The problem, addressed by the workshop
In qualitative and quantitative empirical studies, we all collect data and try to make sense of it (for potential strategies for this sense-making, see the second part of this workshop). For handling this data there are a lot of rules, how-to-do-guidelines, and standards which we follow and we finally end up with an aggregation of data (categories, codes, means, variances or other coefficients) that we publish and that is the backbone of our study. In this process of collecting and aggregating data we necessarily focus on details and cannot always connect them to the big picture. When publishing or presenting our study we need to establish a good link between our data and this big picture. Then a number of questions arise, that need to be answered:
- What data do I need to present; what does the recipient expect?
- How much data do I need, what is sufficient?
- How can I prove that my interpretation of data is valid?
- Is my presentation of data clear enough?
Sometimes even the researcher her/himself gets lost in the mass of data and is less than sure what it all means.