’ The result was that a discouraging 33% had an interest in contemporary art A follow-up question was open-ended, about why the participant was interested or not interested The results helped the researchers to define a population for their planned survey as ‘people living a maximum of 60 km from Helsinki, 15–74 years of age and interested in any form of cultural activities, or, failing that, are interested in new experiences’ The reasoning behind this was that a person who was interested in at least some form of culture would more easily be persuaded to come to Kiasma Determine the sampling frame Sampling frame A representation of the elements of the target population that consists of a list or set of directions for identifying the target population A sampling frame is a representation of the elements of the target population It consists of a list or set of directions for identifying the target population Examples of a sampling frame include the telephone book, an association directory listing the firms in an industry, a customer database, a mailing list on a database purchased from a commercial organisation, a city directory, a map or, most frequently in marketing research, an access panel.8 If a list cannot be compiled, then at least some directions for identifying the target population should be specified, such as random-digit dialling procedures in telephone surveys With growing numbers of individuals, households and businesses, it may be possible to compile or obtain a list of population elements, but the list may omit some elements of the population or may include other elements that not belong Therefore, the use of a list will lead to sampling frame error (which was discussed in Chapter 3).9 www.downloadslide.net 416 Marketing Research In some instances, the discrepancy between the population and the sampling frame is small enough to ignore In most cases, however, the researcher should recognise and attempt to treat the sampling frame error One approach is to redefine the population in terms of the sampling frame For example, if a specialist business directory is used as a sampling frame, the population of businesses could be redefined as those with a correct listing in a given location Although this approach is simplistic, it does prevent the researcher from being misled about the actual population being investigated 10 Ultimately, the major drawback of redefining the population based upon available sampling frames is that the nature of the research problem may be compromised Who is being measured and ultimately to whom the research findings may be generalised may not match the target group of individuals identified in a research problem definition Evaluating the accuracy of sampling frames matches the issues of evaluating the quality of secondary data (see Chapter 4) Another way is to account for sampling frame error by screening the participants in the data collection phase The participants could be screened with respect to demographic characteristics, familiarity, product usage and other characteristics to ensure that they satisfy the criteria for the target population Screening can eliminate inappropriate elements contained in the sampling frame, but it cannot account for elements that have been omitted Yet another approach is to adjust the data collected by a weighted scheme to counterbalance the sampling frame error These issues were presented in the opening example ‘Down with random sampling’ (and will be further discussed in Chapters 15 and 19) Regardless of which approach is used, it is important to recognise any sampling frame error that exists, so that inappropriate inferences can be avoided Select a sampling technique Bayesian approach A selection method where the elements are selected sequentially The Bayesian approach explicitly incorporates prior information about population parameters as well as the costs and probabilities associated with making wrong decisions Sampling with replacement A sampling technique in which an element can be included in the sample more than once Sampling without replacement A sampling technique in which an element cannot be included in the sample more than once Selecting a sampling technique involves several decisions of a broader nature The researcher must decide whether to use a Bayesian or traditional sampling approach, to sample with or without replacement, and to use non-probability or probability sampling In the Bayesian approach, the elements are selected sequentially After each element is added to the sample, the data are collected, sample statistics computed and sampling costs determined The Bayesian approach explicitly incorporates prior information about population parameters, as well as the costs and probabilities associated with making wrong decisions.11 This approach is theoretically appealing Yet it is not used widely in marketing research because much of the required information on costs and probabilities is not available In the traditional sampling approach, the entire sample is selected before data collection begins Because the traditional approach is the most common approach used, it is assumed in the following sections In sampling with replacement, an element is selected from the sampling frame and appropriate data are obtained Then the element is placed back in the sampling frame As a result, it is possible for an element to be included in the sample more than once In sampling without replacement, once an element is selected for inclusion in the sample it is removed from the sampling frame and therefore cannot be selected again The calculation of statistics is done somewhat differently for the two approaches, but statistical inference is not very different if the sampling frame is large relative to the ultimate sample size Thus, the distinction is important only when the sampling frame is small compared with the sample size The most important