QUANTITATIVE RESEARCH METHODS IN CONSUMER PSYCHOLOGY Quantitative consumer research has long been the backbone of consumer psychology producing insights with peerless validity and reliability.This new book addresses a broad range of approaches to consumer psychology research along with developments in quantitative consumer research Experts in their respective fields offer a perspective into this rapidly changing discipline of quantitative consumer research.The book focuses on new techniques as well as adaptations of traditional approaches and addresses ethics that relate to contemporary research approaches The text is appropriate for use with university students at all academic levels Each chapter provides both a theoretical grounding in its topic area and offers applied examples of the use of the approach in consumer settings Exercises are provided at the end of each chapter to test student learning Topics covered are quantitative research techniques, measurement theory and psychological scaling, mapping sentences for planning and managing research, using qualitative research to elucidate quantitative research findings, big data and its visualization, extracting insights from online data, modelling the consumer, social media and digital market analysis, connectionist modelling of consumer choice, market sensing and marketing research, preparing data for analysis, and ethics The book may be used on its own as a textbook and may also be used as a supplementary text in quantitative research courses Paul M W Hackett’s main area of research is in the theory and application of categorical ontologies Paul has developed the qualitative or philosophical facet theory approach He has almost 200 publications, including 10 books Paul is a visiting professor at the Universities of Suffolk and Gloucestershire, a visiting researcher in psychology at Cambridge University and teaches at Emerson College QUANTITATIVE RESEARCH METHODS IN CONSUMER PSYCHOLOGY Contemporary and Data-Driven Approaches Edited by Paul M W Hackett First published 2019 by Routledge 711 Third Avenue, New York, NY 10017 and by Routledge Park Square, Milton Park, Abingdon, Oxon, OX14 4RN Routledge is an imprint of the Taylor & Francis Group, an informa business © 2019 Taylor & Francis The right of Paul M W Hackett to be identified as the author of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988 All rights reserved No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe Library of Congress Cataloging-in-Publication Data Names: Hackett, Paul, 1960– editor Title: Quantitative research methods in consumer psychology : contemporary and data driven approaches / edited by Paul M W Hackett Description: Edition | New York : Routledge, 2019 | Includes bibliographical references and index | Identifiers: LCCN 2018030734 (print) | LCCN 2018031723 (ebook) | ISBN 9781315641577 (master eb) | ISBN 9781317280415 (epub) | ISBN 9781317280422 (pdf ) | ISBN 9781317280408 (mobi) | ISBN 9781138182691 (hb : alk paper) | ISBN 9781138182721 (pb : alk paper) | ISBN 9781315641577 (eb) Subjects: LCSH: Consumers—Psychology | Quantitative research Classification: LCC HF5415.32 (ebook) | LCC HF5415.32 Q36 2019 (print) | DDC 658.8/3420721—dc23 LC record available at https://lccn.loc.gov/2018030734 ISBN: 978-1-138-18269-1 (hbk) ISBN: 978-1-138-18272-1 (pbk) ISBN: 978-1-315-64157-7 (ebk) Typeset in Bembo by Apex CoVantage, LLC To my wife, Jessica Schwarzenbach, who in so many ways, is significantly responsible for the completion of this book CONTENTS List of Figures,Tables and Boxes ix List of Contributors xiii Acknowledgmentsxvi Prefacexvii Quantitative Research: Its Place in Consumer Psychology Cathrine V Jansson-Boyd Using Contemporary Quantitative Techniques Or Shkoler 22 Measurement Theory and Psychological Scaling Daniel P Hinton and Tracey Platt 59 Identify, Interpret, Monitor, and Respond to Quantitative Consumer Data on Social Media Dr Amy Jauman, SMS Alternative Research Methods: Introducing Market Sensing—A Qualitative and Interpretive Perspective on Research David Longbottom and Alison Lawson 88 124 viii Contents Big Data: Data Visualization and Quantitative Research Apps Vaidas Lukošius and Michael R Hyman 166 Exploring Ways of Extracting Insights From Big Data Peter Steidl 194 Contemporary Approaches to Modelling the Consumer Debbie Isobel Keeling 222 Connectionist Modelling of Consumer Choice Max N Greene, Peter H Morgan, and Gordon R Foxall 247 10 Uniting Theory and Empirical Research: Marketing Research and Market Sensing Melvin Prince, Gillie Gabay, Constantinos-Vasilios Priporas, and Howard Moskowitz 11 Ethical Issues in Conducting Psychological Research David B Resnik 272 298 12 A User-Friendly Practical Guide to Preparing Data for Analysis Kerry Rees 326 13 Integrating and Writing Up Data-Driven Quantitative Research: From Design to Result Presentation Paul M.W Hackett, Lydia Lu and Paul M Capobianco 376 Index407 FIGURES, TABLES AND BOXES Figures 