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Marketing Research Methods: Quantitative and Qualitative Approaches [Minkštas viršelis]

(Universidad Carlos III de Madrid), (Universidad Carlos III de Madrid)
  • Formatas: Paperback / softback, 882 pages, aukštis x plotis x storis: 245x187x40 mm, weight: 1870 g, Worked examples or Exercises
  • Išleidimo metai: 28-Jan-2021
  • Leidėjas: Cambridge University Press
  • ISBN-10: 1108792693
  • ISBN-13: 9781108792691
Kitos knygos pagal šią temą:
  • Formatas: Paperback / softback, 882 pages, aukštis x plotis x storis: 245x187x40 mm, weight: 1870 g, Worked examples or Exercises
  • Išleidimo metai: 28-Jan-2021
  • Leidėjas: Cambridge University Press
  • ISBN-10: 1108792693
  • ISBN-13: 9781108792691
Kitos knygos pagal šią temą:
Covering both quantitative and qualitative market research methods and including rigorous academic theory, this book explains the breadth of modern methods for upper level business and social science students.

Covering both quantitative and qualitative methods, this book examines the breadth of modern market research methods for upper level students across business schools and social science faculties. Modern and trending topics including social networks, machine learning, big data, and artificial intelligence are addressed and real world examples and case studies illustrate the application of the methods. This text examines potential problems, such as researcher bias, and discusses effective solutions in the preparation of research reports and papers, and oral presentations. Assuming no prior knowledge of statistics or econometrics, discrete chapters offer a clear introduction to both, opening up the quantitative methods to all students. Each chapter contains rigorous academic theory, including a synthesis of the recent literature as well as key historical references, applied contextualization and recent research results, making it an excellent resource for practitioners. Online resources include extensive chapter bibliographies, lecture slides, an instructor guide and extra extension material and questions.

Recenzijos

'Mercedes and Jose have written a comprehensive and modern book on marketing research methods that covers classical approaches as well as new methods such as machine learning and artificial intelligence. Using real-life examples and case studies, the book illustrates complex methods with great clarity. It is a highly valuable resource for all teachers, students, and practitioners of market research.' Sunil Gupta, Harvard Business School, University of Harvard 'This book is an amazing combination of traditional and newer topics presented in an accessible style filled with examples. I especially liked the coverage of new topics like machine learning and the discussions of issues like causality. A wonderful resource for teachers, students, and practitioners.' Donald R. Lehmann, Columbia Business School, Columbia University 'The depth, breadth, and relevance of the work presented make the book unique, and a must-have as a key reference in advanced social and marketing research methods.' Mesfin Habtom, London Metropolitan University, UK 'Excellent and solidly founded guide for practitioners and scholars to develop social and market research. From the definition of objectives, through the compilation and analysis of qualitative and quantitative information, to the reporting of results and conclusions, a rigorous, advanced, as well as comprehensible description of most relevant modern processes and techniques involved in market research is provided.' Óscar Gonzįlez-Benito, Universidad de Salamanca 'Due to the increasing number of data sources to which companies have access, marketing and marketing research are changing fundamentally. Marketing managers and marketing researchers need to have extensive knowledge not only of traditional market research methods but also of the latest quantitative and qualitative methods. Mercedes Esteban-Bravo and Jose M. Vidal-Sanz present and summarize in this book - in a very intuitive, comprehensive, and applied way - traditional and newest methods of market research. Specifically, the authors manage to bridge the gap between traditional methods, such as regression analysis, and machine learning and artificial intelligence. Complex methods of computational statistics, machine learning, and artificial intelligence are illustrated in real-world examples. The wide range of methods and the way they are presented make this book a must for anyone interested in how companies can create value from (large) data available for various marketing purposes.' Florian Stahl, University of Mannheim 'Proliferation of data has provided numerous opportunities for marketing professionals to transform their businesses. Esteban-Bravo and Vidal-Sanz provide managers, policymakers, and scholars with an excellent roadmap that helps them to understand the old, the modified, and the new research methods. These methods are necessary to transform data from the market into insights and, in turn, value for consumers, firms, and society.' Oded Koenigsberg, London Business School

Daugiau informacijos

Academically thorough and up-to-date quantitative and qualitative market research methods text for business and social science students.
