3/14/2023 0 Comments Quant theory![]() Nineteen of the 4,826 unique studies identified were included in the review. ![]() Peer‐reviewed publications (i.e., articles and conference papers) were identified and coded for study focus, keywords, participants and institution(s), journal discipline, study type, methods used, and work referenced, in addition to academic outcomes, mental health measures, and mental health findings. Inclusion and exclusion criteria were applied during the screening process. Five research databases, EBSCO: CINAHL, EBSCO: PsycINFO, ProQuest: ERIC, PubMed, and Scopus, were searched in a scoping literature review. We investigate the literature concerning engineering graduate students' mental health, focusing on academic outcomes, mental health measures, and mental health findings, to highlight gaps in current literature and the need for further research. In particular, engineering graduate students have lower help‐seeking tendencies, which can impact the severity and length of their mental health problems. ![]() Graduate students experience unique stressors that impact their mental health. Prevalence of these issues has an established impact on students' personal, professional, and academic outcomes. Mental health issues reported among college‐aged individuals have increased. Those studies that are truly examining race should reflect on their research question and seek more relevant racial questions for data collection. If possible, data librarians should also consult on alternatives to habitual use of the Census racial categories.Ĭonclusions: We suggest that many studies are harmed by including race and should remove it entirely. Therefore, social justice requires that data librarians should expose researchers to this fact. Nevertheless, the Census categories explicitly say that they have no basis in research or science. ![]() Results: There are good reasons why patrons who are experts in topics other than racism can find it challenging to change habits from Interoperable approaches to race. Finally, we apply the Model of Domain Learning to explain why data science and data management experts can and should expose experts in subject research to the idea of critically examining demographic data collection. We then present examples of how racial categories can hide, rather than reveal, racial disparities. Methods: We synthesize historical context with modern critical thinking about race and data to examine the origins of current assumptions about data. We attempt to apply QuantCrit thinking, particularly to demographic datasheets. Objective: We consider how data librarians can take antiracist action in education and consultations. Only then can quantitative approach be re-imagined and rectified. We argue that quantitative approaches cannot be adopted for racial justice aims without an ontological reckoning that considers historical, social, political, and economic power relations. Second, we examine the legacy and genealogy of QuantCrit traditions across the disciplines to uncover a rich lineage of methodological possibilities for disrupting racism in research. Informed by this work, and 15 years later, this article reconsiders the possibilities of CRT applications to quantitative methodologies through ‘QuantCrit.’ We ask the question: Can quantitative methods, long critiqued for their inability to capture the nuance of everyday experience, support and further a critical race agenda in educational research? We provide an abbreviated sketch of some of the key tenets of CRT and the enduring interdisciplinary contributions in race and quantitative studies. CRT scholars have applied a critical race framework to advance research methodologies, namely qualitative interventions. Critical race theory (CRT) in education centers, examines, and seeks to transform the relationship that undergirds race, racism, and power.
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