Marketing Research

* 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 d...

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Detalles Bibliográficos
Autor principal: Bajpai, Naval (-)
Autor Corporativo: Naval Bajpai (-)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Noida : Pearson India 2015.
Edición:1st ed
Colección:Always learning.
Materias:
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009820525706719
Tabla de Contenidos:
  • Intro
  • Brief Contents
  • Contents
  • About the Author
  • Preface
  • I Introduction to Marketing Research
  • 1 Marketing Research: An Introduction
  • 1.1 Introduction
  • 1.2 Difference Between Basic and Applied Research
  • 1.3 Defining Marketing Research
  • 1.4 Roadmap to Learn Marketing Research
  • 1.5 Marketing Research: A Decision Making Tool in the Hands of Management
  • 1.6 Use of Software in Data Preparation and Analysis
  • 1.7 Ethical Issues in Marketing Research
  • Summary 19
  • Key Terms
  • Discussion Questions
  • Case Study
  • 2 Marketing Research Process Design
  • 2.1 Introduction
  • 2.2 Marketing Research Process Design
  • Summary 41
  • Key Terms
  • Discussion Questions
  • Case Study
  • II Research Design Formulation
  • 3 Measurement and Scaling
  • 3.1 Introduction
  • 3.2 What Should be Measured?
  • 3.3 Scales of Measurement
  • 3.4 Four Levels of Data Measurement
  • 3.5 The Criteria for Good Measurement
  • 3.6 Measurement Scales
  • 3.7 Factors in Selecting an Appropriate Measurement Scale
  • Summary
  • Key Terms
  • Discussion Questions
  • Case Study
  • 4 Questionnaire Design
  • 4.1 Introduction
  • 4.2 What is a Questionnaire?
  • 4.3 Questionnaire Design Process
  • Summary
  • Key Terms
  • Discussion Questions
  • Case Study
  • 5 Sampling and Sampling Distributions
  • 5.1 Introduction
  • 5.2 Sampling
  • 5.3 Why Is Sampling Essential?
  • 5.4 The Sampling Design Process
  • 5.5 Random Versus Non-Random Sampling
  • 5.6 Random Sampling Methods
  • 5.7 Non-Random Sampling
  • 5.8 Sampling and Non-Sampling Errors
  • 5.9 Sampling Distribution
  • 5.10 Central Limit Theorem
  • 5.11 Sample Distribution of Sample Proportion p
  • Summary
  • Key Terms
  • Discussion Questions
  • Numerical Problems
  • Case Study
  • III Sources and Collection of Data
  • 6 Secondary Data Sources
  • 6.1 Introduction
  • 6.2 Meaning of Primary and Secondary Data.
  • 6.3 Benefits and Limitations of Using Secondary Data
  • 6.4 Classification of Secondary Data Sources
  • 6.5 Roadmap to Use Secondary Data
  • Summary
  • Key Terms
  • Discussion Questions
  • Case Study
  • 7 Data Collection: Survey and Observation
  • 7.1 Introduction
  • 7.2 Survey Method of Data Collection
  • 7.3 A Classification of Survey Methods
  • 7.4 Evaluation Criteria for Survey Methods
  • 7.5 Observation Techniques
  • 7.6 Classification of Observation Methods
  • 7.7 Advantages of Observation Techniques
  • 7.8 Limitations of Observation Techniques
  • Summary
  • Key Terms
  • Discussion Questions
  • Case Study
  • 8 Experimentation
  • 8.1 Introduction
  • 8.2 Defining Experiments
  • 8.3 Some Basic Symbols and Notations in Conducting Experiments
  • 8.4 Internal and External Validity in Experimentation
  • 8.5 Threats to the Internal Validity of the Experiment
  • 8.6 Threats to the External Validity of the Experiment
  • 8.7 Ways to Control Extraneous Variables
  • 8.8 Laboratory Versus Field Experiment
  • 8.9 Experimental Designs and their Classification
  • 8.10 Limitations of Experimentation
  • 8.11 Test Marketing
  • Summary
  • Key Terms
  • Discussion Questions
  • Case Study
  • 9 Fieldwork and Data Preparation
  • 9.1 Introduction
  • 9.2 Fieldwork Process
  • 9.3 Data Preparation
  • 9.4 Data Preparation Process
  • 9.5 Data Analysis
  • Summary
  • Key Terms
  • Discussion Questions
  • Case Study
  • IV Descriptive Statistics and Data Analysis
  • 10 Descriptive Statistics: Measures of Central Tendency
  • 10.1 Introduction
  • 10.2 Central Tendency
  • 10.3 Measures of Central Tendency
  • 10.4 Prerequisites for an Ideal Measure of Central Tendency
  • 10.5 Mathematical Averages
  • 10.6 Positional Averages
  • 10.7 Partition Values: Quartiles, Deciles, and Percentiles
  • Summary
  • Key Terms
  • Discussion Questions
  • Numerical Problems
  • Case Study.
