Patrick R. Mullen, Nancy Chae, Adrienne Backer
The Recognized American School Counselor Association Model Program (RAMP) designation aims to acknowledge school counselors who deliver comprehensive data-driven programs. However, there is little research to date that examines RAMP schools and associated factors with this designation. Therefore, we compared the characteristics of schools that earned the RAMP designation with a random sample of schools without this designation to examine if differences exist. Data was accessed using the Elementary/Secondary Information System through the U.S. Department of Education. The results indicated that non-RAMP schools in this study were more likely to: (a) be eligible for Title I; (b) be located in city, rural, and township communities; and (c) have fewer students and full-time equivalent employees. Furthermore, non-RAMP schools had higher rates of students eligible for free or reduced lunch. The development of support mechanisms for the RAMP-seeking process for these schools may be beneficial along with further research on this topic.
Keywords: Recognized ASCA Model Program (RAMP), school counseling, school characteristics, U.S. Department of Education, data-driven
School counselors provide an array of services to students and families across elementary and secondary schools. The American School Counselor Association (ASCA) created the ASCA National Model (ASCA, 2012), a framework for school counselors to identify the appropriate roles and duties of a school counselor. Additionally, the ASCA National Model outlines the tenets of comprehensive school counseling programs. Currently, the ASCA National Model is the only structured framework promoted by ASCA that recommends job duties and different roles that will help to support the school community (ASCA, 2012). For example, ASCA recommends that school counselors spend 80% or more of their time in providing direct or indirect service with the students in their buildings and 20% or less in program planning or school support (ASCA, 2012). Thus, this model is taught in school counselor training programs and used for professional development of practicing school counselors across the United States. One initiative by ASCA to encourage and recognize rigorously implemented school counseling programs is to facilitate the Recognized ASCA Model Program (RAMP) designation program (ASCA, 2019). RAMP is earned by school counseling programs that consistently adhere to the ASCA National Model and demonstrate its implementation and outcomes through data-driven practices. Programs with the RAMP designation are highlighted at ASCA-related events and publications. The RAMP initiative has encouraged many school counseling programs to implement comprehensive services and requires evaluation of their effectiveness through data-driven practices.
While the RAMP recognition intends to highlight accomplished school counseling programs, the general development of the ASCA National Model helped to structure the efforts and experiences of school counselors and students. Researchers have previously asserted that the ASCA National Model can benefit student achievement and promote effective school counseling programs (Brigman & Campbell, 2003; Carey, Harrity, & Dimmitt, 2005; Sink & Stroh, 2003). In a study of secondary school counselors from Michigan, Pyne (2011) suggested that school counselors who implemented a comprehensive school counseling program, like the ASCA National Model, experienced greater job satisfaction compared to school counselors without such programs. Specifically, school counselors exhibited greater job satisfaction when school counseling programs had administrative support, included communication among school faculty members, possessed a clear program philosophy, identified clear roles of the school counselor, served all students in the school, and included time for planning and evaluation of the school counseling program and related activities (Pyne, 2011).
In studies of state-based school counseling programs, researchers have found positive features of student outcomes in schools with comprehensive school counseling programs. Carey, Harrington, Martin, and Hoffman (2012) assessed school counseling programs in suburban and rural Nebraska high schools, and found that well-implemented and differentiated programs with features of the ASCA National Model enhanced student outcomes, including lower suspension rates, lower discipline incident rates, higher attendance rates, and higher math proficiency. By contrast, high school counselors in Nebraska who spent more time providing responsive services were associated with schools with higher suspension and disciplinary incident rates and lower graduation rates. Moreover, Carey, Harrington, Martin, and Stevenson (2012) assessed school counseling programs in Utah high schools, and found that high schools that reflected components of the ASCA National Model improved student achievement, such as ACT scores, number of students taking the ACT, and percentage of students with proficient reading and math scores on the state assessments. The researchers suggested that programmatic focus and use of data were strongly associated with academic achievement and college aspirations in Utah high schools (Carey, Harrington, Martin, & Stevenson, 2012). Carey, Harrington, Martin, and Stevenson (2012) also found that more favorable or lower student-to-school counselor ratios were connected to decreased disciplinary issues and increased student attendance.
Lapan, Gysbers, and Petroski (2001) found that students who attended Missouri middle schools with fully implemented comprehensive school counseling programs reported feeling safer and having fewer conflicts with peers, having improved relationships with teachers, and believing their education was applicable to their future, as compared to students who attended schools with lower implementation fidelity. Additionally, Sink, Akos, Turnbull, and Mvududu (2008) compared student achievement in middle schools in Washington with and without fully implemented comprehensive school counseling programs and found student achievement was significantly higher in schools with fully implemented comprehensive school counseling programs for at least five years. Both studies indicated positive student outcomes associated with the implementation of comprehensive school counseling programs. However, despite a call for schools and school counselors to implement comprehensive school counseling programs for more than 30 years, Martin, Carey, and DeCoster (2009) found that 17 states have fully implemented these programs and 24 states have at least partially implemented these programs.
