Session 1: Introduction to Research Methods, Variables & Misconceptions
Introduce types of research methodologies: quantitative, qualitative, and mixed-methods; understand positivist vs. interpretivist paradigms.
Define types of variables, research aims, and identify common data interpretation mistakes.
Practice: Learners analyze real-life research scenarios and classify the methodology, paradigm, and variables used.
Session 2: Sampling Techniques & Questionnaire Design
Understand sampling strategies: probability vs. non-probability, and how to determine an appropriate sample size.
Explore principles of questionnaire design for data validity and reliability; consider ethical concerns like informed consent.
Practice: Design a short questionnaire using Google Forms or SurveyMonkey.
Session 3: The Art of Story-telling: How to bring emotion and knowledge into Data Interpretation
Build effective dashboards and apply storytelling frameworks (setup-insight-action).
Tailor presentations for different audiences (academic, executive, NGO) and avoid misleading visuals.
Practice: Create a dashboard for data storytelling.
Session 4&5: Quantitative Research (2 sessions)
Conduct statistical analysis: descriptive statistics, ANOVA-tests, correlation, and regression.
Clean and process quantitative data, define variable types, and interpret outputs.
Practice: Input a survey dataset, run descriptive statistics, and create visual summaries.
Session 6&7: Qualitative Research (2 sessions)
Analyze textual data: code transcripts, organize nodes, and identify key themes and narratives.
Understand the interpretivist approach, qualitative rigor, and the logic of open/axial coding.
Practice: Analyze data from diverse industries (marketing, retail, sales, human resources, hospitality, etc) to explore and communicate insights.
Session 8: Mixed Methods, Data Triangulation & Integration Tools
Combine qualitative and quantitative results through data triangulation for deeper insights.
Visualize integrated findings and draw well-supported conclusions.
Practice: Apply mixed methods to analyze data from multiple industry cases.