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Marketing Research

Book Description

* The Research in Action feature links the concepts discussed in the chapter to actual industry practice * The case study at the end of each chapter acquaints learners with a variety of organizational scenarios that they may encounter in the future * Numerous examples and problems framed using real data from Indiastat.com and CMIE highlight the business applications of marketing research methods * Marginal definitions reinforce critical concepts and provide simple descriptions for complex theories * Modern statistical software programs explain multivariate statistical techniques using a step-by-step approach

Table of Contents

  1. Cover
  2. Title Page
  3. Contents
  4. About the Authors
  5. Preface
  6. I Introduction to Marketing Research
    1. 1 Marketing Research: An Introduction
      1. 1.1 Introduction
      2. 1.2 Difference Between Basic and Applied Research 5
      3. 1.3 Defining Marketing Research 6
      4. 1.4 Roadmap to Learn Marketing Research 7
      5. 1.5 Marketing Research: A Decision Making Tool in the Hands of Management
      6. 1.6 Use of Software in Data Preparation and Analysis
      7. 1.7 Ethical Issues in Marketing Research
      8. Summary
      9. Key Terms
      10. Discussion Questions
      11. Case Study
    2. 2 Marketing Research Process Design
      1. 2.1 Introduction
      2. 2.2 Marketing Research Process Design
      3. Summary
      4. Key Terms
      5. Discussion Questions
      6. Case Study
  7. II Research Design Formulation
    1. 3 Measurement and Scaling
      1. 3.1 Introduction
      2. 3.2 What Should be Measured?
      3. 3.3 Scales of Measurement
      4. 3.4 Four Levels of Data Measurement
      5. 3.5 The Criteria for Good Measurement
      6. 3.6 Measurement Scales
      7. 3.7 Factors in Selecting an Appropriate Measurement Scale
      8. Summary
      9. Key Terms
      10. Discussion Questions
      11. Case Study
    2. 4 Questionnaire Design
      1. 4.1 Introduction
      2. 4.2 What is a Questionnaire?
      3. 4.3 Questionnaire Design Process
      4. Summary
      5. Key Terms
      6. Discussion Questions
      7. Case Study
    3. 5 Sampling and Sampling Distributions
      1. 5.1 Introduction
      2. 5.2 Sampling
      3. 5.3 Why Is Sampling Essential?
      4. 5.4 The Sampling Design Process
      5. 5.5 Random Versus Non-Random Sampling
      6. 5.6 Random Sampling Methods
      7. 5.7 Non-Random Sampling
      8. 5.8 Sampling and Non-Sampling Errors
      9. 5.9 Sampling Distribution
      10. 5.10 Central Limit Theorem
      11. 5.11 Sample Distribution of Sample Proportion
      12. Summary
      13. Key Terms
      14. Discussion Questions
      15. Numerical Problems
      16. Case Study
  8. III Sources and Collection of Data
    1. 6 Secondary Data Sources
      1. 6.1 Introduction
      2. 6.2 Meaning of Primary and Secondary Data
      3. 6.3 Benefits and Limitations of Using Secondary Data
      4. 6.4 Classification of Secondary Data Sources
      5. 6.5 Roadmap to Use Secondary Data
      6. Summary
      7. Key Terms
      8. Discussion Questions
      9. Case Study
    2. 7 Data Collection: Survey and Observation
      1. 7.1 Introduction
      2. 7.2 Survey Method of Data Collection
      3. 7.3 A Classification of Survey Methods
      4. 7.4 Evaluation Criteria for Survey Methods
      5. 7.5 Observation Techniques
      6. 7.6 Classification of Observation Methods
      7. 7.7 Advantages of Observation Techniques
      8. 7.8 Limitations of Observation Techniques
      9. Summary
      10. Key Terms
      11. Discussion Questions
      12. Case Study
    3. 8 Experimentation
      1. 8.1 Introduction
      2. 8.2 Defining Experiments
      3. 8.3 Some Basic Symbols and Notations in Conducting Experiments
