Table of Contents
1a. Introduction to Marketing Research ... 4
What is the relationship of marketing research to marketing, the marketing concept and marketing strategy ... 4
How to define marketing research ... 4
The function and uses of marketing research ... 4
How to describe a marketing information system (MIS) and understand why marketing research occupies a place in a MIS ... 5
1b. The Marketing Research Industry ... 6
Evolution of an Industry ... 6
Who Conducts Marketing Research? ... 6
The Industry Structure ... 7
Challenges to the Marketing Research Industry ... 7
Industry Initiatives ... 7
2. The Marketing Research Process ... 9
The Steps in the Marketing Research Process ... 9
The Importance and Process of Defining the Problem ... 11
How to Formulate Research Objectives ... 13
What an Action Standard is and Why it can be Helpful ... 14
The Components of the Marketing Research Proposal ... 14
3a. Research Design and Secondary Data ... 16
What research design is and why it is important ... 16
The three major types of research design: exploratory, descriptive, and causal ... 16
How exploratory research design helps the researcher gain understanding of a problem ... 16
The fundamental questions addressed by descriptive research and the different types of descriptive research ... 18
What is meant by causal research and to describe types of experimental research designs ... 19
The different types of test marketing and how to select test-market cities ... 21
3b. Secondary Data ... 24
The meaning of the term big data ... 24
The differences between primary and secondary data ... 24
The different classifications of secondary data, including internal and external databases ... 24
The advantages and disadvantages of secondary data ... 25
How to evaluate secondary data ... 26
What packaged information is and the differences between syndicated data and packaged services ... 26
The advantages and disadvantages of packaged information ... 27
The applications of packaged information ... 28
The uses of social media data and their advantages and disadvantages ... 28
What the Internet of Things is and its future potential ... 30
4a. Qualitative Research Techniques ... 31
The differences between quantitative and qualitative research techniques ... 31
The pros and cons of using observation as a means of gathering data ... 31
What focus groups are and how they are conducted and analysed ... 32
What enthnographic research is and its strengths and weaknesses ... 35
To learn how marketing research online communities (MROCs) are used ... 36
Other qualitative methods used by marketing researchers, including in-depth interviews, protocol analysis, projective techniques, and neuromarketing ... 37
4b. Evaluating Survey Data Collection Methods ... 40
Advantages of surveys ... 40
The various modes of survey administration based on whether or not an interviewer or a computer is present ... 40
Descriptions of nine different methods of data collection ... 43
How marketing researchers work with panel companies to collect data ... 48
Various considerations to ponder when selecting a specific method of data collection ... 49
5. Understanding Measurement, Developing Questions and Designing the Questionnaire ... 50
What the basic concepts of measurement are ... 50
Types of Measures ... 51
Three interval scales that are commonly used in marketing research ... 52
Reliability and Validity of Measurements ... 54
Designing a Questionnaire ... 54
How to develop questions including dos and don’ts ... 55
The recommended organisation of questions and sections of a questionnaire ... 56
How computer-assisted questionnaire design simplifies and expedites this process ... 58
What coding and pretesting entail ... 59
6. Developing the Sampling Plan ... 60
Basic concepts involved with samples and sampling ... 60
The reasons for taking a sample ... 60
Differences between probability and nonprobability sampling ... 60
How to perform each of four different types of probability sampling ... 61
Nonsampling Methods ... 65
Online Sampling Techniques ... 67
The steps involved with developing a sampling plan ... 67
7. Dealing with Fieldwork and Data Quality Issues ... 68
What constitutes sampling error ... 68
Possible Errors in Field Data Collection ... 68
Field Data Collection Quality Controls ... 70
Control of Intentional Fieldworker Error ... 70
Control of Unintentional Fieldworker Error ... 71
Control of Intentional Respondent Error ... 71
Control of Unintentional Respondent Error ... 72
Nonresponse Error ... 72
What is a Completed Interview? ... 73
Measuring Response Rate in Surveys ... 73
How Panel Companies Control Errors ... 73
Data Quality Issues in Data Sets ... 74
8. Using Descriptive Analysis, Performing Population Estimates and Testing Hypotheses ... 76
Types of Statistical Analyses Used in Marketing Research ... 76
Understanding Descriptive Analysis ... 77
Measures of Central Tendency: Summarising the “Typical” Respondent ... 77
Measures of Variability: Relating the Diversity of Respondents ... 78
When to Use a Particular Descriptive Measure ... 79
Reporting Descriptive Statistics to Clients ... 79
Reporting Scale Data (Ratio and Interval Scales) ... 79
Reporting Nominal or Categorical Data ... 80
Statistical Inference: Sample Statistics and Population Parameters ... 81
Parameter Estimation: Estimating the Population Percent or Mean ... 81
Reporting Confidence Intervals to Clients ... 83
Hypothesis Tests ... 83
How to report hypothesis tests to clients ... 84
10: Implementing Basic Differences Tests ... 85
Why Differences are Important ... 85
Small Sample Sizes: The Use of a t Test or a z Test ... 86
Testing for Significant Differences Between Two Groups ... 86
Testing for Significant Differences in Means Among More Than Two Groups: Analysis of Variance ... 87
Reporting Group Differences Tests to Clients ... 89
Differences Between Two Means Within the Same Sample (Paired Sample) ... 89
Null Hypotheses for Differences Tests Summary ... 90
11. Making Use of Associations Test ... 91
Types of Relationships Between Two Variables ... 91
