|
Instructors: Dr. Yvonne Feucht; Prof. Dr. Thorsten Teichert
Event type:
Lecture + practical course
Displayed in timetable as:
Quant. Research Meth
Hours per week:
3
Credits:
6,0
Language of instruction:
English
Min. | Max. participants:
- | -
Comments/contents:
This course applies a hands-on approach to market research by diving into the current topic of meat consumption: Reduction and shift to meat substitutes). It will enable you to conduct a market research project from A to Z. The course comprises all steps from the generation of research questions to the analysis of self-collected data to the deductions of implications for marketing. We will look into implicit as well as explicit attitude measures as well as psychological covariates. We will apply segmentation techniques as well as causal-effects models.
The focus of our research will be the uptake of meat substitutes and willingness to reduce meat consumption. We will look into drivers of the consumption of meat and its’ substitutes and derive from our findings ideas for the marketing of meat substitutes.
Learning objectives:
The course enables students to handle and solve complex research questions in marketing and social science. Based on examples of (business) strategy students get to know complex multivariate analysis methods and how to apply them on their own. By obtaining knowledge and the ability to use statistical software packages, students also qualify to perform operational empirical analysis in a research project, consultancy, and professional practice.
By the end of the course, it is expected that students will be able to:
1. Demonstrate the ability to transform marketing problems into research hypotheses and proposal.
2. Display the ability to develop proper measurement to collect data on the variables that are relevant to address the research hypotheses.
3. Demonstrate the ability to select and utilize proper statistic tools to assess the dataset and the research hypotheses.
The skills mentioned above are key for a prospective student becoming an empirical researcher in the fields of social science, especially in market research.
Didactic concept:
Schedule
Integrated lecture and exercise < in italics >
Homework: Execution of own empirical research (survey, a pre-designed questionnaire will be available and developed further during the lecture)
1 Course Introduction and Overview <KW 42>
- Deriving Hypotheses
- Primary and Secondary Data
- Deriving a Research Proposal
- Our Topic: “Meat consumption”
< Teams & Selection of Topics & Hypotheses >
2 Foundations of Questionnaire Design <KW 43>
< Students present their ideas on their research topics>
- Measurement, Errors and Data for Consumer Research
- Explicit Measurement Instruments
- Implicit Measurement Instruments
- Implicit Attention
-
- Implicit Attitudes
-
- Approach and Avoidance
- Vignettes for Experimental Research
Homework: < Pretest Questionnaire Start in KW43 after LV, End in KW45 before LV>
<KW 44 National Holiday, no lecture>
3 Questionnaire Specification & Data Collection <KW 45 + KW46>
< Students present their insights from the pretest / data collection >
- Manipulation Checks
- Refine Questionnaire
- Sampling
<KW 45 after Lecture; Start: Three Weeks of Data Collection >
4 Data Preparation <KW47>
<Students discuss data issues and finalize Data Preparation within teams >
<KW48 48hours before Lecture; End: Three Weeks of Data Collection>
Descriptive Statistics <KW48>
- Visualization: Scatter and Box-Plots
- Outlier Treatment
<Students derive descriptive statistics for their topics >
Data Reduction <week 7 KW49>
- Factor Analysis
- Scoring Techniques (e.g. IAT-score)
<Students implement data reduction for their topics >
Basic Methods for Hypothesis Testing <KW50>
- Mean Comparisons
- Analysis of Variance
- Association Analysis
<Students perform initial hypotheses tests >
Segmentation and Positioning Techniques <KW 51 /KW 52>
- Target Segment Strategy
- Cluster Analysis
- Multidimensional Scaling
- Correspondence Analysis
<Students apply data reduction methods for their topics >
Causal-Effects Models <KW53 / KW54>
- Correlation and Regression
- Logit Analyses and Discrete Choice Models
- Complex Causal-Effects Models (latent variables)
<Students derive causal effects models for their topics >
KW55 Wrap-Up
Additional examination information:
Written research report (60%), and presence and active participation in exercises as well as participation in a market research project including two presentations (40%). To pass the course, passing the research report and taking part in the exercises and the presentations is a precondition. Both parts have to be passed at least with a 4.0 in order to pass this course. Exceptions like an exam are not possible.
|
