TPC Journal-Vol 9- Issue 2-Full-Issue

160 The Professional Counselor | Volume 9, Issue 2 Participants The sample ( N = 266) in this study included RAMP ( n = 133, 50%) and non-RAMP ( n = 133, 50%) schools from across the United States. On average, the schools in this sample reported 940.96 ( SD = 753.76, Mdn = 706.00, Range = 35 to 4,190) students, a mean teacher-to-pupil ratio of 16.80 ( SD = 4.72, Mdn = 16.18, Range = 8.57 to 53.56), and a mean FTE of 55.43 ( SD = 42.69, Mdn = 43.60, Range = 0 to 270.96). In addition, the average percentage of students eligible for free or reduced lunch was 48.33% ( SD = 26.81, Mdn = 46.30, Range = 2.32 to 100), and the majority of schools were eligible for Title I funding ( n = 159, 59.8%) as compared to not being eligible for Title I funding ( n = 107, 40.2%). We used percentages of the student body that make up each race and ethnicity group by dividing the number of students for each group by the total number of students in the school and multiplying it by 100. Across all the schools that reported the race and ethnicity rates in this study ( N = 261), White students had the highest mean percentage ( M = 52.30%, Mdn = 55.38%, SD = 29.26%) followed by Hispanic ( M = 19.94%, Mdn = 12.44%, SD = 21.82%), Black ( M = 17.47%, Mdn = 8.28%, SD = 22.20%), Asian ( M = 4.93%, Mdn = 2.04%, SD = 7.54%), Two or more races/ethnicities ( M = 3.99%, Mdn = 3.33%, SD = 3.13%), Hawaiian or Pacific Islander ( M = .74%, Mdn = .05%, SD = 5.81%), and American Indian ( M =.69%, Mdn = .22%, SD = 2.78%). Regarding location, the ELSi portal identifies locales, which measure schools’ locations relative to the populated areas in which they are situated, as city, suburban, town, and rural settings. There are 12 subdomains to indicate varied levels within the broad domains: City: Large, Midsize, and Small; Suburb: Large, Midsize, and Small; Town: Fringe, Distant, and Remote; and Rural: Fringe, Distant, and Remote (National Center for Education Statistics, 2018). For this study, we condensed these subcategories into four broad areas to simplify the analyses. Most schools were located in suburban communities ( n = 120, 45.1%) followed by city ( n = 71, 26.7%), rural ( n = 53, 19.9%), and town ( n = 22, 8.3%). The majority of the schools were primary level ( n = 111, 41.7%) followed by secondary level ( n = 79, 29.7%), middle ( n = 65, 24.4%), and other levels ( n = 8, 3.0%), with three (1.1%) cases of missing data. ELSi denotes two school-choice programs: (a) charter schools—schools that offer elementary and secondary education for students who are eligible under a charter approved by the state legislature or some other applicable authority and (b) magnet schools—schools that offer programs to draw students of varied racial and ethnic backgrounds with the aim to decrease racial isolation and offer an academic and social focus. Two-hundred and forty-three (91.4%) of the schools were not charter schools, 11 (4.1%) schools identified as charter schools, and 12 schools did not have data for this category. Only 29 (10.9%) schools in the sample identified as magnet schools, 222 (83.5%) schools were not magnet schools, and 15 (5.6%) schools had missing data. Study Variables The two-level independent variable in this study was whether a school achieved RAMP status. The dependent variables included general descriptive data and demographic data on students. The general descriptive dependent variables of school characteristics (Research Question 1) included grade level served by the school (i.e., elementary, middle, high school), geographic location of the school (i.e., city, suburban, town, and rural), FTE, and total number of attending students. Furthermore, the student demographic data dependent variables (Research Question 2) included percentage of students eligible for free or reduced lunch, Title I status of the school, and percentage of race and ethnicity in the student body. For percentage of students eligible for free or reduced lunch and percentage of race/ethnicity in the student body, we calculated these variables using the frequency count data. All dependent variables were selected by using the filter option in ELSi.