Data Flow Diagram Use to Plan Empirical Research Projects

Yourdon and Constantine (1979), De Marco (1979), and Gane and Sarson (1979) introduced the data flow diagram (DFD) more than a quarter of a century ago, as a systems planning tool that is particularly useful in the fields of software engineering and information systems development. But the DFD is not restricted to those fields. Empirical research projects are systems too (which consist of interconnected sources, data, collection processes, files, analysis processes, knowledge, and users), and those systems are similar to information systems. This article reports how the DFD can also be useful in planning empirical research projects. This finding should be advantageous to research planners, individual researchers, research advisors, research supervisors, or research managers. And it should be especially advantageous to research planners in information and communication technology (ICT) because they know DFDs already, so they can get the planning advantages with little or no extra learning effort. This finding was obtained from two research projects. The first was planned without the aid of a DFD and failed. It was then replanned with a DFD and redone in a second project, which succeeded. The DFD that turned failure into success is Figure 1. The second project had the exploratory aim of demonstrating that DFDs can be useful in planning empirical research projects (the more ambitious aim should calls for further research). Figure 1 shows that the aim was achieved by means of DFD examples that speak for themselves: so no elaborate data collection was necessary and neither was any sophisticated data analysis. First, textbooks of research methods were surveyed to develop a structural model of empirical research projects: that model identifies seven major types of components as key planning issues. Then DFDs were drawn from the research proposals of three recent research projects to demonstrate that those DFDs explicitly identify the major decision components. This means that DFDs can be useful in focusing attention on the key planning issues. Third, faulty DFDs were drawn, some from initial proposals of old research projects, and others by conducting thought experiments that distorted the structural model in various ways (Brown, 1992): these examples demonstrate that DFDs readily expose planning errors. This means that DFDs can be useful in identifying planning errors. Therefore, by focusing