Advances in text mining together with the widespread adoption of the Internet have opened up new possibilities for qualitative researchers in the information systems and business and management fields. Easy access to large amounts of textual material through search engines, combined with automated techniques for analysis, promise to simplify the process of qualitative research. In practice this turns out not to be so easy. We outline a design research approach for building a five stage process for low tech, low cost text mining, which includes insights from the text mining literature and an experiment with trend analysis in business intelligence. We summarise the prototype process, and discuss the many difficulties that currently stand in the way of high quality research by this route. Despite the difficulties, the combination of low cost text mining with qualitative research is a promising methodological avenue, and we specify some future paths for this area of study. 

The possibility of there being an investigator with the knowledge and ability to understand, let alone digest the data embraced by the boundaries of social scientific disciplines seems very remote. However research investigators in the human sciences might in their quest for greater validity and wider generalization in their concepts and theories, adopt a different scientific perspective in addition to its currently most prevalent hypothetico‑deductive approach. The proposed approach made in the paper corresponds more to Baconian‑type methods and those adopted by Darwin in his theory of evolution and involves the use of large data sets of the kind made available by the methodological approaches of social physics and crowdsourcing. The paper suggests that large data sets can be analyzed effectively in multi‑disciplinary, cross‑cultural collaborative research contexts, and a case study illustrating a collaborative research process is described in detail to demonstrate this point. The paper maintains that through the combined utilization of scientific approaches and large data gathering techniques made available through new technologies, it may be possible to generate comprehensively valid empirical and theoretical wholes across the human sciences. 

The purpose of this paper is to describe the application of a Knowledge Café as an academic research technique. Knowledge Cafés are a tool for sharing knowledge among those individuals who participate in the conversations it encourages. Used in a novel way to non‑intrusively listen in on a group of well‑experienced e‑Government individuals, the Knowledge Café has provided a technique for critiquing a theoretical conjecture which has lead to its refinement. This paper describes the steps involved in this refinement process, as well as the how the transcript of the final plenary session was treated. The previous and the revised theoretical conjectures are provided and the differences addressed. The application of the Knowledge Cafés technique described here is that the major beneficiaries of this event were not the participants but the facilitators. However, participants also benefited, but the transcript of the final plenary session of the Knowledge Café was coded, analysed and used to enhance the theoretical conjecture resulting from the original research. The critical success factors for the use of a Knowledge Café in this way include finding a group of well‑informed participants, the facilitator/s conducting a well‑focused briefing and employing a well‑experienced facilitator to manage the event. In this research, the resulting refined theoretical conjecture is a more satisfactory understanding of how e‑Government operates within its organisational setting. 

Multiple imputation by chained equations (MICE) is an effective tool to handle missing data ‑ an almost unavoidable problem in quantitative data analysis. However, despite the empirical and theoretical evidence supporting the use of MICE, researchers in the social sciences often resort to inferior approaches unnecessarily risking erroneous results. The complexity of the decision process when encountering missing data may be what is discouraging potential users from adopting the appropriate technique. In this article, we develop straightforward step‑by‑step graphical guidelines on how to handle missing data based on a comprehensive literature review. It is our hope that these guidelines can help improve current standards of handling missing data. The guidelines incorporate recent innovations on how to handle missing data such as random forests and predictive mean matching. Thus, the data analysts who already actively apply MICE may use it to review some of the newest developments. We demonstrate how the guidelines can be used in praxis using the statistical program R and data from the European Social Survey. We demonstrate central decisions such as variable selection and number of imputations as well as how to handle typical challenges such as skewed distributions and data transformations. These guidelines will enable a social science researcher to go through the process of handling missing data while adhering to the newest developments in the field.