decision about the choice of sampling technique is whether to use non-probability or probability sampling Non-probability sampling relies on the judgement of the researcher, while probability sampling relies on chance Given its importance, the issues involved in this decision are discussed in detail below, in the next section If the sampling unit is different from the element, it is necessary to specify precisely how the elements within the sampling unit should be selected With home face-to-face interviews www.downloadslide.net Chapter 14 Sampling: design and procedures 417 and telephone interviews, merely specifying the address or the telephone number may not be sufficient For example, should the person answering the doorbell or the telephone be interviewed, or someone else in the household? Often, more than one person in a household may qualify For example, both the male and female head of household, and even their children, may be eligible to participate in a study examining family leisure-time activities When a probability sampling technique is being employed, a random selection must be made from all the eligible persons in each household A simple procedure for random selection is the ‘next birthday’ method The interviewer asks which of the eligible persons in the household has the next birthday and includes that person in the sample Determine the sample size Sample size The number of elements to be included in a study Sample size refers to the number of elements to be included in the study Determining the sample size involves several qualitative and quantitative considerations The qualitative factors are discussed in this subsection, and the quantitative factors are considered in Chapter 15 Important qualitative factors to be considered in determining the sample size include: (1) the importance of the decision; (2) the nature of the research; (3) the number of variables; (4) the nature of the analysis; (5) sample sizes used in similar studies; (6) incidence rates; (7) completion rates; and (8) resource constraints In general, for more important decisions more information is necessary, and that information should be obtained very precisely This calls for larger samples, but as the sample size increases, each unit of information is obtained at greater cost The degree of precision may be measured in terms of the standard deviation of the mean, which is inversely proportional to the square root of the sample size The larger the sample, the smaller the gain in precision by increasing the sample size by one unit The nature of the research also has an impact on the sample size For exploratory research designs, such as those using qualitative research, the sample size is typically small For conclusive research, such as descriptive surveys, larger samples are required Likewise, if data are being collected on a large number of variables, i.e many questions are asked in a survey, larger samples are required The cumulative effects of sampling error across variables are reduced in a large sample If sophisticated analysis of the data using multivariate techniques is required, the sample size should be large The same applies if the data are to be analysed in great detail Thus, a larger sample would be required if the data are being analysed at the subgroup or segment level than if the analysis is limited to the aggregate or total sample Sample size is influenced by the average size of samples in similar studies Table 14.2 gives an idea of sample sizes used in different marketing research studies These sample sizes have been determined based on experience and can serve as rough guidelines, particularly when non-probability sampling techniques are used Finally, the sample size decision should be guided by a consideration of the resource constraints In any marketing research project, money and time are limited The sample size required should be adjusted for the incidence of eligible participants and the completion rate The quantitative decisions involved in determining the sample size are covered in detail in the next chapter Execute the sampling process Execution of the sampling process requires a detailed specification of how the sampling design decisions with respect to the population, sampling unit, sampling frame, sampling technique and sample size are to be implemented While individual researchers may know how they are going to execute their sampling process, once more than one individual is involved a specification for execution is needed to ensure that the process is conducted in a consistent manner www.downloadslide.net 418 Marketing Research For example, if households are the sampling unit, an operational definition of a household is needed Procedures should be specified for empty housing units and for call-backs in case no one is at home Table 14.2 Usual sample sizes used in marketing research studies Type of study Minimum size Typical range Problem identification Problem-solving research Product tests Test marketing studies TV, radio, print or online advertising Test-market audits Focus groups 500 200 200 200 150 10 stores groups 1,000–2,500 research (e.g market potential) 300–500 (e.g pricing) 300–500 300–500 200–300 (per advertisement tested) 10–20 stores 6–12 groups Validate the sample Sample validation aims to account for sampling frame error by screening the participants in the data collection phase Participants can be screened with respect to demographic characteristics, familiarity, product usage and other characteristics to ensure that they satisfy the criteria for the target population Screening can eliminate inappropriate elements contained in the sampling frame, but it cannot account for elements that have been omitted The success of the validation process depends upon the accuracy of base statistics that describe the structure of a target population Once data are collected from a sample, comparisons