2.1 2.2 2.3 2.4 2.5 2.6 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 5.1 5.2 5.3 6.1 7.1 8.1 Model for a Direct Relationship Model for a Covariance/Correlation Type Relationship Model for a Spurious Relationship Model for a Mediational Relationship Model for a Conditioned (‘Moderated’) Relationship An After-Only Experimental Design Illustration The Research Process Social Media Poll Sample Online Poll Survey Design Choices Dropdown Boxes Semantic Differential Scale Visual Analog Sliding Scale Multiple Textboxes Reactive Content Objective Analytical Scales Objective Analytical Scales Objective Analytical Scales Responding to Responses Exploratory and Explanatory Research Designs Deep Value Mining Depth Gauge (Hancock & Longbottom, 2017) The Data Reduction Process Process for Extracting Insights From Big Data Identified Dopamine Segments The (Simplified) Theory of Planned Behaviour (Ajzen, 1991) 33 34 34 34 34 47 93 95 97 98 101 102 103 104 111 114 116 116 119 127 148 151 170 199 224 400 Paul M W Hackett et al which are in need of improvement? Do consumers prefer the colour of the new package rather than the colour of the old wrapper? What consumers report will be the effect of discounting the price of tickets on their likelihood to purchase tickets? Think about the research area that you are looking at and the questions you plan to ask Now identify a sample of respondents that would be appropriate for the research question(s) you have identified Write down the sample and provide an explanation for your choice Examples for stage 3: For example, a sample for the shopping mall study could be a group of individuals who regularly use the mall where regular use is determined to be visiting the mall at least twice a week For the chocolate bar study, a sample could be individuals between the ages of and 15, of both genders No experiential requirement is made as both regular purchasers and people who have never purchased the bar are of interest to the brand owners For the ticket sale study, a sample could be drawn on the basis of how frequently respondents attended live performances (not necessarily opera) This sample would be appropriate as the concert hall may wish to see if price reductions could attract different types of users of the opera hall (regular attendees, irregular attendees, people who have never been to an opera) Write an informative title for this piece of research The title should include the subject, event, state-of-affairs that you are interested in, the broad question(s) you are asking and the sample you will be investigating Examples for stage 4: For example, for the mall study—“An investigation of regular users of a shopping mall: identifying successful aspects of the mall and those needing improvement”; for the chocolate bar—“Reactions to new packaging of a chocolate bar”; for the opera tickets—“A study into the reported willingness to purchase discounted opera tickets amongst a sample of regular live event attendees” Integrating and Writing Up 401 5a (For research PLANNING and MANAGING mapping sentence) Identify the one or more research techniques/methods/approaches that are appropriate ways of gathering data in order to answer your research questions and write down the methods you have chosen Examples for stage 5a: For example, with the shopping mall project you may have chosen the following research methods: structured questionnaire; structured observation study of different user groups With the chocolate bar packaging: experiment investigating preference between different packaging With the opera tickets: eye-tracking to investigate where on promotional material readers attend This would be followed by a brief questionnaire 6a (For research PLANNING and MANAGING mapping sentence) From your knowledge of research procedures and the types of data and understanding that each of these procedures usually produces, write down the expected types of data and understanding that each of your techniques is likely to produce Examples for stage 6a: For example, with the shopping mall project: structured questionnaire may be given quickly and inexpensively to a large number of respondents and will potentially yield opinions about the mall that may be generalised to other mall users; structured observations of different user groups may allow the identification of how the mall is used by different user groups With the chocolate bar packaging: an experiment with a group of respondents presented with the old chocolate packaging, and several possible alternatives to this, are asked to rate the packaging on several criteria each along a 7-point scale With the opera tickets: eyetracking: a sample of respondents may be asked to look at promotional material for live events that include price details, with some of the material including the words “savings” and “discount”, and other material not including these words Eye-tracking may be used to identify the effects of the inclusion of