Although previous research addressed how components of the ASCA National Model offer benefits to school counseling programs and schools, there is little known about how schools that earn a RAMP designation uniquely aid students’ academic, social and emotional, and postsecondary outcomes. In other words, there is limited research about the differences between schools with a RAMP designation versus schools without a RAMP designation (henceforward non-RAMP). In one study, Wilkerson, Pérusse, and Hughes (2013) compared RAMP and non-RAMP designated schools on their Adequate Yearly Progress scores for Math and English/Language Arts and found that the elementary schools with RAMP performed better than non-RAMP schools. However, the researchers only collected data from a single state, had a limited sample size resulting in issues related to power, and did not control for school factors (e.g., funding, size, and student characteristics) that may have impacted the results. Outside of this single study, no other research has been done that provides empirical evidence for RAMP designated schools being more effective at addressing students’ educational outcomes over non-RAMP schools.
Other studies about RAMP schools connected the benefits of data-driven decision making, supervisory practices, and administrative support. In a study of school counselors from RAMP schools, Young and Kaffenberger (2011) found that participants who earned RAMP actively used data to drive and inform school counseling program development and impact student outcomes. In addition, school counselors reported that undergoing the RAMP application process transformed their beliefs in using data to address gaps and develop interventions (Young & Kaffenberger, 2011). In addition, Blakely, Underwood, and Rehfuss (2009) found that supervisors in a RAMP school provided significantly more supervisory activities related to the ASCA National Model for school counseling trainees in RAMP schools than trainees in traditional schools (i.e., non-RAMP schools), which may help to maintain consistency in school counseling training and support trainees to apply their university training in their professional practice. Moreover, in a study of administrators’ perceptions of school counselors in RAMP versus non-RAMP schools, Dodson (2009) found that participants from RAMP schools more often perceived school counselors to deliver classroom guidance lessons, counsel students with disciplinary concerns, consult with teachers, and interpret student records, compared to participants from non-RAMP schools. According to these studies, there are benefits of understanding the RAMP process in schools to inform training practices and elicit administrative support.
One topic related to becoming a RAMP-designated school is the ability of a counseling program to implement the components of the ASCA National Model with fidelity. To implement a comprehensive school counseling program, school counselors need the financial and time resources to implement the services. For example, the school or school counselor may need to put forth money to purchase various curricula for group or classroom interventions. Moreover, ASCA suggests that the recommended timeline of the RAMP process includes one to two years of planning (e.g., developing the foundational and management components, such as calendars, an advisory council, and advisory agreement) and approximately one year to collect and evaluate data (ASCA, 2019). A minimum 2-year commitment can be burdensome for school counseling programs with a single school counselor and even for a team of school counselors, which may require coordination. In addition, school counselors often have high student caseloads and do not always have the time to implement the various components of the ASCA National Model because they focus on responding to immediate student needs and non–counselor-related duties (McCarthy, van Horn Kerne, Calfa, Lambert, & Guzmán, 2010). Increased financial resources and counselors in a school (i.e., lower student-to-counselor ratio) impact the ability of school counselors to implement the ASCA National Model (Lapan, Whitcomb, & Aleman, 2012). As a result, schools with fewer staff allocations and fewer financial supports may be less likely to put forth time and resources to the RAMP designation.
In addition, the application for RAMP costs $250 for ASCA members and $500 for non-members, which adds to the financial burden of schools to pay to implement these services. There also is a perceived lack of benefit for earning RAMP designation. School districts and states have yet to incentivize the RAMP designation, making the use of time and financial effort toward this status resultant in only professional recognition (ASCA, 2019). Given the emphasis placed on the ASCA National Model and the RAMP designation, those schools with the fewest resources may likely have the least amount of opportunity to implement. However, there has been no research on the differences in school characteristics for those sites that have earned the RAMP designation in comparison to those schools who have not earned this recognition. Therefore, the purpose of this study was to compare the characteristics of RAMP-designated schools to a sample of non-RAMP schools to provide information about how these schools differ.
While earning the RAMP designation is an indicator of the comprehensive implementation of the ASCA National Model, little is known about characteristics of schools that have attained RAMP recognition in comparison to non-RAMP schools. The lack of research on RAMP schools is notable due to ASCA’s efforts to train and encourage practitioners to earn this recognition, which may take school counselors away from other responsibilities or burden them with more commitments. It is likely that school counseling programs that pursue RAMP have unique qualities as compared to non-RAMP schools, given the requirements of RAMP, which necessitate resources and organizational support. Some differences between RAMP and non-RAMP schools might lie in the school counselors’ individual qualities (e.g., professional identity, training, motivation); however, there could be characteristics of the school that differ (e.g., school size or location) and facilitate or hinder the achievement of RAMP designation. Therefore, we compared differences in school characteristics based on whether a school has achieved RAMP status. The following exploratory research questions guided our study: (1) Do schools whose school counseling programs have achieved RAMP differ in general school characteristics when compared to schools with school counseling programs that have not achieved RAMP status? (2) Do schools whose school counseling programs have achieved RAMP differ in student body characteristics when compared to schools with school counseling programs that have not achieved RAMP status?
The analyses in this study utilized school-level data publicly available from the Common Core of Data’s (CCD) Elementary/Secondary Information System (ELSi; National Center for Education Statistics, 2018) to retrieve the school characteristics for a sample of RAMP schools and non-RAMP schools. The CCD is a census database that provides information on all public elementary and secondary schools along with school districts and additional administrative and operational entities in the United States. Education agencies submit data to the National Center for Education Statistics on an annual basis (National Center for Education Statistics, 2018). In the data set, three types of information are collected: (a) general descriptive data (e.g., school grade level and locale), (b) demographic data on staff and students, and (c) fiscal data.
