
In qualitative research projects, questions often arise about the intersubjectivity of the analysis. Given the same interview passage, for example, does my fellow researcher see the same topics addressed as I do, and do they draw the same conclusions? To what extent do we agree on our understanding of categories? With these questions we are entering the field of quality criteria, which should not be neglected in qualitative research. In category-based approaches, the focus is placed on the question to what extent two people identify the same topics, aspects, and phenomena in the data and assign these to the same categories. It is quite possible for two people to agree in terms of content, but assign different categories to a phenomenon, because the category definitions have not yet been clearly formulated. MAXQDA offers numerous (partly interactive) functions, which enable systematic analysis, improvement, and verification of the agreement between coders. Problematic categories, misleading instructions, and blurred category definitions can be identified to improve the quality of analysis step-by-step.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 10 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
