Advanced Statistics

Department
  • Master's Program International Health & Social Management
Course unit code
  • IHSM-M2.2
Number of ECTS credits allocated
  • 5.0
Name of lecturer(s)
  • FH-Prof. Dr. Mevenkamp Nils
Mode of delivery
  • -
Recommended optional program components
  • none
Recommended or required reading
  • Bruce, N., Pope, D., & Stanistreet, D. (2008). Quantitative methods for health research: A practical interactive guide to epidemiology and statistics. Chichester: Wiley.
    Diez, D. M., Barr, C. D., & Cetinkaya-Rundel, M. (2015). OpenIntro Statistics. URL: https://www.openintro.org/stat/textbook.php
    Field, A. (2009). Discovering statistics using SPSS: (and sex and drugs and rock'n'roll) (3rd ed.). Los Angeles, Calif.: Sage.
    Flick, U. (2011). Introducing research methodology: A beginner's guide to doing a research project. Los Angeles: Sage.
    Lane, D. M. Online Statistics Education: A Multimedia Course of Study. URL: http://onlinestatbook.com/
    Lowry, R. (1998-2013). Concepts and Applications of Inferential Statis-tics. URL: http://vassarstats.net/textbook/
    Siegel, S. (1956). Nonparametric Statistics for the Behavioral Scienc-es. New York u.a.: McGraw-Hill.
Level of course unit
  • Master
Year of study
  • Spring 2026
Semester when the course unit is delivered
  • 2
Language of instruction
  • English
Learning outcomes of the course unit
  • By the end of this course, students will be able to:
    • demonstrate basic knowledge and awareness of the role of mul-tivariate statistics in public health
    • understand concepts and applications of statistical models
    • understand and critically reflect empirical studies in the field of public health
Course contents
  • • Introduction & Repetition of Basic Concepts: Frequency distribu-tions, descriptive statistics, random sampling, theoretical distribu-tions, central limit theorem, hypothesis testing
    • Test hypotheses of differences: Contingency tables, chi-square-tests, rank size analysis, Compare means, t-test, ANOVA
    Test hypotheses of associations: Simple & multiple regression, nonlinear transformations
    Logistic regression: Contingency tables & odds ratios, multiple binary logistic regression
    Questionnaires: Questionnaire design, dos & don'ts in survey based research
    Indirect measurement of complex dimensions: Likert scaling, sum scores, reliability analysis, Cronbach's alpha
    Validation of item sets: Concepts of validity, factor analysis
Planned learning activities and teaching methods
  • The course comprises an interactive mix of lectures, discussions and individual and group work.
Work placement(s)
  • none

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