We accessed the ELSi to retrieve information on general descriptive data and demographic data. In our first step, we downloaded a dataset of every U.S. public school from the most recent year available (2015–2016) that contained characteristics for each school. We captured information about free and reduced lunch rates (i.e., based on family size and income criteria, students eligible for free or reduced-price lunches at school under the National School Lunch Act), Title I status (i.e., per state and federal regulations, Title I schools are eligible for participation in programs authorized by Title I of Public Law 103-382), geographic region in which the school is located, grade level, number of students at the school, race and ethnicity demographics for each school, and school full-time–equivalent (FTE) teachers. Then, we removed schools (n = 133) that attained RAMP status in 2015 or 2016 and created a new dataset with these schools. We selected the RAMP schools from the 2015–2016 school year to match the years in which the CCD was represented. The list of RAMP schools was acquired through the ASCA website. After removing RAMP schools, we generated an equal-sized simple random sample of schools (n = 133) from the remaining schools in the CCD database. The resulting aggregated and de-identified sample included data for 266 schools across the United States. There were some cases in which data was missing (e.g., three schools didn’t report grade level served).
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.
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.
We employed the Mann-Whitney U Test and chi-square analyses for this study due to the data characteristics. Specifically, each analysis included RAMP status as a nominal and dichotomous independent variable. The dependent variables were nominal with four groups or continuous data. However, the distribution of the continuous dependent variables violated assumptions for normality; thus, we applied non-parametric approaches of data analysis to this data. The Mann-Whitney U Test was used with continuous dependent variables. For the Mann-Whitney U Tests, we interpreted the effect sizes by computing the approximate value of r (Pallant, 2011), which could be interpreted using 0.1, 0.3, and 0.5 for small, medium, and large effect sizes, respectively (Cohen, 1988). We also utilized chi-square tests for independence when the dependent variables were nominal. In the case of a two-by-two chi-square table, we used Yates’ continuity correction statistics for interpretation and the phi coefficient to evaluate the effect size. The phi coefficient can be interpreted in a similar fashion as the r statistic. For analyses with chi-square tables of two-by-four, we studied the Pearson chi-square statistic and the Cramer’s V effect size statistic. We interpreted the Cramer’s V based on criteria for four categories (0.06, 0.17, and 0.29 were small, medium, and large effect sizes, respectively; Pallant, 2011). An initial a priori power analysis for the Mann-Whitney U Test using G*Power with an alpha level of .05, power established at .95, and a moderate effect size of 0.5 (Cohen, 1988) identified a minimum sample size of 184. Similarly, we conducted an a priori power analysis for the chi-square tests for independence using G*Power with an alpha level of .05, power established at .95, and a moderate effect size of 0.3 (Cohen, 1988) and identified a minimum sample of 191. We used a Bonferroni corrected value of .003 as a means to reduce the likelihood of Type I errors.
General School Characteristics
Our first research question examined whether schools whose school counseling programs have achieved RAMP (i.e., RAMP schools) differ in general school characteristics when compared to schools with school counseling programs that have not achieved RAMP status (i.e., non-RAMP schools). We facilitated a Mann-Whitney U Test to compare the total number of students per school for both RAMP and non-RAMP schools. The Mann-Whitney U Test revealed a statistically significant difference in RAMP schools (Mrank = 159.90, Mdn = 925, M = 1,201.81, SD = 853.67) versus non-RAMP schools (Mrank = 103.96, Mdn = 575, M = 687.96, SD = 534.56, U = 4,915.50, z = -5.97, p < .001, r = .37). Similarly, we completed the Mann-Whitney U Test to analyze FTEs for both RAMP and non-RAMP schools. The Mann-Whitney U Test revealed a statistically significant difference in FTE for schools that had RAMP (Mrank = 159.20, Mdn = 51.37, M = 69.38, SD = 48.49) and those schools that did not have RAMP (Mrank = 105.80, Mdn = 32.48, M = 41.49, SD = 30.27, U = 5,187.00, z = -5.68, p < .001, r = .35).
A chi-square test for independence indicated a statistically significant association between RAMP and geographic location among the schools in this study: χ2 (3, N = 266) = 22.94, p < .001, Cramer’s V = .29. Table 1 provides a breakdown of the frequency and percentage for each geographical location by RAMP status. Non-RAMP schools were more often located in city, town, and rural settings than RAMP schools, whereas RAMP schools were more often located in suburban locations. A chi-square test for independence indicated no statistically significant association between RAMP and school level among the schools in this study: χ2 (3, N = 263) = 22.94, p = .06, Cramer’s V = .17 (Bonferroni corrected p value of .003).
