Research design from scratch - You will learn how to turn a research problem into a clear goal, scope, questions and hypotheses, so you can build a coherent study design and avoid collecting data in a random, unfocused way.
Right method for the goal - You will learn when to use quantitative, qualitative and experimental approaches, and how to match each technique to the problem so your findings are relevant, comparable and useful for decisions.
Stronger sampling decisions - You will master sampling principles and sample size planning, which will help you design studies more reliably, reduce errors and increase the credibility of the conclusions drawn from your data.
Efficient data analysis - You will practice processing, coding, categorizing and analyzing data, making it easier to organize research material, draw sound conclusions and prepare datasets for reporting and further use.
Statistics without confusion - You will understand how to use means, standard deviation, regression and hypothesis testing in practice, so you can read results correctly and avoid common statistical interpretation mistakes.
Modern research tools - You will explore eye tracking, facial expression analysis and gamification in research, helping you design more innovative measurements and better capture participant behavior and reactions.
Clear reports and charts - You will learn how to present findings through reports and charts, so you can highlight key relationships, make data easier to understand and prepare materials ready for practical use.
More confident interpretation - You will strengthen your ability to interpret social phenomena and track trends, allowing you to connect numbers with context, spot meaningful shifts and formulate more useful recommendations.
Defining the objective, scope, research questions, and hypotheses sets the direction for the entire study. This section concentrates on the evaluation and research process at its starting point, helping translate a general problem into a structured design that can be examined empirically.
At the organisational level, social research must be arranged so that methods and techniques fit the purpose, scope, and available resources. The section frames how research activities are structured, creating a basis for later choices between quantitative, qualitative, and experimental approaches.
For studies based on measurable variables, the process begins with a clear hypothesis and research objective. This section follows the logic of sample selection, data collection, processing, and analysis, while grounding each stage in practical examples of quantitative research execution.
Working with qualitative data involves a sequence from defining the research objective and selecting the sample to collection, categorization, coding, and analysis. The section also explains how material can be converted into pseudo-quantitative form and illustrated through practical research examples.
Experimental design is presented through laboratory, field, semi-field, framework, and natural experiments. The focus is on how different settings influence control, realism, and interpretation, so participants can select an experimental format suited to the research problem and operating conditions.
Mean, standard deviation, regression, sample size determination, and hypothesis testing form the statistical foundation for evidence-based research. Rather than treating them as isolated formulas, the section shows how these methods support measurement quality, analytical rigor, and defensible inference.
When organisations need to detect change over time, trend research helps distinguish temporary signals from durable shifts. The section addresses how to observe developments in attitudes, behaviors, or markets and use longitudinal insight to support planning, evaluation, and strategic decisions.
Eye-tracking, facial expression analysis, and gamification expand how researchers collect and enrich evidence beyond standard questionnaires. The section examines where these technologies fit into research design, what they measure, and how they support quantitative studies with additional behavioral signals.
Social phenomena rarely speak for themselves, which is why interpretation must connect empirical material with context and meaning. The section focuses on reading behaviors, opinions, and interactions in a way that supports explanation, avoids simplistic conclusions, and informs further analytical work.
Choosing the right research technique depends on the problem, target group, and expected type of evidence. This part compares surveys, case analyses, in-depth interviews, focus groups, observation, and experiments, then shows how to match methods and techniques to a specific research project.
Statistical analysis is addressed as the stage where collected data is examined for patterns, differences, and relationships relevant to the research objective. The section links processing steps with interpretation, so numerical results can support valid conclusions rather than isolated calculations.
Effective reporting requires more than listing results, so the focus here is on structuring findings for decision-making and evaluation purposes. Participants work on presenting conclusions clearly, linking evidence to research questions, and maintaining consistency between analysis, visuals, and narrative.
Charts turn processed data into visual material that supports interpretation and communication of findings. The section covers creating appropriate chart types, using them in data analysis, and exporting results for reports, presentations, or further circulation across teams.
The closing module consolidates the full research workflow, from defining questions and choosing methods to analysis, reporting, and visual presentation of results. It is used to connect quantitative, qualitative, experimental, and technological approaches into one coherent evaluation process.
Basic data handling - You should be comfortable working with tables and simple data summaries so you can organize information efficiently and follow the results discussed during the training.
Introductory statistics - You should know basic statistical concepts such as mean, variation and relationships between variables, so you can move more easily into analysis and hypothesis testing.
Understanding research basics - You should understand what a research goal, research questions and hypotheses are, so you can actively take part in study design and assess whether methods fit the task.
Readiness for case analysis - You should be ready to analyze research examples and draw conclusions from case studies, because the training is strongly based on practical applications of methods and techniques.
