Research Methods II: Quantitative Analysis
Niveau
2nd study cycle, Master
Learning outcomes of the courses/module
The students are able to:
• distinguish causality from correlation and design empirical analyses accordingly.
• implement and interpret multivariate methods of regression analysis.
• transfer research questions from business practice into a model framework and test them by hypothesis formation.
• explain the standard model of OLS regression and critically reflect limitations / potentials of results.
• use statistical software such as STATA or R to independently implement empirical analyses.
• distinguish causality from correlation and design empirical analyses accordingly.
• implement and interpret multivariate methods of regression analysis.
• transfer research questions from business practice into a model framework and test them by hypothesis formation.
• explain the standard model of OLS regression and critically reflect limitations / potentials of results.
• use statistical software such as STATA or R to independently implement empirical analyses.
Prerequisites for the course
Course Research Methods I
Course content
• Multivariate methods and OLS regression
• Estimation of coefficients with hypothesis tests
• Interpretation of indicators for goodness of fit model
• Multicollinearity and heteroskedasty
• Statistical software like STATA or R
• Estimation of coefficients with hypothesis tests
• Interpretation of indicators for goodness of fit model
• Multicollinearity and heteroskedasty
• Statistical software like STATA or R
Recommended specialist literature
• Wooldridge, Jeffrey: Introductory Econometrics A Modern Approach. Cengage Learning (latest edition)
• Heiss, Florian: Using R for Introductory Econometrics. CreateSpace Independent Publishing Platform (latest edition)
• Stock, James; Watson, Mark: Introduction to Econometrics. Pearson Education Limited (latest edition)
• Heiss, Florian: Using R for Introductory Econometrics. CreateSpace Independent Publishing Platform (latest edition)
• Stock, James; Watson, Mark: Introduction to Econometrics. Pearson Education Limited (latest edition)
Assessment methods and criteria
Online tasks, term paper, exam
Language
English
Number of ECTS credits awarded
4
Share of e-learning in %
25
Semester hours per week
2.0
Planned teaching and learning method
Blended Learning
Semester/trimester in which the course/module is offered
2
Name of lecturer
Prof. (FH) Dr. Peter Dietrich
Academic year
Key figure of the course/module
05.MV.RSM.2
Type of course/module
integrated lecture
Type of course
Compulsory
Internship(s)
none