Preface xv
Part I Research Methods
1(34)
1 Introduction To Social And Marketing Research
3(32)
1.1 The Role of Information in Science
3(4)
1.1.1 Positivism
4(2)
1.1.2 Research
6(1)
1.1.3 Information and Power
7(1)
1.2 Marketing Research
7(6)
1.2.1 Typical Applications of Marketing Research
10(2)
1.2.2 Some Misconceptions on MR
12(1)
1.3 Sources of Information
13(6)
1.3.1 Primary Sources
13(1)
1.3.2 Secondary Sources
14(2)
1.3.3 Analyzing Secondary Information
16(2)
1.3.4 Database Management
18(1)
1.4 Types of Social and MR Studies
19(7)
1.4.1 Exploratory vs. Conclusive Research
19(5)
1.4.2 Qualitative and Quantitative Research
24(2)
1.5 History of Social and Market Research Methods
26(2)
1.6 Information Systems
28(4)
1.6.1 Decision Support Systems
29(1)
1.6.2 A More General Perspective on Information Systems
30(2)
1.7 Ethics in Social Research
32(2)
1.7.1 Ethics in MR: Scholarly and Professional Concern
33(1)
1.8 Further Resources
34(1)
Part II Qualitative Methods
35(116)
2 Qualitative Research Based On Direct Questioning
37(46)
2.1 What is Qualitative Research?
37(7)
2.1.1 Main Procedures
38(2)
2.1.2 The Quantitative Fallacy
40(3)
2.1.3 Qualitative Biases
43(1)
2.2 Introspection
44(3)
2.3 Depth Interviews
47(9)
2.3.1 Conducting Depth Interviews
50(1)
2.3.2 Special Techniques
51(5)
2.3.3 Online Depth Interviews
56(1)
2.4 Narrative Inquiry and Storytelling
56(2)
2.5 Focus Groups
58(5)
2.5.1 Online Focus Groups
62(1)
2.5.2 Other Types of FGs
63(1)
2.6 Technical Issues in IDIs and FGs
63(7)
2.6.1 Writing the Discussion Agenda (or Script of Questions)
64(2)
2.6.2 Transcriptions
66(4)
2.7 Analysis of Transcripts and Written Texts
70(7)
2.7.1 Content Analysis and Thematic Analysis
70(4)
2.7.2 Linguistic and Discourse Analysis
74(1)
2.7.3 Text Analysis
74(2)
2.7.4 Using Non-verbal Information
76(1)
2.8 Reporting
77(1)
2.9 Case Study Research
77(3)
2.10 Further Resources
80(3)
3 Indirect Questioning In Qualitative Research
83(20)
3.1 Handling Sensitive or Complex Issues
83(1)
3.2 Projective Methods
84(14)
3.2.1 Association Techniques
85(3)
3.2.2 Construction Techniques
88(4)
3.2.3 Completion Techniques
92(4)
3.2.4 Ordering or Choice Techniques
96(1)
3.2.5 Expression Techniques
96(2)
3.3 Creative Methods
98(3)
3.3.1 Brainstorming
98(1)
3.3.2 Delphi Method
99(2)
3.3.3 Other Qualitative Prediction Methods
101(1)
3.4 Further Resources
101(2)
4 Observation Methods
103(48)