these words on where participants gaze A brief questionnaire may then be given asking about the likelihood of each respondent buying tickets from the different types of promotional material 402 Paul M W Hackett et al 5b (For research DESIGN mapping sentence) Identify the variables that are important to the question(s) you have identified Write these down Examples for stage 5b: For example, previous research may lead us to identify the following variables as being important in users’ experiences—in the study into mall experience: mall location; facilities; design features For the chocolate bar packaging: bar characteristics, whether individuals purchased other brands of chocolate bars For the opera tickets: price, performance characteristics 6b (For research DESIGN mapping sentence) Consider the variables you have identified in stage 6a and identify sub-divisions of each of these Examples for stage 6b: For example, in the study into mall experience: the following sub-variables may be identified: mall location with sub-divisions of: access, transportation linkages; facilities with sub-divisions of: shops, services, parking; design features with sub-divisions of: temperature, lighting levels, noise levels For the chocolate bar packaging: bar characteristics with sub-divisions of: text, images, colour; bar relationships with sub-divisions of: perceived comparative quality, perceived comparative value For the opera tickets: price with sub-divisions of: cheap, reasonable, expensive; perceived saving with sub-divisions of: appealing savings, makes no difference; performance characteristics with subdivisions of: time of day; day of week (For research DESIGN mapping sentence) Identify a range of responses that you will use to quantitatively assess respondents in your study Examples for stage 7: For example, in the study into mall experience: satisfaction with the specified mall feature For the chocolate bar packaging study: liking of Integrating and Writing Up 403 the specified feature of the packaging For the opera ticket: preference for the specified features of the promotion It should be noted that multiple methods may be used within a single study and that the earlier examples are given simply as illustrative possibilities Write a research project mapping sentence for a consumer psychology research project Fill in the following (add the contents you developed at each of thve stages indicated by the (number) in parentheses): Provide a title for the research project (4) The research project into (1) that has been designed to answer the following questions (2) will be undertaken utilizing (3) as respondents, by employing the research techniques of (5a), which will, respectively, provide the following types of data which can answer the following types of questions (6a) Write a research design mapping sentence for a consumer psychology study Fill in the following (add the contents you developed at each of the stages indicated by the (number) in parentheses): Provide a title for the research study (4) The research study into (1) that has been designed to answer the following questions (2) will be undertaken utilizing (3) as respondents and will investigate the characteristics of (write each variable you identified) (5b) and under each of these write their respective sub-divisions (6b) in which respondents will express their reactions on the range of (7) 10 Write a paragraph that explains how you can use the research project mapping sentence to plan and guide your research project 11 Write a paragraph that explains how you can use the research design mapping sentence to plan and guide the design of one of your research studies 404 Paul M W Hackett et al Output from exercises You should have the following written materials when you complete these exercises: 5a 6a 5b 6b 10 11 The subject/topic of your research Your research question(s) A sample of respondents suitable for your research study An informative title for your research A list of the important variables that are of interest to the question(s) you have identified A list of the most appropriate methods/approaches that you can use in your research project A list of the types of data and understanding that each of the techniques you identified in 5a will produce A list of the variables pertinent to your research study A list of the sub-divisions of the variables you listed in 5b The range of responses you will gather data on to assess your research study question(s) A research project planning and management mapping sentence A research study design mapping sentence A paragraph explaining the use of your research project mapping sentence in the planning and managing of your research project A paragraph explaining the use of your research design mapping sentence in the planning and guiding of how you design one of your research studies References Aghaei, S., & Naeini, B (2018) Consumer attitudes toward new pasta products in Iran market: A qualitative and