  • 11 Descriptive Statistics: Measures of Dispersion
  • 11.1 Introduction
  • 11.2 Measures of Dispersion
  • 11.3 Properties of a Good Measure of Dispersion
  • 11.4 Methods of Measuring Dispersion
  • 11.5 Empirical Rule
  • 11.6 Empirical Relationship Between Measures of Dispersion
  • 11.7 Chebyshev's Theorem
  • 11.8 Measures of Shape
  • 11.9 The Five-Number Summary
  • 11.10 Box-and-Whisker Plots
  • 11.11 Measures of Association
  • Summary
  • Key Terms
  • Discussion Questions
  • Numerical Problems
  • Case Study
  • 12 Statistical Inference: Hypothesis Testing for Single Populations
  • 12.1 Introduction
  • 12.2 Introduction to Hypothesis Testing
  • 12.3 Hypothesis Testing Procedure
  • 12.4 Two-Tailed and One-Tailed Tests of Hypothesis
  • 12.5 Type I and Type II Errors
  • 12.6 Hypothesis Testing for a Single Population Mean Using the z Statistic
  • 12.7 Hypothesis Testing for a Single Population Mean Using the t Statistic (Case of a Small Random Sample When n < 30)
  • 12.8 Hypothesis Testing for a Population Proportion
  • Summary
  • Key Terms
  • Discussion Questions
  • Numerical Problems
  • Case Study
  • 13 Statistical Inference: Hypothesis Testing for Two Populations
  • 13.1 Introduction
  • 13.2 Hypothesis Testing for the Difference Between Two Population Means Using the z Statistic
  • 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)
  • 13.4 Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)
  • 13.5 Hypothesis Testing for the Difference in Two Population Proportions
  • 13.6 Hypothesis Testing About Two Population Variances (F Distribution)
  • Summary
  • Key Terms
  • Discussion Questions
  • Numerical Problems
  • Case Study.
  • 14 Analysis of Variance and Experimental Designs
  • 14.1 Introduction
  • 14.2 Introduction to Experimental Designs
  • 14.3 Analysis of Variance
  • 14.4 Completely Randomized Design (One-Way ANOVA)
  • 14.5 Randomized Block Design
  • 14.6 Factorial Design (Two-Way ANOVA)
  • Summary
  • Key Terms
  • Discussion Questions
  • Numerical Problems
  • Case Study
  • 15 Hypothesis Testing for Categorical Data (Chi-Square Test)
  • 15.1 Introduction
  • 15.2 Defining x2-Test Statistic
  • 15.3 x2 Goodness-of-Fit Test
  • 15.4 x2 Test of Independence: Two-Way Contingency Analysis
  • 15.5 x2 Test for Population Variance
  • 15.6 x2 Test of Homogeneity
  • Summary
  • Key Terms
  • Discussion Questions
  • Numerical Problems
  • Case Study
  • 16 Correlation and Simple Linear Regression Analysis
  • 16.1 Measures of Association
  • 16.2 Introduction to Simple Linear Regression
  • 16.3 Determining the Equation of a Regression Line
  • 16.4 Using MS Excel for Simple Linear Regression
  • 16.5 Using Minitab for Simple Linear Regression
  • 16.6 Using SPSS for Simple Linear Regression
  • 16.7 Measures of Variation
  • 16.8 Statistical Inference About Slope, Correlation Coefficient of the Regression Model, and Testing the Overall Model
  • Summary
  • Key Terms
  • Discussion Questions
  • Numerical Problems
  • Case Study
  • 17 Multivariate Analysis I: Multiple Regression Analysis
  • 17.1 Introduction
  • 17.2 The Multiple Regression Model
  • 17.3 Multiple Regression Model with Two Independent Variables
  • 17.4 Determination of Coefficient of Multiple Determination (R2), Adjusted R2, and Standard Error of the Estimate
  • 17.5 Statistical Significance Test for the Regression Model and the Coefficient of Regression
  • 17.6 Indicator (Dummy Variable Model)
  • 17.7 Collinearity
  • Summary
  • Key Terms
  • Discussion Questions
  • Numerical Problems
  • Case Study.
  • 18 Multivariate Analysis lI: Discriminant Analysis and Conjoint Analysis
  • 18.1 Discriminant Analysis
  • 18.2 Conjoint Analysis
  • Summary
  • Key Terms
  • Discussion Questions
  • Case Study
  • 19 Multivariate Analysis III: Factor Analysis, Cluster Analysis, Multidimensional Scaling and Correspondence Analysis
  • 19.1 Factor Analysis
  • 19.2 Cluster Analysis
  • 19.3 Multidimensional Scaling
  • 19.4 Correspondence Analysis
  • Summary
  • Key Terms
  • Discussion Questions
  • Case Study
  • 20 Sales Forecasting
  • 20.1 Introduction
  • 20.2 Types of Forecasting Methods
  • 20.3 Qualitative Methods of Forecasting
  • 20.4 Time Series Analysis
  • 20.5 Components of Time Series
  • 20.6 Time Series Decomposition Models
  • 20.7 The Measurement of Errors in Forecasting
  • 20.8 Quantitative Methods of Forecasting
  • 20.9 Freehand Method
  • 20.10 Smoothing Techniques
  • 20.11 Exponential Smoothing Method
  • 20.12 Double Exponential Smoothing
  • 20.13 Regression Trend Analysis
  • 20.14 Seasonal Variation
  • 20.15 Solving Problems Involving all Four Components of Time Series
  • 20.16 Autocorrelation and Autoregression
  • Summary
  • Key Terms
  • Discussion Questions
  • Numerical Problems
  • Case Study
  • V Result Presentation
  • 21 Presentation of Result: Report Writing
  • 21.1 Introduction
  • 21.2 Organization of the Written Report
  • 21.3 Tabular Presentation of Data
  • 21.4 Graphical Presentation of Data
  • 21.5 Oral Presentation
  • Summary
  • Key Terms
  • Discussion Questions
  • Case Study
  • VI Applications of Marketing Research
  • 22 Marketing Mix Research: Product, Price, Place and Promotion Research
  • 22.1 Introduction
  • 22.2 Marketing Mix: Meaning
  • 22.3 New Product Research
  • 22.4 Pricing Research
  • 22.5 Distribution (Place) Research
  • 22.6 Promotional Research
  • Summary
  • Key Terms
  • Discussion Questions
  • Case Study
  • Appendix
  • Index.