4.1 How Can We Use Observation?
103(2)
4.2 Direct Observation of Behavior
105(9)
4.2.1 Naturalistic Observation With a Relatively Passive or Mechanical Observer
105(4)
4.2.2 Active Observer Participation
109(5)
4.3 Indirect Observation (or Trace Analysis) of Behavior
114(1)
4.4 Observation of Digital Behavior
115(4)
4.5 Social Networks Analysis
119(13)
4.5.1 Background on Graph Theory
120(2)
4.5.2 Centrality of a Vertex
122(8)
4.5.3 Models of Network Formation: Random Graphs
130(2)
4.6 Psychophysiology (Biometry)
132(18)
4.6.1 Psychophysical Response
132(11)
4.6.2 Neuropsychology
143(5)
4.6.3 Genetic Research
148(2)
4.7 Further Resources
150(1)
Part III Quantitative Data Analysis
151(418)
5 Uncertainty And Probability
153(52)
5.1 Probability Theory
153(17)
5.1.1 Sets and Sigma Algebras
154(4)
5.1.2 Probability Measure
158(2)
5.1.3 Random Variables/Vectors and Integrals
160(8)
5.1.4 Conditional Probability
168(2)
5.2 Common Probability Distribution Families
170(15)
5.2.1 Univariate Discrete Families
171(2)
5.2.2 Univariate Continuous Families
173(3)
5.2.3 Multivariate Discrete Families
176(2)
5.2.4 Multivariate Continuous Families
178(2)
5.2.5 Continuous Distributions Derived From a Normal Distribution
180(5)
5.3 Convergence of Random Variables and Distributions
185(10)
5.3.1 Laws of Large Numbers
189(2)
5.3.2 Central Limit Theorems
191(4)
5.4 Random Number Generation and Monte Carlo
195(1)
5.5 Stochastic Processes
196(7)
5.5.1 Asymptotic Theory for Stochastic Processes
200(2)
5.5.2 Random Elements on Metric Spaces
202(1)
5.6 Further Resources
203(2)
6 Statistical Analysis I: Parameters And Estimation
205(77)
6.1 Statistical Analysis
205(2)
6.2 Point Estimation
207(15)
6.2.1 Parameters and Point Estimation
208(4)
6.2.2 Finite-Sample Properties of Estimators
212(6)
6.2.3 Asymptotic Properties of Estimators
218(4)
6.3 Some General Families of Estimators
222(15)
6.3.1 M-Estimators
222(7)
6.3.2 Z-Estimators
229(2)
6.3.3 Generalized Method of Moments
231(2)
6.3.4 Numerical Computation
233(2)
6.3.5 Other Related Estimators
235(2)
6.4 Parametric Models
237(33)
6.4.1 Cramer--Rao Bound
238(2)
6.4.2 Maximum Likelihood Estimators
240(14)
6.4.3 Bayesian Estimators
254(7)
6.4.4 Other Parametric Model Estimators
261(2)
6.4.5 Selection of Parametric Models
263(5)
6.4.6 Data Heterogeneity and Mixtures
268(2)
6.5 Nonparametric Density Estimation
270(9)
6.5.1 Histogram
272(2)
6.5.2 Kernel Density Estimators
274(4)
6.5.3 K-Nearest Neighbor Density Estimate
278(1)
6.6 Robustness
279(1)
6.7 Further Resources
280(2)
7 Statistical Analysis II: Confidence Regions And Hypothesis Testing
282(68)
7.1 Statistical Inference
282(1)
7.2 Confidence Intervals and Regions
283(15)
7.2.1 Asymptotic Confidence Intervals
284(6)
7.2.2 Exact Confidence Intervals
290(2)
7.2.3 Confidence Regions for Multiple Parameters
292(4)
7.2.4 Bayesian Confidence Regions
296(1)
7.2.5 Warning on Misleading Interpretations
297(1)
7.3 Hypothesis Testing
298(33)
7.3.1 Significance Tests
300(1)
7.3.2 Asymptotic Significance Tests for a Single Parameter
301(9)
7.3.3 Some Alternatives to Asymptotic Tests
310(3)
7.3.4 Tests for a Vector of Parameters
313(3)
7.3.5 Classical Econometric Tests
316(11)
7.3.6 Questionable Research Practices, Misleading Interpretations, and Controversies
327(4)
7.4 Multiple Testing Methods
331(8)
7.4.1 The Impact of Preliminary Testing on Estimation
334(5)
7.5 Analysis of Variance
339(5)
7.5.1 One-Way ANOVA
339(2)
7.5.2 Fixed vs. Random Effects
341(2)
7.5.3 Two-Way and Multiway ANOVA
343(1)
7.6 Empirical Processes
344(5)
7.6.1 Goodness-of-Fit Tests
348(1)
7.7 Further Resources
349(1)
8 Regression Analysis I: General Linear Model
350(88)