Chi-square Tests of Independence Comparing RAMP Versus Non-RAMP Schools
(n = 133)
|Non-RAMP (n = 133)||Pearson χ2||Cramer’s V|
|City (n = 71)||28 (39.4%)||43 (60.6%)|
|Suburban (n = 120)||79 (65.8%)||41 (34.3%)|
|Town (n = 22)||6 (27.3%)||16 (72.7%)|
|Rural (n = 53)||30 (37.7%)||33 (62.3%)|
|Primary (n = 111)||45 (40.5%)||66 (59.5%)|
|Middle (n = 65)||33 (50.8%)||32 (49.2%)|
|Secondary (n = 79)||48 (60.8%)||31 (39.2%)|
|Other (n = 8)||4 (50.0%)||4 (50.0%)|
|Title I Eligible||33.08**||-.36|
|Yes (n = 159)||56 (35.2%)||103 (64.8%)|
|No (n = 107)||77 (71.0%)||30 (28.0%)|
|Yes (n = 11)||1 (9.1%)||10 (90.9%)|
|No (n = 243)||120 (49.4%)||123 (50.6%)|
|Yes (n = 29)||21 (72.4%)||8 (27.6%)|
|No (n = 222)||102 (45.9%)||120 (54.1%)|
|Note. * = p < .05, ** = p < .001, Bonferroni correction of .003 for significant p value.|
A chi-square test for independence using Yates’ continuity correction indicated a non-statistically significant association between RAMP status and identity as a charter school among the schools in this study: χ2 (1, N = 254) = 5.33, p < .05, phi = -.16 (Bonferroni corrected p value of .003). Of the 11 schools that were charter schools, 10 (90.9%) were non-RAMP schools and one (9.1%) was a RAMP school. However, schools that were not charter schools were evenly split between RAMP schools (n = 120, 49.4%) and non-RAMP schools (n = 123, 50.6%). Similarly, another chi-square test for independence using Yates’ continuity correction indicated no statistically significant association between RAMP status and identification as a magnet school among the schools in this study: χ2 (1, N = 251) = 6.17, p < .05, phi = .17 (Bonferroni corrected p value of .003). Nonetheless, schools that identified as magnet schools (N = 29) were more often RAMP schools (n = 21, 72.4%) compared to non-RAMP schools (n = 8, 27.6%). Of the schools that did not identify as a magnet school (n = 222), 45.9% (n = 102) were RAMP and 54.1% (n = 120) were not RAMP.
Student Body Characteristics
The second research question examined whether schools whose school counseling programs have achieved RAMP differ in student body characteristics when compared to schools with school counseling programs that have not achieved RAMP status. A chi-square test for independence using Yates’ continuity correction indicated a significant association between RAMP status and Title I eligibility among the schools in this study: χ2 (1, N = 266) = 33.08, p < .001, phi = -.36. Of the schools eligible for Title I (n = 159), 56 (35.2%) were RAMP schools and 103 (64.8%) were non-RAMP schools. Conversely, 77 (71.0%) of the schools not eligible for Title I (n = 107) were RAMP schools, whereas 30 (28.0%) were non-RAMP schools. A Mann-Whitney U Test revealed a significant difference in the percentage of students eligible for free and reduced lunch based on RAMP (Mrank = 114.19, Mdn = 38.71, M = 42.23, SD = 26.16) and those schools that did not have RAMP (Mrank = 148.29, Mdn = 53.63, M = 54.24, SD = 26.18, U = 6,345.00, z = -3.64, p < .001, r = .23).
Table 2 provides a detailed breakdown of the percentages of students’ race and ethnicity for RAMP and non-RAMP schools. The percentages were calculated by dividing the total number of students identified for each race/ethnic category by the total number of students at each school. Percentages were utilized versus total frequency counts to help understand the rates of students for each race and ethnicity category in the contexts of their schools. Of the race and ethnicity categories, one produced significant differences based on RAMP status. The RAMP schools in this study had a greater percentage of Asian students when compared to non-RAMP schools.
Breakdown of Percentages of Students’ Race/Ethnicity for RAMP and Non-RAMP Schools
|Percentages for Each Race/Ethnicity Classification by RAMP Status|
|Hawaiian Pacific Islander||137.85||1.24||8.23||124.30||0.24||0.60||7,630.00||-1.54||–|
|Two or more races||146.31||4.33||3.12||119.79||3.56||3.13||7,021.50||-2.81*||–|
|Note. * = p < .001|
The first research question compared school characteristics of RAMP and non-RAMP schools, and we found that RAMP schools were more likely to have a larger student enrollment and more full-time teachers compared to non-RAMP schools. In addition, RAMP schools were more likely to be located in suburban areas, whereas non-RAMP schools were more often in city, town, and rural settings. RAMP schools were more likely to be magnet schools and less likely to be charter schools; however, this was not found to be significant with the Bonferroni corrected p value. There were no differences in school level (i.e., elementary, middle, high) and pupil-to-teacher ratios as variables in either RAMP or non-RAMP schools. The second research question compared student body characteristics of RAMP and non-RAMP schools, and we found that non-RAMP schools were more likely to be Title I schools and serve low-income students compared to RAMP schools. Moreover, RAMP schools likely had more Asian students. There is little known about RAMP schools in relationship to students’ demographic breakdown, and this finding provides some insight into the topic for continued research. This finding has a medium effect size, which indicates moderate practical significance. More research on the racial/ethnic breakdown of RAMP compared to non-RAMP schools is needed to make significant claims about this difference.
