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There is an identified need in higher education research for methods which have the capacity to generate conceptual insights grounded in concrete local practice but with wider applicability in understanding and facilitating research-based change. This chapter outlines an intermediate approach to qualitative data analysis which can support theoretical knowledge advancement from practice-based research, which I call the difference-within-similarity approach. It involves a particular way of conducting dialogues with our data: of interanimating similarities and differences within our qualitative datasets. The approach outlined involves first identifying a similarity, then systematically examining differences within that similarity to generate theoretical explanations. Drawing on sociocultural theorising, particularly dialogic theory and cultural–historical activity theory, the approach is based on the idea that new meanings arise from a comparison of multiple perspectives on the ‘same’ phenomenon. The tensions between such perspectives are seen as a key driver for change in educational practice. Therefore, articulating and examining such tensions in our data gives an opportunity to simulate the possibility of change in our analysis and, hence, develop insights which can inform change beyond local settings. Important here is that the differences examined are bound together by an analytically productive similarity. Through multiple research examples, the chapter identifies and illustrates a range of ways of articulating productive analytical similarities for comparison in our data: through theory/literature, through forward and backwards processing of data itself and through a process termed ‘weaving’.