TPC Journal V8, Issue 4- FULL ISSUE

The Professional Counselor | Volume 8, Issue 4 301 counseling students. The appraisal of barriers to seeking counseling among adults in the United States is an essential first step in understanding why prospective clients do, or do not, seek counseling. If validated, researchers and practitioners can potentially use the results of the Revised FSV Scale to aid in the early identification of specific barriers and to inform the development of interventions geared toward reducing barriers to counseling among adults in the United States. Thus, we sought to answer the following research questions (RQs): RQ 1: Is the three-dimensional hypothesized model of the Revised FSV scale confirmed with a stratified random sample of adults in the United States? RQ 2: To what extent do adults in the United States attend counseling? RQ 3: Are there demographic differences to the FSV barriers among adults in the United States? Method The psychometric properties of the Revised FSV Scale were tested with a confirmatory factor analysis (CFA) based on structural equation modeling (RQ 1). Descriptive statistics were used to compute participants’ frequency of attendance in counseling (RQ 2). A factorial multivariate analysis of variance (MANOVA) was computed to investigate demographic differences in respondents’ sensitivity to the FSV barriers (RQ 3). A minimum sample size of 320 (10 participants for each estimated parameter) was determined to be sufficient for computing a CFA (Mvududu & Sink, 2013). An a priori power analysis was conducted using G*Power to determine the sample size for the factorial MANOVA (Faul, Erdfelder, Lang, & Buchner, 2007). Results revealed that a minimum sample size of 269 would provide an 80% power estimate (α = .05), with a moderate effect size, f 2 = 0.25 (Cohen, 1988). Participants and Procedures After obtaining IRB approval, an online sampling service (Qualtrics, 2018) was contracted to survey a stratified random sample (stratified by age, gender, and ethnicity) of the general U.S. population based on the 2016–2017 census data. A Qualtrics project management team generated a list of parameters and sample quota constraints for data collection. Once the researchers reviewed and confirmed these parameters, a project manager initiated the stratified random sampling procedure and data collection by sending an electronic link to the questionnaire to prospective participants. A pilot study was conducted using 41 participants and no formatting or imputation errors were found. Data collection for the main study was initiated and was completed in less than one week. A total of 431 individuals responded to the survey. Of these, 21 responses were omitted because of missing data, yielding a useable sample of 410. Participants ranged in ages from 18 to 84 ( M = 45, SD = 15). The demographic profile included the following: 52% ( n = 213) identified as female, 44% ( n = 181) as male, 0.5% ( n = 2) as transgender, and 3.4% ( n = 14) did not specify their gender. For ethnicity, 63% ( n = 258) identified as White, 17% ( n = 69) as Hispanic/Latinx, 12% ( n = 49) as African American, 5% ( n = 21) as Asian, 1% ( n = 5) as American Indian or Alaska Native, 0.5% ( n = 2) as Native Hawaiian or Pacific Islander, and 1.5% ( n = 6) did not specify their ethnicity. For highest degree completed, 1% ( n = 5) held a doctoral degree, 7% ( n = 29) held a master’s degree, 24% ( n = 98) held a bachelor’s degree, 16% ( n = 65) had completed an associate degree, 49% ( n = 199) had a high school diploma, and 3% ( n = 14) did not specify their highest level of education. Eighty-four percent ( n = 343) of participants had health insurance at the time of data collection. The demographic profile of our sample is consistent with those found in recent surveys of the general U.S. population (Lumina Foundation, 2017; U.S. Census Bureau, 2017).