Overall goal:
Upon successful completion of this course, students are expected to be able distinguish between research questions of behavioral and design research, to select adequate statistical techniques to answer different types of research questions, to analyse quantitative data, to interpret findings related to the research question/problem statement, and to draw respective conclusions.
Intended Learning Outcomes
Students are expected to be able to:
- Formulate research questions of design and/or behavioral research that they can answer using quantitative methods.
- Independently conduct multivariate analysis, for instance as part of their master thesis (application level)
- Systematically use a basic methodological toolbox for business analytics (application level)
- Characterise requirements and assumptions of various multivariate statistical techniques (comprehension cognition level)
- Understand the methods and results section of scientific articles
- Better judge the quality of analyses and the validity of empirical findings.
- Make well-grounded decisions with regard to the empirical part of the master thesis (integration level)
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The course provides all MSc Business Administration students with both active and passive knowledge on multivariate statistical techniques typically used in empirical business research studies. Active knowledge comprises the abilities to choose the adequate multivariate technique for combinations of data and research questions, to autonomously conduct multivariate analyses using statistical software such as SPSS, R, or ADANCO, and to interpret and report the obtained results. Passive knowledge refers to the ability to critically reflect upon the assumptions made as well as the reliability and validity of multivariate analyses.
This course conveys this knowledge by covering four families of multivariate techniques:
1) Factor analysis (more specifically, principal component analysis and common factor analysis) as a technique for data reduction and modelling of latent variables which is often used in behavioral research,
2) Analysis of (Co)Variance AN(C)OVA) as comprehensive techniques to analyse the outcomes of experiments and to validate interventions and other artifacts resulting from design research,
3) Multiple linear regression as the cornerstone of econometrics to analyse (causal) relationships between one dependent and several independent variables.
4) Structural equation modelling as a technique for theory-testing combining factor analysis and multiple linear regression.
The multivariate techniques will be presented in different forms. First, each statistical technique will be explained in detail. Subsequently, the technique will be central in a corresponding assignment. For the assignments, the techniques have to be applied using statistical software, e.g. SPSS, R, or ADANCO, to analyse a given dataset. The assignments have to be carried out independently by small groups of students. In the corresponding assignment submission, students have to elaborate on the assumptions, choices made that are relevant for the specific statistical technique, to interpret and to report the relevant findings, and to discuss the scientific and/or managerial implications. The actual statistical analyses of the data will be facilitated by computer classes (tutorials).
As a follow-up in the so-called application lectures, findings will be presented by student groups and discussed. Furthermore, additional information on the strengths and limitations of the techniques and analyses will be discussed.
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Individual written exam that is weighted 100% of the final grade
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