Although RAMP schools tended to have larger student enrollments than non-RAMP schools, RAMP schools were also likely to have more full-time teachers. With larger student bodies, more full-time staff might be needed and budgeted to address the capacity of students served. However, the data showed that larger school enrollments were often located in suburban areas. This finding raises the question about how certain contextual factors of schools play a role in comprehensive school counseling program development. For instance, it is possible that largely populated urban, township, or rural schools may have fewer full-time teachers, making it difficult to implement comprehensive counseling programs (Gagnon & Mattingly, 2016). With more full-time staff, school counselors who are pursuing the RAMP application process may benefit from increased access to full- and part-time staff to support program development; however, a report by Scafidi (2013) found that an increase in staffing in U.S. public schools did not necessarily appear to have positive outcomes for student achievement, such as test scores and graduation rates. More research is needed to understand how numbers of school staff members can support school counselors and counseling program development, implementation, and recognition. More importantly, students and their families can benefit from having increased access to full-time personnel to address their academic, social and emotional, and postsecondary needs. For example, Sink (2008) suggested that when elementary school teachers work collaboratively with school counselors, student learning and academic outcomes have the potential to improve and narrow achievement gaps among students. On the other hand, fewer full-time staff might be budgeted in schools with lower enrollments, thus having to share and delegate the many daily roles and responsibilities among fewer staff. Furthermore, having fewer FTE teachers may increase staff members’ burdens, and the RAMP process could be perceived as additional tasks that take time away from their primary responsibilities.
Our results indicated the allocation of the RAMP designation differed based on location. The greater likelihood of RAMP schools being in suburban locations suggested that RAMP schools are often located in areas of increased access to school-based and community resources (Wright, 2012). With greater access to physical and financial resources, counselors can bridge and enhance their program planning and delivery for students. Since non-RAMP schools in this study were likely to be located in rural, township, and urban areas as well as serve more low-income students, these student populations might have less access to counseling services due to the challenges of funding and resource availability in their local communities. Also, these communities might serve higher populations of minority and low-income students (Gagnon & Mattingly, 2016; Lapan, Gysbers, & Sun, 1997; Lee, 2005; Sutton & Pearson, 2002).
Although magnet and charter schools offer attractive nontraditional school and program choices to students and families, Archbald (1996) suggested that magnet schools either appealed to parents of higher educational attainment, or parents of higher educational attainment were better able to gain access to magnet schools. Parents of higher educational attainment are likely to have greater financial resources, and in addition, because of specialized programming, some magnet schools have even received increased educational funding (Archbald, 1996). It is possible that families of higher educational attainment and greater funding can afford schools and their school counseling programs with more resources to implement comprehensive counseling programs. Moreover, in a case study of a college counseling program in a charter high school, researchers suggested that the innovative nature of the charter school framework and structure may support the work of college counseling; however, school counselors may experience difficulties in implementing a comprehensive college counseling model due to the organizational challenges of sustaining a new school (Farmer-Hinton & McCullough, 2008). Furthermore, charter schools may likely have smaller student enrollments and thus fewer full-time teachers budgeted for the programs, which connects to the present study’s findings about non-RAMP schools. Both magnet and charter programs attract students based on various program characteristics, and further studies about school counselors’ roles in school-choice programs is warranted. The ways in which schools are funded and managed can impact school counselors’ access to developing and implementing comprehensive school counseling programs. Further research is needed to explore the characteristics of these school-choice programs and their connections with comprehensive school counseling programs.
Teacher-to-student ratios were not different when comparing RAMP and non-RAMP schools in our study, which is consistent with the mixed evidence about the impact of teacher-to-student ratios on student achievement. For instance, one study found that lower teacher-to-student ratios did not necessarily equate to higher test achievement (Alspaugh, 1994), while another study showed that lower teacher-to-student ratios increased student achievement (Schwartz, Schmidt, & Lose, 2012). Further research is not only needed about the potential impact of teacher-to-student ratios on school counseling programming, but also student-to-school counselor ratios on program development and delivery. Researchers found that Connecticut, Missouri, Nebraska, and Utah high schools with comprehensive school counseling programs and lower student-to-school counselor ratios were connected to lower disciplinary rates and higher attendance rates (Carey, Harrington, Martin, & Hoffman, 2012; Carey, Harrington, Martin, and Stevenson, 2012; Lapan, Gysbers, Stanley, & Pierce, 2012; Lapan, Whitcomb, & Aleman, 2012). It also could be beneficial to further understand how student-to-school counselor ratios impact RAMP programming.
School counselors and the programs they develop play critical roles in closing the achievement gap (Holcomb-McCoy, 2007). RAMP schools submit closing-the-gap results reports as a component of the RAMP application to address an achievement or attainment gap within the context of their school and community, demonstrating that comprehensive school counseling programs work toward closing such gaps. It is possible that RAMP schools work toward closing the achievement and attainment gaps specific to their local settings; however, the findings of this study demonstrate that RAMP schools in totality might not be addressing the national educational gaps among students from low-income backgrounds. This study demonstrated that fewer low-income students and students who attended Title I schools are in RAMP schools, which highlights the issue of equity and access to comprehensive school counseling programs to support the academic, social and emotional, and postsecondary development of students. Dimmitt and Wilkerson (2012) found that schools in Rhode Island with higher percentages of minority students and those receiving free and reduced lunch were less likely to have implemented comprehensive school counseling programs, which supports the findings of the present study. In addition, researchers found that students who attended poorer, diverse, and city school districts had less access to school counselors (Gagnon & Mattingly, 2016). However, research has demonstrated that when schools reduce the student-to-school counselor ratio to 250:1, as recommended by ASCA, students receiving free and reduced lunch at high-poverty schools had better academic outcomes (Lapan, Gysbers, Stanley, & Pierce, 2012). Research should continue to explore and question how RAMP schools work toward more globally closing the achievement gap in addition to addressing the gaps within their own local contexts.
