P.ı\RT 1: FlJND.ı\MENT.ı\LS OF MARKETiNG RESE.ı\RCH 1 . lntroduction, Evolution, and Emerging lssues Role ofMarketing Rescarch in a Marketing Plan 3 Marketing lntelligence versus Marketing Research 4 Who Does the Marketing Research? 6 Typical Applications of Marketing Research 6 Concept Rcsearch 7 Product Rescarch 8 Pricing Rcsearch 8 Distribution Rcsearch 9 Advertising Research /O When To Do Marketing Research? / / Limitations ofMarketing Research /1 Differences in Methodology 12 Complemcntary lnputs for Decision-making 12 Secondary and Primary Research 12 Ethical Considerations in Marketing Research / 3 Consumer's Right to Privacy 14 Emcrging lssues 14 Marketing Rescarch in the İnternet Era I 4 Onliııe Research 15 Data Warehousing and Data Mining 16 3 Contents ( Sumınary 17 Assignment Questions 18 2. The Marketing Research Process-An Overview lnformation Need 19 Defining the Research Objective 21 Research Designs: Exploratory, Descriptive, and Causal 21 Exploratory Research 22 Descriptive Research 22 Causal Researclı Designs 23 Designiııg the Research Methodology 24 Survey 24 Obscrvation 25 Experimentation 25 Qualitative Techniques 26 Spccialised Techniques 26 Plan for Sampling, Field Work, and Analysis 27 Sampling Plan 27 Field Work Plan 28 Briefing 28 Debriefing 29 Analysis Plan and Expected Outcome 29 Expectcd Outcorne 30 Budget and Cost Estimation 30 Presentation, Report, and Marketing Action 31 Case Study 1 32 Summary 33 Assignment Questions 34 3. Research Methods and Design-Additional lnputs Sources of Secondary Data 36 Disadvantages of Secondary Data 37 Exploratory and Conclusive Research 38 Major Qualitative Research Techniques 39 Depth Jnterview 39 Focus Group 40 Projective Teclıniques 40 19 36 Contents Validity of Research 41 Expcriments 41 Test Marketing 41 Case Sıudy: Consumer Perception o/High-eııd 1T Education 43 Summary 45 Assignment Questions 47 4. Questionnaire Design: A Customer-centric Approach Designing Questionnaires for Market Research 48 Language 48 Diffıculty Level 49 Fatigue 49 Cooperation with Researcher 49 Social Desirability Bias 49 Ease of Recording 50 Coding 50 Purpose ofa Questionnaire 50 Sequerıcing of Questions 50 Biased and Leading Questions 50 Monotony 51 Analysis Required 51 Scales ofMeasurement Used in Marketing Research 51 Nominal Scale 51 Ordinal Scale 52 Interval Scale 52 Ratio Scale 52 Structured and Unstructured Questionnaires 53 Structured Questions 53 Structured Answers 53 Open-erıded and Closed-ended Questions 54 Disguised Versus Undisguised Questions 54 Types of Questions 55 Open-ended Question 56 Dichotomous Questions 56 Multiple-choice Questions 56 Ratings or Rankings 56 Paired Comparisons 57 Semantic Differential 57 48 How to Choose a Scale and Question Type 57 Transformırıg Informarion Needs into a Questi·onnai.rc 58 Example of Infonnation Needs 58 Double-barrelled Questions 61 Good Questionnaires and Bad Questionnaires 61 Reliability and Validity ofa Questionnaire 62 Reliability and Validity 62 What is a Construct? 62 Content Validity 62 Criterion Validity or Predictive Validity 63 Construct Validity 63 Reliability ofa Scale 64 Summary 65 Case Study 1: TamarindMenswear 68 Case Study 2: Casual C!othing Rejerences of Youth 69 Case Study 3: Parryware-A Survey on Consumers Perception ofBathrooın and Sanitaryware 76 Assignınent Questions 83 5. Sampling Methods-Theory and Practice Basic Tenninology in Sanıpling 90 Sampling Element 90 Population 91 Sanıpling Frarne 91 Sampling Unit 91 The Sanıple Size Calculation 91 Formula for Sample Size Calculation when Estimating Means (for Continuous or Interval-scaled Variables) 92 Fornıula for Sample Size Calculation when Estimating Proportions 94 Other Issues tlıat Affect Sample Size Decisions 96 l. Number of Centres 96 2. Multiple Questions 96 3. Celi Size in Analysis 96 4. Time and Budget Constraints 97 5. The Role of Experience in Detennination of Sample Size 97 Sanıpling Teclıniques 97 Probability Samplin Techniques 97 Non-probability