Kategorie:M.Sc. Human Geography and Sustainability P4 Quantitative Methods

Aus GEOWiki@LMU
Wechseln zu:Navigation, Suche

M.Sc. Human Geography and Sustainability P4 Quantitative Methods


Zugeordnete Module

Lehrform Veranstaltung (Pflicht) Turnus Präsenzzeit Selbststudium ECTS
Vorlesung P 4.1 Quantitative Methods and Statistics (Lecture) WS 30 h (2 SWS) 60 h (3)
Übung P 4.2 Quantitative Methods and Statistics (Exercise) WS 30 h (2 SWS) 60 h 3

Informationen LSF
Verantwortliche(r) Prof. Dr. Claudia Binder
Verwendbarkeit des Moduls in anderen Studiengängen -
Ort der Lehrveranstaltung Luisenstr. 37
Zeitpunkt im Studienverlauf Empfohlenes Semester: 1
Dauer Das Modul erstreckt sich über 1 Semester


Modulinhalte

Lecture Quantitative Methods and Statistics

  • Quantitative methods of data analysis employed within the areas of Human-Environment Relations and Human Geography
  • Provides an opportunity to identify common sources and types of quantitative data and critically reflect on the statistical methods applied to these


Exercise Quantitative Methods and Statistics

  • The module revises basic topics in statistics, including types of data variables, probability theory, sampling theory, and descriptive, inferential and goodness-of-fit statistical analyses
  • Additional topics include multivariate methods (factor analysis, ANOVA and multivariate regression), logistic regression, Bayesian statistics, and methodological concerns
  • Advanced topics methods to obtain more information
    • Cluster analysis
    • Non-parametric methods
    • Monte Carlo simulation
    • Q-Methodology
    • multi-level modelling
    • structural equation modelling
    • network analysis
  • Examples and exercises given during the module employ software supported by LMU
    • Excel
    • SPSS


Qualifikationsziele

  • Professional and methodological competences
    • understand the major sources of quantitative data in the areas of Human-Environment Relations and Human Geography
    • apply suitable statistical analysis methods to these data
    • critically reflect on analyses found in social science literature


  • Social and personal competences
    • perform several common types of statistical analyses with the aid of computer software
    • discuss more advanced statistical techniques and how to learn more about their use




Diese Kategorie enthält zurzeit keine Seiten oder Medien.