between the structure of the sample and the target population should be made, as practised in the following example Once data have been collected and it is found that the structure of a sample does not match the target population, a weighting scheme can be used (this is discussed in Chapter 19) Real research How consumers are affected by online banking layouts12 A study was conducted to examine banking store layout effects on consumer behaviour The target population for this study was adult heavy internet users who used either offline or online banking services in Greece Three versions of a web banking store were developed and tested Two of the layout types were transformed from conventional banking and one type was designed by incorporating users’ preferences and suggestions The study was conducted in three phases Phase involved a series of semistructured in-depth interviews with design experts from four major multinational banks in Greece Phase involved a series of focus groups with banking users and heavy online shoppers to evaluate requirements as far as the most preferred layout type was concerned Phase consisted of a within-group laboratory experiment to test three alternative versions of a virtual e-banking store Sample validation was conducted, enabling the researchers to demonstrate that the sample used satisfied the population criteria Validation was further strengthened as participants were further questioned upon completion of their questionnaires in a semi-structured face-to-face interview conducted by the experiment’s administrator www.downloadslide.net Subject Index psychographics 110, 140, 920 variables 709–10 psychological segmentation 140 public data on social media 509 public domain, social media 506 public records 874 public sector, b2b sampling 871 published sources of secondary data 100–4 pupilometers 293, 920 purchase panels 108, 112–13, 920 purchasing decisions, b2b see business-to-business mobile devices playing key part in process 515 Q Q-sort scaling 346–7, 920 qualitative research 10–12, 70–2, 147–50, 920 action research 148, 171–4 American style 156–8 classification 182–3 comparisons 226–7 cross-tabulation 249, 570 direct approach 182–3, 912 ethics 183, 892–5 European style 156–8 expenditure 16–17 grounded theory 148, 168–70, 251–4 holistic dimension 154–5 indirect approach 182–3, 914 intellectual traditions 152 interpretation 155 interviews 894–5 listening 166–7 nature 151–2 new theory development 155 objective viewpoint 169–70 philosophy and 155–62 quantitative research compared 150–2 rationale 152–5 sensitive information 153 see also ethnographic research; focus groups; in-depth interviews; projective techniques; ……….surveys quality control 481–2, 483 data 135 individuals 71 responses see responses variable in presentations 835 quantitative observation techniques 289, 920 quantitative research 10–12, 69–72, 920 content analysis 256 ethics 895–6 expenditure 16–17 experiments 895 qualitative research compared 150–2 questionnaire design 896 scales 895 quantity of data 282–3 quasi-experimental designs 315–16, 318–20, 920 questioning errors 84 questionnaires 6 checking assigning missing values 533 data integrity 531–2 unsatisfactory responses 532–3 definition 374–5, 920 design 371–3 ambiguity 391–2 assumptions and alternatives 393 bias 896 checklist 400–1 colour coding 398 content of questions 380–1 cross-cultural 402–3 data analysis 377 defining issues 390 encouraging answers 384–5 estimates 393 ethics 896 exchange of values 374–5 flow chart 396 form 396–7 funnel approach 395, 914 generalisations 393 information required 378, 394 interviewing method 379–80 layout 396–7 negative statements 394 numbering 396–7 order of questions 394–6 pilot-testing 398–9, 402 positive statements 394 printed questionnaires 397–8 process 375–8 refusal rates decrease 460 wording 389–94 discards 533 fieldworkers and 476–7 information 394 objectives 374–5 reproducing 397–8 questions 53–4, 921 bias 392 branching 395–6, 909 context 384 dichotomous 388–9, 912 difficult 395 diversity 280 double-barrelled 381, 912 fieldwork 476–7 filler 381 filter 382, 913 fixed-response alternative 269–70, 913 in focus groups 188–9 framing, social media 507 inability to remember 382–3 943 www.downloadslide.net 944 Marketing Research questions (continued) layers of response 63–7 leading 392, 915 logical order 395–6 multiple-choice 387–8 neutral 381, 478 objective 478 open-ended 385, 539 opening 394 repeating 477 response errors 83, 84–5, 375 rules use, social media 507 structured 387–9, 923 unstructured 385–7, 532, 925 unwillingness to answer 381–5 wording 389–94 quota sampling 419, 420, 422–4, 434, 920 R R-square 698, 766, 772 random errors 358, 920 random sampling see sampling randomisation 314, 920 randomised block design 321, 921 range 563, 921 rank order scaling 344–5, 921 rapport 285–6 ratio scales 338, 341–2, 921 reality 159 reassuring 478 rebates 674–5, 726–7 reciprocity ensuring 893 recording answers 479–81 recording errors 84 recruitment see participants recursive model 799 reduction of data 243–7, 739, 911 reference points, mood boards as 192 refusal rates 459 regression analysis 632–4, 641, 921 accuracy 650 ANOVA and 604–5, 664 assumptions 651 bivariate 641–51 cross-validation 662–3 dependent variables 641 discriminant analysis relationships 676 double cross-validation 663 dummy variables 663–4, 782 estimated or predicted value 646, 650 F distribution 649–50 F tests 652–3 general model 645 independent variables 641, 662 least squares procedure 644–5 logistic 697 logit analysis relationships 676 multicollinearity 661–2 multiple regression 651–61 parameters 645–6 partial regression coefficients 653–4 predictor variables 641, 662 residuals 657–8 scatter diagrams 642–5 significance of association 647–50 significance test 656 standardised regression coefficient 646–7 statistical terms 642, 652–3 stepwise regression 658–61, 