      4. 8.4 Internal and External Validity in Experimentation
      5. 8.5 Threats to the Internal Validity of the Experiment
      6. 8.6 Threats to the External Validity of the Experiment
      7. 8.7 Ways to Control Extraneous Variables
      8. 8.8 Laboratory Versus Field Experiment
      9. 8.9 Experimental Designs and their Classification
      10. 8.10 Limitations of Experimentation
      11. 8.11 Test Marketing
      12. Summary
      13. Key Terms
      14. Discussion Questions
      15. Case Study
    4. 9 Fieldwork and Data Preparation
      1. 9.1 Introduction
      2. 9.2 Fieldwork Process
      3. 9.3 Data Preparation
      4. 9.4 Data Preparation Process
      5. 9.5 Data Analysis
      6. Summary
      7. Key Terms
      8. Discussion Questions
      9. Case Study
  9. IV Descriptive Statistics and Data Analysis
    1. 10 Descriptive Statistics: Measures of Central Tendency
      1. 10.1 Introduction
      2. 10.2 Central Tendency
      3. 10.3 Measures of Central Tendency
      4. 10.4 Prerequisites for an Ideal Measure of Central Tendency
      5. 10.5 Mathematical Averages
      6. 10.6 Positional Averages
      7. 10.7 Partition Values: Quartiles, Deciles, and Percentiles
      8. Summary
      9. Key Terms
      10. Discussion Questions
      11. Numerical Problems
      12. Case Study
    2. 11 Descriptive Statistics: Measures of Dispersion
      1. 11.1 Introduction
      2. 11.2 Measures of Dispersion
      3. 11.3 Properties of a Good Measure of Dispersion
      4. 11.4 Methods of Measuring Dispersion
      5. 11.5 Empirical Rule
      6. 11.6 Empirical Relationship Between Measures of Dispersion
      7. 11.7 Chebyshev’s Theorem
      8. 11.8 Measures of Shape
      9. 11.9 The Five-Number Summary
      10. 11.10 Box-and-Whisker Plots
      11. 11.11 Measures of Association
      12. Summary
      13. Key Terms
      14. Discussion Questions
      15. Numerical Problems
      16. Case Study
    3. 12 Statistical Inference: Hypothesis Testing for Single Populations
      1. 12.1 Introduction
      2. 12.2 Introduction to Hypothesis Testing
      3. 12.3 Hypothesis Testing Procedure
      4. 12.4 Two-Tailed and One-Tailed Tests of Hypothesis
      5. 12.5 Type I and Type II Errors
      6. 12.6 Hypothesis Testing for a Single Population Mean Using the z Statistic
      7. 12.7 Hypothesis Testing for a Single Population Mean Using the t Statistic (Case of a Small Random Sample When n < 30)
      8. 12.8 Hypothesis Testing for a Population Proportion
      9. Summary
      10. Key Terms
      11. Discussion Questions
      12. Numerical Problems
      13. Case Study
    4. 13 Statistical Inference: Hypothesis Testing for Two Populations
      1. 13.1 Introduction
      2. 13.2 Hypothesis Testing for the Difference Between Two Population Means Using the z Statistic
      3. 13.3 Hypothesis Testing for the Difference Between Two Population Means Using the t Statistic (Case of a Small Random Sample, n1, n2 < 30, When Population Standard Deviation is Unknown)
      4. 13.4 Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)
      5. 13.5 Hypothesis Testing for the Difference in Two Population Proportions
      6. 13.6 Hypothesis Testing About Two Population Variances (F Distribution)
      7. Summary
      8. Key Terms
      9. Discussion Questions
      10. Numerical Problems
      11. Case Study
    5. 14 Analysis of Variance and Experimental Designs
      1. 14.1 Introduction
      2. 14.2 Introduction to Experimental Designs
      3. 14.3 Analysis of Variance
      4. 14.4 Completely Randomized Design (One-Way ANOVA)
      5. 14.5 Randomized Block Design
      6. 14.6 Factorial Design (Two-Way ANOVA)
      7. Summary
      8. Key Terms
      9. Discussion Questions
      10. Numerical Problems
      11. Case Study
    6. 15 Hypothesis Testing for Categorical Data (Chi-Square Test)
      1. 15.1 Introduction