8.1 What Does "Regression" Mean?
350(6)
8.1.1 Regression with Finite Variance
353(3)
8.2 Other Related Curves
356(6)
8.2.1 Best Linear Predictors
356(4)
8.2.2 Modeling Functional Data Discretely Sampled
360(2)
8.3 Estimating the General Linear Model
362(33)
8.3.1 Ordinary Least Squares Estimator
365(7)
8.3.2 Partitioned Regression and Frisch--Waugh--Lovell Theorem
372(3)
8.3.3 Finite Sample Properties of OLS
375(6)
8.3.4 Other Common Estimators Related to Least Squares
381(11)
8.3.5 Linear Dynamic Regression and Distributed Lag Models
392(3)
8.4 Asymptotic Theory for Least Squares
395(11)
8.4.1 Consistency of OLS
395(3)
8.4.2 Asymptotic Normality of OLS
398(3)
8.4.3 Estimating the Asymptotic Covariance Matrix of OLS
401(3)
8.4.4 Bootstrap Approximation
404(2)
8.5 Inference in the General Linear Model
406(9)
8.5.1 Forecasting with Least Squares
406(2)
8.5.2 Testing Linear Hypothesis under Normality
408(4)
8.5.3 Asymptotic Tests
412(3)
8.6 Diagnosis in the General Linear Model
415(10)
8.6.1 Basic Residual Analysis
416(1)
8.6.2 Multicollinearity and Near-Collinearity
416(6)
8.6.3 Structural Change
422(1)
8.6.4 Inferences about Σ using Residuals
422(3)
8.7 Transformations to Achieve Linearity
425(3)
8.8 Dummy Regressors: Reconsidering Classical ANOVA
428(8)
8.8.1 One-Way ANOVA with Fixed Effects
429(3)
8.8.2 Analysis of Covariance and Panels
432(1)
8.8.3 Two-Way and Multiway ANOVA
433(1)
8.8.4 General Random Coefficients and Multi-level Modeling
434(2)
8.9 Further Resources
436(2)
9 Regression Analysis II: Flexible Methods And Machine Learning
438(56)
9.1 Variable Selection in Large Databases
438(10)
9.1.1 Classical Variable Selection
439(4)
9.1.2 Variable Screening Methods
443(1)
9.1.3 Regularization: Ridge, LASSO, and Related Procedures
444(4)
9.2 Nonlinear Parametric Regression Models
448(15)
9.2.1 Nonlinear Least Squares
451(2)
9.2.2 Regression Using Z-Estimators and GMM
453(1)
9.2.3 Maximum Likelihood and Bayes
454(3)
9.2.4 Models for Dependent Variables with Limited Support
457(6)
9.3 Recursive Estimation (Learning); Stochastic Approximation
463(4)
9.4 Nonparametric Regression Estimation
467(20)
9.4.1 Regressograms (Partitions)
470(3)
9.4.2 Kernel Estimators
473(1)
9.4.3 K-Nearest Neighbor Estimators
474(1)
9.4.4 Projections: Sieves Estimators and Regularization Methods
474(6)
9.4.5 Recursive Estimators
480(1)
9.4.6 Machine Learning
481(5)
9.4.7 Ensemble Methods
486(1)
9.5 Quantile Regression
487(3)
9.6 General Test for Goodness-of-Fit
490(1)
9.7 Further Resources
491(3)
10 Multivariate Statistics And Econometrics
494(75)
10.1 Inferences for a Multivariate Normal
494(7)
10.1.1 Correlation and Partial Correlation
498(3)
10.2 Multi-equational Regression Models
501(15)
10.2.1 Linear SURE Models
502(7)
10.2.2 Dynamic SURE Models
509(4)