quantitative study Management Science Letters, 8, 109–120 Ausberg, K., & Hinz, T (2014) Factorial survey experiments, quantitative applications in social sciences Thousand Oaks, CA: Sage Publications Ltd Balnaves, M., & Caputi, P (2001) Introduction to quantitative research methods: An investigative approach Thousand Oaks, CA: Sage Publications Ltd Berger, J., & Sellke, T (1987) Testing a point null hypothesis: The irreconcilability of P values and evidence Journal of the American Statistical Association, 82(397), 112–122 doi:10.2307/2289131 Boettke, P (2011) How can we avoid closed-ended and single exist theories in economics and political economy: Reflections on Richard Wagner’s mind, society and human action Studies in Emergent Order, 4, 149–155 Borg, I., & Shye, S (1995) Facet theory: Form and content (Advanced Quantitative Techniques in the Social Sciences) Thousand Oaks, CA: Sage Publications Ltd Canter, D (ed.) 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Cambridge: Cambridge University Press Szucs, D., & Ioannidis, J (2017) When null hypothesis significance testing is unsuitable for research: A reassessment Frontiers in Human Neuroscience, 11, 390 Thyer, B A (2012) Quasi-experimental research designs Oxford: Oxford University Press Webster, M., & Sell, J (2014) Laboratory experiments in the social sciences Cambridge, MA: Academic Press INDEX Note: Page numbers in italics indicate a figure and page numbers in bold indicate a table on the corresponding page Aaker, J L 64 accessibility 40, 48, 239, 241, 388 – 389 accuracy bias 184 – 185 Aghaei, S 384 ambiguities 8, 27, 138; see also ambiguity bias ambiguity bias 184 – 185 American Psychological Association 298, 308, 312 analysis 67; analysis of covariance (ANCOVA) 362 – 370, 364 – 370; analysis of variance (ANOVA) 6, 71, 330 – 333, 339, 346 – 347, 349, 368; assessing distribution 344; and big data 168, 170 – 174, 194 – 198, 207, 210, 212; competitor analysis 107 – 111; and consumer choice 249, 251 – 259, 261 – 262, 264; in consumer psychology 5, 6, 7, 10, 12; and data presentation 378 – 381, 383, 388 – 389, 391 – 392, 397 – 398; and ethics 301, 308, 318; homogeneity of variance 345 – 349, 347, 348; homoscedasticity 360; independent errors 360 – 362, 360, 361, 362; inputting data 327 – 332, 327, 328, 329; MANOVA 370 – 373, 372, 373; and marketing research 277, 279 – 280, 285; and market sensing 131 – 132, 132 – 133, 136 – 139, 141, 143, 150; and modelling 222 – 223, 225 – 228, 230, 233 – 235, 237 – 238, 240 – 242; multiple regression 353 – 360, 354 – 359; normal distribution 337, 343, 336 – 341, 343 – 344; outliers 349 – 350, 351 – 352; preparing data for 326 – 327; in quantitative techniques 27, 47 – 49; screening data 332; and social media 88, 102, 106; software for conducting 15; see also data analysis; discourse analysis; factor analysis; multiple regression; sentiment analysis analytics 96, 171, 173, 176, 231, 239; big data analytics 166, 170, 174; cloud computing 180 – 181, 180; cognitive computing 181 – 182; gamification 184; machine learning 181; multimedia analytics 182; platform analytics 113 – 114; social media analytics 116, 182 – 183; social network analytics 183 – 184 animal research 299, 313 – 315 artificial intelligence (AI) 211 – 213, 241 – 242, 247, 249 artificial neural networks (NNs) 211 – 212, 248 – 251, 249, 250, 264 – 266, 269n2; as black box 256 – 258, 258; and consumer behavior 251 – 254; and Logit models 254; and overfitting 258 – 263 association 13, 75, 226, 390 – 391; and big data 174, 180; hypotheses’ direction of 30 – 31; and market sensing 134, 142, 408 Index 279, 285; and preparing data 328, 330, 345, 371; and quantitative techniques 24, 32 – 36, 33 – 34 audience research 88, 106; see also social media audio analytics 171 Ausberg, K 379 authorship 300, 307 – 310 Ayeh, J K 240 Azjen, I 223 Bell, E see Bryman, A Bentler, P M see Hu, L bias 70, 97, 237; avoidance of 112; and big data 167, 184 – 185, 212; bigness bias 184; and consumer psychology 12 – 14; and ethics 298 – 299, 304, 306, 310 – 313; and market sensing 124 – 125, 128, 130, 131 – 132, 142, 144; measurement bias 78, 82; and preparing data 349, 356, 358 – 360, 363; and quantitative techniques 28, 46, 48 – 49 big data 27, 65, 166 – 168, 187n2, 195 – 197, 241; and analytics 180; and artificial intelligence 211 – 213; assumptions about 168 – 169; and cloud computing 180 – 181, 180; and cognitive computing 181 – 182; and consumer behavior 175 – 176; and consumer sentiment 176; extracting insights from 194 – 198; gaining insights from 169 – 172, 170; and gamification 184; and Implicit Goal Segmentation 198 – 206, 199; and machine learning 181; and marketing research 172 – 173; and marketing strategy 177 – 180; and multimedia analytics 182; and sharing 206 – 210; and social media analytics 182 – 183; and social network analytics 183 – 184; troubleshooting 184 – 185, 213 – 217; viewing marketing through 173 – 175, 174, 175 Black, T R black boxes 