| Short description of the master´s programme |
Year 1: Strong foundation
In the first year, the Research Master students will gain a strong foundation in research methodology and applied statistics.
Year 2: Elective EMOS and (preparation for) master's thesis
In the second year, the Research Master students will take elective courses. In this year all activities related to EMOS will take place
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| Entrance criteria (note of Bachelor degree, language, other) |
A Dutch academic bachelor’s degree in the social, biomedical or behavioural sciences, or a foreign academic bachelor degree that equals the level of that Dutch academic bachelor’s degree, for the programme’s admissions committee to decide. Students with another academic bachelor’s degree and a thorough training in formal models and/or quantitative analysis such as students with an academic bachelor’s degree in computer sciences, econometrics or mathematics can also be admitted to MSBBSS;
In addition for all programmes requirements for consideration for admission are:
- GPA:
- a minimum GPA in undergraduate studies of 3.4 (7.5 in the Dutch system). Candidates who do not meet this criterion, will be considered for admission in case they have at least a minimum GPA of 3.0 (7.0 in the Dutch system) and can compensate for the insufficient GPA by other capacities they have, according to the programme’s admissions committee;
- students who attend one of the pre-master courses referred to in the first paragraph must earn a minimum GPA of 3.4 (7.5 in the Dutch system) for the courses in that pre-master course.
- advanced knowledge of methods and statistics (approximately 20 credits) which entails (i) a basic course in introductory statistics including topics such: univariate descriptive statistics, correlation, univariate regression, one-way analysis of variance (both descriptive and inferential) and a basic course in the methodology of behavioural and/or social science research (experimentation, surveys, observational studies); (ii) knowledge of multivariate analysis tools such as factor analysis, reliability, multiple regression, analysis of variance, dummy variables; (iii) hands on experience with the tools just mentioned (for example, experience with SPSS);
- one letter of recommendation for candidates with a Dutch preliminary training, two letters of recommendation for candidates with a foreign preliminary training.
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