Characterising Relationships Between Variables ... 92
Correlation Coefficients and Covariation ... 93
The Pearson Product Moment Correlation Coefficient ... 94
Reporting Correlation Findings to Clients ... 94
Cross-Tabulations ... 95
Chi-Square Analysis ... 97
Reporting Cross-Tabulation Findings to Clients ... 98
Special Considerations in Association Procedures ... 98
8. Using Descriptive Analysis, Performing Population Estimates and Testing Hypotheses
Types of Statistical Analyses Used in Marketing Research
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Dataset – an arrangement of numbers (mainly) in rows and columns. The columns represent answers to the various questions on the survey questionnaire, and the rows represent each respondent
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The problem confronting the marketing researcher when faced with a dataset is data analysis
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Data analysis is the process of describing a dataset by computing a small number of statistics that characterise various aspects of the dataset
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Data analysis distils the dataset while retaining enough information so the client can mentally envision its salient characteristics
Descriptive Analysis
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Descriptive analysis is used to describe the variables (question responses) in a dataset (all respondents’ answers)
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Certain measures such as the mean, mode, standard deviation and range are forms of descriptive analysis used by marketing researchers to describe the sample dataset in such a way as to portray the “typical” respondent and to reveal the general pattern of responses
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Descriptive measures are typically used early in the analysis process and become foundations for subsequence analysis
Inference Analysis
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When statistical procedures are used by marketing researchers to generalise the results of the sample to the target population that it represents, the process is an inference analysis
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Inference analysis is used to generate conclusions about the population’s characteristics based on the sample data
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Such statistical procedures allow a researcher to draw conclusions about the population based on information contained in the dataset
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Inferential statistics include hypothesis testing and estimating true population values using confidence intervals
Difference Analysis
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Difference analysis is used to compare the mean of the responses of one group to that of another group
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The researcher uses difference analysis to determine the degree to which real and generalisable differences exist in the population in order to help the manager make an enlightened decision on which advertising theme to use
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Statistical differences analyses include the t-test for significant differences
Association Analysis
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Association analysis investigates if and how two variables are related
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Association analysis determines the strength and direction of relationships between two or more variables (questions in the survey)
Relationships Analysis
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Statistical procedures and models are available to the marketing researcher to help make forecasts about future events; these fall under the category of relationships analysis
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Relationships analysis allows insights into multiple relationships among variables
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Regression analysis is commonly used by the marketing researcher to understand these connections
Understanding Descriptive Analysis
Two sets of measures are used to describe the information obtained in a sample:
1. Measures of central tendency or measures that describe the “typical” respondent or response 2. Measures of variability or measures that describe how similar (dissimilar) respondents or responses
are to (from) “typical” respondents or responses
Measures of Central Tendency: Summarising the “Typical” Respondent
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The basic data analysis goal involved in all measures to central tendency is to report a single piece of information that describes the most typical response to a question
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Central tendency applies to any statistical measure used that somehow reflects a typical or frequent response
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3 measures:
Mode
•With a set of numbers, the mode is that number appearing most often
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The mode is a relative measure of central tendency, for it does not require that a majority of responses occurred for this value. Instead, it simply specifies the value that occurs most frequently and there is no requirement that this occurrence is 50% or more
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It can only take on any value as long as it is the most frequently occurring number
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If a tie for the mode occurs, the distribution is considered to be “bimodal”, or
“trimodal” for a three-way tie
Median
•The median expresses the value whose occurrence lies in the middle of a set of ordered values
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The median tells us the approximate halfway point Mean
(Average)
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It approximates the typical value in that set of values
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