The Researcher as Insider Versus the Researcher as Outsider: Enhancing Rigour Through Language and Cultural Sensitivity
Evidence suggests that in research studies involving minority language users, rigour is enhanced when researchers share a common language and culture with research participants and thus are considered to be “insiders”. However, it is clear that the use of “insiders” is not always possible and where the researchers and the researched do not share a common culture and language, measures can be taken to ensure that the research is rigorous. Furthermore, cultural and linguistic concordance does not in itself guarantee rigour; researchers must also demonstrate that their approach stands up to judgement against criteria that are congruent with the relevant research paradigm.
In this chapter, we consider best practice in research studies involving more than one language when the researchers are either insiders or outsiders. We draw on examples from published literature and from our research experiences as insiders and outsiders to examine approaches for enhancing rigour when conducting qualitative research. Attention is given to cultural issues, which we argue are inextricably linked to language, and therefore require cultural sensitivity on the part of the researcher in order to effectively capture and interpret data. We also examine sampling and recruitment, in which we demonstrate the measures that can be taken to facilitate the recruitment of participants with different language backgrounds. We give some consideration to data collection and identify strategies that can be adopted to gain rich data from participants where researchers either share or do not share the same language as the researched. Translation and interpretation are both identified as approaches that can facilitate rigorous cross-cultural research if addressed appropriately. The process of transcription, which is rarely addressed in the literature, is also examined and suggestions are given about how this can be tackled when operating in more than one language. Finally, we consider data analysis and offer suggestions for best practice when analysing data collected in two or more languages.
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