252, 256 – 258, 258, 261 Branthwaite, A see Cooper, P Brodie, R J 241 Bryman, A 125, 129 – 130, 135, 142, 152, 155 Canter, D 386 – 387 Cassell, C 126, 129, 135, 145 Cheung, M W L 241 Churchill, Jr, G A 83 Classical Test Theory 60, 62 – 63 cloud computing 180 – 181, 180 C-OAR-SE 60, 82 – 83 cognitive computing 180, 181 – 182 compatibility 29 – 30 complexity 75, 130, 377 – 385; and big data 181, 212, 216; complexity bias 184 – 185; and conflicts of interest (COI) 137, 298, 200 – 301, 303, 311 – 313 connectionist modelling 247 – 248, 263 – 266; and black boxes 256 – 258, 258; model specification 255 – 256; and overfitting 258 – 263; predetermined vs flexible models 254 – 255; see also artificial neural networks consistency motif 13 constants 57n2 consumer behavior 22 – 24, 174 – 175, 175, 177, 181, 183; and artificial neural networks 251 – 254; and big data 175 – 176 consumer neuroscience 4, 9 – 12, 378 consumer psychology xvii – xviii, 1, 15, 22, 290, 376, 398; and complexity 377 – 384; and ethics 307, 314; future directions of modelling in 239 – 242; and measurement theory 61, 75, 78, 83; and modelling 222 – 225, 227 – 228, 237 – 238, 255 – 258, 264; psychometrics in 59, 63 – 65; quantitative research in 1 – 2, 8 – 9, 14 – 16; and relationship complexity 226, 231; research in xviii – xix consumer sciences 3 – 4, 8, 12 – 13; see also consumer neuroscience; consumer psychology consumer sentiment 174 – 176, 175 context 257, 380 – 385, 387, 392, 394 – 396; and big data 170, 176, 182, 199, 203, 213 – 214; and consumer psychology 6 – 8; and marketing research 272, 291; and market sensing 129 – 130, 133 – 134, 136 – 138, 142 – 145, 156 – 157, 161; and measurement theory 67, 78; and modelling 222 – 224, 226, 228, 232 – 233, 235, 238 – 239; and preparing data 326, 350; for qualitative research 126 – 128; and quantitative techniques 23, 26, 27; and social media 90, 92 control groups 12, 45 – 47, 318, 379 Cooper, P 146 Index 409 Corley, K G see Gioia, D A Creswell, J W 125, 377 – 379 Curry, B 251 Dark Social 206 data 187n1, 205, 376 – 379, 383, 388 – 392, 394 – 398; analysis of covariance (ANCOVA) 362 – 370, 364 – 370; assessing distribution 344; and consumer psychology 6 – 7, 6, 11; and ethics 298 – 303, 308 – 312, 318 – 319, 321; historical 214; homogeneity of variance 345 – 349, 347, 348; homoscedasticity 360; independent errors 360 – 362, 360, 361, 362; inputting data 327 – 332, 327, 328, 329; MANOVA 370 – 373, 372, 373; and marketing research 272 – 273, 277 – 278, 280, 283, 285, 290; and market sensing 127, 130, 136 – 139, 142, 143, 144; and measurement theory 67 – 68, 69, 71 – 73, 75 – 77, 80 – 82; multiple regression 353 – 360, 354 – 359; normal distribution 333 – 340, 336 – 341, 342 – 344, 343 – 344; outliers 349 – 350, 351 – 352; and preparing data 326 – 335, 329, 337 – 340, 342 – 345, 349 – 350, 362 – 363; and quantitative techniques 23 – 29, 27 – 28, 33, 35, 48 – 49; screening data 332; and social media 91 – 92, 93, 96, 98 – 103, 112 – 114, 117 – 121; see also big data; data analysis; data collection; data management; data presentation; modelling; multiple regression data analysis 304, 392; and big data 171, 179, 181 – 183, 185, 195, 214; computer aided qualitative data analysis (CAQDAS) 154; and consumer psychology 14 – 15; and market sensing 136, 150 – 151, 151, 154 – 155, 157 – 158, 158 – 160 data collection 67, 272, 308, 326; and market sensing 124, 132, 132 – 133, 134 – 135, 136, 161; methods for 37 – 38; and modelling 222, 230, 237 – 238; overview of methods for 37 – 39; primary methods of 39 – 47; in qualitative research process 145 – 150; and quantitative techniques 24, 26, 44; secondary methods for 47 – 48; and social media 88 – 89, 94, 105, 107 – 109, 112, 117 – 118; see also survey data management 170, 239, 304 – 307 data presentation 124, 132, 150 – 151, 158 – 161, 159 – 160; analysis and evaluation 157 – 158, 158; recording and transcribing 151 – 154, 151, 153; summarising data 155 – 157, 156 data subset bias 184 – 185 Davies, F M 253 Descartes, R 36 – 37 determinism 29 – 30 Dickson, P R discourse analysis 135, 138 – 139, 157 distribution 277 – 278, 345 – 346, 371; assessing 344 – 345; normal 333 – 335, 337 – 340, 341, 342 – 344 dopamine 196, 198 – 204, 199, 206 – 209 Douglas, S P 244 electroencephalography (EEG) 9, 11 Elmore, S A see Resnik, D B emojis 92 – 93, 176 emoticons see emojis empiricism 29 engagement 236, 240 – 241, 382, 387; and big data 168, 175, 184, 202 – 203, 205, 209; and social media 92, 108, 110, 111, 114, 118 epistemology 4, 130 – 131, 130, 133, 306 equations 14, 24, 62, 177, 279 – 280; and modelling 222, 225, 228 – 229; see also Structural Equation Modelling (SEM) error 76, 386, 390; and big data 171, 184, 205, 218; and connectionist modelling 248, 250 – 251, 254, 259, 261; and ethics 299 – 301, 305, 310; independent errors 360 – 362, 360, 361, 362; measurement error 62, 231; and modelling 229, 236 – 237, 239; normally distributed errors 360, 361; and preparing