662 strength of association 647–50, 655 reidentification 902–3, 904 relationships b2b 858–9, 872–3 building 60–1 customer relationship management see customers focal 859–60 relative importance weights 777 relative non-centrality index (RNI) 806, 807 relevance of information 62–3 reliability 160, 921 cluster analysis 748–9 conjoint analysis 785–6 measurement and scaling 359–61, 363, 921 multidimensional scaling (MDS) 772–3 structural equation modelling 805–9, 816, 818–21 reluctance of participants 166 repeated measures ANOVA 622–3, 921 repeating questions and responses 477 repertory grid technique (RGT) 217–19 replacement, sampling with 416, 921 replacement, sampling without 416, 921 reporting 260, 921 solutions providers 19 reports 12, 832 appendices 840 caveats 840 client questions 849 conciseness 841 conclusions 839–40 data analysis 839 data collection 839 digital dashboards 832, 845–7 distribution 845 ethics 898 executive summaries 838–9 format 837–40 graphs 841, 842–5 importance 835–6 infographics 847 limitations 840 managers’ thoughts about 836 market research, 18, 916 mistakes 842 objectives 839 objectivity 841 oral presentations 847–8 oversimplification avoidance 849 www.downloadslide.net Subject Index preparation 836–42, 898 presentation 836–7, 898 problem definition 839 project evaluation 850 proposals 38 readership 840–1 recommendations 839–40 research design 839 research follow-up 849–50 results 839–8 submission letters 838 tables 841 tables of contents 838 title page 838 writing 840–2 requests, critical 284, 911 research approach components 50–1 development 9, 39–41 ethics 888 briefs 32–3, 921 administration 35–6 background information 33–4, 37 components 33–6 constraints 35 findings, use 34–5 objectives 34 target groups 34 design 9–10, 59–61, 921 classification 69–73 conclusive research see conclusive research cross-sectional designs see cross-sectional designs decision-makers’ perspective 62–3 definition 61–2 descriptive research see descriptive research errors 82–5 ethics 889–90 evolving 161, 913 exploratory research see exploratory research flexible 164 multi-method designs 82 participants’ perspective 63–9 proposals 38 reports 839 secondary data 97 theory and 160 follow-up 460, 849–50 objectives 34 organisation 38 problems see problems process, ethics 888–96 proposals 36–9, 921 questions see questions social media see social media targets 34 users, preferences and/or experience 152–3 Research 2.0: 23 researchers 945 action research 172 experience 152 language 160 participants and, relationships 160 preferences 152 on social media research 509 residuals 657–8, 712, 721, 802, 806–7, 809–10, 921 respectification, variables 544, 925 respondent trust, ethics 900–1 responses bias 79, 461 errors 83, 84–5, 375, 867, 921 imputed, substitution 542 latency 293, 294, 921 layers 63–7 missing 541–2, 917 non-response see non-response quality mobile research 523 online surveys 273 rates 283–4, 921 calculation 456–7 compliance techniques 473–4 fieldwork 483 following-up 460 improving 459–61 imputation 463 incentives 460 missing responses 541–2 not-at homes 461 online surveys 283–4 personalisation 460 prior notification 459 questionnaire design 460 refusal rates 459 replacement 462 subjective estimates 462 subsampling of non-participants 462 substitution 462 surveys and 458 trend analysis 462–3 unwillingness to answer 381–5, 867–8 weighting 463 repeating 477 unsatisfactory 532–3 responsibilities 25 results see findings retail audits 115–16 retrieval 259 return on investment 19–21 rewards, social media 507 RGT (repertory grid technique) 217–19 rigour 260 RMSEA see root mean square error of approximation RMSR (root mean square residual) 806–7 RNI (relative non-centrality index) 806, 807 role playing 225, 226, 921 root mean square error of approximation (RMSEA) 806, 807, 816–19, 822 www.downloadslide.net 946 Marketing Research root mean square residual (RMSR) 806–7 rotation of factors 719–20 round charts 842–3 runs tests 589, 921 S SaaS (software as a service) 19 samples 413, 921 control 280–2, 921 covariance matrices 799 independent 547 paired 547 size 416, 442–3, 921 completion rates 455 confidence intervals 419, 443, 445, 448–54, 910 ethics 891 finite population correction 445, 450 incidence rates 455 means 445, 449–51 multiple parameters 454 non-response issues 457–64 normal distribution 443, 446 proportions 446–9, 451–4 standard deviation 445, 448–52, 563–4 standard error 445, 446–7, 450 statistical approach 447 statistical inference 446 structural equation modelling 804–5 symbols 445 z values 447, 448–52 sampling 10, 260, 409–12 area sampling 430–1 b2b research 862, 870–1 Bayesian approach 416 classification of techniques 419–20 cluster sampling 419, 425, 429–32, 434, 435, 909 confidence intervals 419, 443, 445, 448–54, 910 control 482, 921 convenience sampling 419, 420–1, 434, 911 design errors 413, 443–4 design process 414–18 distribution 446–7, 921 double 432–3 elements 414, 417 errors 82–5, 413, 443–4 fieldwork 482 frames 84, 274, 281–2, 415–16, 871, 921 international research 436–7 judgemental 419, 420, 421–2, 434, 915 non-probability 419, 420–5, 433–4, 870–1, 918 population parameters 445–6, 451 probability 419, 425–34 probability proportionate to size (PPS) 431–2 quota sampling 419, 420, 422–4, 434, 920 random 411–12, 419, 425–7, 434, 435 errors 82–3, 920 with replacement 416, 921 without replacement 416, 921 sequential 432 snowball sampling 419, 420, 424–5, 434, 922 stratified 419, 425, 428–9, 430, 434, 435, 923 systematic 419, 425, 427–8, 434, 435, 870, 923 target populations 414–15 techniques 416–17 theoretical 161, 253, 924 units 414, 417, 921 validation 418 sarcasm 500 SAS (Small Area Statistics) 192 scales bias 895 ethics 895 transformation 545, 921 scaling see measurement and scaling scanner data 108, 106, 114–15, 922 scanner panels 114, 922 scanner panels with cable TV 108, 114–15, 922 scatter