Implications for Practice and Research
The findings of this study indicate potential inequalities between RAMP-designated schools and non-RAMP schools. Specifically, the RAMP designation appears to be more often received in schools that: (a) have fewer students on free and reduced lunch, (b) have more students and FTEs, and (c) are less likely to be eligible for Title I. Thus, there are several implications for practice and research. School counselors whose principals are supportive and knowledgeable about school counselors’ roles and programming can better facilitate implementation of comprehensive school counseling programs (Dodson, 2009; Fye, Miller, & Rainey, 2018). When school counselors are burdened by non-counseling duties, such as administrative tasks, substitute teaching, and lunch duty, they are less likely to devote the time, energy, and resources required to effectively implement components of the ASCA National Model. Therefore, it is critical that school counselors and principals view the ASCA National Model not as an added task, but rather an inherent element that guides program development, enhances student achievement, and supports underrepresented student groups who would not otherwise have access. School counselors can work with school administrations to advocate for the time and financial resources needed to implement components of the ASCA National Model.
As a tool to advocate for the merit of the ASCA National Model and the RAMP designation, scholars can develop and implement research studies that test and evaluate the effectiveness of this approach. For instance, Martin and Carey (2014) developed a logic model to guide evaluation of ASCA National Model programs, which offered a step toward understanding the connection between comprehensive school counseling programs and addressing issues related to the student achievement gap and outcomes. Also, Villares and Dimmit (2017) identified the top research priorities in the school counseling field, indicating that determining best practices related to school counseling interventions persists as highly ranked, as does evaluating the impact of comprehensive school counseling programs on students’ academic development and achievement. Additional studies to test the effectiveness of the ASCA National Model are needed to attest to its merit as an evidence-based practice. For example, many evidence-based registries require interventions to have been researched using experimental or quasi-experimental designs, used an inactive control group, and been published in high quality journals (Brigman, Villares, & Webb, 2018; Mullen, Stevens, & Chae, 2019). Thus, researchers may want to develop rigorous study designs that provide merit for the ASCA National Model’s effectiveness—an endeavor that has yet to be fulfilled in the literature despite the vast implementation of this model. Similarly, ASCA as an organization would likely benefit from providing resources and support to researchers to take on such endeavors. The need for increased use of the ASCA National Model is predicated on its effectiveness at enhancing students’ educational, social and emotional, and career outcomes; consequently, research is vital to establish its credibility. Research on the effectiveness of the ASCA National Model will help develop its merit for stakeholders and enhance the ability to advocate for its implementation.
A key finding of our study is that schools that are lower staffed, smaller, and have students with lower SES are less likely to receive the RAMP designation. Based on the concept that higher implementation of the ASCA National Model will result in better student outcomes, it is imperative to increase access for schools with lower resources and higher needs. As the ASCA National Model asserts and ASCA as an organization believes school counselors to be agents of social justice, it is reasonable that measures are taken to increase the access to service implementation for smaller, lower staffed schools with a higher rate of students with lower SES. For example, ASCA could provide training materials or programs at a reduced rate for qualified schools or waive the application fee for schools that may not have access to such support locally. Similarly, ASCA could provide or facilitate mentor support for schools that may not have access to this type of support locally. Moreover, ASCA can support school counselors, especially those in Title I schools who serve larger populations of students and families who are from low SES backgrounds, by offering supervision or mentoring at no or limited cost to facilitate strengths-based partnerships with schools, families, and communities that have the potential to provide necessary resources and supports for students’ academic, social and emotional, and postsecondary development (Bryan & Henry, 2008). School counselors, school counseling trainees, and school counselor educators are encouraged to be self-reflective as well as to engage in professional development practices connected to supporting students and families from low SES backgrounds (Cole & Grothaus, 2014). School counselors can gain awareness of and advocate for the challenges experienced by these students and families and also highlight their strengths and assets. While it is unlikely that any one individual or organization can cause a school to increase the number of school counselors at that site, it is relevant to continue advocacy efforts related to decreasing student ratios.
Limitations and Future Research Directions
This study compares school and student characteristics of RAMP and non-RAMP schools; however, the results do not attribute causality. Based on the findings, we can only make predictions based on the given characteristics of RAMP and non-RAMP schools. Another limitation is that CCD ELSi data neither identifies if schools have a presence of school counselors nor clarifies if schools include school counselors in the FTE category. We can be assured that the RAMP schools in this study have at least one school counselor, but it is unclear if school counselors are represented in our simple random sample of non-RAMP schools. Moreover, since there were only 133 RAMP schools in the 2015–2016 school year, the 133 non-RAMP schools selected for this study might not necessarily be an accurate representation of all U.S. public schools. Also, this study cannot account for or consider the individual qualities of school counselors in RAMP schools and how individual school counselors’ professional identity, training, motivation, and other unique factors contribute to RAMP achievement.