data 333 – 334, 337, 342, 344 – 345, 349, 353; and quantitative techniques 24, 48; and social media 92 – 93, 117 ethics: and animal research 313 – 315; and authorship 307 – 310; and conflicts of interest (COI) 312 – 313; and data management 304 – 307; and human subjects 315 – 321; and peer review 310 – 312; research misconduct 300 – 304; and scientific research 298 – 300; and social responsibility 321 Ettenson, R see Klein, J G experiment 329, 377 – 380, 401; and big data 166, 196 – 197; and consumer 410 Index psychology 2, 5 – 7, 6, 10 – 12; and ethics 301 – 302, 306 – 307, 309, 311, 313 – 318; and marketing research 272, 276 – 278, 280, 286, 290; and market sensing 125 – 126, 134 – 135; and modelling 228, 230, 241, 253, 256, 263 – 266; and quantitative techniques 27, 31 – 32, 37 – 38, 44 – 48, 45, 47 experimental studies 5, 24 experiment groups 46 Facebook 169, 206; and quantitative consumer data 99, 108, 111, 112, 115, 118 facet theory 376, 379, 384, 395 – 396; advantages of 388 – 392; and mapping sentences 386 – 388, 397 – 398 factor analysis 14, 64, 72 – 75, 173, 198, 234; confirmatory factor analysis (CFA) 61, 75 – 77, 80 – 82, 233; pitfalls of 390 – 391 falsifiability 29 Fisher, R A 389 Foxall, G R 380 Fryer, D 130 functional magnetic resonance imaging (fMRI) 9 – 11 Gabay, G 272, 277, 278 gamification 140, 180, 184 Gevrey, M 262 Gibbert, M 67 Gioia, D A 67 Glaser, B 67, 136 – 137 Green, D P see LaCour, M J Greene, M N 265 Hackett, P M W xxii – xxiii, 380, 395, 396 Hair, J F 240 Hamilton, A L see Gioia, D A Hancock, C 146, 148 Harkness, J A 79 Hayes, A F 228 Heidegger, M Herrnstein, R J 263 Hinkin, T R 68, 72 Holgado-Tello, F P 81 homoscedasticity 360, 362 Hooley, G see Lee, N Hu, L 76 Huberman, A M see Miles, B M human subjects 299 – 300, 306, 313, 315 – 321 Hyman, M 170 hypotheses 227, 291, 353, 360, 392; and big data 181 – 182, 205; and consumer psychology 7, 14; and ethics 305, 309, 316; null vs alternative 35 – 36; and quantitative techniques 23, 26 – 27, 27 – 28, 29 – 33, 49; theoretical background as basis for 36 – 37 Iacobucci, D 14 image content analysis 171 Implicit Association Test (IAT) 13 Implicit Goal Segmentation 198 – 206 information 187n1, 187n2, 240, 381 – 385, 388 – 389, 395 – 396; and big data 167 – 171, 175 – 183, 203, 207 – 208, 219; and consumer psychology 5, 6, 8, 13; and ethics 301, 304, 307, 311, 317, 319; and marketing research 272 – 274, 278, 280, 288 – 291; and market sensing 143, 148 – 149; and measurement theory 63, 65, 67, 82; and modelling 248 – 249, 254, 257 – 258, 261 – 262, 264, 266; and preparing data 327 – 328, 330 – 331, 350, 353; and quantitative techniques 23 – 24, 27, 28, 30, 37, 47 – 48; see also data; social media research inner model 228 intellectual property 298 – 300, 304 – 305, 311 – 312 interpretation 67, 241, 382, 383, 392, 394; and big data 170, 172, 187n1, 197 – 198, 213, 220; and consumer psychology 7, 14; and ethics 299, 308, 312, 318; and marketing research 272, 279, 291; and market sensing 130 – 132, 133, 138, 142, 150 – 151, 156 – 157; and modelling 248, 252; and preparing data 353, 373; and quantitative techniques 26, 27; and social media 92, 112 – 113, 117, 120 interpretive methods 125 – 126 interview 6, 67, 395; analysis and presentation of data 151 – 154, 153, 157, 161; and context 126; and data collection 146, 148, 149, 150; and ethics 301, 304, 315, 318 – 319; and philosophy 128; and planning 142 – 143, 143; and process 136, 138, 139, 141; and quantitative techniques 26, 37, 39 – 40, 43; and research approach 133; and research strategy 134 – 135; and social media 105 – 107 item information curve (IIC) 63 Item Response Theory (IRT) 60, 62 – 63, 78, 82 Index 411 items 327, 331, 394; and measurement theory 60 – 63, 66, 68, 70 – 77, 79 – 83; and modelling 228, 231; and quantitative techniques 41 – 43; and social media 89, 102 Jackson, D A see Olden, J D Jak, S see Cheung, M W L Jang, S 240 Jansson-Boyd, C.V 1, 4, 5, 377 Jerath, K 240 Kano Model 229, 230 Kim, J 391 King, J 385 King, N 145 Klein, J G 63 – 64 Knutson, B 10 – 11 Krishnan, B C see Klein, J G Kuhn, T S 167 LaCour, M J 304 Lawson, A see Longbottom, D Lee, N 73 – 74 Lehto, T 240 Likert scale 6, 42 – 43, 129; and measurement theory 61, 69, 73, 76 Lin,Y K 240 Longbottom, D 126; see also Hancock, C machine learning 180, 181 – 182, 184 mapping sentences 384 – 386; declarative 396 – 397; as expanded hypothesis 393; facet theory and 386 – 388, 397 – 398; as interpreter of data 394; and questionnaire design 393 – 394; salience of 392; as a tool 395 – 396 Marinova, D 240 marketing research 26, 83; and big data 166, 169 – 170, 172 – 173; case study abstract 273 – 274; case study analysis 279 – 286, 280 – 282, 283 – 284, 285 – 286; case study background 274 – 275; case study experimentation 276 – 277; case study messaging 275 – 276; case study sampling 277 – 279; managerial implications of case study in 286 – 291, 287, 288; and market sensing 126, 137, 138, 272 – 273, 290 – 