diagrams 642–5 scattergrams 642–5 scents 67 schematic figures 845 Schwarz’s Bayesian Information Criterion (BIC) 744 scientific scrutiny, focus groups 187 S:Comm Leisure Time Survey interval scale 341 nominal scale 339 ordinal scale 340 ratio scale 342 scores, discriminant 677 scree plots 712, 718 search 259 second-order factor model 799, 813–14, 817–23 secondary data 90–2 advantages 94–5 analysis 45–6 classification 99–100 collection 45–6 databases 104–5, 122–8 definition 92–3, 922 disadvantages 96 ethics 891 evaluation 96–9 external sources government 102–4 published 100–4 internal sources 125–8, 915 linking data 139–44 syndicated sources 106–17 uses 94–5 security, focus groups 187 segmentation 765 baby milk market 131–2 cluster analysis 738, 752 conjoint analysis 777 methods 140 parents and children 426–7 research 14 www.downloadslide.net Subject Index selection bias 313, 922 self reflection 235–6 self-regulation 884, 886, 899, 904 self-selection bias 505–6 SEM see structural equation modelling semantic differential scales 348, 351–2, 922 semiotics 922 advertising and 258 data analysis 256–9 sensitive information 65–6, 153, 197, 287, 384, 395 sensitive questions 896 sensitivity 170 moderators 194 sentence completion 224, 922 sentiment analysis 499–501 sequential sampling 432, 922 sequential threshold method 743, 922 serendipity, focus groups 187 set-top box (STB) data 292 seven Cs 30, 43, 888 shadow teams 94, 922 shame, participants 166 shape measures 564–5 sharing data 135–6 showrooming 515 sign tests 547, 592, 922 significance of association 647–50 of interaction effects 615, 922 level 567–8, 915 level choice, hypothesis testing 567–8 of main effects of each factor 615, 922 of overall effects 614–15, 922 tests correlation and regression 656 factor analysis 718 logit analysis 698 one-way ANOVA 608–9 n-way ANOVA 614–15 silence 241, 477 silos 30–1 similarity/distance coefficient matrices 739 similarity judgements 765 simple correlation coefficient 662 see also product moment correlation simple random sampling (SRS) 419, 425, 426–7, 434, 435, 922 simulated test markets 328, 922 single cross-sectional designs 74–5, 872, 922 single linkage 742, 922 skewness 564, 922 skills data analysis 135 gap 22–5 specialist 834 Slovenia, advertising agencies 422 Small Area Statistics (SAS) 192 smartphones 516 smartwatches 514, 525 SMC (squared multiple correlations) 799, 809, 817 SMS research 517–18 snowball sampling 419, 420, 424–5, 434, 922 snowballing 186 social desirability 287, 922 social listening 496 social media b2b research 855 blogs 503 co-creative nature of 899 communities 501–4 data access 497–9 collection 899–900 examples 499 reliability 500 ethics 899–900 informed consent 899–900 meaning 492–4, 922 participative nature of 899 privacy policies 899–900, 901 private data on 509 public data on 509 research 22, 141–2, active 495, 496–7, 908 approaches to 495–7 blogs 503 challenges 495 communities 501–4 crowdsourcing 504–6 emergence 494–5 entity recognition 501 gamification 507–8 with image data 508–9 limitations 509 methods 499–508 passive 495–6, 919 sentiment analysis 499–501 strengths 495 with video data 508–9 researchers on 509 uninformed consent 899–900 social networks 493, 922 social responsibility 382 Social Trends 103 social values 234–6 soft gamification 507 software data analysis 259–62 data display 247–8 providers, 19, 922 software as a service (SaaS) 19 Solomon four-group design 318, 922 songs 373 Sophisticated Singles 130 source derogation arguments 347 spatial maps 765, 769 special-purpose databases 105, 922 947 www.downloadslide.net 948 Marketing Research specialisation, focus groups 187 specialist skills 834 specific components of problems 50, 922 specification search 810, 922 spectators, b2b 862 speed 260 focus groups 187 online surveys 273 spelling 500 sphericity 711, 714 split-half reliability 360, 718, 923 sponsorship 278 spontaneity 187, 494 sports ticket prices 659 spreadsheets 247–8 SPSS practice data analysis ANOVA 625–6 cluster analysis 757–8 conjoint analysis 788–9 correlation and regression 665–6 data analysis 593–5 data integrity 549–51 discriminant analysis 702–3 factor analysis 729–30 MDS 788–9 spurious relationships 574 squared multiple correlations (SMC) 799, 809, 817 SRMR (standardised root mean residual) 806–7, 818, 822 SRS see simple random sampling staff retention 685–6 standard deviation 445, 448–52, 563–4, 891, 923 standard errors 445, 446–7, 450, 642, 923 standard test markets 327–8, 923 standardisation 545, 646–7, 923 standardised regression coefficient 646–7 standardised residuals 809–10, 923 standardised root mean residual (SRMR) 806–7, 818, 822 Stapel scales 348, 352, 923 statements of problem 50, 909 static group design 315, 316–17, 320, 923 statistical adjustments 543–5 statistical approaches 770 statistical control 314, 923 statistical designs 315–16, 320–3, 923 statistical inference 446, 923 statistical regression 313, 662, 923 statistical technique selection, hypothesis testing 567 statistical test, power of 567, 919 STB (set-top box) data 292 stepwise discriminant analysis 680, 696, 923 stepwise regression 658–61, 923 stimulation, focus groups 187 stimuli 280, 779–80 storage 259 story completion 224, 923 strategic marketing managers 172 strategic use of dashboards 846 strategy formulation 590–1 stratified sampling see sampling streaming, online 19, 918 street surveys 278 strength of association 647–50, 655 stress values 765, 769–70, 772 structural equation modelling (SEM) 795–7, 923 applications 814–23 chi-square test 806 communality 805 compared to other techniques 814 concepts 797–8 conclusions and recommendations 812–13, 817, 823 constructs 797 endogenous 798, 801 exogenous 798, 801 individual, defining 802–3, 815, 817–18 correlational relationships 800–1 dependence relationships 800–1 first-order factor model 799, 813–17 foundations 800–2 higher order models 813–14 