Quantitative Research Methods
Niveau
2nd semester Master: 1st Study Cycle
Learning outcomes of the courses/module
The students:
• are able to calculate basic statistical parameters.
• are able to develop variables on the basis of scales.
• are able to create a data set with different variables for software-based statistical analysis.
• know the difference between descriptive and inferential statistics.
• are able to apply advanced tools and techniques of statistics to business-related problems.
• are able to interpret the results of statistical analyses and to derive business-related decisions from them.
• are able to set up a system for the early detection of business crises.
• are able to calculate basic statistical parameters.
• are able to develop variables on the basis of scales.
• are able to create a data set with different variables for software-based statistical analysis.
• know the difference between descriptive and inferential statistics.
• are able to apply advanced tools and techniques of statistics to business-related problems.
• are able to interpret the results of statistical analyses and to derive business-related decisions from them.
• are able to set up a system for the early detection of business crises.
Prerequisites for the course
2. Semester: no information
Course content
I. Basics:
• Differentiation of quantitative from qualitative research
• Empirical data and distributions of data (discrete vs. continuous distribution, distribution functions, visualization of distributions, etc.)
• Data search and creation of a database for software-based analysis
II. Descriptive Statistics:
• Definition and calculation of selected statistical parameters (mean, median, maximum, minimum, variance, standard deviation, etc.)
• Measures of correlation between several series of measurements (covariance, correlation)
• Dealing with outliers in data
III. Closing statistics:
• Tests for differences (ANOVA, t-test, U-test, H-test etc.)
• Supplementary correlation analysis (factor analysis, principal component analysis)
IV. Questionnaire design and scale evaluation
• Application of scales and development of a questionnaire
• Implementation of a pre-test
• Development of constructs
• Confirmatory factor analysis and Cronbach's alpha
V. Selected statistical techniques
• Univariate and multivariate regression analysis
• Multivariate linear discriminant analysis
• Logistic regression
• Differentiation of quantitative from qualitative research
• Empirical data and distributions of data (discrete vs. continuous distribution, distribution functions, visualization of distributions, etc.)
• Data search and creation of a database for software-based analysis
II. Descriptive Statistics:
• Definition and calculation of selected statistical parameters (mean, median, maximum, minimum, variance, standard deviation, etc.)
• Measures of correlation between several series of measurements (covariance, correlation)
• Dealing with outliers in data
III. Closing statistics:
• Tests for differences (ANOVA, t-test, U-test, H-test etc.)
• Supplementary correlation analysis (factor analysis, principal component analysis)
IV. Questionnaire design and scale evaluation
• Application of scales and development of a questionnaire
• Implementation of a pre-test
• Development of constructs
• Confirmatory factor analysis and Cronbach's alpha
V. Selected statistical techniques
• Univariate and multivariate regression analysis
• Multivariate linear discriminant analysis
• Logistic regression
Recommended specialist literature
Exler, M. & Situm M. (Hrsg.) (2025) Transformations- und Turnaround-Management: Strategien, Erfolgsfaktoren und Best Practice, Berlin: Erich Schmidt.
Fahrmeir, L., Heumann, C., Künstler, R., Pigeot, I., & Tutz, G. (2023). Statistik: Der Weg zur Datenanalyse. Berlin: Springer.
Jones, S. (2023). Distress risk and corporate failure modelling: The state of the art. New York, NY: Routledge.
Puhani, J. (2025). Statistik: Einführung mit praktischen Beispielen. Wiesbaden: Springer.
Wagner-Huang, W. E. (2026). Using SPSS® for research methods and social statistics. Thousand Oaks, CA: Sage.
Yhip, T. M., & Alagheband, B., M. D. (2020). The practice of lending: A guide to credit analysis and credit risk. Cham: Springer.
Fahrmeir, L., Heumann, C., Künstler, R., Pigeot, I., & Tutz, G. (2023). Statistik: Der Weg zur Datenanalyse. Berlin: Springer.
Jones, S. (2023). Distress risk and corporate failure modelling: The state of the art. New York, NY: Routledge.
Puhani, J. (2025). Statistik: Einführung mit praktischen Beispielen. Wiesbaden: Springer.
Wagner-Huang, W. E. (2026). Using SPSS® for research methods and social statistics. Thousand Oaks, CA: Sage.
Yhip, T. M., & Alagheband, B., M. D. (2020). The practice of lending: A guide to credit analysis and credit risk. Cham: Springer.
Assessment methods and criteria
Exam
Language
German
Number of ECTS credits awarded
2
Semester hours per week
Planned teaching and learning method
A part of the course will be covered through e-learning. This involves a combination of online phases (an inductive method for independently developing knowledge and practicing tasks) and in-person phases (a deductive method in which support is provided during the learning process and knowledge is also conveyed through lectures).
Semester/trimester in which the course/module is offered
2