Observement as Universal Measurement
This work addresses the fundamental problem of formally measuring and interpreting non-numerical data, which is crucial for any field dealing with large volumes of qualitative information.
This study proposes "observement" as a generalization of traditional measurement theory to handle non-numerical data, using alternative mathematical models like strings and graphs. It demonstrates that these representations are already prevalent and identifies implicit interpretive methodologies within string and graph-based data.
Measurement theory is the cornerstone of science, but no equivalent theory underpins the huge volumes of non-numerical data now being generated. In this study, we show that replacing numbers with alternative mathematical models, such as strings and graphs, generalises traditional measurement to provide rigorous, formal systems (`observement') for recording and interpreting non-numerical data. Moreover, we show that these representations are already widely used and identify general classes of interpretive methodologies implicit in representations based on character strings and graphs (networks). This implies that a generalised concept of measurement has the potential to reveal new insights as well as deep connections between different fields of research.