hypothesised relationships 812 measurement model 799, 800, 803–4, 815–16, 818–21 model fit 801–2, 805–6, 811 model identification 802 modification index 810 path diagrams 801 reliability 805–9, 816, 818–21 residuals 802, 806–7, 809–10 sample size 804–5 second-order factor model 799, 813–14, 817–23 specification search 810 statistics and terms associated with 798–9 structural models 799, 800, 801, 810–13, 817, 821–3 validity 805–10, 811–12, 816–17 structural errors 799 structural models see structural equation modelling structural relationships 799 structure correlations 678 focus groups 187 structured data collection 269, 923 structured direct surveys 269 structured observation 290, 923 structured questions 387–9, 923 subconscious feelings 153 subjective estimates 462 submission letters 838 subsampling of non-participants 462 substitution 462, 923 sufficiency of information 62 sugging 905, 923 sum of squared errors 642 supervising fieldworkers 481–2 suppliers external 17–19, 913 full-service 17, 913 limited-service 18, 915 see also agencies www.downloadslide.net Subject Index support arguments 347 suppressed association 574–5 surrogate information errors 83 surrogate variables 721, 923 surveys 2, 4, 6, 108, 109, 267–9, 923 anonymity 286 classification 270 completion rates 455 computer-assisted personal interviews 278, 280, 379 content 523 cost 286 diversity of questions 280 drop-off surveys 288 email 272 environment control 284 evaluation of methods 279–88 face-to-face 276–9, 280–8 field force control 284–5 fixed-response alternative questions 269–70 flexibility 280 general 111 home 276–7, 280–8 incidence rates 287–8 interviewer bias 285 invitations 523 kiosk-based 288 methods 269–71, 923 mixed-mode 289 mobile see mobile surveys omnibus 109–11, 277, 918 online see online research participants 270, 286, 287 physical stimuli 280 postal 278–9, 280–8 probing 285 quantity of data 282–3 questionnaire design 379–80 rapport 285–6 response rates 283–4, 457–64 sample control 280–2 sampling frames 281–2 sensitive information 287 situational factors 284 social desirability 287 speed 286 street 278 structured direct 269 techniques 270, 278–81, 923 telephone see telephone surveys workplace 276–7, 280–8 see also fieldwork surveytainment 273–4 symmetric lambda 579, 923 syndicated data 106–17 syndicated services 18, 923 syndicated sources 106–9, 923 synergy, focus groups 186 systematic errors 358, 923 systematic sampling see sampling T t distribution 582, 924 t statistic 582, 924 t tests 547, 581, 582–7, 587–8, 925 tables in reports 841 tablets 514 tactical use of dashboards 846 TAM (technology acceptance model) 814–17 Tango Facebook 376 target customers, action research 172 target groups, online surveys 274 target population 160, 414–15, 924 targets, research 34 task factors, surveys 279 TAT (thematic apperception tests) 225 tau b 580, 924 tau c 580, 924 taxonomy, numerical see cluster analysis teams 260 technical competence 865 technical problems, online surveys 274 technology acceptance model (TAM) 814–17 teenagers 213–14, 255 telecommunications industry 431–2 telemarketing 905 telephone-based research 521 telephone focus groups 194 telephone interviewing, computer-assisted see CATI television set-top box (STB) data 292 telephone surveys 275, 280–8, 386–7, 458 see also mobile surveys telescoping 383, 867–8, 924 television motivations 74 reception 617–18 users or non-users 687–8 ten Ss186–7 territorial maps 693, 924 test marketing 326–8, 924 test markets 326–8, 738, 924 test-retest reliability 359, 924 test statistic 567, 567–8, 924 test units 308–9, 924 testing effects 312, 924 text-based mobile research 517 text, extended 247 Thailand 738, 787–8 thematic apperception tests (TAT) 225 thematic maps 128, 924 theoretical base 23 theoretical framework 51–2 theoretical sampling 161, 253, 924 theoretical understanding 237, 250 theory 51–2, 155, 168, 924 building 260 thermometer scales 355 third-person techniques 225, 924 time 325 time order of occurrence of variables 306 949 www.downloadslide.net 950 Marketing Research time series designs 319, 320, 924 TLI (Tucker Lewis index) 806, 807 Tokyo Girls Collection (TGC) 237–8 top performers, mindless mimicry of 834 topic guides 188–9, 924 total correlation matrix 678 total errors 82–3, 924 total field of information tourism destinations 602–3, 738 trace analysis 294, 295, 924 training fieldworkers 476–9, 480 transcripts 243–4, 259, 539–41, 924 transformation of scale 545, 921 transition questions 189 transitivity of preference 343, 924 transparency, maintaining 893 travel companies 137, 305–6, 322, 602–3 treatment 604, 924 trend analysis 462–3, 924 triangulation 251, 924 true experimental designs 315–16, 317–18, 320, 924 true feelings 863–4 True Loyalty 76 true score model 358, 924 trust 23 participants 893 Tucker Lewis index (TLI) 806, 807 TV see television two-group discriminant analysis 675, 676, 679, 925 two independent samples non-parametric tests 589–91 parametric tests 581, 583–6 two-sample median test 590, 925 two-tailed tests 566–7, 925 two-way focus groups 194 two-way mirrors, focus groups 894 TwoStep clustering 744, 752–4 Type I errors 567–8, 925 Type II errors 567–8, 925 U U statistic 589–90, 678 unbalanced scales 353 undisguised observation 290–1 unethical employee behaviour 728–9 unfolding 766, 773 unidimensionality 799 uninformed consent, social media 899–900 unit definition, b2b sampling 871 United Nations Statistics Division 102 univariate techniques 546–7, 925 unsatisfactory responses 532–3 unstructured observation 290, 925 unstructured questions 385–7, 532, 925 unwillingness errors 85, 867–8 unwillingness to answer 381–5, 867–8 users, b2b 861 utility functions 777 V validation samples 678–9, 925 validity 160, 925 causality 310–11 cluster analysis 748–9 conjoint analysis 785–6 construct validity 362, 910–11 content validity 362, 911 convergent 362, 808, 911 criterion