291 marketing strategy 64, 126, 139, 174, 175, 381; and big data 177 – 180 market sensing 124, 128 – 130, 138, 139, 142, 148; see also marketing research Maxwell, S E 379 McCarthy, E J 173 McCulloch, W 249 measurement 6, 301, 386, 388 – 389, 397; and big data 168, 184; and market sensing 129, 134, 277 – 278; and modelling 231, 233, 235, 252, 265; objective 112 – 117; and psychological scaling 59 – 61, 66, 69, 79; and quantitative techniques 27, 41 – 43, 47; and social media 109, 118; subjective 117; see also measurement theory; scales; see also under bias measurement model see outer model measurement theory 62 – 63 Merleau-Ponty, M meta-analysis 26, 47 – 49 methods 33, 376 – 377, 383, 386, 389 – 391, 397 – 398; and big data 167, 170 – 171, 173 – 174, 182, 185, 195; and connectionist modelling 248 – 249, 251 – 257, 260 – 262, 264 – 266, 269n1; and consumer psychology 2, 4 – 11, 6, 15; creative methods 13 – 14; for data collection 37 – 38; difficulties using quantitative methods 12 – 13; and ethics 299, 301, 304, 311, 315, 320 – 321; and market sensing 136 – 141, 143, 146, 157, 158; and measurement theory 61, 63, 71 – 73; mixed methods approach 28 – 29; and modelling the consumer 223, 225, 227, 232, 237 – 241; and preparing data 342 – 344, 346, 350, 358, 362, 366; qualitative 26 – 27; quantitative 26 – 27; and social media 103, 106; see also C-OAR-SE; data collection; market sensing; survey Miles, B M 126, 131 – 132, 146, 155, 157 Milgram, S 316 – 317 Minsky, M 269n3 misconduct 298, 300 – 304, 308, 312 modelling 222 – 224, 224; building blocks of models 228 – 232, 229, 230; dependence techniques 232 – 234, 234; future directions of 239 – 242; interdependence techniques 234 – 236, 236; software for 242; troubleshooting 236 – 239; see also connectionist modelling; standard statistical modelling; Structural Equation Modelling (SEM) Mogilner, C 378 – 379 Moore, R S 253 Morgan, P H 251, 252, 260 Moskowitz, H R see Gabay, G Mueller, W see Kim, J 412 Index multimedia analytics 180, 182 multiple regression 266, 330, 332, 353 – 360, 354 – 359, 381; assumptions in 362 multivariate analysis of variance (MANOVA) 370 – 373, 372, 373 Netnography 139 neuroscience see consumer neuroscience Neyman, J 390 normative measures 62, 70 norm group 62, 77 – 78 Novick, M R 62 observation 67, 273, 333; and big data 175, 177, 197, 203; and consumer psychology 6, 9; and data presentation 378, 388, 393, 395 – 397, 401; and market sensing 130, 132 – 133, 134 – 135, 136 – 138, 141, 149 – 150; and modelling 223, 225 – 226, 228, 230; and quantitative techniques 23, 26, 37 – 38, 43 – 44, 45, 47 Oinas-Kukkonen, H see Lehto, T Olden, J D 262 ontology 124, 130 – 132, 131, 132, 174 operationalisation 205 – 206, 210, 226, 231 outer model 228 outliers 301, 304, 349 – 350, 352, 354, 359 – 360 overfitting 252 – 253, 258 – 263 overfitting bias 184 Papert, S see Minsky, M paradigm 2 – 3, 27 – 29, 28, 43, 128, 180 parsimony see simplicity Pearce, J M 263 Pearson, E S see Neyman, J peer review 149, 310 – 312 Peirce, C H 386 percentile 62, 77 Pham, M T 3 – 4 pilot study 24, 354 Plano Clark,V L see Creswell, J W Plassmann, H 11 Platt, T 64 population 37, 326 – 327, 331 – 334, 386 – 388; and market sensing 132 – 134, 133, 276 – 278; and measurement theory 63 – 65; and modelling 239 – 240; population parameters 38, 169 positivism 125 – 131, 127, 130, 131, 133 positron emission tomography (PET) 9, 11 predictive power 30, 252, 257 – 258, 264 – 265, 280 Prince, M 273 principle component analysis 390 – 391 Priporas, C.V see Prince, M psychometric properties 60, 72, 75 psychometrics 59, 60 – 65, 78 – 79, 82 – 83, 284; see also psychometric properties; scale design p-value 389 – 390 qualitative research: caution regarding 128; context of 126 – 128; and data analysis 150 – 158, 151, 153, 156, 158 – 160; and data collection 145 – 150, 146, 147, 148, 149, 150; nature of 124 – 125; philosophy of 128 – 132, 130, 131, 132; planning for 142 – 145, 143; and quantitative research 5 – 8, 6, 25 – 29, 27 – 28; research approach for 133 – 134, 133; strategies for 134 – 141, 136 – 138, 139 – 140, 141; see also market sensing quantitative research 22 – 23, 376 – 377; and complexity 377 – 384; in consumer psychology 1 – 2, 15 – 16; cross-sectional 38 – 39; difficulties using 12 – 13; framework of 23; mapping sentences 384 – 388, 392 – 398; method in 5; and qualitative research 5 – 8, 6, 25 – 29, 27 – 28; and replicating research 14 – 15; theory in 3 – 4; types of 23 – 25; see also data collection; measurement; observation; research questions questionnaires 304 – 306; and consumer psychology 5 – 7, 6; and mapping sentences 393 – 394; and marketing research 286 – 288, 287 – 288; and modelling 230 – 231; and quantitative techniques 39 – 43 raw score 62 Reeve, J 23 relationship see association relevance 30, 136, 142, 238 – 239 reliability 48, 238, 331; and market sensing 131 – 132, 132, 142; and measurement theory 63, 66 – 67, 72; and scale development 60 – 61 replication 