Future research can explore the barriers and supports of pursuing and sustaining RAMP, like in Fye et al. (2018). Continued research is needed to understand how RAMP schools specifically address and work toward closing the achievement gap, which impacts students of color and students from low-income backgrounds. Furthermore, although there are existing state-level studies of school counseling programs and their connections to student outcomes within individual states (Burkard, Gillen, Martinez, & Skytte, 2012; Carey, Harrington, Martin, & Hoffman, 2012; Carey, Harrington, Martin, & Stevenson, 2012; Dimmitt & Wilkerson, 2012; Lapan, Gysbers, Stanley, & Pierce, 2012; Lapan, Whitcomb, & Aleman, 2012; Martin et al., 2009; Sink et al., 2008; Wilkerson et al., 2013), cross-comparison studies of state-by-state programs can be useful to see which states are highly represented among RAMP schools, and how these states’ RAMP schools effectively facilitate the RAMP process. Such state-based studies also can explore the extent to which state-level funding and supports impact school counseling program development.
This study explored whether schools whose school counseling programs have achieved RAMP designation differ in general school and student body characteristics when compared to schools with school counseling programs that have not achieved RAMP status. The study utilized publicly available data from the CCD’s ELSi to retrieve the school characteristics for RAMP schools and an equal-sized simple random sample of non-RAMP schools. The results showed that general school characteristics of RAMP schools differed from non-RAMP schools. Non-RAMP schools tended to be eligible for Title I, had more students eligible for free and reduced lunch, and were more likely to be in city, rural, and township communities. Non-RAMP schools also had fewer students and full-time teachers compared to RAMP schools. This study not only addressed issues of social justice as it pertains to socioeconomic status, geographic location, and race, but also explored the disparities in the types of schools and student populations that have or lack access to school counseling programs. School counselors, schools, and ASCA can collaborate and advocate on behalf of students to ensure that comprehensive school counseling programs serve and are equitably accessed by all students.
Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.
Alspaugh, J. W. (1994). The relationship between school size, student teacher ratio and school efficiency. Education, 114, 593–602.
American School Counselor Association. (2012). The ASCA National Model: A framework for school counseling programs (3rd ed.). Alexandria, VA: Author.
American School Counselor Association. (2019). Recognized ASCA Model Program (RAMP). Retrieved from https://www.schoolcounselor.org/school-counselors/recognized-asca-model-program-(ramp)
Archbald, D. (1996). SES and demographic predictors of magnet school enrollment. Journal of Research and Development in Education, 29, 152–162.
Blakely, C., Underwood, L. A., & Rehfuss, M. (2009). Effectiveness of school counselor supervision with trainees utilizing the ASCA Model. Journal of School Counseling, 7.
Brigman, G., & Campbell, C. (2003). Helping students improve academic achievement and school success behavior. Professional School Counseling, 7, 91–98.
Brigman, G., Villares, E., & Webb, L. (2018). Evidence-based school counseling: A student success approach. New York, NY: Taylor & Francis.
Bryan, J., & Henry, L. (2008). Strengths-based partnerships: A school-family-community partnership approach to empowering students. Professional School Counseling, 12, 149–156. doi:10.1177/2156759X0801200202
Burkard, A. W., Gillen, M., Martinez, M. J., & Skytte, S. L. (2012). Implementation challenges and training needs for comprehensive school counseling programs in Wisconsin high schools. Professional School Counseling, 16, 136–145. doi:10.5330/PSC.n.2012-16.136
Carey, J., Harrington, K., Martin, I., & Hoffman, D. (2012). A statewide evaluation of the outcomes of the implementation of ASCA National Model school counseling programs in rural and suburban Nebraska high schools. Professional School Counseling, 16, 100–107. doi:10.1177/2156759X0001600202
Carey, J., Harrington, K., Martin, I., & Stevenson, D. (2012). A statewide evaluation of the outcomes of the implementation of ASCA National Model school counseling programs in Utah high schools. Professional School Counseling, 16, 89–99. doi:10.1177/2156759X0001600203
Carey, J., Harrity, J., & Dimmitt, C. (2005). The development of a self-assessment instrument to measure a school district’s readiness to implement the ASCA National Model. Professional School Counseling, 8, 305–312.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Earlbaum Associates.
Cole, R. F., & Grothaus, T. (2014). A phenomenological study of urban school counselors’ perceptions of low-income families. Journal of School Counseling, 12(5).
Dimmitt, C., & Wilkerson, B. (2012). Comprehensive school counseling in Rhode Island: Access to services and student outcomes. Professional School Counseling, 16, 125–135. doi:10.1177/2156759X0001600205
Dodson, T. (2009). Advocacy and impact: A comparison of administrators’ perceptions of the high school counselor role. Professional School Counseling, 12, 480–487. doi:10.1177/2156759X0901200606
Farmer-Hinton, R. L., & McCullough, R. G. (2008). College counseling in charter high schools: Examining the opportunities and challenges. The High School Journal, 91(4), 77–90. doi:10.1353/hsj.0.0006
Fye, H. J., Miller, L. G., & Rainey, J. S. (2018). Predicting school counselors’ supports and challenges when implementing the ASCA National Model. Professional School Counseling, 21, 1–11. doi:10.1177/2156759X18777671
Gagnon, D. J., & Mattingly, M. J. (2016). Most U.S. school districts have low access to school counselors: Poor, diverse, and city school districts exhibit particularly high student-to-counselor ratios. Retrieved from University of New Hampshire Carsey School of Public Policy, Carsey Research: https://scholars.unh.edu/cgi/viewcontent.cgi?article=1285&context=carsey
Holcomb-McCoy, C. (2007). School counseling to close the achievement gap: A social justice framework for success. Thousand Oaks, CA: Corwin Press.
