Handbook of Research Methods in Migration
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Handbook of Research Methods in Migration

Edited by Carlos Vargas-Silva

Covering both qualitative and quantitative topics, the expert contributors in this Handbook explore fundamental issues of scientific logic, methodology and methods, through to practical applications of different techniques and approaches in migration research.
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Chapter 27: Three Mistakes and Corrections: On Reflective Adaptation in Qualitative Data Collection and Analysis

Johanna Shih


Johanna Shih I recall being told, in a moment early on as a graduate student, that analysing qualitative data was a bit like ‘osmosis’ because the story ‘just suddenly comes to you’ (or something to this effect). Since I was, at the time, drowning in data that I could not make sense of, this explanation did not engender the illuminating ‘Aha!’ that it was meant to (perhaps more of a ‘What?!’ instead), although I have over time come to truly appreciate the truth in this description. Notwithstanding my belated understanding, this chapter takes a different explanatory tack by offering three examples of mistakes and subsequent corrections that I made during the course of two research projects. Put differently, these examples focus on the process of reflective adaptation at various stages of research, a practice that contributes to producing sound qualitative data analysis. 27.1 TWO EXAMPLES FROM SILICON VALLEY 27.1.1 The Study in Brief I conducted a case study of hi-tech engineers (focusing on white women and Asian immigrants) in Silicon Valley, based primarily on a set of indepth, semi-structured interviews. Silicon Valley, a region in Northern California, is home to a hi-tech industry that achieved exponential growth in the past three decades and has been the subject of worldwide attention. Aside from its economic success, the region was also quite interesting from a sociological standpoint for a number of reasons. First, there was a pervasive discourse, both popular and academic, about Silicon Valley that depicted it as meritocratic...

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