book

Marketing Research

by Naval Bajpai

April 2015

Intermediate to advanced

656 pages

24h 23m

English

Pearson Education India

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1.1 Introduction1.2 Difference Between Basic and Applied Research 51.3 Defining Marketing Research 61.4 Roadmap to Learn Marketing Research 71.5 Marketing Research: A Decision Making Tool in the Hands of Management1.6 Use of Software in Data Preparation and Analysis1.7 Ethical Issues in Marketing ResearchSummaryKey TermsDiscussion QuestionsCase Study
2.1 Introduction2.2 Marketing Research Process DesignSummaryKey TermsDiscussion QuestionsCase Study
3.1 Introduction3.2 What Should be Measured?3.3 Scales of Measurement3.4 Four Levels of Data Measurement3.5 The Criteria for Good Measurement3.6 Measurement Scales3.7 Factors in Selecting an Appropriate Measurement ScaleSummaryKey TermsDiscussion QuestionsCase Study

4.1 Introduction4.2 What is a Questionnaire?4.3 Questionnaire Design ProcessSummaryKey TermsDiscussion QuestionsCase Study
5.1 Introduction5.2 Sampling5.3 Why Is Sampling Essential?5.4 The Sampling Design Process5.5 Random Versus Non-Random Sampling5.6 Random Sampling Methods5.7 Non-Random Sampling5.8 Sampling and Non-Sampling Errors5.9 Sampling Distribution 5.10 Central Limit Theorem5.11 Sample Distribution of Sample Proportion SummaryKey TermsDiscussion QuestionsNumerical ProblemsCase Study
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 DataSummaryKey TermsDiscussion QuestionsCase Study
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 TechniquesSummaryKey TermsDiscussion QuestionsCase Study
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 Classification8.10 Limitations of Experimentation8.11 Test MarketingSummaryKey TermsDiscussion QuestionsCase Study
9.1 Introduction 9.2 Fieldwork Process 9.3 Data Preparation 9.4 Data Preparation Process 9.5 Data AnalysisSummaryKey TermsDiscussion QuestionsCase Study
10.1 Introduction10.2 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 PercentilesSummaryKey TermsDiscussion QuestionsNumerical ProblemsCase Study
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 Summary11.10 Box-and-Whisker Plots11.11 Measures of AssociationSummaryKey TermsDiscussion QuestionsNumerical ProblemsCase Study
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 ProportionSummaryKey TermsDiscussion QuestionsNumerical ProblemsCase Study
13.1 Introduction13.2 Hypothesis Testing for the Difference Between Two Population Means Using the z Statistic13.3 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 Proportions13.6 Hypothesis Testing About Two Population Variances (F Distribution)SummaryKey TermsDiscussion QuestionsNumerical ProblemsCase Study
14.1 Introduction 14.2 Introduction to Experimental Designs14.3 Analysis of Variance14.4 Completely Randomized Design (One-Way ANOVA)14.5 Randomized Block Design 14.6 Factorial Design (Two-Way ANOVA)SummaryKey TermsDiscussion QuestionsNumerical ProblemsCase Study
15.1 Introduction 15.2 Defining χ2-Test Statistic15.3 χ2 Goodness-of-Fit Test15.4 χ2 Test of Independence: Two-Way Contingency Analysis15.5 χ2 Test for Population Variance15.6 χ2 Test of HomogeneitySummaryKey TermsDiscussion QuestionsNumerical ProblemsCase Study
16.1 Measures of Association16.2 Introduction to Simple Linear Regression16.3 Determining the Equation of a Regression Line16.4 Using MS Excel for Simple Linear Regression16.5 Using Minitab for Simple Linear Regression16.6 Using SPSS for Simple Linear Regression16.7 Measures of Variation16.8 Statistical Inference About Slope, Correlation Coefficient of the Regression Model, and Testing the Overall ModelSummaryKey TermsDiscussion QuestionsNumerical ProblemsCase Study
17.1 Introduction17.2 The Multiple Regression Model17.3 Multiple Regression Model with Two Independent Variables17.4 Determination of Coefficient of Multiple Determination (R2), Adjusted R2, and Standard Error of the Estimate17.5 Statistical Significance Test for the Regression Model and the Coefficient of Regression17.6 Indicator (Dummy Variable Model)17.7 CollinearitySummaryKey TermsDiscussion QuestionsNumerical ProblemsCase Study
18.1 Discriminant Analysis18.2 Conjoint AnalysisSummaryKey TermsDiscussion QuestionsCase Study
19.1 Factor Analysis19.2 Cluster Analysis19.3 Multidimensional Scaling19.4 Correspondence AnalysisSummaryKey TermsDiscussion QuestionsCase Study
20.1 Introduction20.2 Types of Forecasting Methods20.3 Qualitative Methods of Forecasting20.4 Time Series Analysis20.5 Components of Time Series20.6 Time Series Decomposition Models20.7 The Measurement of Errors in Forecasting20.8 Quantitative Methods of Forecasting20.9 Freehand Method20.10 Smoothing Techniques20.11 Exponential Smoothing Method20.12 Double Exponential Smoothing20.13 Regression Trend Analysis20.14 Seasonal Variation20.15 Solving Problems Involving all Four Components of Time Series20.16 Autocorrelation and AutoregressionSummaryKey TermsDiscussion QuestionsNumerical ProblemsCase Study
21.1 Introduction21.2 Organization of the Written Report21.3 Tabular Presentation of Data21.4 Graphical Presentation of Data21.5 Oral PresentationSummaryKey TermsDiscussion QuestionsCase Study
22.1 Introduction22.2 Marketing Mix: Meaning22.3 New Product Research22.4 Pricing Research22.5 Distribution (Place) Research 22.6 Promotional ResearchSummaryKey TermsDiscussion QuestionsCase Study

Overview

* 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