validity 362, 911 cross-validation 662–3 data 251 discriminant analysis 686–8, 694 discriminant validity 362, 809, 912 experimentation 310–11, 320 external 311, 913 fieldwork 482 focus groups 197 internal 310–11, 777, 915 logit analysis 698–9 measurement and scaling 361–3 multidimensional scaling (MDS) 772–3 participant validation 251 sampling design 418 structural equation modelling 805–10, 811–12, 816–17 triangulation 251 values 160, 216 variability measures 563, 916 variables clustering 754–6 confounding 313, 910 criterion 641 dependent 80, 308–9, 606, 641, 912 dummy 544, 663–4, 782 extraneous see extraneous variables independent 80, 308–9, 641, 662, 914 latent variable analysis 798, 823 operationalised 155, 918 predictor 641, 662 psychographic 709–10 respectification 544, 925 selection, cluster analysis 740 surrogate 721, 923 time order of occurrence 306 variance 563, 925 method 743–4, 925 percentage of 712, 718 standard deviation see standard deviation see also ANOVA (analysis of variance) variation coefficient 564, 910 variety 24–5, 135 varimax procedure 719–20, 925 velocity, data 135 veracity, data 135 verbal descriptions 354 verbal models 52–3, 925 verbal protocols 347, 925 verification of data 250–1, 259, 911 www.downloadslide.net Subject Index video data collection 521–2 social media research 508–9 video recordings focus groups 184, 894 interviews 894–5 Vietnam, sampling 436 vision 5 visual ethnography 154, 242–3 visuals use, social media 507 voice-pitch analysis 293, 925 voice recognition systems 540 volume data 135 obsession with 261 tracking data 114, 115, 925 voters, European 71 W Ward’s procedure 743, 744–6, 925 wearable devices 525 Web 2.0: 23 web access, online surveys 274 web analytics 136–9, 925 see also online research 951 web portals 460–1 web scraping 498 web surveys 22 websites or mobile apps 519–20 weighting 463, 543, 777, 925 Wilcoxon matched-pairs signed-ranks test 547, 581, 590, 591, 925 Wilks’ λ 678, 684 Women in Muslim Countries (WIMC) study 411 word association 223–4, 925 wording of questions 389–94 working environments 25 workplace surveys 276–7, 280–8 writing reports 840–2 wrongdoing to participants, prevention 892 Y yea-saying 392, 908 Z z tests 547, 583–5, 925 z values 447, 448–52, 586, 925 Zaltman metaphor elicitation technique (ZMET) 219–21 www.downloadslide.net Name Index Aalto, Juha 40 Angell, Ian 272 Attfield, John 283 Bevolo, Marco 150 Bradford Smith, Kim 486 Bruce, Clare 124 Clark, Moira 892 Cooper, Peter 158 Dahl, Stephan 493 Derrida, J 259 Dibb, S 857 Donato, Paul 114 Dorsey, Jack 494 Drummond-Dunn, Denyse 139 Dyson, James 21 Lavrakas, Paul 458 Lenhart, A 497 Lewin, Kurt 171–2 Likert, Rensis 349 Lynn, Peter 458 Maguire, Louise 517 Mariampolski, Hy 80 Meax, Dimitri 142 Mecklin, Taina 40 Mellor, Stephen 525 Merkel, Angela 77 Metcalf, Peter 163 Milgram, Stanley 883 Mouncey, Peter 464 Murray, Gavin 40 Nunan, D 899 Nuzzo, Regina 849 Ekman, Paul 51 Obama, Barrack 499 Ford, David 857, 858, 860 Frost, Jane 905 Glaser, B.G 168, 251 Gnädig, Ayobamidele 163 Gobé, Marc 68 Goldfarb, Jamie 541 Gordon, Wendy 20 Griffiths, John 901–2 Gutman, J 216 Hair, Neil 892 Harristhal, Johan 531 Hill, Alistair 516 Jobs, Steve 3–4, 20 Kakar, Sudhir 45 Kaminska, Olena 458 Keller, Peter 411–12 Kelly, George 217 Kroll, Bob 593 Pearson, Karl 634 Pointer, Ray 882 Prasad, Shobha 241 Rapaille, Clotaire, 45 Reichheld, Frederick 76 Reidenbach, R.E 756 Robin, D.P 756 Roddick, Anita 20 Ruohomaa, Erja 40 Scheffler, Hartmut 412 Schieleit, Oliver 163 Shearer, Colin 124 Smith, Patten 412 Spaeth, Jim 124 Stapel, Jan 352 Storry, Grant 398 Strauss, A.L 168, 251 Sutton, Bob 833 Sweeney, Latanya 902–3 www.downloadslide.net Name Index Tilvanen, Jukka 40 Tufte, Edward 834 Wind, Y 861 Wolf, Sharon 80 Webster, Frederick 858, 861 Weld, William, Governor 902–3 Whiting, Mark 840 Yenicoglu, B 899 Zelin, Andrew 412 953 www.downloadslide.net Company Index @BRINT 101 2CV 522 7-Up 763–4 ABCe 97–8 A.C Nielsen 292 A.C Nielsen Homescan 113 Access Testing 293 Adidas Japan 268–9 Adobe Analytics 137 Air New Zealand 398 Allied Domecq 60–1 AMA (American Marketing Association) 7, 26 Amazon 493, 507 American Association for Public Opinion Research 436 American Marketing Association (AMA) 7, 26 Amos 826 Apple 3–4, 68, 514, 525 Apple Pay 516 Apple Watch 525 Arỗelik192 Ariel44 Asda301 AT&T 726–7 Atlas.ti 260 ATUO BILD 217 Auchan 126 Audi 775 Avon Products 633–4 Bailey’s Irish Cream 66 Bally 779 Barclays Bank 476 Barilla Alimentare 80 BBDO Worldwide 756 BBH 376 Becks 767–74 Beetle 728 BEHAVIORSCAN 115 BH&HPA (British Holiday & Home Parks Association) 271–2 bizeurope.com 102 BlackPlanet 167 BluMis 36 BMW 832–3, 869 BMWG (Broadband Measurement Working Group) 98 Boden 123 The Body Shop 20 Boots 326, 764 Bose 22, 23 BrainJuicer 51, 195–6 Brand Tracker 415 Brass 484–5 Braun 497 Bristol City Council 505–6 British Airways 496 British Gas 496 British Holiday & Home Parks Association (BH&HPA) 271–2 British Office for National Statistics 102, 103 Broadband Measurement Working Group (BMWG) 98 BSkyB 111, 114 Budvar 343–4, 767–74 Budwieser 767–74 Bureau Van Dijk 116–17 Business Intelligence Group (BIG) 856 CACI 129, 130, 132 Cadillac 775 Canadian Marketing Research and Intelligence Association 896 Carlsberg 343–4, 767–74 Caterpillar 779 CBOS (Centrum Badania Opinii Spotecznej) 75–5 CCR Data 129 Central Bureau voor de Statistiek Nederlands 102 Centrum Badania Opinii Spotecznej (CBOS) 75–5 Chartered Institute of Purchasing and Supply (CIPS) 863 Cheeseborough Ponds 327 Christian Dior 208–9, 215 Chrysler 774–5 Cint 437 Circle Research 856, 868 Clausthaler 652 Clear Channel 269 Cobalt Sky Ltd 19 Coca-Cola 38–9, 138, 343, 381, 763–4 www.downloadslide.net Company Index Colgate 392, 593, 787–8 Colgate Palmolive 293 Conquest Metaphorix 220 Consumer Zoom 126 Cooperative Bank 867 Coors Light 225–6 Corning Display Techniques 74 Corona 767–74 Crazy Egg 137 CRM Metrix 