Syllabus for Quantitative Methods

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A revised version of the syllabus is available.

  • 7.5 credits
  • Course code: 2ST106
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Statistics A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (G), Pass with distinction (VG)
  • Established: 2008-05-29
  • Established by: The Department Board
  • Revised: 2011-02-28
  • Revised by:
  • Applies from: week 35, 2011
  • Entry requirements: To be admitted to the course, undergraduate degree of at least 180 credits is required, of which at least 90 credits are within Social Science, or equivalent.
  • Responsible department: Department of Statistics

Learning outcomes

A student who has taken the course should:
- have obtained practice in applying analytical methods for the social and the behavioural sciences
- be able to use statistical software packages for the analysis of statistical data
- be able to interpret the results of a statistical analysis
- be aware of limitations and possible sources of errors in the analysis
- have ability to both in oral and written form present results of statistical analysis

Content

Practical application of statistical methods used within the social and the behavioural sciences Data collection and sampling methods. Inference, relationships between variables, correlation, regression analysis, models for dichotomous/polytomous dependent variables and analysis of questionnaire data are examples that are treated during the course.

Instruction

Teaching is given in the form of lectures and seminars.

Assessment

Examination and assignments.

Reading list

The reading list is missing. For further information, please contact the responsible department.