This theoretical paper offers different ways in which qualitative researchers can establish quality, which is analogous to reliability and validity in quantitative research, when working with linguistically and culturally diverse populations. This paper uses Walther, Sochacka, and Kellam’s (2013) article from the Journal of Engineering Education as a starting point for identifying strategies for ensuring quality in qualitative research throughout the data generation and analysis process. This article contributed significantly to the field of engineering educational research by establishing a typology of validation constructs and process reliability for interpretive engineering educational research. At the same time, this article did not include the word “culture,” name the race of the research participants, or account for the ways in which quality might include cultural responsiveness to populations, such as Native Americans, which have historically been exploited and misrepresented by researchers and educators from dominant cultures. Drawing from Walther et al.’s constructs and drawing from constructivist and critical theories, this paper identifies strategies for establishing quality in qualitative research with historically marginalized populations. The authors argue that cultural responsiveness, as well as a commitment to research that actively benefits marginalized communities, are two core components of quality which were not considered in Walther et al.’s original typology.
Moreover, this paper identifies methods of establishing quality in qualitative research with historically marginalized populations, such as the following: (a) establishing participant selection criteria that avoid generalizations about particular racial, ethnic, or linguistic groups (e.g., “Native American,” “English learner”); (b) using data generation procedures that are culturally and linguistically congruent; (c) developing educational strategies (if any) that begin with the needs and preferences of the studied group or community; and (d) involving participants in data analysis using methods specifically designed to diminish and dismantle power differentials between “researcher and researched.” Given that many engineering educational researchers come from privileged backgrounds, such as from middle-class families, it is important that these researchers have pro-active strategies for conducting rigorous research in ways that foreground, honor, and sustain cultural and linguistic plurality when they work with research participants from marginalized populations who have been historically underserved in both educational and research efforts.
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