      2. 15.2 Defining χ2-Test Statistic
      3. 15.3 χ2 Goodness-of-Fit Test
      4. 15.4 χ2 Test of Independence: Two-Way Contingency Analysis
      5. 15.5 χ2 Test for Population Variance
      6. 15.6 χ2 Test of Homogeneity
      7. Summary
      8. Key Terms
      9. Discussion Questions
      10. Numerical Problems
      11. Case Study
    7. 16 Correlation and Simple Linear Regression Analysis
      1. 16.1 Measures of Association
      2. 16.2 Introduction to Simple Linear Regression
      3. 16.3 Determining the Equation of a Regression Line
      4. 16.4 Using MS Excel for Simple Linear Regression
      5. 16.5 Using Minitab for Simple Linear Regression
      6. 16.6 Using SPSS for Simple Linear Regression
      7. 16.7 Measures of Variation
      8. 16.8 Statistical Inference About Slope, Correlation Coefficient of the Regression Model, and Testing the Overall Model
      9. Summary
      10. Key Terms
      11. Discussion Questions
      12. Numerical Problems
      13. Case Study
    8. 17 Multivariate Analysis I: Multiple Regression Analysis
      1. 17.1 Introduction
      2. 17.2 The Multiple Regression Model
      3. 17.3 Multiple Regression Model with Two Independent Variables
      4. 17.4 Determination of Coefficient of Multiple Determination (R2), Adjusted R2, and Standard Error of the Estimate
      5. 17.5 Statistical Significance Test for the Regression Model and the Coefficient of Regression
      6. 17.6 Indicator (Dummy Variable Model)
      7. 17.7 Collinearity
      8. Summary
      9. Key Terms
      10. Discussion Questions
      11. Numerical Problems
      12. Case Study
    9. 18 Multivariate Analysis lI: Discriminant Analysis and Conjoint Analysis
      1. 18.1 Discriminant Analysis
      2. 18.2 Conjoint Analysis
      3. Summary
      4. Key Terms
      5. Discussion Questions
      6. Case Study
    10. 19 Multivariate Analysis III: Factor Analysis, Cluster Analysis, Multidimensional Scaling and Correspondence Analysis
      1. 19.1 Factor Analysis
      2. 19.2 Cluster Analysis
      3. 19.3 Multidimensional Scaling
      4. 19.4 Correspondence Analysis
      5. Summary
      6. Key Terms
      7. Discussion Questions
      8. Case Study
    11. 20 Sales Forecasting
      1. 20.1 Introduction
      2. 20.2 Types of Forecasting Methods
      3. 20.3 Qualitative Methods of Forecasting
      4. 20.4 Time Series Analysis
      5. 20.5 Components of Time Series
      6. 20.6 Time Series Decomposition Models
      7. 20.7 The Measurement of Errors in Forecasting
      8. 20.8 Quantitative Methods of Forecasting
      9. 20.9 Freehand Method
      10. 20.10 Smoothing Techniques
      11. 20.11 Exponential Smoothing Method
      12. 20.12 Double Exponential Smoothing
      13. 20.13 Regression Trend Analysis
      14. 20.14 Seasonal Variation
      15. 20.15 Solving Problems Involving all Four Components of Time Series
      16. 20.16 Autocorrelation and Autoregression
      17. Summary
      18. Key Terms
      19. Discussion Questions
      20. Numerical Problems
      21. Case Study
  10. V Result Presentation
    1. 21 Presentation of Result: Report Writing
      1. 21.1 Introduction
      2. 21.2 Organization of the Written Report
      3. 21.3 Tabular Presentation of Data
      4. 21.4 Graphical Presentation of Data
      5. 21.5 Oral Presentation
      6. Summary
      7. Key Terms
      8. Discussion Questions
      9. Case Study
  11. VI Applications of Marketing Research
    1. 22 Marketing Mix Research: Product, Price, Place and Promotion Research
      1. 22.1 Introduction
      2. 22.2 Marketing Mix: Meaning
      3. 22.3 New Product Research
      4. 22.4 Pricing Research
      5. 22.5 Distribution (Place) Research
      6. 22.6 Promotional Research
      7. Summary
      8. Key Terms
      9. Discussion Questions
      10. Case Study
  12. Appendix