Important Information

This course is only offered for Master students.

Prerequisites

Erasmus students must have successfully completed a basic/introductory marketing course at their home university. To be able to attend the course they must hand in a relevant transcript/certificate by March 6th, 2023.

Course Objectives

The course seeks to introduce core concepts and techniques associated with the design and execution of marketing research projects. It focuses particularly on the research process, data collection issues, questionnaire design, sampling and measurement. Students also gain hands-on experience by con-ducting a marketing research project.

Reading List

The reading list will be available on Moodle.

Prerequisites

It is recommended that Erasmus students have successfully completed a basic/introductory marketing course at their home university. DAMD is a prerequisite for the Seminar Marketing.

Course Objectives

Sound knowledge of statistics and data analysis is an essential requirement for marketing research and managerial decision-making – more than one would expect! The present course discusses (a) key concepts of statistics and statistical inference (e.g., NHST, Type I and II error, confidence intervals) and (b) different methods of data analysis (e.g., t-test, χ² test, ANOVA, regression analysis) in a series of lectures that combine theory, illustrative examples, and hands-on exercises. Although IBM SPSS software is used in the context of DAMD, the course is not a tutorial on the SPSS software package. Rather it focuses on the logic, the implementation, and the interpretation of statistical data analysis in general. Students who successfully complete DAMD will be equipped with the ability to effectively carry out data analysis projects in their later academic and professional career.

Prerequisites

It is recommended that Erasmus students have successfully completed a basic/introductory marketing course at their home university.

Course Objectives

Quantitative methods, data analytics, and statistical analysis is a key component of decision-making for business practitioners and policy makers. This course aims to equip students with the hands-on knowledge and skills necessary to implement, interpret, and communicate quantitative data analysis using computer software and other tools.

The course assumes that students already have a theoretical understanding of statistical inference and basic knowledge of key concepts in research methods. Hence, the emphasis is not placed on analytical theory, but on training students in analyzing data to predict behavioral tendencies (e.g., relative prod-uct preferences, purchase choices, and willingness to pay), make forecasts about future outcomes (e.g., likelihood of customer switching, probability of being hired/fired, and expected product sales), make comparisons (e.g., across gender, nationality, or market segments), and assess the efficacy of alterna-tive interventions.