167 Crowd DNA 522 Cspace 19, 22 D3 Systems 411 Dagmar 415 Dalgety Animal Feeds 424 Danmarks Statistik 102 Datamonitor 101, 116 DataSift 498 Datastar 105 Debenhams 133 Dell 675 Department for Education 426–7 Desgrippes Gobé Group 68 Deutsche Telekom 174 Diageo 8, 503–4 Diet Coke 763–4 Digitab 19 Directory of Open Access Journals 105 Dr Martens 779 Dr Pepper 763–4 Dubit 894 Dun and Bradstreet 116–17 Dunnhumby 138 Durex 65–6 Dyson 21 e-Rewards 19 easyJet 4 eBay 209–10, 522, 857 ECB (European Central Bank) 104 EgeBank 602 Emerald Insight 105 ESOMAR 6, 7, 12, 16, 519 Directory of Research Organisations 102 Global Market Research Industry Study 15, 19–20 see also entry in Subject Index ESP Properties 53 Euromonitor 18, 102 Europages 102 European Central Bank (ECB) 104 European Directories 105 European Society for Opinion and Market Research see ESOMAR Eurostat 103 Experian 129–31 Experian MOSAIC 105, 123, 129–31 Eyebox2 269 Facebook 22, 135, 272, 376, 493, 494, 497, 498, 508, 521–2, 900 FashionLife 116 Federal Statistical Office of Germany 102 Firefish 213 Flickr 494 FocusVision 19 FreshMinds 19 Gale Newspaper Database 105 Gartner 17 GfK 17, 18 BEHAVIORSCAN 115 Custom Research 531 FashionLife 116 Girlswalker.com 237–8 Givaudan 67, 180 Global Change Network 382 GlobalPark 123, 143 Google 498, 506, 525 Google Analytics 137 Google Glass 525 Google Vision 508 Grenson 125 Grolsch 343–4, 767–74 Häagen-Dazs 66, 127, 549, 736–7 Hall and Partners 376 Harp 767–74 Harvey Research 110 Heineken 846 Hennessy Cognac 167, 840 Hermès 48 Hewlett-Packard 701 Hitachi Europe 866, 867 Hollis 101 Holsten 343–4, 767–74 Honda 774–5 H.T.P Concept 163 Huober Brezeln 453 Hyundai 775 HYVE Innovation Research 497 IAG Cargo 868 IBM 134–5, 859–60 ICI Agricultural Products 787 The iD Factor 199 IDEO 250, 398 IFABC (International Federation of Audit Bureaux of Circulation) 98 IFM Sports Marketing 64–5 IKEA 388 IMF (International Monetary Fund) 104 IMRI 105 IMS Health Inc 17 In/situm 68 Indiefield 19 Information Resources Symphony 110 955 www.downloadslide.net 956 Marketing Research ING Bank NV 130 Inside Flyer 98 Insight Research Group 864 Instagram 272, 494 Institut National de la Statistique et des Études Economiques, France 102 International Basketball Federation FIBA 415 International Federation of Audit Bureaux of Circulation (IFABC) 98 International Monetary Fund (IMF) 104 International Olympic Committee (IOC) 421 Interview NSS 76 Ipsos 17, 24 Ipsos MORI 76, 109, 412, 473 Jaguar 775 JCDecaux 269 Jigsaw Research 293 Kantar 17, 39, 113 Kellogg’s 13–14, 15 Keynote 102 Kiasma Museum of Contemporary Art 415 Kissmetrics 137 Knorr 237–8 KPMG Nutwood 82 Kraft 875 Labbrand 180 Lego 165 Lexus 775 LG Electronics 149–50 LightspeedGMI 19 LinkedIn 493, 900 LISREL 826 London School of Economics 71 Maersk 855 Marktest 386 MAXQDA 260 Mercedes 322–3, 609–10, 774–5 Microsoft 153 Millward Brown 39, 110, 277 Millward Brown’s Vermeer 19 Mintel 18, 847 Mosaic Global 130 Moving Motor Show 64–5 MTV 40 MTV Networks 154 Mueller Dairy Group 149 Mullen’s 92 Mumsnet 502 NASA 834 Natural Marketing Institute 110 NatWest 81–2 Nestlé 35–6, 138, 139 Neustar 903 Nielsen Claritas 737 Nielsen Company 17, 114 Nielsen Media Research 458 NielsenConnect 114 Nike 68, 165 Nikon 755 Nintendo 123 Nivea 164–5 Nudge 376 NVivo 260, 262 NYC Taxi and Limousine Commission 903 OgilvyOne New York 142 OnePoll 18 OpenMx 826 Opinium Research 71 Optimisa 213 Orbitz 309 PARC (Pan Arab Research Centre) 276–7 Pegram Walters International 60 Pepsi Cola 763–4 Performance Research 421 Perrier 652 Persil 44 Philips 76, 150, 181 Pinterest 900 Porsche 774–5 Prada 182 Premier Farnell 856 Princess Cruises 540–1 Procter and Gamble 44, 327–8, 497 Progressive Sports Technologies 260, 262 QSR 169, 260 Qualtrics 16, 19 R 826 Radio Ad Effectiveness Lab (RAEL) 110–11 Readers Digest 40 Red Cross 303 Rémy Martin 166 Renault 538, 869 Repères 208, 215 Research 2.0: 23 The Research Box 271–2 Research International 40 ResearchNow 131, 485–6 Rexona 257 Saatchi & Saatchi 640 San Miguel 767–74 Scholler 36 Schwan 875 SCI INTAGE 112–13 Scottish Epilepsy Association 423 Sense Scotland 128–9 Simmons 92 www.downloadslide.net Company Index Sky Media 111–12 Sky UK 473 SMA 131–2 smartFOCUS 123 Snap surveys 271 Society of Competitive Professionals (SCIP) 873–4 Sony 222, 273–4, 303 SPSS 133 SSI (Survey Sampling International) 371, 421 Statistical Office of the European Community 103 Statistical Yearbook 102 Stella Artois 343–4, 767–74 Suncorp 192 Surf 43–4 Survey Sampling International (SSI) 371, 421 Surveymonkey 16, 19 Swedish Information ‘Smorgasbord’ 102 Swedish Tourism Trade Association 102 Synovate 398 Taloustutkimus 40 Tango 763 Taylor Nelson Sofres see TNS Tesco 138–9 TfL (Transport for London) 3, Thomson Holidays 137 Timberland 779 TNS 18, 39, 77, 79, 107, 111, 114, 132, 222, 278, 478–9, 496 957 TNS Infratest 412 TOMORROW FOCUS Media 143–4 Toyota 774–5 Transport for London (TfL) 3, Trinity McQueen 30 TripAdvisor 493 Twitter 494, 497, 900 UBS 848 Unilever 43–4, 138, 213, 257 United Nations Economic Commission for Europe (UNICE) 104 uSamp 524 Virgin 91–2, 98 VW 774–5 VW Beetle 728 WARC 101 Weihenstephan 149 WGSN 105 WhatsApp 498 Wikipedia 504–5 World Statistics Pocketbook 102 Yankelovich and Partners 651–2 YLE 40 YouGov 18, 76, 411 YouTube 494, 508 ... Mode Afghanistan Bangladesh Egypt Iran Iraq Jordan Kosovo Pakistan Saudi Arabia Turkey Face-to-face nationwide Face-to-face nationwide Face-to-face nationwide, seven main cities and suburbs CATI... Totals Totals 25 % 40% 15% 20 % Male 48% Female 52% Have a flag No flag Have a flag No flag 50% 50% 50% 50% 30 48 18 24 120 30 48 18 24 120 33 52 19 26 130 32 52 20 26 130 24 0 26 0 Totals 125 20 0... of research panels in more than 40 countries The company has an extensive list of clients and partners spanning most of the large market research groups, media and web-based companies, branding