TPC Journal V7, Issue 2 - FULL ISSUE

160 The Professional Counselor | Volume 7, Issue 2 hospitalization units in Ohio and Texas, requesting that they forward recruitment information for the study to potential subjects. Snowball sampling was also used to recruit participants when participants who had completed the study recommended colleagues who might be willing to participate in the research. Data were collected from participants by sending packets in the mail that consisted of an informed consent, demographic questionnaire, Q sort, post-Q sort questions and a postage prepaid return envelope. Thirty-two participants met the criteria for inclusion in the study and completed the Q sorting process. In Q methodology a sample size only needs to be large enough for factors (i.e., groups of shared viewpoints) to emerge and is typically 20 and 60 participants (Brown, 1980). Seventy-two percent (n = 23) of the participants in the study were 20–30 years old; 28% (n = 9) were between 31–40 years old. Seventy-two percent (n = 23) of the participants identified as female and 28% (n = 9) of the participants identified as male. Fifty-nine percent (n = 19) of the participants reported they worked in a community counseling agency; 22% (n = 7) reported they worked in a private practice; and 19% (n = 6) reported they worked in a hospital setting. Thirty-eight percent (n = 12) of the participants indicated they had accrued 400–1,000 direct clinical hours working with clients; 22% (n = 7) indicated they had accrued 1,001–1,500 direct clinical hours working with clients; 3% (n = 1) indicated they accrued 1,501–2,000 direct clinical hours working with clients; 9% (n = 3) indicated they accrued 2,001–2,500 direct clinical hours working with clients; and 28% (n = 9) indicated they had accrued more than 2500 direct clinical hours working with clients. Eighty-two percent (n = 26) of participants identified as Caucasian, 9% (n = 3) of participants identified as African American, and 9% (n = 3) of participants identified as Hispanic. Data Analysis Data were entered into the PQMethod software program (Schmolck, 2014) and were factor analyzed using principle components analysis (PCA). After the PCA was initiated, a varimax rotation was used to determine reliability, scores and factor loadings. A 3-factor solution was selected for the data because it accounted for each participant loading onto at least one factor. Due to each participant being accounted for by a 3-factor solution, it was unnecessary to search for a fourth factor. In Q methodology, factor scores are used for interpretation rather than factor loadings. The factor narratives presented in the results section were created through a factor interpretation method developed by Watts and Stenner (2012). This method was designed to consistently approach each factor in the context of all other factors and to provide a holistic factor interpretation by taking into consideration all differences between factors. First, a worksheet was created from the factor array for each individual factor. The worksheet contained the highest (+4) and lowest (-4) ranked items within the factor (note: items of consensus were not included and were analyzed separately) and those items ranked higher or lower within the factor compared to the other two factors. Second, items in the worksheet were compared to participants’ demographic information and qualitative responses associated with that factor to add depth and detail before the final step. Finally, the finished worksheet was used to construct the factor narratives, which were written as stories that reflected the shared viewpoint of each factor. Results Of the three factors produced by the PCA of the 32 Q sorts, Factor 1 contained 12 of the participants and accounted for 17% of the variance; Factor 2 contained nine participants and accounted for 13% of the variance; and Factor 3 contained nine participants and accounted for 14%