Market Research & Methods
Niveau
1st Master cycle
Learning outcomes of the courses/module
The participants:
• know qualitative and quantitative methods of analysis
• know the design aspects of questionnaires: Question types, scale levels, alignment and dimensionality
• can evaluate questionnaires and analyze and interpret them using descriptive and simple inferential statistics: Data collection, processing and evaluation
• can develop and formulate research questions and create and process research hypotheses
• can create and send an online questionnaire and collect and evaluate the data accordingly
• can apply inferential statistical evaluation procedures (e.g. in SPSS, Jasp, Datatab, Excel etc.)
• can create an interview guideline, conduct and evaluate expert interviews
• can conduct a qualitative content analysis
• can perform and interpret descriptive analyses and inferential statistical tests to describe data, test hypotheses and draw generalizable conclusions
• can select, apply and critically evaluate various AI-supported tools for data analysis and visualization
• can adapt their research results to different target groups and communicate them effectively
• can explain and apply the ethical principles of research with regard to data protection, anonymity and informed consent
• know qualitative and quantitative methods of analysis
• know the design aspects of questionnaires: Question types, scale levels, alignment and dimensionality
• can evaluate questionnaires and analyze and interpret them using descriptive and simple inferential statistics: Data collection, processing and evaluation
• can develop and formulate research questions and create and process research hypotheses
• can create and send an online questionnaire and collect and evaluate the data accordingly
• can apply inferential statistical evaluation procedures (e.g. in SPSS, Jasp, Datatab, Excel etc.)
• can create an interview guideline, conduct and evaluate expert interviews
• can conduct a qualitative content analysis
• can perform and interpret descriptive analyses and inferential statistical tests to describe data, test hypotheses and draw generalizable conclusions
• can select, apply and critically evaluate various AI-supported tools for data analysis and visualization
• can adapt their research results to different target groups and communicate them effectively
• can explain and apply the ethical principles of research with regard to data protection, anonymity and informed consent
Prerequisites for the course
Academic Methods
Course content
• Secondary, qualitative, quantitative research
• Questionnaire development and design
• Data modeling, coding and test procedure selection
• Introduction to online questionnaire design: Question variants, dynamic content, sending and evaluating responses
• Selected descriptive indicators, extension by inferential statistical methods
• Procedure and execution of interviews, transcription, coding of texts (category formation and generalization)
• The content-analytical procedure model (qualitative content analysis)
• Basics of data analysis: descriptive statistics and inferential statistics
• Use of AI-supported tools for research and data visualization
• Presentation and communication of research results
• Ethics in research: data protection, anonymity, informed consent
• Questionnaire development and design
• Data modeling, coding and test procedure selection
• Introduction to online questionnaire design: Question variants, dynamic content, sending and evaluating responses
• Selected descriptive indicators, extension by inferential statistical methods
• Procedure and execution of interviews, transcription, coding of texts (category formation and generalization)
• The content-analytical procedure model (qualitative content analysis)
• Basics of data analysis: descriptive statistics and inferential statistics
• Use of AI-supported tools for research and data visualization
• Presentation and communication of research results
• Ethics in research: data protection, anonymity, informed consent
Recommended specialist literature
• Magerhans, A. (2016): Marktforschung, Eine praxisorientierte Einführung
• Kuß, A./ Wildner, R./ Kreis, H. (2014): Marktforschung, Grundlagen der Datenerhebung und Datenanalyse
• Koch, J./ Gebhardt, P. (2016): Marktforschung: Grundlagen und praktische Anwendungen
• Theobald, A. (2017): Praxis Online-Marktforschung, Grundlagen - Anwendungsbereiche - Durchführung
• Porst, R. (2014): Fragebogen, Ein Arbeitsbuch
• Kaiser, R. (2014): Qualitative Experteninterviews, Konzeptionelle Grundlagen und praktische Durchführung
• Mayring, P. (2015): Qualitative Inhaltsanalyse: Grundlagen und Techniken
• Cleff, T. (2015): Deskriptive Statistik und Explorative Datenanalyse
• Duller, C. (2013): Einführung in die Statistik mit EXCEL und SPSS, 3. Aufl.
• Kuß, A./ Wildner, R./ Kreis, H. (2014): Marktforschung, Grundlagen der Datenerhebung und Datenanalyse
• Koch, J./ Gebhardt, P. (2016): Marktforschung: Grundlagen und praktische Anwendungen
• Theobald, A. (2017): Praxis Online-Marktforschung, Grundlagen - Anwendungsbereiche - Durchführung
• Porst, R. (2014): Fragebogen, Ein Arbeitsbuch
• Kaiser, R. (2014): Qualitative Experteninterviews, Konzeptionelle Grundlagen und praktische Durchführung
• Mayring, P. (2015): Qualitative Inhaltsanalyse: Grundlagen und Techniken
• Cleff, T. (2015): Deskriptive Statistik und Explorative Datenanalyse
• Duller, C. (2013): Einführung in die Statistik mit EXCEL und SPSS, 3. Aufl.
Assessment methods and criteria
Projektarbeit
Language
German
Number of ECTS credits awarded
6
Share of e-learning in %
35
Semester hours per week
3.0
Planned teaching and learning method
ILV (Blended Learning, Inverted Classroom)
Semester/trimester in which the course/module is offered
2
Name of lecturer
n.N.
Academic year
1
Key figure of the course/module
6_MFM
Type of course/module
integrated lecture
Type of course
Compulsory
Internship(s)
no