14 – 15, 29 – 30, 237 research: business-led 8 – 9; exploratory and explanatory 126, 127; the research process 93; see also ethics; market sensing; positivism; qualitative research; quantitative research; research questions; social media research research groups see experiment groups Index 413 research questions 27 – 30, 133, 134 Resnik, D B 310 – 311; see also Shamoo, A E Rossiter, J R 60, 83; see also C-OAR-SE samples 142 – 143, 168, 197 – 198, 334 – 335, 342 – 344, 349 – 350 sampling 37 – 39, 47, 142; see also samples Sartre, J P Saunders, M 125, 129, 134, 145 Sawyer, A G see Dickson, P R scale design 60, 65 – 68, 80 – 81 scales 60 – 61, 65 – 66; the construct in 66 – 67; content validity in 70 – 72; item set and response format in 68 – 70, 69; item trialling in 72 – 74; measurement bias in 78; measurement by 41 – 43; norming the tool in 77 – 78; second item trial in 75 – 77, 76; software and 81 – 82; translating 79 – 80; troubleshooting 80 – 81; see also scale design Schmarzo, B 173 Schubring, S 241 scope 30 Seitz, M J 241 sentiment analysis 90 – 92, 107, 175, 176, 196 Shamoo, A E.: on animal research 313 – 315; on authorship 307 – 310; on conflicts of interest 312 – 313; on data management 304 – 307; on ethics and scientific research 298 – 300; on peer review 310 – 312; on research misconduct 300 – 304; on research with human subjects 315 – 321 Shkoler, O xix, 32, 33 Sigurdsson,V 382 – 383 Simon, H A 247 simplicity 30, 241, 280, 386 smallest space analysis (SSA) 388, 391 Smith, A 382 social desirability 11 – 13 socially desirable responding (SDR) behaviour 69, 70, 78 – 79 social media analytics 180, 182 – 183 social media research 111, 120 – 121; adding relevant information 111; and assessing activity 96 – 97; and bias 112; and competitor analysis 107 – 110; and content 88 – 93; and experiential questions 104 – 105; information and 94; and interviews 105 – 107; and interpretation 112 – 117, 114, 116; and monitoring data 117 – 119; participants in 94; and proactive searching 97 – 104, 97 – 98, 101 – 104; questions and surveys 94 – 96, 95; and reactive content 111; and responding 119 – 120, 119; strategic changes to 120; and subjective measurement 117 social network analytics 180, 183 – 184 social responsibility 299, 321 software 26 – 27, 27, 152, 154, 194 – 196, 223 – 224; AMOS 14, 82, 233 – 234; for conducting analysis 15; for modelling 241 – 242; and scale development 81 – 82; see also SPSS SPSS 14 – 15, 81 – 82; and ANCOVA 364; and homogeneity of variance 346 – 347; and independent errors 360; and inputting data 331; and MANOVA 371; and multiple regression 354 – 355; and outliers 350; and screening data 335, 337 standard statistical modelling 224 – 228 statistics 6 – 7, 338 – 339, 377 – 378; descriptive 338, 350; inferential 37; sample statistics 38, 171; statistical method bias 184 – 185; statistical model 228 sten 77 – 78 Strauss, A see Glaser, B Structural Equation Modelling (SEM) 225, 227, 231, 233 – 234, 239 – 242; and consumer psychology 14 – 15; and measurement theory 82 structural model see inner model subject matter experts (SMEs) 61, 67, 71 – 72, 81 survey 125 – 126, 303 – 306, 315 – 316; and quantitative techniques 26 – 27, 27, 37 – 41, 41, 43 – 44, 46 – 47; and social media 94 – 96, 98 – 104, 104 Swani, K 240 Symon, G see Cassell, C Targowski, A 272 – 273 Tashakkori, A see Teddlie, C Teddlie, C 125 test information curve (TIC) 63 text analytics 171, 180 Thomas, A B 142, 145 timing 46, 94, 142, 147, 156, 177 tools 8 – 11, 59 – 60, 98 – 99, 183 – 185, 187n2, 377 usefulness 3, 9, 30, 202, 253, 263, 384 414 Index validity: big data and 241; and market sensing 128 – 129, 131 – 132, 132, 136; and measurement theory 60 – 64, 66 – 68, 70 – 73, 75 – 81; p-values and 390; reliability and 238 van de Schoot, R 240 variables 378 – 379, 386 – 391; and the building blocks of models 228 – 232, 229; and connectionist modelling 248 – 249, 252 – 255, 261 – 262, 264 – 266; and measurement theory 81 – 82; and modelling the consumer 222 – 223, 232 – 238, 236; and preparing data 343 – 345, 366 – 367, 370 – 371; and quantitative techniques 26 – 27, 31 – 33, 45 – 46; and standard statistical modelling 225 – 228; see also association variance 32 – 33, 345 – 347, 349, 358 – 360; see also analysis verifiability 29 video content analysis 171 Wakefield, A J 303 Wakefield, K 240 – 241 Walker, E 49 Wislar, J S 307 Yoh, E 33 ... Research? Increasingly, businesses have shown an interest in conducting consumer psychology? ?? based research in order to further the understanding of human behaviour, ultimately in the hope of increasing... method In Creating models in psychological research: Springer briefs in psychology Cham: Springer Minichiello, V., Aroni, R., Timewell, E., & Alexander, L (1990) In- depth interviewing: Researching.. .QUANTITATIVE RESEARCH METHODS IN CONSUMER PSYCHOLOGY Quantitative consumer research has long been the backbone of consumer psychology producing insights with peerless validity