The Professional Counselor | Volume 12, Issue 1 71 Skills Subscale. This subscale of the GICCS consists of 11 items focused on counselors’ experiences and skills with trans clients, including statements like “I have experience counseling [trans male] clients” and “I have received adequate clinical training and supervision to counsel [trans] clients” (Bidell, 2005, p. 273). Cronbach’s alpha for the Skills subscale was reported as .91 (Bidell, 2005) but was .75 in the present sample. Counselors working with trans students need to understand the importance of evolving language and terminologies; utilize affirmative, celebratory, and liberating counseling; and have knowledge of and connection to medical providers who support gender-affirming interventions. Data Analysis Procedures Data Cleaning We first screened the data to ensure it was usable, reliable, and valid to proceed with statistical analyses. We continued data cleaning by coding the demographic variable of GI 1 through 4: cisfemale (1); cismale (2); nonbinary, trans, and/or genderqueer (3); and agender (4). Racial-ethnic identities were coded 1 through 10: American Indian or Alaska Native (1); Asian or Asian American (2); Black or African American (3); Hispanic, Latino, or Spanish Origin (4); Middle Eastern or North African (5); Native Hawaiian or Other Pacific Islander (6); White (7); Some Other Race, Ethnicity, or Origin (8); Prefer Not to Answer (9); and Multiracial Identity (10). PSC location was also coded 1 through 6: Midwest (1), Northeast (2), South (3), West (4), Puerto Rico or other U.S. Territories (5), and Other (6). Last of the demographic variables, we coded PSC School Level 1 through 4: Elementary (1), Middle School (2), High School (3), and Other (4). In addition, we cleaned variables highlighting PSC professional and personal training and experiences with trans persons. The first variable was dummy coded to reflect participants who had worked with trans students (1; n = 297, 76.3%) and participants who indicated not working with trans students (0; n = 92, 23.7%). The next variable, PSC postgraduate training, was dummy coded for use in data analyses, reflecting those who indicated they engaged in postgraduate training (1; n = 193, 49.6%) and participants who indicated they did not engage in postgraduate training (0; n = 196, 50.4%). The final variable was dummy coded to reflect participants who know someone who is trans outside of the school setting (1; n = 93, 23.9%) and those participants who do not know someone who is trans outside of the school setting (0; n = 296, 76.1%). Per Bidell (2005), we started by reverse scoring coded GICCS items and created new variables for the GICCS total mean score, attitudinal Awareness, Skills, and Knowledge subscales. Data Analysis Post–data cleaning, we entered all the data from the demographic questionnaire and the GICCS into SPSS 26. To best answer the research questions, we used a series of standard multiple regression analyses to determine “the existence of a relationship and the extent to which variables are related, including statistical significance” (Sheperis et al., 2017, p. 131). Although multiple regression analysis can be used in prediction studies, it can also be used to determine how much of the variation in a dependent variable is explained by the independent variables, which is what we intended to measure (Johnson, 2001). Our independent variables were four categorical variables measured by our demographic questionnaire: PSC GI, postgraduate training, PSC work with trans students, and PSC personal relationships with someone who is trans. Our dependent variable was school counselor competence in working with trans students, as measured by the GICCS (Bidell, 2005). There are many assumptions to consider when conducting a multiple regression analysis, including (a) two or more continuous or categorical independent variables, (b) a continuous dependent variable, (c) independence of residuals (or observations), (d) linearity (both between dependent variable and each of the independent variables, and between the dependent variable and the independent variables
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