Interpretative structural modeling to social sciences: designing better datasets for mixed method research

The multiplication of complex datasets in empirical social sciences calls for methods that can improve the design of complex datasets before the actual gathering of data. Yet mainstream scholars in related fields have rarely explored such methods. In this study, we introduce Interpretive Structural Modeling (ISM) as such a method. As a mixed method, ISM integrates Boolean algebra, matrix theory, and directed graph theory to impose a formal structure to qualitative understanding of a complex system. ISM’s final output is a directed graph that can be visually and easily interpreted. We show that ISM can structure indicators graphically into a multilayered and multi-blocked model, thus uncovering hidden interactions among indicators. By doing so, ISM can reveal hidden and undesired redundancies and incoherencies among indicators within a complex dataset. Most critically, ISM achieves these goals without relying on statistical analysis and hence before the actual gathering of any data. Deploying ISM when designing complex datasets thus facilitates more rigorous conceptualization and understanding of complex social phenomena, steers us away from badly designed complex datasets, and saves precious resource. We use ISM to probe several complex datasets to demonstrate its potentials.