We've used a lot of keywords to help you find everything quickly and define them in the glossary at the end of the book. Online appendices to the book are freely available on the accompanying website and provide additional information on analysis techniques not covered in the book and datasets.
Acknowledgments
His research has been published in journals such as Journal of Marketing Research, Journal of the Academy of Marketing Science, Organizational Research Methods, MIS Quarterly and International Journal of Research in Marketing. His research has been published in journals such as Journal of Marketing, Journal of Marketing Research, International Journal of Research in Marketing and Journal of Business Research.
Introduction to Market Research
- Introduction – 2
- What Is Market and Marketing Research? – 3
- Market Research by Practitioners and Academics – 4 1.4 When Should Market Research (Not)
- Who Provides Market Research? – 5 1.6 Review Questions – 8
- Introduction
- What Is Market and Marketing Research?
- Market Research by Practitioners and Academics
- When Should Market Research (Not) Be Conducted?
- Who Provides Market Research?
- Review Questions
Many organizations have people, departments or other companies that work for them to provide market research. In larger organizations, a subdivision of the marketing department usually conducts internally supplied market research.
A choice between these full-service and limited-service market research firms boils down to a trade-off between what they can provide (if this is highly specialized, you may not have much choice) and the cost of doing so. American Marketing Association at http://www.marketingpower.com British Market Research Society at http://www.mrs.org.uk.
The Market Research Process
- Introduction – 12
- Identify and Formulate the Problem – 13 2.3 Determine the Research Design – 13
- Design the Sample and Method of Data Collection – 22
- Collect the Data – 22 2.6 Analyze the Data – 22
- Interpret, Discuss, and Present the Findings – 23 2.8 Follow-Up – 23
- Review Questions – 23 References – 23
- Introduction
- Identify and Formulate the Problem
- Determine the Research Design
- Exploratory Research
- Uses of Exploratory Research
- Descriptive Research
- Uses of Descriptive Research
- Causal Research
- Uses of Causal Research
- Design the Sample and Method of Data Collection
- Collect the Data
- Analyze the Data
- Interpret, Discuss, and Present the Findings
- Follow-Up
- Review Questions
For example, where exploratory research can help formulate problems accurately or structure them, causal research provides accurate insight into how variables relate. For example, compare Qualtrics' marketing research process (http://www.qualtrics.com/blog/marketing-research-process) with the process discussed above.
Data
Dependence of Observations – 32
Review Questions – 44 References – 44
Armstrong and Overton procedure • Case • Census • Constant • Construct • Construct validity • Content validity • Criterion validity • Dependence on observations • Discriminant validity • Equidistance • Face validity • Formative constructs • Index • Index construct • Internal consistency reliability • Inter-rater reliability • Items • Latent concept • Latent variable • Measurement scaling • Multi-item constructs • Net Promoter Score (NPS) • Nomological validity • Non-probability sampling • Observation • Operationalization • Population • Predictive validity • Primary data • Probability sampling • Qualitative data • Quantitative data • Reflective constructs • Reliability • Sample size • Sampling • Sampling Error • Scale Development • Secondary Data • Single-Item Constructs • Test-Retest Reliability • Unit of Analysis • Validity • Variable.
Introduction
Types of Data
- Primary and Secondary Data
- Quantitative and Qualitative Data
For example, constructs such as satisfaction, loyalty and brand trust cannot be measured directly. For example, in .Table 3.1, brand_1, brand_2, and brand_3 are items belonging to a construct called brand trust (as defined by Erdem and Swait 2004).
Unit of Analysis
Dependence of Observations
Dependent and Independent Variables
Measurement Scaling
For example, if the temperature in a car showroom is 23°C, we know that if it drops to 20°C, the difference is exactly 3°C. The difference between the interval and ratio scales can be ignored in most statistical methods.
Validity and Reliability
- Types of Validity
- Types of Reliability
For example, if you want to measure trust, using items like "this company is honest and truthful" makes a lot of sense, while "this company is not well known" does not make sense. For example, consider a set of questions commonly used to measure brand trust (i.e., "This brand's product claims are believable," "This brand delivers what it promises," and "This brand has a name that can you trust). ").
Population and Sampling
- Probability Sampling
- Non-probability Sampling
- Probability or Non-probability Sampling?