10.2.3 Nonlinear Multi-equational Regression
513(3)
10.3 Models with Endogeneity
516(8)
10.3.1 Proxies and Measurement Errors
517(1)
10.3.2 Instrumental Variables
518(5)
10.3.3 Control Function Method
523(1)
10.4 Econometric Structural Models: Linear Specifications
524(16)
10.4.1 Identification
528(6)
10.4.2 Estimation with Full Information
534(4)
10.4.3 Estimation with Limited Information
538(1)
10.4.4 Dynamic Linear Structural Models
539(1)
10.5 Econometric Nonlinear Structural Models
540(3)
10.6 Dimension Reduction Methods
543(14)
10.6.1 Principal Components
543(3)
10.6.2 Canonical Correlation (Canonical Variables)
546(2)
10.6.3 Factor Analysis
548(3)
10.6.4 Correspondence Analysis
551(1)
10.6.5 Multidimensional Scaling
552(2)
10.6.6 Dimension Reduction with Nonlinear Relationships
554(3)
10.7 Discriminant Analysis (Supervised Classification/Pattern Recognition)
557(5)
10.7.1 Bayes Rule
558(2)
10.7.2 Parametric Discriminant Analysis
560(1)
10.7.3 Nonparametric and Other Machine Learning Approaches
561(1)
10.8 Cluster Analysis (Unsupervised Classification)
562(4)
10.8.1 Worst-Case and K-Means Methods
563(1)
10.8.2 The EM Algorithm
564(2)
10.9 Further Resources
566(3)
Part IV Quantitative Data Collection
569(250)
11 Quantitative Measurement
571(65)
11.1 Types of Quantitative Data
571(3)
11.1.1 Measurement Theory
572(2)
11.2 Measuring Attitudinal Magnitudes
574(3)
11.3 A Taxonomy of Measurement Scales
577(9)
11.3.1 Distortions Induced by the Scale
580(2)
11.3.2 Stevens's Classification of Scales
582(4)
11.4 Main Attitudinal Scales
586(36)
11.4.1 Nominal Scales
586(6)
11.4.2 Ordinal Scales
592(25)
11.4.3 Interval Scales
617(3)
11.4.4 Ratio Scales
620(2)
11.5 Multi-item Scales and Constructs
622(4)
11.6 Measurement Errors
626(8)
11.6.1 Classical Approach
628(3)
11.6.2 Rasch Model and Item Response Theory
631(3)
11.7 Further Resources
634(2)
12 Sampling Methods
636(57)
12.1 Key Concepts in Sampling
636(5)
12.1.1 Target Population
636(2)
12.1.2 Samples
638(3)
12.2 Sample Representativeness and Biases
641(1)
12.3 Non-probabilistic Sampling
642(4)
12.3.1 Main Types
642(3)
12.3.2 Recruiting Participants for Qualitative Research
645(1)
12.4 Probabilistic Sampling Methods
646(8)
12.4.1 Main Types
646(8)
12.5 Statistical Inference Using Probabilistic Samples
654(24)
12.5.1 Simple Random Sampling
656(11)
12.5.2 Stratified Sampling
667(3)
12.5.3 General Procedures for Non-EPSEM Sampling
670(8)
12.6 Superpopulation Models
678(3)
12.7 Sample Size Selection
681(6)
12.7.1 Optimal Sizes in Stratified Sampling
684(3)
12.8 Nonresponse and Item-Nonresponse
687(3)
12.8.1 Item-Nonresponse and Imputation
689(1)
12.8.2 Dual and Multiple-Frame Sampling
690(1)
12.9 Combined Sampling and Other Shortcuts
690(1)
12.10 Further Resources
691(2)
13 Survey And Questionnaire Design
693(70)