Sampling Techniques 104 Census Versus Sample 105 90 Contents Types of Errors in Marketing Research 106 Sampling Error 106 Non-sanıpling Error 106 Total Error 106 Sunınıary 107 Assignment Questions 108 6. Field Procedures 109 Design ofField Work 109 Selection of Cities/Centres 109 Organising Field Work 110 Quotas 110 Selection of Respondents 11 O Control Procedures on the Field 111 Briefıng 111 Debriefıng 111 Summary 111 Assignmenı Questions 112 7. Planning the Data Analysis Processing of Data with Computer Packages Statistical and Oata Processing Packages il 5 Types of Analysis 115 Data Processing 115 Data lnput Format Jl6 Coding 116 Variables and Variable Labels 116 Variable Format 117 Value Labels 117 Record Number/Case Number 117 Missing Data 118 Statistical Analysis 118 Hypothesis Testing and Probability Values (p-values) 119 114 114 Approaches to Analysis 119 Three Types of Analysis 119 Hypothesis Testing 123 Sunıınary 127 Assignment Questions 128 SPSS Data lnput aııd t-test Coınınands 128 Contents Integrated Case Studies for Part 1 130 Case Study 1: Crocin 130 Case Study 2: Detergents 141 Case Study 3: BPL 155 PART 2: DATA ANALYSIS 8. Simple Tabulation and Cross-tabulation Univariate and Bivariate Analysis 181 Dependent and Independent Variables 181 Demographic Variables 182 First Stage Analysis-Simple Tabulation 182 Computer Tabulation 183 Percentages 183 Simple Tabulation for Ranking Type Questioııs 184 Tabulating Ratings 185 Second Stage Aııalysis-Cross-Tabulatioıı l86 Calculatiııg Perceııtages in a Cross-tabulatioıı 186 Cross-tabulation of More Than Two Variables 187 Lack of Causal Inference in Cross-tabulations 187 The Chi-squared Test for Cross-tabulations /87 Clıi-squared Test: An I!lustration J 88 Measures of the Strength ofAssociation Between Variables 190 Doing More with Data (Transformation ofVariables and Use of Part-samples) 191 Summary 194 Assignment Questions 195 SPSS Commands.for Frequency Tables, and Cross-tabs with Chi-squared Test 196 Case Study 1: Chi-square Testfar Cross-ıabs 197 Case Study 2: Chi-square Testfor Cross-tabs 200 Case Study 3: Chi-square Test 204 9. ANOVA and the Design of Experiments 208 Introduction 208 Applications 208 Methods 209 Variables 209 Experimental Designs 209 Completely Randomised Design in a One-way ANOVA 209 Randomised Block Design 210 18 Contents Latin Square Design 212 Factorial Design with Two or More Factors 212 Additional Cornrnents 213 Pairwise Tests 213 Summary 217 Assignment Questions 218 SPSS Commandsfor ANOVA 220 Case Study 1: ANOVA 221 Case Study 2: ANOVA 226 Case Study 3: ANOVA 235 1 O. Correlation and Regression: Explaining Association and Causation 242 Application Areas 242 Methods 243 Recornmended LJsage 244 Worked Example 244 Problem 244 Iııput Data 245 Correlation 245 Regression 245 Regressioıı Output 245 Predictions 246 Forward Stepwise Regression 247 Backward Stepwise Regression 247 Additional Comnıents 248 Summary 252 Assignment Questions 253 SPSS Commands for Correlation and Regression 254 Case Study J: Correlation and Regression 256 Case Study 2: Correlation and Regression 267 Case Study 3: Correlation and Regression 273 1 1. Discriminant Analysis for Classifıcation and Prediction Application Areas 280 Metlıods 281 Variables and Data 281 Predictiııg the Group Membership fora New Data Point 281 Accuracy of Classification 281 Stepwise/Fixed Model 282 280 Contents Reiative Importaııce of Iııdepeııdent Variables 282 Apriori Probabi I ity of Classifıcatioıı iııto Groups 282 Worked ExampJe 282 Problem 282 Iııput Data 283 Iııterpretatioıı of Computer Output 283 Additional Comments 285 Summary 289 Assignment Questions 290 SPSS Commandsfh» Üiscriminant Analysis 292 Case Study 1: Discriminant Analysis 293 Case Study 2: Discriminant Analysis 297 Case Study 3: Discriminant Analysis 302 12. Logistic Regression for Classifıcation and Prediction Applicatioıı Areas 308 Methods 308 The AlgoritJım 310 Logisı... Regression Versus Liııear Discrirninant Aııalysis 310