The most important aspect of sampling is that the selected sample is representative of the population. First you need to find out what proportion of the population are men and what proportion are women.
Sample Sizes
Review Questions
Getting Data
Collecting Primary Data through Experimental Research – 81
Introduction
Secondary Data
- Internal Secondary Data
- External Secondary Data
Take for example comments posted on a Facebook group site like BMW or Heineken. These pages also provide statistics, such as the number of mentions or complaints, thereby providing insight into.
Conducting Secondary Data Research
- Assess Availability of Secondary Data
- Assess Inclusion of Key Variables
- Assess Construct Validity
- Assess Sampling
For example, on many commercial mailing lists, 25% (or more) of the companies regularly included have outdated contact information. For example, many companies have changed their definitions of loyalty from behavioral (actual purchases) to attitudinal (commitment or intentions).
Conducting Primary Data Research
- Collecting Primary Data Through Observations
- Collecting Quantitative Data: Designing Surveys
Then determine the questions and scale, as well as the design of the questionnaire. In such a case, respondents tend to choose categories in the middle of the scale (Rammstedt and Krebs 2007).
Basic Qualitative Research
- In-depth Interviews
- Projective Techniques
- Focus Groups
There are ethical issues associated with conducting research if the participants are not aware of the research purpose. Although participants in projective techniques know that they are participating in market research, they may not be aware of the specific purpose of the research.
Collecting Primary Data through Experimental Research
- Principles of Experimental Research
- Experimental Designs
After the treatment, we wait for reactions to it and then measure the outcome of the manipulation (labeled O1), such as the participants' willingness to buy the advertised product. The random assignment of participants to an experimental and a control group (denoted by R) is an important element of this experimental design.
Oddjob Airways (Case Study)
Review Questions
The State of Survey Methodology Challenges, Dilemmas, and New Frontiers in the Age of Adapted Design, Field Methods. Effect of number of response categories and anchor labels on coefficient alpha and test-retest reliability.
Descriptive Statistics
Cadbury and the UK Chocolate Market (Case Study) – 149
Review Questions – 149 References – 150
Agreement • Aggregation • Bar chart • Bivariate statistics • Box plot • Codebook • Construction score • Correlation • Covariance • Cross tabulations • Data entry errors • Dummy variables • Extreme response styles • Frequency table • Histogram • Inconsistent responses • Index • Interquartile range • Interviewer fraud • Item non-response • Line chart • Listwise removal • Little's MCAR test • Log transformation • Mean • Measures of centrality • Measures of dispersion • Median • Middle response styles • Missing (completely or not) at random • Missing data • Multiple imputation • Outliers • Pie chart • Range • Standardization of range • Scale transformation • Scatterplot • Skewed data • SPSS • Standard deviation • Standardization variables • Straight-lining • Survey non-response • Suspicious response patterns • Transformative data • Univariate statistics • Variable respecification • Variance • Workflow • z-standardization.
The Workflow of Data
Create Structure
In the Data Files subfolders we distinguish between two files with the suffix rev1 and rev2. Descriptives.spv Factor analysis.spv Regression analysis.spv Temporary files Missing data analysis rev1.spv.
Enter Data
Clean Data
- Interviewer Fraud
- Suspicious Response Patterns
- Data Entry Errors
- Outliers
- Missing Data
When data are MCAR, observations with missing data are indistinguishable from those with complete data. According to the literature, the decision on the number of imputations, m, can be very challenging, especially when the patterns of the missing data are unclear.
Describe Data
- Univariate Graphs and Tables
- Univariate Statistics
- Bivariate Graphs and Tables
- Bivariate Statistics
If the mean and median are approximately the same, the variable is likely symmetrically distributed (that is, the left side of the distribution reflects the right side). If the observations form almost a straight diagonal line in the scatterplot (top left and top right of Figure 5.6), the two variables have a high (absolute) correlation.
Transform Data (Optional)
- Variable Respecification
- Scale Transformation
For example, we can use a dummy variable to indicate whether advertising was used in a given period (dummy value is 1) or not (dummy value is 0). For example, the three levels of flying intensity (low, medium, and high) can be represented by two dummy variables: the first has the value 1 if the intensity is high (0 otherwise), the second also has the value 1 if the intensity is medium (0 else).
