Chapter 17. Emotional Labor, Mentoring, and Equity for Doctoral Student and Faculty Writers

Untangling Methodological Commitments in Writing Research: Using Collaborative Secondary Data Analysis to Maximize Interpretive Potentials of Qualitative Data

“…Indeed, developing the codes and coding each disagreement collaboratively helped us understand more deeply the needs of the faculty and graduate student writers who participated in our studies and enabled us to identify important subthemes related to the experiences of writers in each group. As we explain elsewhere (Madden & Tarabochia, 2020), the role of emotional labor was a latent subtheme that we came to recognize through coding collaboratively. Moreover, calculating and comparing frequencies became an exploratory tool that shaped our next set of questions about their experiences.…”

“…Taken together, these evaluative comments suggested that simple recommendations for improving writing mentorship practices are insufficient. As we argue elsewhere (Madden & Tarabochia, 2020), those invested in supporting advanced writers need to think creatively about how to work within and against institutional structures that are not conducive to—and often preclude—meaningful mentoring. Practices such as collaborative authorship, cochaired committees, peer-based and multiple mentoring models, and emotionally aware programming offer ways to mitigate emotional labor in mentoring interactions (Madden & Tarabochia, 2020).…”

“…As we argue elsewhere (Madden & Tarabochia, 2020), those invested in supporting advanced writers need to think creatively about how to work within and against institutional structures that are not conducive to—and often preclude—meaningful mentoring. Practices such as collaborative authorship, cochaired committees, peer-based and multiple mentoring models, and emotionally aware programming offer ways to mitigate emotional labor in mentoring interactions (Madden & Tarabochia, 2020). Through tacking in and tacking out, using evaluation coding to analyze the details of our data set in relation to the broader view cultivated through the first two rounds of coding, we were able to make recommendations for mentoring structures responsive to writers’ needs.…”

“…For example, by creating a map of writers’ evaluative observations, we noticed that emotion manifested across concerns and magnitudes. In recent scholarship, we interrogate how writing emotions can instantiate forms of emotional labor, how emotional labor can prohibit healthy writer development, and how writers from historically oppressed groups experience emotional labor disproportionately relative to other groups (Madden & Tarabochia, 2020). Table 1 shows four different segments of data recorded in our evaluation coding document that represent writers’ distinct needs related to structure and mentoring.…”

“…We then reflect on how our collaborative process of combining two distinct studies addresses these issues. Findings from our collaboration are published in Tarabochia and Madden (2018) and Madden and Tarabochia (2020). Here, we enact Rickly and Cook’s (2017) call for methodological transparency by foregrounding “the mess” of research, and submit a meta-analysis of our process in the spirit of Driscoll et al’s (2017) study of “failed” research attempts.…”