Main Field of Study and progress level:
Statistics: Second cycle, has only first-cycle course/s as entry requirements
Grading scale: Pass with distinction, Pass, Fail
Responsible department: Department of Statistics
Contents
Module 1: Scientific Method and Observational Studies - 1.5 ECTS credits
Module 2: Analysis of Data - 3 ECTS credits (seminars run in collaboration with different departments upon decision of the different Master Programs)
Module 3: Workshop/Clinic (subject-matter dependent) - 3 ECTS credits (run by different departments upon decision of the different Master programs)
Module 1:
Scientific method: A general discussion of the philosophy and role of probability and statistics in scientific method, with the aim to help the student see the wider context of different research strategies used in different fields of study. For instance, issues to be discussed include the nature of inferences (e.g. hypothesis testing) and predictions in the context of scientific method as well as issues such as the measurement process, publication bias, negative results and causality.
Observational studies: The nature of observational studies, prospective, retrospective; Survey research methods; Examples of good and bad study designs; Stratification; Avoidance of pitfalls (e.g. in multiple regression); Simpsons paradox, causality, and
evaluation studies with longitudinal register databases.
Module 2- Analysis of data:
The focus is on the understanding of statistical reasoning in analysis of data and research.
Type and structure of data: different measurement levels, different types of variables (e.g. qualitative and quantitative), normal distributed data, censored data and longitudinal data.
Types of analysis: Comparing two or several groups, analyzing relationships between variables (e.g. response and explanatory variables), model based analysis (regression, multi-level, etc.), and the role of the likelihood function and validation diagnostics.
Common to Module 1-2: Small group seminars (homogenous within master program) where the focus is on interpretation of scientific articles including quantitative analysis. Participation to seminars is part of the examination.
Module 3:
This module deals with specific problems arising in different scientific fields. The module is run in small groups (homogenous within a master program). Problems are specified in collaboration with scientists from other disciplines (across the Faculty of
Social Sciences) depending on the students program affiliation attending the course. The problems are worked out within the small groups with supervision. A short presentation by students with discussion, and a final written report are part of the examination.
Expected learning outcomes
Upon successful completion of the course, the student is expected to:
- Have knowledge of relevant statistical terminology
- Have knowledge of the roll of statistics as a scientific method
- Be aware of the difficulties and dangers of applying methods available in statistical software without having a sound understanding of the assumptions made
- Be able to understand and critically evaluate the content of scientific articles in their own scientific field
- Have acquired experience in the process of analyzing data within the relevant scientific context
- Be able to present orally and in writing results from their own quantitative analysis.
Required Knowledge
University: Courses of the amount of 90 ECTS credits within an bachelor in social sciences, or the necessary pre-requisites to be admitted at a Master program at Umeå University, or corresponding knowledge.
English proficiency equivalent to IELTS Academic Training minimum score 5.0 with no individual score below 4.5 (Tests taken before January 2005 not admissible or TOEFL minimum score 500 on paper based test and not below 4.0 on the TWE, Alternatively 173 on computer based test with iBT61 is also required as well as basic entrance requirements for higher studies in Swedish language proficiency if the course is taught in Swedish.
Form of instruction
Lectures, seminars, computer assignments, and problem solving in group with supervision (see Content section above). All teaching will be in English.
Examination modes
Active participation at the lectures and seminars, as well as oral and written presentations at the workshop
Academic credit transfer
Academic credit transfers are reviewed individually. For more information, please see the Universitys set of rules and academic credit transfer regulations.
