The course is composed by two modules: Quantitative Marketing Research and Statistical Data Analysis
The two modules of the course will be assessed separately as described below. Both parts are worth 50% of the final grade of the course. To pass the course, you need to pass both parts, with a minimum grade of 5 for each part.
Quantitative Marketing Research
Marketing professionals rely on information gathered through marketing research to determine the course of action of a marketing plan. Leveraging upon research to know more about customers, consumers, and citizens is crucial for effective marketing decisions.
In this Course, students will learn how to be smart producers and consumers of information, when it comes to marketing research. They will also learn to be more effective marketing decision makers. Specifically, this course will enable students to:
Class content follows the key steps of the research process: research design, sampling, data collection, data analysis, and presentation of the findings.
The Course is organized interactively, and alternates face-to-face lectures with in-class exercise, and group assignments with software-assisted training (SPSS, Qualtrics). It also offers guest lectures and self-assessment moments (i.e. in class exercises).
40% group assignment
60% final written exam
K. MALHOTRA NARESH, Marketing Research: An Applied Orientation, Pearson Education. (selected chapters).
Selected readings (a detailed reading list will be available on iCorsi).
Statistical Data Analysis
This course addresses market research design, data collection, and data interpretation based-upon quantitative data and techniques.
Methods that are specifically developed along the course include questionnaire design and survey, experiments, and panel data. Data analysis familiarizes participants with statistical data analysis - the art of examining, summarising and drawing conclusions from data. This includes the organisation of a coherent database and its use to produce statistical summaries and inference. Statistical software is essential in this respect. The course builds on students´ knowledge of introductory level statistics, such as frequency, distribution and correlation and
introduces new topics like hypothesis testing on means, percentage and regression, multiple regression, logistic regression, factor analysis and cluster analysis