Lapan, R. T., Gysbers, N. C., & Sun, Y. (1997). The impact of more fully implemented guidance programs on the school experiences of high school students: A statewide evaluation study. Journal of Counseling & Development, 75, 292–302. doi:10.1002/j.1556-6676.1997.tb02344.x
Lapan, R. T., Gysbers, N. C., & Petroski, G. F. (2001). Helping seventh graders be safe and successful: A statewide study of the impact of comprehensive guidance and counseling programs. Journal of Counseling & Development, 79, 320–330. doi:10.1002/j.1556-6676.2001.tb01977.x
Lapan, R. T., Gysbers, N. C., Stanley, B., & Pierce, M. E. (2012). Missouri professional school counselors: Ratios matter, especially in high-poverty schools. Professional School Counseling, 16, 108–116. doi:10.1177/2156759X0001600207
Lapan, R. T., Whitcomb, S. A., & Aleman, N. M. (2012). Connecticut professional school counselors: College and career counseling services and smaller ratios benefit students. Professional School Counseling, 16, 117–124. doi:10.1177/2156759X0001600206
Lee, C. C. (2005). Urban school counseling: Context, characteristics, and competencies. Professional School Counseling, 8, 184–188.
Martin, I., & Carey, J. (2014). Development of a logic model to guide evaluations of the ASCA National Model for school counseling programs. The Professional Counselor, 4, 455–466. doi:10.15241/im.4.5.455
Martin, I., Carey, J., & DeCoster, K. (2009). A national study of the current status of state school
counseling models. Professional School Counseling, 12, 378–386.
McCarthy, C., van Horn Kerne, V., Calfa, N. A., Lambert, R. G., & Guzmán, M. (2010). An exploration of school counselors’ demands and resources: Relationship to stress, biographic, and caseload characteristics. Professional School Counseling, 13, 146–158. doi:10.1177/2156759X1001300302
Mullen, P. R., Stevens, H., & Chae, N. (2019). School counselors’ attitudes toward evidence-based practices. Professional School Counselor, 22, 1–11. doi:10.1177/2156759X18823690
National Center for Education Statistics. (2018). Elementary/secondary information system. Retrieved from https://nces.ed.gov/ccd/elsi
Pallant, J. (2011). SPSS survival manual: A step by step guide to data analysis using SPSS (4th ed.). Buckingham, UK: Open University Press.
Pyne, J. R. (2011). Comprehensive school counseling programs, job satisfaction, and the ASCA National Model. Professional School Counseling, 15, 88–97. doi:10.1177/2156759X1101500202
Scafidi, J. (2013). The school staffing surge: Decades of employment growth in America’s public schools (Part II). Retrieved from https://files.eric.ed.gov/fulltext/ED543118.pdf
Schwartz, R. M., Schmidt, M. C., & Lose, M. K. (2012). Effects of teacher-student ratio in response to intervention approaches. The Elementary School Journal, 112, 547–567. doi:10.1086/664490
Sink, C. A. (2008). Elementary school counselors and teachers: Collaborators for higher student achievement. The Elementary School Journal, 108, 445–458. doi:10.1086/589473
Sink, C. A., Akos, P., Turnbull, R. J., & Mvududu, N. (2008). An investigation of comprehensive school counseling programs and academic achievement in Washington state middle schools. Professional School Counseling, 12, 43–53. doi:10.1177/2156759X0801200105
Sink, C. A., & Stroh, H. R. (2003). Raising achievement test scores of early elementary school students through comprehensive school counseling programs. Professional School Counseling, 6, 350–364.
Sutton, J. M., Jr., & Pearson, R. (2002). The practice of school counseling in rural and small town schools. Professional School Counseling, 5, 266–276.
U.S. Department of Education, Common Core of Data. (2016). Elementary/secondary information system [ElSi], 2015-2016 [Data set]. Retrieved from https://nces.ed.gov/ccd/elsi/tableGenerator.aspx
Villares, E., & Dimmitt, C. (2017). Updating the school counseling research agenda: A Delphi study. Counselor Education and Supervision, 56, 177–192. doi:10.1002/ceas.12071
Wilkerson, K., Pérusse, R., & Hughes, A. (2013). Comprehensive school counseling programs and student achievement outcomes: A comparative analysis of RAMP versus non-RAMP schools. Professional School Counseling, 16, 172–184. doi:10.1177/2156759X1701600302
Wright, W. (2012). The disparities between urban and suburban American education systems: A comparative analysis using social closure theory. Proceedings of The National Conference on Undergraduate Research (NCUR). Ogden, UT: Webster State University.
Young, A., & Kaffenberger, C. (2011). The beliefs and practices of school counselors who use data to implement comprehensive school counseling programs. Professional School Counseling, 15, 67–76. doi:10.1177/2156759X1101500204
Patrick R. Mullen, NCC, is an assistant professor at the College of William & Mary. Nancy Chae, NCC, is a doctoral candidate at the College of William & Mary. Adrienne Backer is a doctoral student at the College of William & Mary. Correspondence can be addressed to Patrick Mullen, P.O. Box 8795, Williamsburg, VA 23187-8795, firstname.lastname@example.org