New Directions in Science Literacy Education

While Grossman calls for a rebalancing in methodological approach and targeted subjects, each individual study would not necessarily employ mixed methods. Instead, the underlying assumption is that useful research, in the aggregate, will avail itself of both quantitative and qualitative approaches, both small-scale and large-scale studies. However, chapters in Part III argue for mixed methods and offer up examples of research in which a single research team has used both quantitative and qualitative data. Although Nieswandt and McEneaney (see Chap. 10) have a much more quantitative emphasis than Levin and Wagner (see Chap. 11), both chapters can be viewed as employing mixed methods and sitting relatively close to one another on the continuum. The differences in method choice and orientation can be justified because the target constructs under consideration were at different stages of conceptual development and instrumentation. The chapter by Norton-Meier and colleagues (see Chap. 9) further highlights potential differences in approach to mixed-methods research. Because they are seeking to examine interactions that occur at the project level rather than at the individual study level, they have employed a combination of approaches that enables them to address a problem space related to teacher implementation and the consequential impact on student learning. In this case, quantitative and qualitative approaches are used to study a variety of science content, grade levels, and classroom settings across the overall project; and the choice of method is determined and informed by both the larger question posed and the available data.

Munby (2003) suggested that discussions on appropriate research, in fact, should be framed around questions of purpose and rigor. He argued that we need to move past a purely technical view of reliability and validity and focus on the essence of research, that is, to persuade others of the trustworthiness of the results. Rigorous studies are designed so that the argument reflects the quality of the data and analysis and also responds appropriately and convincingly to the questions posed. For Munby, the argument needs to show a strong connection among the question(s), claims, and evidence.