The Professional Counselor | Volume 12, Issue 1 89 We then independently contrasted the tentative categories with the data to verify that they aligned accurately. We discussed the verifications until consensus was met on all categories. Lastly, we classified the categories into three themes and two subthemes found across all participants (Stake, 2005). During these later processes, we were mindful of Yin’s (2017) remaining two principles of high-quality data analysis: consider rival interpretations of data and use previous expertise when interpreting the case. Accordingly, we reflected on possible contrary explanations of the themes and considered the findings in light of previous literature on the topic. Trustworthiness We addressed trustworthiness in three ways in this study. First, before data collection, we engaged in reflexivity through acknowledging personal biases and assumptions with one another (Hays & Singh, 2012; Yin, 2017). For example, Merlin-Knoblich acknowledged that her lived experience supervising the participants might impact the interpretation of data during analysis and noted that these perceptions could potentially serve as biases during the study. Merlin-Knoblich perceived that the supervisees grew in their understanding of social justice, but also acknowledged doubt over whether the social justice supervision model impacted participants’ advocacy skills. She also noted her role as a supervisor evaluating the three participants prior to the study taking place. These power dynamics may have influenced her interpretations in the analysis process. Newman shared that his lack of familiarity with social justice supervision might impact perceptions and biases to question whether or not supervisees grew in their understanding of social justice. We agreed to challenge one another’s potential biases during data analysis in an attempt to prevent one another’s experiences from interfering with interpretations of the findings. In addition, we acknowledged that our identities as White, English-speaking, educated, heterosexual, cisgender, middle-class researchers studying social justice inevitably was informing personal perceptions of the supervisees’ experiences. These privileged identities were likely blinding us to experiences with oppression that participants and their clients encountered and that we are not burdened with facing. Throughout the study, we discussed the complexity of studying social justice in light of such privileged identities. We spoke further about our identities and potential biases when interpreting the data. Second, investigator triangulation was addressed by collaboratively analyzing the study’s data (Hays & Singh, 2012). Because data included both interview transcripts and journals, we confirmed that study findings were reflected in both data sources, rather than just one information source (Hancock et al., 2021). This process helped prevent real or potential biases from informing the analysis without constraint. We also were mindful of saturation of themes while comparing data across participants and sources during the analysis process. Lastly, an audit trail was created to further address credibility. The study recruitment, data collection, and data analysis were documented so that the research can be replicated (Hays & Singh, 2012; Roulston, 2010). Findings In case study research, researchers use key quotes and descriptions from participants to illuminate the case studied (Hancock et al., 2021). As such, we next describe the themes and subthemes identified in study data using participants’ journal and interview quotes to illustrate the findings. Three overarching themes were identified in the data: 1) intersection of supervision experiences and external factors, 2) feelings about social justice, and 3) personal and professional growth. Two subthemes, 3a) increased understanding of privilege and 3b) increased understanding of clients, further expand the third theme.
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