The Professional Counselor | Volume 14, Issue 2 139 Data Analysis Qualitative Data Analysis To analyze the qualitative data, we used Consensual Qualitative Research-Modified (CQR-M; Spangler et al., 2012), which was based on Hill et al.’s (2005) CQR but modified for larger numbers of participants with briefer responses. In contrast to the in-depth analysis of a small number of interviews, CQR-M was ideal for our data, which consisted of brief written responses from 139 participants. CQR-M involves a consensus process rather than interrater reliability among judges, who discuss and code the narratives, and relies on a bottom-up approach, in which categories (i.e., themes) are derived directly from the data rather than using a pre-existing thematic structure. Frequencies (i.e., how many participants were represented in each category) are then calculated. We analyzed the beginning and advanced students’ responses separately, as the questions were adjusted for their time spent in the program. After immersing themselves in the data, the first two authors, Sapna B. Chopra and Rebekah Smart, met to outline a preliminary coding structure, then met repeatedly to revise the coding into more abstract categories and subcategories. The computer program NVivo was used to organize the coding process and determine frequencies. After all data were coded, the fifth author, Eric W. Price, served as auditor and provided feedback on the overall coding structure. Both the consensus process and use of an auditor are helpful in countering biases and preconceptions. Brief quantitative data, as used in this study, can be used effectively as a means of triangulation (Spangler et al., 2012). Quantitative Data Analysis To examine for significant differences in the self-perceptions of multicultural competencies and advocacy competencies between White and BIPOC students as well as between beginning and advanced students, a two-way (2x2) ANOVA was conducted with the overall MCCT as the criterion variable and student levels (beginning, advanced) and race (White, BIPOC) as the two independent variables. In addition, two (5x2) multivariate analyses of variances (MANOVAs) were conducted with the five factors of multicultural competencies (knowledge, awareness, definition of terms, racial identity, and skills) as criterion variables and with student levels (beginning, advanced) and student races (White, BIPOC) as independent variables in each analysis. Data for beginning and advanced students were analyzed separately to assess whether time in the counseling program helped to expand their interest and commitment to social justice. Research Team We were intentional in examining our own social identities and potential biases throughout the research process. Chopra is a second-generation South Asian American, heterosexual, cisgender woman. Smart is a White European American, heterosexual, cisgender woman. Yuying Tsong identifies as a genderqueer first-generation Taiwanese and Chinese American immigrant. Olga L. Mejía is an Indigenous-identified Mexican immigrant, bisexual, cisgender woman. Price is a White, gay, cisgender male. All have experience as counselor educators and in qualitative research methods, and all have been actively engaged in decolonizing their syllabi and incorporating multicultural and social justice into their pedagogy.
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