Create a Codebook
For example, the log (ticket price) is much more difficult to interpret and less intuitive than simply using the ticket price.
The Oddjob Airways Case Study
- Introduction to SPSS
- Finding Your Way in SPSS
- SPSS Statistics Data Editor
- SPSS Statistics Viewer
- SPSS Menu Functions
When selecting user-defined missing values, take those that would not otherwise appear in the data. SPSS displays value labels in the SPSS Data Editor window by clicking ► View and then Value Labels.
Data Management in SPSS
- Split File
- Select Cases
- Compute Variables
- Recode Variables
Note that we chose to display the variable names in the variable box on the left side of the dialog box. You have now created a new dichotomous variable (ie, age_dummy) located at the bottom of the Variable View.
Example
- Clean Data
To do this, go to ► Analyze ► Descriptive Statistics ► Descriptions, which will open a dialog similar to .Fig. In the dialog box that opens (.Fig. 5.18), select Line under Selection and drag the first item into the chart builder box.
Cadbury and the UK Chocolate Market (Case Study)
Review Questions
Preventing Human Error: The Impact of Data Entry Methods on Data Accuracy and Statistical Results. Missing data in a multi-item instrument was best handled by multiple imputation at the item-score level.
Hypothesis Testing and ANOVA
Introduction – 153
Comparing More Than Two Means: Analysis of Variance (ANOVA) – 176
Example – 193
Customer Spending Analysis with IWD Market Research (Case Study) – 206
Review Questions – 207 References – 208
Inflation • α error • Adjusted R2 • Alternative hypothesis • Analysis of variance (ANOVA) • β error • Bonferroni correction • Confidence interval • Degrees of freedom • Directional hypothesis • Effect size • Eta squared • Variance explained • Level of factors • Factor variable • F-test • F-test of sample variance • Familywise error rate • Grand mean • Independent samples • Independent samples t-test • Kruskal-Wallis rank test • Left hypothesis • Levene's test • Mann-Whitney U test • Marginal mean • Noise • Non-parametric test. Non-Directional Hypothesis • Null Hypothesis • Omega-Squared • One-Sample T-Test • One-Tail Test • One-Way ANOVA • p-Value • Paired Samples • Paired-Sample T-Test • Parametric Test • Post Hoc Tests • Power Analysis • Power of a statistical test • Practical significance • Quantile plot • Random noise • R2 • Right-tailed hypothesis • Sampling error • Shapiro-Wilk test • Level of significance • Standard error • Statistical significance • T-test • Test statistics • Tukey's Honestly significant difference test • Two-sample t-test • Two-tailed test • Two-way ANOVA • Type I error • Type II error • Unexplained variance • Welch's correction • Signed-rank test with Wilcoxon matched pairs • Wilcoxon signed rank test • z-test.
Introduction
Understanding Hypothesis Testing
Hypothesis testing is performed to conclude that the stated hypothesis is likely to be true in the population of interest (Agresti and Finlay 2014). Then, depending on the claim made in the hypothesis, we must decide on the exact type of test to be performed.
Testing Hypotheses on One Mean
- Formulate the Hypothesis
- Choose the Significance Level
- Select the Appropriate Test
- Calculate the Test Statistic
- Make the Test Decision
- Interpret the Results
The null hypothesis of the Kruskal-Wallis rank test is that the distribution of the test variable across the subsamples of the group is identical (Schuyler 2011). If the value of the test statistic is greater than the critical value, we can reject the H0.
Two-Samples t -test
- Comparing Two Independent Samples
- Comparing Two Paired Samples
We can therefore reject the null hypothesis at a significance level of 5 % and conclude that the absolute difference between means of the point of sale display's sales (μ1) and those of the free trial stand (μ2) is significantly different from 0. In this we want for example, test whether the sales are significantly different with or without the installation of the point of sale display.
Comparing More Than Two Means: Analysis of Variance (ANOVA)
- Check the Assumptions
- Calculate the Test Statistic
- Make the Test Decision
- Carry Out Post Hoc Tests
- Measure the Strength of the Effects
- Interpret the Results
As the name already suggests, SSW describes the change in the dependent variable within each of the groups. eta squared is the ratio of between-group variation (SSB) to the total.