13.1 What is a Survey?
693(4)
13.1.1 Survey Methodologies
693(4)
13.2 Planning a Survey
697(4)
13.2.1 Privacy: Anonymity, Confidentiality, and Disclosure
697(3)
13.2.2 Explicit Informed Consent
700(1)
13.3 Typical Survey Types
701(11)
13.3.1 Personal or Face-to-Face Surveys
701(3)
13.3.2 Telephone Surveys
704(2)
13.3.3 Mail Surveys
706(1)
13.3.4 Web Surveys
707(2)
13.3.5 Summary of Survey Modes and Response Rates
709(3)
13.4 Questionnaires
712(35)
13.4.1 Priming with Accessible Information
713(1)
13.4.2 Question Types
714(3)
13.4.3 How to Build a Good Questionnaire
717(15)
13.4.4 Questions for Sensitive Issues
732(9)
13.4.5 Complementary Elements
741(5)
13.4.6 Language Translations
746(1)
13.5 Sources of Survey Errors
747(7)
13.5.1 Sampling Errors
747(4)
13.5.2 Non-sampling Errors
751(3)
13.6 Data Processing and Analysis
754(6)
13.7 Concluding Remarks
760(1)
13.8 Further Resources
761(2)
14 Experimental Research
763(56)
14.1 What are Experiments?
763(3)
14.2 Historical Overview
766(2)
14.3 Validity of Experimental Inferences
768(6)
14.3.1 Internal Validity
769(3)
14.3.2 External Validity
772(2)
14.4 Field vs. Lab Experiments
774(4)
14.4.1 Laboratory Experiments
774(1)
14.4.2 Field Experiments
775(3)
14.5 Ethics
778(1)
14.6 Typical Types of Experiment
778(4)
14.7 Analysis of Experimental Data
782(13)
14.7.1 Basic Inference Setup
783(4)
14.7.2 Multiple Treatment Experiments
787(1)
14.7.3 Factorial Analysis
788(2)
14.7.4 Response Surface Models
790(3)
14.7.5 Difference in Differences and Dynamics
793(2)
14.8 Randomization
795(5)
14.8.1 Blocking
797(1)
14.8.2 Classical Procedures to Randomize
798(2)
14.9 Optimal Experimental Designs
800(2)
14.10 Quasi-experiments
802(12)
14.10.1 Natural Experiments
803(1)
14.10.2 Potentially Harmful Econometrics
804(10)
14.11 Concluding Remarks
814(3)
14.12 Further Resources
817(2)
Part V Research Planning and Reporting
819(28)
15 Planning Social Research
821(12)
15.1 First Stage: Cost-Benefit Analysis
821(6)
15.1.1 The Value of Information
822(5)
15.2 Planning the Research Process
827(4)
15.2.1 Program Evaluation and Review Technique
828(1)
15.2.2 Fieldwork Planning
829(2)
15.3 Commissioning MR
831(1)
15.4 Further Resources
832(1)
16 Reporting Social And Market Research Studies
833(13)
16.1 Communicating the Findings
833(1)
16.2 Written Report
834(9)
16.2.1 Report Structure in a MR Report
837(1)
16.2.2 Plagiarism and References
838(2)
16.2.3 Visual Displays
840(3)
16.3 Presentations
843(2)
16.4 Further Resources
845(1)
17 Afterword
846(1)
Index 847
Mercedes Esteban-Bravo is a Professor of Marketing and Market Research at the Department of Business Administration and Director of Master's degree in Marketing at Universidad Carlos III de Madrid. She is a quantitative researcher connecting operations research/management science and marketing. Her research has been published in academic journals such as Marketing Science, International Journal of Research in Marketing, Marketing Letters, Journal of Advertising, Technological Forecasting and Social Change, European Journal of Operational Research, Journal of the Operational Research Society, and Statistics and Computing, among others. She is a life member of Clare Hall, University of Cambridge. Jose M. Vidal-Sanz is a Professor of Marketing and Market Research at the Department of Business Administration of Universidad Carlos III de Madrid. His research has dealt with analytical research methods and their applications to marketing and business economics. He has published in journals such as Marketing Science, International Journal of Research in Marketing, Marketing Letters, Journal of Advertising, Technological Forecasting and Social Change Annals of the Institute of Statistical Mathematics, Bernoulli, European Journal of Operational Research, Statistics and Computing, among others. He is a life member of Clare Hall College, University of Cambridge.