It makes sense to define codes and use recording procedures that work best for the observers. After all, if we expect good work, we should accommodate observers’ comfort; the data as recorded can be modified later into forms that facilitate analysis. In Chapter 4, we argued the utility of representing observational data with a few standard formats (i.e., single-code event, timed-event, interval, and multicode event data) and then conceptualizing such data as a code-unit grid – partly for the order and organization doing so brings to observational data, but also for the opportunities it presents for later data modification. In this chapter we describe, among other matters, data modification, that is, specific ways new codes can be created from existing codes – new codes that are faithful to and accurately reflect our research questions and that extend the range of our data-analytic efforts.

Given the benefits, it is a bit puzzling that data modification of observational data is not more common. Perhaps it is because data modification occupies something of a middle ground. On the one hand, there are a number of systems for computer-assisted coding that facilitate the initial recording of observational data; most produce the sorts of summary scores described in Chapter 8. On the other hand, there are a number of statistical packages that permit often quite complex data recoding and transformation of summary scores. But no coding or analysis programs we know of address the need to modify sequential data before summary scores are computed. In this respect, GSEQ, with its extensive and flexible data modification capabilities, may be uniquely helpful.