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
Bonart, T. & Bär, J. (2018) Quantitative Betriebswirtschaftslehre - Band I: Grundlagen, Operations Research, Statistik, Wiesbaden.
Burns, A. & Burns, A. (2008) Business research methods and statistics using SPSS, London.
Eckstein, P. P. (2016) Angewandte Statistik mit SPSS: Praktische Einführung für Wirtschaftswissenschaftler, Wiesbaden.
Exler, M. W. & Situm, M. (2019) (Hrsg.) Restrukturierungs- und Turnaround-Management: Strategien, Erfolgsfaktoren und Best Practice für die Transformation, Berlin.
Foster, J., Barkus, E. & Yavorsky, C. (2006) Understanding and using advanced statistics, London.
Burns, A. & Burns, A. (2008) Business research methods and statistics using SPSS, London.
Eckstein, P. P. (2016) Angewandte Statistik mit SPSS: Praktische Einführung für Wirtschaftswissenschaftler, Wiesbaden.
Exler, M. W. & Situm, M. (2019) (Hrsg.) Restrukturierungs- und Turnaround-Management: Strategien, Erfolgsfaktoren und Best Practice für die Transformation, Berlin.
Foster, J., Barkus, E. & Yavorsky, C. (2006) Understanding and using advanced statistics, London.
Assessment methods and criteria
• Written exam
• Online questions
• Online questions
Language
German
Number of ECTS credits awarded
3
Share of e-learning in %
33
Semester hours per week
1.0
Planned teaching and learning method
• Blended Learning
Semester/trimester in which the course/module is offered
2
Name of lecturer
Situm Mario
Academic year
1
Key figure of the course/module
1
Type of course/module
seminar-degree
Type of course
Compulsory
Internship(s)
none